Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
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We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
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Throughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\n
We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"6187",leadTitle:null,fullTitle:"Advanced Applications for Artificial Neural Networks",title:"Advanced Applications for Artificial Neural Networks",subtitle:null,reviewType:"peer-reviewed",abstract:"In this book, highly qualified multidisciplinary scientists grasp their recent researches motivated by the importance of artificial neural networks. It addresses advanced applications and innovative case studies for the next-generation optical networks based on modulation recognition using artificial neural networks, hardware ANN for gait generation of multi-legged robots, production of high-resolution soil property ANN maps, ANN and dynamic factor models to combine forecasts, ANN parameter recognition of engineering constants in Civil Engineering, ANN electricity consumption and generation forecasting, ANN for advanced process control, ANN breast cancer detection, ANN applications in biofuels, ANN modeling for manufacturing process optimization, spectral interference correction using a large-size spectrometer and ANN-based deep learning, solar radiation ANN prediction using NARX model, and ANN data assimilation for an atmospheric general circulation model.",isbn:"978-953-51-3781-8",printIsbn:"978-953-51-3780-1",pdfIsbn:"978-953-51-4057-3",doi:"10.5772/intechopen.68505",price:119,priceEur:129,priceUsd:155,slug:"advanced-applications-for-artificial-neural-networks",numberOfPages:296,isOpenForSubmission:!1,isInWos:1,isInBkci:!1,hash:"c7fb38ad3b189551aa9a91eaa3da04d1",bookSignature:"Adel El-Shahat",publishedDate:"February 28th 2018",coverURL:"https://cdn.intechopen.com/books/images_new/6187.jpg",numberOfDownloads:21887,numberOfWosCitations:31,numberOfCrossrefCitations:46,numberOfCrossrefCitationsByBook:3,numberOfDimensionsCitations:75,numberOfDimensionsCitationsByBook:4,hasAltmetrics:1,numberOfTotalCitations:152,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 11th 2017",dateEndSecondStepPublish:"May 2nd 2017",dateEndThirdStepPublish:"July 29th 2017",dateEndFourthStepPublish:"October 27th 2017",dateEndFifthStepPublish:"December 26th 2017",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"193331",title:"Dr.",name:"Adel",middleName:null,surname:"El-Shahat",slug:"adel-el-shahat",fullName:"Adel El-Shahat",profilePictureURL:"https://mts.intechopen.com/storage/users/193331/images/system/193331.jpg",biography:"Dr. Adel El-Shahat received a B.Sc. in Electrical Engineering from Zagazig University, Egypt, in 1999. the M.Sc. in Electrical Engineering (Power and Machines) from Zagazig University, Egypt, in 2004, and the Ph.D. degree (Joint Supervision) from Zagazig University, Egypt, and The Ohio State University (OSU), Columbus, OH, USA, in 2011. He is currently an Assistant Professor - Energy Technology, School of Engineering Technology at Purdue University, USA. He is the Founder and Director of Advanced Power Units and Renewable Distributed Energy Lab (A_PURDUE). His research focuses on Modeling, Design, Multi-Objectives Optimization, Simulation, Analysis, and Control of various aspects such as Smart Nano & Micro- Grids; Electric Mobility & Transportation Electrification, Renewable Energy Systems; Wireless Charging of Electric Vehicles; Electric Vehicles; Special Purposes Electric Machines; Deep Learning Techniques; Distributed Generation Systems; Thermoelectric Generation; Special Power Electronics Converters; Power Systems; Energy Storage & Conservation; and Engineering Education. So far, He has 9 books, 5 chapters in books, 63 journal papers, 73 conference papers, and 106 other publications with his collaborators, and students related to his research interests. He has more than 20 years of working experience in academia and industry. He has experience in funding grant proposals, and He got some awards and recognitions due to his research work. He has good experience directing research for both graduate and undergraduate students for funded projects. He holds full-time academic positions at Purdue University, Georgia Southern University, the University of Illinois at Chicago, Ohio State University, USA, and Suez University, Egypt, along with some full-time and part-time positions in Egyptian companies as an electrical engineer, and consultant as a professional engineer. Additionally, He has distinguished professional training, and He is a Senior Member in the IEEE and IRED institutions along with 21 professional memberships in other societies. Finally, He served as a book editor for 4 books, and a reviewer for 8 books. He is a guest editor and editor-in-chief for three international journals. Also, He is a reviewer for other 35 international journals. Moreover, He served as invited conference sessions chair and reviewer for 31 international conferences along with other community and academic services.",institutionString:"Georgia Southern University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"4",institution:{name:"Purdue University West Lafayette",institutionURL:null,country:{name:"United States of America"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"522",title:"Neural Network",slug:"computer-and-information-science-artificial-intelligence-neural-network"}],chapters:[{id:"59037",title:"Introductory Chapter: Artificial Neural Networks",doi:"10.5772/intechopen.73530",slug:"introductory-chapter-artificial-neural-networks",totalDownloads:1637,totalCrossrefCites:8,totalDimensionsCites:8,hasAltmetrics:1,abstract:null,signatures:"Adel El-Shahat",downloadPdfUrl:"/chapter/pdf-download/59037",previewPdfUrl:"/chapter/pdf-preview/59037",authors:[{id:"193331",title:"Dr.",name:"Adel",surname:"El-Shahat",slug:"adel-el-shahat",fullName:"Adel El-Shahat"}],corrections:null},{id:"57086",title:"Modulation Format Recognition Using Artificial Neural Networks for the Next Generation Optical Networks",doi:"10.5772/intechopen.70954",slug:"modulation-format-recognition-using-artificial-neural-networks-for-the-next-generation-optical-netwo",totalDownloads:1382,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Transmission systems that use advanced complex modulation schemes have been driving the growth of optical communication networks for nearly a decade. In fact, the adoption of advanced modulation schemes and digital coherent systems has led researchers and industry communities to develop new strategies for network diagnosis and management. A prior knowledge of modulation formats and symbol rates of all received optical signals is needed. Our approach of modulation formats identification is based on artificial neural networks (ANNs) in conjunction with different features extraction approaches. Unlike the existing techniques, our ANN-based pattern recognition algorithm facilitates the modulation format classification with higher accuracies.",signatures:"Latifa Guesmi, Habib Fathallah and Mourad Menif",downloadPdfUrl:"/chapter/pdf-download/57086",previewPdfUrl:"/chapter/pdf-preview/57086",authors:[{id:"208922",title:"Ph.D.",name:"Latifa",surname:"Guesmi",slug:"latifa-guesmi",fullName:"Latifa Guesmi"},{id:"215994",title:"Prof.",name:"Mourad",surname:"Menif",slug:"mourad-menif",fullName:"Mourad Menif"}],corrections:null},{id:"57042",title:"Gait Generation of Multilegged Robots by using Hardware Artificial Neural Networks",doi:"10.5772/intechopen.70693",slug:"gait-generation-of-multilegged-robots-by-using-hardware-artificial-neural-networks",totalDownloads:1350,totalCrossrefCites:4,totalDimensionsCites:5,hasAltmetrics:0,abstract:"Living organisms can act autonomously because biological neural networks process the environmental information in continuous time. Therefore, living organisms have inspired many applications of autonomous control to small-sized robots. In this chapter, a small-sized robot is controlled by a hardware artificial neural network (ANN) without software programs. Previously, the authors constructed a multilegged walking robot. The link mechanism of the limbs was designed to reduce the number of actuators. The current paper describes the basic characteristics of hardware ANNs that generate the gait for multilegged robots. The pulses emitted by the hardware ANN generate oscillating patterns of electrical activity. The pulse-type hardware ANN model has the basic features of a class II neuron model, which behaves like a resonator. Thus, gait generation by the hardware ANNs mimics the synchronization phenomena in biological neural networks. Consequently, our constructed hardware ANNs can generate multilegged robot gaits without requiring software programs.",signatures:"Ken Saito, Masaya Ohara, Mizuki Abe, Minami Kaneko and Fumio\nUchikoba",downloadPdfUrl:"/chapter/pdf-download/57042",previewPdfUrl:"/chapter/pdf-preview/57042",authors:[{id:"157327",title:"Dr.",name:"Ken",surname:"Saito",slug:"ken-saito",fullName:"Ken Saito"},{id:"157328",title:"Dr.",name:"Minami",surname:"Kaneko",slug:"minami-kaneko",fullName:"Minami Kaneko"},{id:"157330",title:"Prof.",name:"Fumio",surname:"Uchikoba",slug:"fumio-uchikoba",fullName:"Fumio Uchikoba"},{id:"219934",title:"Mr.",name:"Masaya",surname:"Ohara",slug:"masaya-ohara",fullName:"Masaya Ohara"},{id:"219935",title:"BSc.",name:"Mizuki",surname:"Abe",slug:"mizuki-abe",fullName:"Mizuki Abe"}],corrections:null},{id:"57311",title:"Using Artificial Neural Networks to Produce High-Resolution Soil Property Maps",doi:"10.5772/intechopen.70705",slug:"using-artificial-neural-networks-to-produce-high-resolution-soil-property-maps",totalDownloads:1455,totalCrossrefCites:1,totalDimensionsCites:4,hasAltmetrics:0,abstract:"High-resolution maps of soil property are considered as the most important inputs for decision support and policy-making in agriculture, forestry, flood control, and environmental protection. Commonly, soil properties are mainly obtained from field surveys. Field soil surveys are generally time-consuming and expensive, with a limitation of application throughout a large area. As such, high-resolution soil property maps are only available for small areas, very often, being obtained for research purposes. In the chapter, artificial neural network (ANN) models were introduced to produce high-resolution maps of soil property. It was found that ANNs can be used to predict high-resolution soil texture, soil drainage classes, and soil organic content across landscape with reasonable accuracy and low cost. Expanding applications of the ANNs were also presented.",signatures:"Zhengyong Zhao, Fan-Rui Meng, Qi Yang and Hangyong Zhu",downloadPdfUrl:"/chapter/pdf-download/57311",previewPdfUrl:"/chapter/pdf-preview/57311",authors:[{id:"20948",title:"Dr.",name:"Fanrui",surname:"Meng",slug:"fanrui-meng",fullName:"Fanrui Meng"},{id:"21145",title:"Mr",name:"Zhengyong",surname:"Zhao",slug:"zhengyong-zhao",fullName:"Zhengyong Zhao"},{id:"209051",title:"Dr.",name:"Qi",surname:"Yang",slug:"qi-yang",fullName:"Qi Yang"},{id:"218568",title:"Dr.",name:"Hangyong",surname:"Zhu",slug:"hangyong-zhu",fullName:"Hangyong Zhu"}],corrections:null},{id:"58149",title:"Dynamic Factor Model and Artificial Neural Network Models: To Combine Forecasts or Combine Models?",doi:"10.5772/intechopen.71536",slug:"dynamic-factor-model-and-artificial-neural-network-models-to-combine-forecasts-or-combine-models-",totalDownloads:1186,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In this chapter, we evaluate the forecasting performance of the model combination and forecast combination of the dynamic factor model (DFM) and the artificial neural networks (ANNs). For the model combination, the factors that are extracted from a large dataset are used as additional input to the ANN model that produces the factor-augmented artificial neural network (FAANN). Linear and nonlinear forecasts combining methods are used to combine the DFM and the ANN forecasts. The results of the best combining method are compared to the forecasts result of the FAANN model. The models are applied to forecast three time series variables using large South African monthly data. The out-of-sample root-mean-square error (RMSE) results show that the FAANN model yields substantial improvement over the individual and best combined forecasts from the DFM and ANN forecasting models and the autoregressive AR benchmark model. Further, the Diebold-Mariano test results also confirm the superiority of the FAANN model forecast’s performance over the AR benchmark model and the combined forecasts.",signatures:"Ali Babikir, Mustafa Mohammed and Henry Mwambi",downloadPdfUrl:"/chapter/pdf-download/58149",previewPdfUrl:"/chapter/pdf-preview/58149",authors:[{id:"208994",title:"Dr.",name:"Ali",surname:"Babikir",slug:"ali-babikir",fullName:"Ali Babikir"},{id:"208999",title:"Prof.",name:"Henry",surname:"Mwambi",slug:"henry-mwambi",fullName:"Henry Mwambi"},{id:"209689",title:"Dr.",name:"Mustafa",surname:"Mohammed",slug:"mustafa-mohammed",fullName:"Mustafa Mohammed"}],corrections:null},{id:"57614",title:"Parameter Recognition of Engineering Constants of CLSMs in Civil Engineering Using Artificial Neural Networks",doi:"10.5772/intechopen.71538",slug:"parameter-recognition-of-engineering-constants-of-clsms-in-civil-engineering-using-artificial-neural",totalDownloads:1174,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Controlled low-strength materials (CLSMs) had been widely applied to excavation and backfill in civil engineering. However, the engineering properties of CLSM in these embankments vary dramatically due to different contents involved. This study is proposed to employ the ANSYS software and two different artificial neural networks (ANNs), that is, back-propagation artificial neural network (BPANN) and radial basis function neural network (RBFNN), to determine the engineering properties of CLSM by considering an inverse problem in which elastic modulus and the Poisson’s ratio can be identified from inputting displacements and stress measurements. The PLANE42 element of ANSYS was first used to investigate a 2D problem of a retaining wall with embankment, with E = 0.02~3 GPa, ν= 0.1~0.4 to obtain totally 270 sampling data for two earth pressures and two top surface settlements of embankment. These data are randomly divided into training and testing set for ANNs. Practical cases of three kinds of backfilled materials, soil, and two kinds of CLSMs (CLSM-B80/30% and CLSM-B130/30%) will be used to check the validity of ANN prediction results. Results showed that maximal errors of CLSM elastic parameters identified by well-trained ANNs can be within 6%.",signatures:"Li-Jeng Huang",downloadPdfUrl:"/chapter/pdf-download/57614",previewPdfUrl:"/chapter/pdf-preview/57614",authors:[{id:"209182",title:"Prof.",name:"Li-Jeng",surname:"Huang",slug:"li-jeng-huang",fullName:"Li-Jeng Huang"}],corrections:null},{id:"57337",title:"Electricity Consumption and Generation Forecasting with Artificial Neural Networks",doi:"10.5772/intechopen.71239",slug:"electricity-consumption-and-generation-forecasting-with-artificial-neural-networks",totalDownloads:1616,totalCrossrefCites:4,totalDimensionsCites:5,hasAltmetrics:0,abstract:"Nowadays, smart meters, sensors and advanced electricity tariff mechanisms such as time-of-use tariff (ToUT), critical peak pricing tariff and real time tariff enable the electricity consumption optimization for residential consumers. Therefore, consumers will play an active role by shifting their peak consumption and change dynamically their behavior by scheduling home appliances, invest in small generation or storage devices (such as small wind turbines, photovoltaic (PV) panels and electrical vehicles). Thus, the current load profile curves for household consumers will become obsolete and electricity suppliers will require dynamical load profiles calculation and new advanced methods for consumption forecast. In this chapter, we aim to present some developments of artificial neural networks for energy demand side management system that determines consumers’ profiles and patterns, consumption forecasting and also small generation estimations.",signatures:"Adela Bâra and Simona Vasilica Oprea",downloadPdfUrl:"/chapter/pdf-download/57337",previewPdfUrl:"/chapter/pdf-preview/57337",authors:[{id:"139804",title:"Prof.",name:"Adela",surname:"Bara",slug:"adela-bara",fullName:"Adela Bara"},{id:"188586",title:"Dr.",name:"Simona Vasilica",surname:"Oprea",slug:"simona-vasilica-oprea",fullName:"Simona Vasilica Oprea"}],corrections:null},{id:"56981",title:"Advanced Process Control",doi:"10.5772/intechopen.70704",slug:"advanced-process-control",totalDownloads:1359,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The debutanizer column is an important unit operation in petroleum refining industries. The top product is liquefied petroleum gas and the bottom product is light naphtha. This system is difficult to handle. This is because due to its non-linear behavior, multivariable interaction and existence of numerous constraints on its manipulated variable. Neural network techniques have been increasingly used for a wide variety of applications. In this book, equation-based multi-input multi-output (MIMO) neural network has been proposed for multivariable control strategy to control the top and bottom temperatures of the column. The manipulated variables for column are reflux and reboiler flow rates, respectively. This neural network model are based on multivariable equation, instead of the normal black box structure. It has the advantage of being robust in nature while being easier to interpret in terms of its input-output variables. It has been employed for set point changes and disturbance changes. The results show that the neural network equation-based model for direct inverse and internal model approach performs better than the conventional proportional, integral and derivative (PID) controller.",signatures:"Nasser Mohamed Ramli",downloadPdfUrl:"/chapter/pdf-download/56981",previewPdfUrl:"/chapter/pdf-preview/56981",authors:[{id:"209483",title:"Dr.",name:"Nasser",surname:"Mohamed Ramli",slug:"nasser-mohamed-ramli",fullName:"Nasser Mohamed Ramli"}],corrections:null},{id:"57365",title:"Breast Cancer Detection by Means of Artificial Neural Networks",doi:"10.5772/intechopen.71256",slug:"breast-cancer-detection-by-means-of-artificial-neural-networks",totalDownloads:1641,totalCrossrefCites:3,totalDimensionsCites:7,hasAltmetrics:1,abstract:"Breast cancer is a fatal disease causing high mortality in women. Constant efforts are being made for creating more efficient techniques for early and accurate diagnosis. Classical methods require oncologists to examine the breast lesions for detection and classification of various stages of cancer. Such manual attempts are time consuming and inefficient in many cases. Hence, there is a need for efficient methods that diagnoses the cancerous cells without human involvement with high accuracies. In this research, image processing techniques were used to develop imaging biomarkers through mammography analysis and based on artificial intelligence technology aiming to detect breast cancer in early stages to support diagnosis and prioritization of high-risk patients. For automatic classification of breast cancer on mammograms, a generalized regression artificial neural network was trained and tested to separate malignant and benign tumors reaching an accuracy of 95.83%. With the biomarker and trained neural net, a computer-aided diagnosis system is being designed. The results obtained show that generalized regression artificial neural network is a promising and robust system for breast cancer detection. The Laboratorio de Innovacion y Desarrollo Tecnologico en Inteligencia Artificial is seeking collaboration with research groups interested in validating the technology being developed.",signatures:"Jose Manuel Ortiz-Rodriguez, Carlos Guerrero-Mendez, Maria del\nRosario Martinez-Blanco, Salvador Castro-Tapia, Mireya Moreno-\nLucio, Ramon Jaramillo-Martinez, Luis Octavio Solis-Sanchez,\nMargarita de la Luz Martinez-Fierro, Idalia Garza-Veloz, Jose Cruz\nMoreira Galvan and Jorge Alberto Barrios Garcia",downloadPdfUrl:"/chapter/pdf-download/57365",previewPdfUrl:"/chapter/pdf-preview/57365",authors:[{id:"19773",title:"Dr.",name:"Jose Manuel",surname:"Ortiz-Rodriguez",slug:"jose-manuel-ortiz-rodriguez",fullName:"Jose Manuel Ortiz-Rodriguez"},{id:"22531",title:"Dr.",name:"Maria Del Rosario",surname:"Martinez-Blanco",slug:"maria-del-rosario-martinez-blanco",fullName:"Maria Del Rosario Martinez-Blanco"},{id:"183473",title:"Dr.",name:"Luis Octavio",surname:"Solis-Sanchez",slug:"luis-octavio-solis-sanchez",fullName:"Luis Octavio Solis-Sanchez"},{id:"211746",title:"Dr.",name:"Margarita de la Luz",surname:"Martinez-Fierro",slug:"margarita-de-la-luz-martinez-fierro",fullName:"Margarita de la Luz Martinez-Fierro"},{id:"221383",title:"Dr.",name:"Carlos",surname:"Guerrero-Mendez",slug:"carlos-guerrero-mendez",fullName:"Carlos Guerrero-Mendez"},{id:"221385",title:"MSc.",name:"Salvador",surname:"Castro-Tapia",slug:"salvador-castro-tapia",fullName:"Salvador Castro-Tapia"},{id:"221386",title:"M.Sc.",name:"Mireya",surname:"Moreno-Lucio",slug:"mireya-moreno-lucio",fullName:"Mireya Moreno-Lucio"},{id:"221387",title:"MSc.",name:"Ramon",surname:"Jaramillo-Martinez",slug:"ramon-jaramillo-martinez",fullName:"Ramon Jaramillo-Martinez"},{id:"221391",title:"Dr.",name:"Idalia",surname:"Garza-Veloz",slug:"idalia-garza-veloz",fullName:"Idalia Garza-Veloz"},{id:"221393",title:"MSc.",name:"Jorge Alberto",surname:"Barrios-Garcia",slug:"jorge-alberto-barrios-garcia",fullName:"Jorge Alberto Barrios-Garcia"}],corrections:null},{id:"57265",title:"Applications of Artificial Neural Networks in Biofuels",doi:"10.5772/intechopen.70691",slug:"applications-of-artificial-neural-networks-in-biofuels",totalDownloads:1476,totalCrossrefCites:0,totalDimensionsCites:3,hasAltmetrics:0,abstract:"This chapter is focused on the application of artificial neural networks (ANNs) in the development of alternative methods for biofuel quality issues. At first, the advances and the proliferation of models and architectures of artificial neural networks are highlighted in the text by the characteristics of robustness and fault tolerance, learning capacity, uncertain information processing and parallelism, which allow the application in problems of complex nature. In this scenario, biofuels are contextualized and focused on issues of quality control and monitoring. Therefore, this chapter leads to a study of prediction and/or classification of biofuels quality parameters by the description of published works on the topic under discussion. Afterwards, a case study is performed to demonstrate, in a practical way, the steps and procedures to build alternative models for predicting the oxidative stability of biodiesel. The procedure goes from the processing of the data obtained by the near infrared until the evaluation of the alternative method developed by the neural network. In addition, some evaluation parameters are described for the assessment of the alternative method built. As a result, the feasibility and practicality of the application of neural networks to the quality of biofuels are proven.",signatures:"Alex Oliveira Barradas Filho and Isabelle Moraes Amorim Viegas",downloadPdfUrl:"/chapter/pdf-download/57265",previewPdfUrl:"/chapter/pdf-preview/57265",authors:[{id:"210152",title:"Dr.",name:"Alex",surname:"Barradas Filho",slug:"alex-barradas-filho",fullName:"Alex Barradas Filho"},{id:"210297",title:"M.Sc.",name:"Isabelle",surname:"Viegas",slug:"isabelle-viegas",fullName:"Isabelle Viegas"}],corrections:null},{id:"57446",title:"ANN Modelling to Optimize Manufacturing Process",doi:"10.5772/intechopen.71237",slug:"ann-modelling-to-optimize-manufacturing-process",totalDownloads:2698,totalCrossrefCites:9,totalDimensionsCites:13,hasAltmetrics:0,abstract:"Neural network (NN) model is an efficient and accurate tool for simulating manufacturing processes. Various authors adopted artificial neural networks (ANNs) to optimize multiresponse parameters in manufacturing processes. In most cases the adoption of ANN allows to predict the mechanical proprieties of processed products on the basis of given technological parameters. Therefore the implementation of ANN is hugely beneficial in industrial applications in order to save cost and material resources. In this chapter, following an introduction on the application of the ANN to the manufacturing process, it will be described an important study that has been published on international journals and that has investigated the use of the ANNs for the monitoring, controlling and optimization of the process. Experimental observations were collected in order to train the network and establish numerical relationships between process-related factors and mechanical features of the welded joints. Finally, an evaluation of time-costs parameters of the process, using the control of the ANN model, is conducted in order to identify the costs and the benefits of the prediction model adopted.",signatures:"Luigi Alberto Ciro De Filippis, Livia Maria Serio, Francesco Facchini\nand Giovanni Mummolo",downloadPdfUrl:"/chapter/pdf-download/57446",previewPdfUrl:"/chapter/pdf-preview/57446",authors:[{id:"210129",title:"Dr.",name:"Francesco",surname:"Facchini",slug:"francesco-facchini",fullName:"Francesco Facchini"},{id:"210244",title:"Dr.",name:"Livia Maria",surname:"Serio",slug:"livia-maria-serio",fullName:"Livia Maria Serio"},{id:"210262",title:"Prof.",name:"Giovanni",surname:"Mummolo",slug:"giovanni-mummolo",fullName:"Giovanni Mummolo"},{id:"210263",title:"Prof.",name:"Luigi Alberto Ciro",surname:"De Filippis",slug:"luigi-alberto-ciro-de-filippis",fullName:"Luigi Alberto Ciro De Filippis"}],corrections:null},{id:"57282",title:"Artificial Neural Networks (ANNs) for Spectral Interference Correction Using a Large-Size Spectrometer and ANN-Based Deep Learning for a Miniature One",doi:"10.5772/intechopen.71039",slug:"artificial-neural-networks-anns-for-spectral-interference-correction-using-a-large-size-spectrometer",totalDownloads:1535,totalCrossrefCites:6,totalDimensionsCites:13,hasAltmetrics:1,abstract:"Artificial neural networks (ANNs) are evaluated for spectral interference correction using simulated and experimentally obtained spectral scans. Using the same data set (where possible), the predictive ability of shallow depth ANNs was validated against partial least squares (PLS, a traditional chemometrics method). Spectral interference (in the form of overlaps between spectral lines) is a key problem in large-size, long focal length inductively coupled plasma-optical emission spectrometry (ICP-OES). Unless corrected, spectral interference can be sufficiently severe to the point of preventing precise and accurate analytical determinations. In miniaturized, microplasma-based optical emission spectrometry with a portable, short focal length spectrometer (having poorer resolution than its large-size counterpart), spectral interference becomes even more severe. To correct it, we are evaluating use of deep learning ANNs. Details are provided in this chapter.",signatures:"Z. Li, X. Zhang, G. A. Mohua and Vassili Karanassios",downloadPdfUrl:"/chapter/pdf-download/57282",previewPdfUrl:"/chapter/pdf-preview/57282",authors:[{id:"60925",title:"Prof.",name:"Vassili",surname:"Karanassios",slug:"vassili-karanassios",fullName:"Vassili Karanassios"}],corrections:null},{id:"56846",title:"Solar Radiation Prediction Using NARX Model",doi:"10.5772/intechopen.70570",slug:"solar-radiation-prediction-using-narx-model",totalDownloads:1663,totalCrossrefCites:2,totalDimensionsCites:2,hasAltmetrics:0,abstract:"The human brain, like every vital organ, is constituted of neurons. It is through this organ that we can learn and reason, reflect and memorize. The geniality of human brain and more particularly of its neurons motivates several researchers to interest to this research and to benefit from its biological aspect. The idea was to reproduce, in an artificial way, the behaviors observed in man. It was in 1943 that the first artificial neural network (ANN) was created by Warren McCulloch and Walter Pitts. It is a simple elementary processor imitating the structure and the functioning from the biological neuron. Artificial neural network is characterized by its capacity to learning and generalizing. It represents a very powerful tool. It provided multiple solutions to different complex problems. In these recent years, its effectiveness is proved in various researches fields. ANN is subdivided on two main groups, the static and dynamic neural network. The choice of the one or the other neural network type depends to the application to be processed and the complexity of model. For static neural network, information propagates in a single direction, layer by layer, and from the inlet to the outlet. They are generally used in various applications such as classifications, pattern recognition, and functions approximation. For the dynamic neural network dynamic neural network is not limited. Each neuron can send and receive information from all other neurons. The dynamic neural network architecture includes frequently one or more cycles which necessarily contain at least one delay connection. This gives rise to the dynamism notion. This neural network type is more complex than the static one, but it is more efficient for some particular applications such as dynamic modeling, monitoring, and process control. In this chapter, nonlinear autoregressive models with exogenous input (NARX) model, as type of dynamic neural network, will be used to the solar radiation prediction. Simulation results will be presented to prove the effectiveness of this model compared to those obtained using the static one.",signatures:"Ines Sansa and Najiba Mrabet Bellaaj",downloadPdfUrl:"/chapter/pdf-download/56846",previewPdfUrl:"/chapter/pdf-preview/56846",authors:[{id:"210487",title:"Dr.",name:"Ines",surname:"Sansa",slug:"ines-sansa",fullName:"Ines Sansa"},{id:"210489",title:"Prof.",name:"Najiba",surname:"Mrabet Bellaaj",slug:"najiba-mrabet-bellaaj",fullName:"Najiba Mrabet Bellaaj"}],corrections:null},{id:"57304",title:"Data Assimilation by Artificial Neural Networks for an Atmospheric General Circulation Model",doi:"10.5772/intechopen.70791",slug:"data-assimilation-by-artificial-neural-networks-for-an-atmospheric-general-circulation-model",totalDownloads:1717,totalCrossrefCites:8,totalDimensionsCites:13,hasAltmetrics:0,abstract:"Numerical weather prediction (NWP) uses atmospheric general circulation models (AGCMs) to predict weather based on current weather conditions. The process of entering observation data into mathematical model to generate the accurate initial conditions is called data assimilation (DA). It combines observations, forecasting, and filtering step. This paper presents an approach for employing artificial neural networks (NNs) to emulate the local ensemble transform Kalman filter (LETKF) as a method of data assimilation. This assimilation experiment tests the Simplified Parameterizations PrimitivE-Equation Dynamics (SPEEDY) model, an atmospheric general circulation model (AGCM), using synthetic observational data simulating localizations of meteorological balloons. For the data assimilation scheme, the supervised NN, the multilayer perceptrons (MLPs) networks are applied. After the training process, the method, forehead-calling MLP-DA, is seen as a function of data assimilation. The NNs were trained with data from first 3 months of 1982, 1983, and 1984. The experiment is performed for January 1985, one data assimilation cycle using MLP-DA with synthetic observations. The numerical results demonstrate the effectiveness of the NN technique for atmospheric data assimilation. The results of the NN analyses are very close to the results from the LETKF analyses, the differences of the monthly average of absolute temperature analyses are of order 10–2. The simulations show that the major advantage of using the MLP-DA is better computational performance, since the analyses have similar quality. The CPU-time cycle assimilation with MLP-DA analyses is 90 times faster than LETKF cycle assimilation with the mean analyses used to run the forecast experiment.",signatures:"Rosangela Saher Cintra and Haroldo F. de Campos Velho",downloadPdfUrl:"/chapter/pdf-download/57304",previewPdfUrl:"/chapter/pdf-preview/57304",authors:[{id:"215748",title:"Dr.",name:"Rosangela",surname:"Cintra",slug:"rosangela-cintra",fullName:"Rosangela Cintra"},{id:"215753",title:"Dr.",name:"Haroldo",surname:"Campos Velho",slug:"haroldo-campos-velho",fullName:"Haroldo Campos Velho"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"5703",title:"Electrical Resistivity and Conductivity",subtitle:null,isOpenForSubmission:!1,hash:"1610778635f74a85054885a032a5554a",slug:"electrical-resistivity-and-conductivity",bookSignature:"Adel El 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1. Introduction
Linear economic model has been constructed on the premise of production, use, and disposal of used resources as wastes. However, there are serious limitations associated with the linear paradigm. These include nonrenewability, unsustainability, and environmental perturbations characterized by negative impacts on air, eco-diversity, soil, and water quality and safety. On the other hand, circular economic model maximizes the 3 (three)Rs of reduce, reuse, and recycle resources. In particular, circular economy applies residue from one process as input material for another process. This approach delivers sustainable benefits for humanity in terms of air, ecology, energy, environment, food, forest, housing, sanitation, soil and water quality, safety and security; as well as improvements in animal and human health, economic, social, and industrial developments.
On the predicate of biorefinery platform, biotechnological upgrading of biomass via biological, chemical, physical or some combinations of these would create bio-based energy, chemicals, and other beneficial metabolites and products within the domain of circular economic model. In this context, anaerobic digestion (AD) is an attractive technology as it would utilize organic resources in waste streams to generate biogas and digestate. However, the quality of digestate is dependent upon variables such as characteristics of feedstock, digester process, and treatment options. Digestates emanating from AD of animal manure, energy crops, agricultural residues, organic fraction of municipal solid wastes (OFMSW), and other feedstocks have been reported in published literature [1, 2, 3]. On the other hand, there is dearth of reports on nutrient properties of digestate generated from AD processes that utilized cassava peeling residue (CPR) as sole feedstock. This chapter presents relevant information on digestates in general, and new results of a technical experiment conducted to secure overview assessment of nitrogen (N), phosphorus (P) and potassium (K) compositions of liquid fraction of CPR digestate.
2. Anaerobic digestion (AD)
AD is a biochemical process that decomposes organic matter to generate flammable biogas and residual digestate. The process is achieved with the assistance of a suite of microorganisms in a near oxygen free environment. Biogas is basically composed of methane and carbon dioxide in the respective range of 40–75% and 25–40%. Other constituents are hydrogen, nitrogen, oxygen, hydrogen sulfide and other trace components ranging from 0.1 to 3% [4]. Successful AD operations are carried out within digester or reactor systems designed to supply nutrients required for metabolic activities of the microbes, as well as prevent conditions or elements that may become stressors or present inhibitory effects. AD digester operations and systems may be classified according to the following [5, 6, 7]:
Optimal temperature regimen: psychrophilic (<20°C), mesophilic (30–38°C), and thermophilic (48–57°C);
Total solid (TS) content: wet digestion (TS < 12%), semi-dry digestion (TS 12–20%), and dry digestion (TS ˃ 20%);
Feeding mode: batch, fed-batch, semi-continuous, and continuous;
Process stage or step: single-stage (where all AD processes—hydrolysis, fermentation, acetogenesis, and methanogenesis are executed in one reactor), and multi-stage (where the processes are separated into two or more reactors);
Fluid-dynamic mode: plug flow, completely stirred or mixed, and hybrid; as well as
Today there are millions of anaerobic digesters (domestic, medium, and large-scale versions) operating in the world and generating tremendous amount of biogas. In 2016 for instance, about 60.8 billion m3 of biogas (1.31 EJ) was generated worldwide; most of it, 84%; in Europe (54%) and Asia (30%) [8]. The technical status of AD plants varies widely. Advanced state-of-the-art systems are prevalent in Europe and more low-tech installations in Africa, Asia and South America. However, irrespective of the level of sophistication, the two fundamental products of AD are biogas and digestate.
3. Digestate
Digestate is the residual organic matter generated as coproduct of biogas production. Digestate is suitable for direct use as bio-fertilizer, as raw material for production of bio-fertilizers, and as amendment material to improve soil physical properties such as bulk density, hydraulic conductivity, and moisture retention capacity. Digestate is also attributed with improved sustainability and veterinary safety; reductions in odors, weed seeds, plant pathogens, food chain contamination risks and greenhouse gas emissions. The three basic types of digestate are: whole digestate, liquor (liquid fraction) digestate, and fiber (solid fraction) digestate. Whole digestate is the digestate as obtained leaving the digester at the end of AD process. It contains less than 15% dry matter. This whole digestate could be separated into liquid and solid fractions using appropriate technology and method. The liquid fraction constitutes up to 90% of the digestate by volume, contains 2–6% dry matter, particles <1.2 mm in size, and most of the soluble nitrogen and potassium, while the solid fraction retains most of the digestate phosphorus, and contains dry matter content ˃ 15% [9, 10].
However, the quality, safety, and utility of digestate are dependent upon variables such as feedstock characteristics (pH, chemical composition, carbon-nitrogen ratio (C/N), particle size), digester process (temperature, inoculum, microbial community, hydraulic retention time (HRT)), as well as pre- and post-digestion treatments. Feedstock should possess balanced nutrients, including optimal C/N to satisfy physiological needs of the microorganisms. High or low C/N would disrupt biogasification and lead to reduced biogas output due to low buffer capacity (high C/N) or ammonia inhibition (low C/N). Generally, for biogas production, C/N of 20–30 is considered optimal. For food wastes, C/N of around 15 could be appropriate. Digestates within C/N range of 15–20 are regarded as safe for application to agricultural land without further treatment [11]. When sole feedstock lacks sufficient nutrients for adequate C/N, feedstocks with complimentary nutrients profile are co-digested to offset the limitations. Table 1 highlights some feedstocks that have been used in AD operations and digestate studies.
S/N
Feedstock
S/N
Feedstock
1
Agro-industrial residues
61
Miscanthus sacchariflorus (Maxim.) Hack silage
2
Animal manure
62
Miscanthus sinesis giganteus Silage
3
Barley straw
63
Molasses
4
Biodegradable plastics
64
Mozzarella Cheese Whey
5
Biodiesel wastewaters
65
Municipal solid waste
6
Biowastes
66
Municipal waste water
7
Blood industry residues
67
Oat silage
8
Buffalo farming wastewater
68
Olive oil mill wastewater
9
Buffalo manure
69
Olive Pomace, olive waste
10
Cacao
70
Orange peel waste
11
Cardboard
71
Organic fraction of municipal solid waste
12
Cattle/cow: manure/slurry
72
Paper
13
Cattle (beef) urine
73
Paper sludge
14
Cereal bran
74
Peach-juice pulp
15
Cereal-WPS
75
Peeled Cassava wash water
16
Cereals
76
Pharmaceutical industry sludge
17
Cheese Whey
77
Phleum pratense L. silage
18
Chicken manure
78
Pig urine
19
Chroococcus sp. (algal biomass)
79
Piggery wastewater
20
Coconut chips
80
Pig/swine effluent; manure; slurry
21
Coffee grounds
81
Plum stones
22
Corn
82
Potato chips production residues
23
Corn cob mix
83
Potato waste
24
Cornmeal
84
Potatoes
25
Corn residue
85
Poultry litter/manure/waste
26
Cover crops
86
Primary sludge
27
Crushed cassava juice
87
Pumpkin waste
28
Dairy manure
88
Rabbit manure
29
Distiller’s waste
89
Rape residue
30
Dried blood of slaughterhouse waste
90
Restaurant food waste
31
Duck slaughterhouse sludge
91
Rice residues
32
Edible oil
92
Rye
33
Energetic crops
93
Sewage sludge
34
Energy maize
94
Sida Hermaphrodita Rusby silage
35
Fennel waste
95
Slaughterhouse waste
36
Fish by-product
96
Sludge from Slaughterhouse wastewater treatment plant
37
Food industry residues
97
Solid farmyard manure
38
Food waste
98
Sorghum silage
39
Fruits and distillery by-products
99
Source-separated organic household waste
40
Fruit Marc
100
Source-separated municipal solid waste
41
Garden wastes
101
Starch processing wastewater
42
Glycerin
102
Straws (cereal, pea)
43
Grape seeds
103
Sugar beet pulp
44
Grass (clover, Sudan); grass silage
104
Sugar sorghum (S. saccharatum L. Moench.) silage
45
Green waste
105
Sunflower residue, sunflower silage
46
Hemp
106
Tea leaves
47
Household kitchen waste
107
Tetraselmis sp. (algal biomass)
48
Household waste
108
Thin stillage (bioethanol by-product)
49
Human excreta
109
Triticale
50
Human urine
110
Triticale silage
51
Industrial and commercial wastes
111
Turkey manure
52
Jute Caddis
112
Vegetable waste
53
Kitchen waste
113
Vinasse
54
Landscape waste
114
Waste-activated sludge
55
Ley silage
115
Waste potato starch
56
Livestock waste
116
Wastewater
57
Maize stover
117
Wastewater sludge
58
Medicago sativa L. silage
118
Wheat
59
Milk (serum, whey)
119
Yeast production wastewater
60
Millet
120
Zea mays L. (corn, maize) silage
Table 1.
Feedstocks used in digestate production and studies.
Source: Assembled from scientific literatures in the public domain, most of them cited in this present work.
4. Regulations, quality, and safety requirements
Perhaps the most important variable affecting the quality and safety of digestate is feedstock. Starting with a high-quality feedstock would virtually guarantee a safe and quality digestate. Source separation can be used to achieve high purity feedstock. The biological, chemical, and physical properties of digestate may be governed by regulations and quality assurance systems. The European Union (EU) and many European national governments have hygienic, quality and safety standards for digestate certification that consider feedstock source and other aspects such as digester process, treatment options, handling and storage requirements. The essential quality and safety requirements for digestate destined as biofertilizer must be achieved regardless of the initial raw material. Essential quality and safety parameters include nutrients content, dry matter and organic dry matter contents, homogeneity, pH, purity (free of inorganic impurities such as glass, metal, plastic, and stones), sanitized and safe for soil organisms and the environment with regards to biological status (pathogenic organisms) and chemical status (organic and inorganic contaminants/pollutants). Furthermore, the digestate should be free of odor, phytotoxicity and weed seeds; and be satisfactorily stabilized.
Quality assurance systems for digestate certification may comprise monitoring to ensure control; standardization to ensure repeatable performance; characterization label to identify product fitness; declaration to describe product constituents; application guidelines to ensure safe and proper use; and documentation to prove that the product received required treatments following approved protocols. Table 2 presents established criteria and characteristics for the production and use of quality and safe digestates. In the EU, conformity with these criteria is enough to ensure that digestate complies with European “End of Waste” criteria; and can be used without further waste management controls.
Criteria
Process/parameter
Requirements
Hygiene
Pasteurization at 70°C
1 h
Sterilization at 133°C
20 min
Weed seeds and sprouting plant parts
≤2/L
Odor
Free of annoying odors
Pathogens
E. coli
≤1000 CFU/g fresh matter
Salmonella spp.
Absent in 25 g fresh matter
Heavy metals
Cadmium (Cd)
0.8–20 mg/kg DM
Chromium (Cr)
75–1000 mg/kg DM
Copper (Cu)
75–1000 mg/kg DM
Lead (Pb)
80–900 mg/kg DM
Mercury (Hg)
0.6–16 mg/kg DM
Nickel (Ni)
30–300 mg/kg DM
Zinc (Zn)
300–4000 mg/kg DM
Organic pollutants
Polycyclic aromatic hydrocarbons
3–6 mg/kg DM
Dioxins and furans
20 ng TE/kg
Chlorinated pesticides
0.5 mg/kg Product
Polychlorinated biphenyls
0.2 mg/kg DM
Absorbable organic halogens
500 mg/kg DM
Linear alkylbenzene sulphonates
1300 mg/kg DM
Nonylphenol and nonylphenolethoxylates
10 mg/kg DM
DEPH: Di (2-ethylhexyl) phthalate
50 mg/kg DM
Inorganic pollutants
Non-stone impurities >2 mm (glass, metal, plastic, etc.)
0.5% m/m dry matter
Stones > 5 mm
8% m/m dry matter
Stability
Volatile fatty acids
0.43 g COD/g VS
Residual biogas potential
0.25 l/g VS
Respiration rate
16 mg CO2 g VS−1 day−1
Declarations
Name of producer, type of product (whole, liquid, solid), mass of product, total nitrogen, ammonium nitrogen, total phosphorus, total potassium, soluble chloride, soluble sodium, dry matter, volatile solids, pH, bulk density, etc.
Relevant units where applicable (e.g., kg; kg/m3; mg/(kg DM); mg/L; %;)
Additives and chemicals
Lime, iron chloride, iron oxide, bentonite, diatomaceous earth
Feedstock sources
Agriculture (e.g., manure, harvesting by-products, silage, energy crops); animal by-products (e.g., manure, stomach intestine, raw milk); food industry (residues from food industry that contain food grade additives); food related shops (e.g., potatoes, dairy waste, bread, meat remnants, flowers, plants); forrest (e.g., bark, wood chips, sludge from the cellulosic industry); parks, gardens (e.g., leaves, grass); greenhouses (e.g., tops, peat products); households, kitchens, restaurants (e.g., fruit and vegetables residues, food, coffee and tea remainders, egg shells); etc.
Table 2.
Quality and safety validation criteria for digestates.
In the context of AD and digestate, we may distinguish between pre- and post-treatment processes. A pretreatment process refers to a processing operation applied upstream, before the digestate emerges from the digester. This could range from size reduction or thermochemical treatment of feedstock substrate; to process management (such as pH, temperature, and retention time control). On the other hand, a posttreatment process is that processing operation applied downstream of digestate harvest. This may also involve size reduction, other unit operations; composting, and end-product requirements that ensure the digestate sanitation. Post treatment may generate nutrient concentrates, liquid and solid fraction digestates conditioned to standardized biofertilizers, and final liquid effluent that could be discharged into a stream or sewage system. Benefits of posttreatment include enhanced marketability, reductions in handling, storage and transportation costs/requirements, and compliance with environmental regulations.
Depending on the starting feedstock and desired end product form of the digestate, similar technologies could be used for pre and post treatment processing. Applied technologies and methods may be classified as biological, chemical, or physical. The methods could also be used in combination. Biological treatment could be accomplished with the use of microorganisms and catalysts; chemical treatment with acids, alkalis and oxidants; and physical treatment by mechanical and thermal means. Physicochemical treatment combines physical and chemical techniques. Ammonia fiber explosion (AFEX), and supercritical CO2 explosion are examples. The major classifications of treatment options and associated technologies are presented in Table 3.
Inorganic acids (hydrochloric, nitric, phosphoric, sulfuric); organic acids (fumaric, maleic). May be used in percolation, plug flow, shrinking-bed, batch, and countercurrent modes
Alkalis
Ammonia, lime
Ammonia recovery
Ion exchange; scrubbing, stripping, precipitation (struvite)
In the service of circular economy, there are many applications management options for digestate. These may include algae cultivation, energy production, bio-adsorbent production, building materials production, nutrients recovery/production, soil creation and other value-added commodities. Perhaps the two most widely recognized utilities of digestate are as land application for soil amendment and as biofertilizer.
6.1 Biofertilizer and soil amendment
Technological aids used in modern agriculture such as inorganic fertilizers and antibiotics have negative impacts on soil, water, and air quality and safety, and therefore pose health risks to humans and the ecosystem. Inorganic fertilizers for instance have caused environmental and soil quality degradation, eutrophication and heavy metals pollution. Similarly, field-spreading agricultural land with raw/untreated manures derived from medicated livestock contributes to dissemination of veterinary antibiotic residues and antibiotic-resistant pathogens. Lincomycin, monensin, and sulfamethazine antibiotics were reported to affect soil microbial community composition and respiration, denitrification and nitrogen transformations [37]. Applications of digestate for biofertilizer and soil amendment purposes could ameliorate some of these adverse effects.
Amendment propensity relates to capability to maintain soil fertility and humus balance. Dairy slurry digestate was found richer in humic substances than raw dairy slurry [38]. Researchers concluded that digestate enhanced soil biological stability, microbial biomass and enzymatic activities [39].
On the other hand, fertilizer properties relate to provision of nutrients necessary for good crop performance. Leaves of alfalfa plant fertilized with digestate had higher contents of N, P, and K in comparison to alfalfa fertilized with mineral fertilizers [40]. Digestate also produced higher yields of dent corn than the application of chemical fertilizers [38]; higher yield of potato (Solanum tuberosum) over the application of compost [41]; and 30% increase in yield over farm yard manure [42].
6.2 Nutrients recovery
Digestate is applied in recovery of nutrients, production of fertilizers and volatile fatty acids (VFAs). Livestock manure contains about 49 g N/kg TS and 6 g P/kg TS; energy crops, 17 g N/kg TS and 2.5 g P/kg TS; and agro-wastes, 27 g N/kg TS and 3 g P/kg TS [43]. Much of these nutrients remain in digestate after AD operation. For example, total N, P, and K values for digestates obtained from wet AD of agricultural wastes were reported respectively in the ranges 44–120, 8–42, and 28–95 g/kg DM [44]. These nutrients could be recovered/harvested with the technologies outlined in Table 3.
VFAs are important input organic acids used extensively in the bioenergy, food, chemical, cosmetic, pharmaceutical, textile, and other industries. Acetic acid (E 260), propionic acid (E 280) and butyric acid are examples; and are GRAS (generally regarded as safe) rated by the FDA. Acetic acid is used to defend against Campylobacter, Escherichia coli, Listeria, Salmonella, and other pathogens in beef, chicken, pork, turkey, carcasses, skin and hides [45]. Butyric acid is used in the textile industry to enhance heat and sunlight resistance of fibers. In the food industry, it is used as additive for flavor formulation and modification [46]. Similarly, propionic acid (E 280) is used as antibacterial and antifungal agent to decontaminate packaging films and coatings, and to protect meat and meat products such as sausages, bologna and ham. VFAs have been harvested from digestates generated from short-term dry AD of swine manure, generated from AD of food waste, and used in recovery of biological nitrogen and phosphorus from sewage sludge [47, 48, 49].
6.3 Energy production
Digestate can be deployed for energy generation. Recirculating digestate into the digester maximizes biogas production, at the same time minimizing methane emissions during digestate storage, transport, and use. Digestate was pyrolyzed (via the use of Pyroformer, quartz rotary kiln reactor, and thermo-catalytic reforming reactor) to produce biofuels: pyrolysis oil (biooil) and pyrolysis gas (syngas). The biooil generated by thermo-catalytic reforming process at 750°C had a higher heating value of 33.9 MJ/kg, and a total acid number of 4.9 mgKOH/g [50].
Algae have widespread applications and potentials in: biofuels, cosmetics, biofertilizer, infant formulas, nutritional supplements, livestock feeds, chemical and allied industries, and biodegradable packaging. Perhaps more importantly, digestate could be used for the cultivation and production of microalgae. In the context of biorefinery platform and circular economy, various compounds produced by microalgae and their applications have been reported [51, 52].
6.4 Other applications
Digestates have other utilities and management options. These include applications in aquaculture, gardening and horticulture, and the production of building materials and biochar.
6.4.1 Biochar
Biochar (charcoal) is the byproduct of thermal pyrolysis of carbonaceous biomass; and has carbon sink properties. Dairy waste and whole sugar beet digestate biochar were effective in eliminating heavy metals (Pb2+, Cu2+, Ni2+, and Cd2+) from aqueous solutions [53].
6.4.2 Gardening and horticulture
Due to its organic origin and physicochemical characteristics, digestate is useful in gardening and horticulture. It could be applied in soil creation or remediation, and has found applications in green houses, plant nurseries, and home gardening [54].
6.4.3 Building materials
A 50% substitution of wood with cattle manure digestate produced particleboard panels that met ANSI performance requirements [55]. USDA reported that medium-density fiberboard and wood/plastic composite engineered materials could be created using digestate solids without compromising mechanical or aesthetic values [56].
6.4.4 Aquaculture
Digestate is better than raw manure in fertilizing fish ponds. Firstly, digestate is hygienic because most of the bacteria, parasites and their eggs are destroyed in the AD process. Thus, pond sanitation is improved; minimizing fish diseases and the cost of veterinary services. Secondly, the digestate is largely stabilized and therefore does not consume and compete with fish for dissolved oxygen. Tilapia, Silver carp, Bighead carp, Silver barb and Mrigal fish species raised in pond fertilized with digestate matured faster and achieved higher net weight gain than counterparts raised in pond fertilized with chemical fertilizer or raw manure. By comparison, while chemical fertilizer increased net yield over raw manure by 27%, digestate increased net yield by 55% [57].
6.4.5 Bio-adsorbents and bedding
Digestates have been applied as bio-adsorbents to scavenge heavy metals from contaminated soils and water [58], and as chicken litter [54], and other livestock bedding [56, 59].
7. Cost implications
The big picture cost elements relevant to AD systems include land acquisition, site preparation/development, plant and machinery (including digester/reactor, pre and post treatment technologies), personnel, feedstock, environmental impact, other operating costs (electricity, logistics, regulations), and revenue from products (biogas and digestate). In the case of digestate, feedstock, treatment processes, and the logistics of storage, transport, handling and field application bear crucial concerns. Cost-effective digestate production process is presaged by efficient feedstock collection and sorting operations. A cost benefit analysis of municipal solid waste management system in Yangon, Myanmar, identified weak organizational structure and ineffective collection methods in the existing system that operated with just 32% waste collection efficiency. An alternative system with increased waste collection efficiency was then proposed. The new system required labor and vehicular productivity; using vehicles with container-hoist handling mechanism. The new system reduced operating and other costs associated with the old system by up to 42% [60]. It is noteworthy that consumer and public environmental behavior and cooperation on waste management could be modified by pecuniary and nonpecuniary information. In Surabaya city, Indonesia, researchers found that in the reference case in which the no information treatment was applied, mean WTP (willingness to pay) for marginal improvements in a waste collection and disposal program was estimated to be US$ 14.65. The researchers reported that pecuniary information increased WTP by 20.5%, whereas non-pecuniary information had a negative but statistically insignificant effect on WTP [61].
A situation where 50% of whole unprocessed digestate was applied on agricultural land near the generating biogas plant and the other 50% transported to a location 20 km away was studied. Cost for digestate utilization near the biogas plant was € 3.34 (US$ 3.73)/t, and that at a location 20 km away was € 5.47 (US$ 6.10)/t [62]. This study highlights the impact that location or site of digestate utilization could have on cost. Such distance related cost also applies to feedstock substrate. Generally, the farther the distance, the higher the cost.
Researchers performed specific cost analysis for six scenarios that involved direct land application of digestate as reference, and various treatment technology options that included screw press and decanter centrifuge separation, belt drying, evaporation concentration, purification by ultrafiltration and reverse osmosis, and nutrients recovery by ammonia stripping and precipitation. Result indicated that net specific costs ranged from € 1.94 (US$ 2.16)/m3 of digestate for the reference scenario, to € 5.45 (US$ 6.08)/m3 for stripping, to € 6.80 (US$ 7.58)/m3 for belt dryer [62]. Similarly, the costs of AD were found to vary up to € 109 (US$ 122)/t of digestate from € 35 (US$ 39)/t for basic storage of digestate for aerobic conditioning, to € 70 (US$ 78)/t for digestate ready for direct land application, to € 79 (US$ 88)/t for on farm co-digestion [63].
Case studies were conducted for separation systems in three regions (Aachen, Borken, and Siegen) of Germany. The researchers determined that investment and variable costs were respectively € 23,000 (US$ 25,536) and € 0.47 (US$ 0.52)/m3 for screw press; € 27,000 (US$ 29,977) and € 0.48 (US$ 0.53)/m3 for screening drum press; and € 163,000 (US$ 180,970) and € 1.46 (US$ 1.62)/m3 for decanter centrifuge. Further analysis revealed the unit cost of digestate disposal for screening drum press varied from € 4.1 (US$ 4.6)/m3 in Aachen to € 4.8 (US$ 5.3)/m3 in Borken, and Siegen [64].
The following were reported about AD in the UK. Least cost post treatment technology for digestate derived from a 10% solids content food waste was biological oxidation at £13.18 (US$ 16.97)/t of feedstock. At 20% solids content, least cost option was direct application of whole digestate to agricultural land at £8.76 (US$ 11.28)/t. The cost of treating 4000 t of slurry with a mechanical screen separator was £0.44 (US$ 0.57)/t per year, and treatment with decanting centrifuge cost £2.21 (US$ 2.85)/t per year. Furthermore, about £3.5M (US$ 4.5 M) would be required to construct a 1 (one) MWe AD plant utilizing farm wastes as feedstock [65, 66, 67].
In the continent of Africa, cost of establishing a 4 m3 anaerobic digester was found to range from US$ 555 in Uganda to US$ 698 in Cameroun to US$ 979 in Rwanda [68]; while that of founding a family size floating drum plant was estimated at US$ 1667 [69].
Techno-economic analyses were performed for post treatment technologies used to recover nutrients from the digestates of five full scale farm AD systems. Results showed membrane technology had specific cost of € 6.97 (US$ 7.72)/m3 of treated digestate. Drying was estimated at € 5.81 (US$ 6.44)/m3, while stripping operated at € 5.44 (US$ 6.03)/m3 [70]. In addition, the process economics of membrane-based nutrients extraction and fractionation from dairy manure digestate indicated cost of solid-liquid separation unit to be US$ 11,000; the microfiltration extraction unit cost US$ 30,000; the nanofiltration fractionation unit was priced at US$ 60,000; and the daily cost of operation (chemicals, energy and water) was approximately US$ 24 [71].
Finally, digestates are used as quilt for cattle bedding and poultry litter due to significant cost offsets to livestock farms. The cost of solid digestate as animal bedding (US$ 55 per dry ton) is cheaper than the cost of alternative wood-based replacement materials such as wood chips at US$ 65 per dry ton or sawdust and shavings at US$ 124 to US$ 248 per tonne [55, 59].
8. Challenges and opportunities
Digestates have good fertilizer qualities: nutrients, safety and other properties required for soil amendment and plants production. However, relative to mineral fertilizers, digestates are not well known in many countries. Therefore, their potential as mineral fertilizer alternative/substitute is limited. Perhaps, standardized quality assurance and control protocols, regulations, certifications, legal and other institutional management systems organized internationally could help demonstrate digestates’ benefits, quality and safety, and thereby engender confidence in their utilization as sustainable fertilizer and soil amendment products. Reconciling and bringing such issues and their benefits to existence present challenges and opportunities. Presented in Table 4 are some of these challenges and opportunities of the waste, AD and digestate system.
Issues
Challenges and opportunities
8.1. Concept of waste
Challenge: the conventional or customary status of looking at waste as a problem presents significant challenge. Opportunity: seeing waste as potential resource would help change perception and attitude, possibly stimulating salient management options. Opportunities may emerge in the areas of prevention, recovery, collection, sorting, reducing, reusing, and recycling. For developing countries these have implications for environmental hygiene and sanitation.
8.2. Biowaste
Challenge: because biodegradable waste could be a source of heavy metals and polluting organic compounds, it presents challenges to life generally, and to the environment. Opportunity: these challenges create opportunities to develop management options (e.g., biological treatments) to protect life, environment, and to benefit agriculture and ecosystem. Biowaste is reported to have potential to tackle climate change in the areas of nitrous oxide (NO2) emissions mitigation, and sequestration capacity of agricultural soils [72].
8.3. E-waste
Challenge: problems and dangers of e-waste, heavy industry products and components; including electrical and electronic equipment, waste batteries, engine blocks, paint, etc. Opportunity: guidance/support for the informal (non or loosely regulated) establishments, to call attention to dangers and health risks that may be associated with used or discarded electronic devices/items (acids, other chemicals, radioactive materials, etc.).
8.4. Mineral waste
Challenge: mining of solid minerals do present health and environmental challenges. Opportunity: chances to implement safeguards for hazardous minerals and to divert safe wastes to beneficial applications. Examples are uses as substitute for backfill material in open pit mining, landfill, or as grit in construction materials. Production of concrete and brick for structural work (bridges, dams, launch pads, highways) are possibilities.
8.5. Source of feedstock
Challenge: the source of digestate feedstock and its treatment could present barriers. PAS 110 in the UK does not approve certification for digestate generated from mechanically biologically treated waste. Such digestates require proof of biodegradability test to be considered suitable for recycling; like land spreading. There is also the issue of digestate originating from co-digestion of industrial waste and household waste. In the Netherlands, the desire in AD electricity regime to maximize biogas production by mixing manure with other organic material conflicts with AD biofertilizer rules for spreading digestate from co-digested manure on farm land. Opportunity: some of these challenges are consumer-induced barriers and lack quantitative elements. Opportunities might lie in the sociocultural realm, such as modifying social and cultural attitudes and behaviors towards waste and its inherent heterogeneity.
8.6. Unrecovered organic matter
Challenge: AD is more adapted to easily putrescible carbohydrates (starch, sugar). Recalcitrant lignocellulosic components (lignin, etc.) remain undigested. Efficiency of organic matter conversion was quite low as ˃97% of lignin in maize stover was found undigested [73]. AD could thus lead to unrecovered organic matter still present in digestate Opportunity: prospects for advanced and innovative pretreatment technologies to fractionate, recover, purify and convert lignin or other recalcitrant organics to more digestible biopolymers. Alkaline treatment, gamma irradiation, membrane technologies, organosolv, steam explosion, wet oxidation, etc. may come to the rescue (Table 3).
8.7. Informal and low status
Challenge: AD and digestate are perceived to be in domain of informal waste management system and service; and therefore, relegated as only appropriate for the rural populace. Opportunity: integration of formal and informal systems. Training to abate misconceptions, lack of awareness, and raise public profile of digestate. These may purge image of biogas and digestate as products that are derived from wastes, and hence belong to poor/rural settings.
8.8. Legal barriers
Challenge: lack of binding global (and for developing countries, own country) coherent rules, laws, directives, regulations and policy frameworks. Opportunity: the formulation of these guidelines and laws on waste governance system. Implementing appropriate technologies and business models for waste management.
8.9. Data and waste reporting system
Challenge: lack of reliable data on waste management systems, design features, standard operating procedures (SOPs), etc. could limit exchange of ideas and retard progress. Opportunity: waste management value chain information is vital. Quantity, type, economic sector, source, and composition data could guide prioritization of strategies and enable trends forecast that deliver better outcomes. Global exchange of briefs would catalyze spread of best practices.
8.10. Standardization
Challenge: although digestate products have similar characteristics as commercial chemical fertilizers, they are not classified in any way, are poorly developed in most countries, and there is no overall guidance [20, 62, 70]. These barriers restrict utilization and trade. Opportunity: these challenges create opportunities to establish frameworks that enable digestate utilization through standardization, fair comparison, commerce development, and international trade.
8.11. Marketing
Challenge: regional nutrient availability, agricultural structure, season, feedstock and degree of upgrading have been reported to challenge and impact digestate prices and marketing [54]. Opportunity: upgraded products offer increased marketability due to their denser nutrients. Marketing to nutrient deficient regions, non-agricultural sectors and purposes represent prospects. Manufacturers of organic soils, particle- and fiber- boards, landscapers, and private customers all represent credible market outlets.
8.12. Cost barrier
Challenge: initial investment fund is a major issue. Cost of establishing a 4 m3 AD digester in the continent of Africa ranges from US$ 555 to US$ 979 [68]; and the price for a family size floating drum reactor was reported at US$ 1667 [69]. In Sri Lanka, a family unit digester generating 6–10 m3 of biogas per day cost Rs. 17,000 (US$ 5459); and described as difficult proposition for low-income families [74]. In the UK, a 1 MWe AD plant utilizing farm wastes as feedstock cost about £3.5M (US$ 4.5 M) to construct [67]. Also, costs associated with animal breeding and maintenance (veterinary care, feed, water, etc.) escalate operating costs, and constrain availability of manure for feedstock. Opportunity: easing cost barriers would require support with appropriate and necessary interventions (policies, credit facilities, subsidy schemes, preventive maintenance that promote solutions, prolong facilities productive lifespan, and minimize operating costs). Furthermore, transparency on proposals and bidding for new plants and projects could build confidence in the process.
8.13. Urban and rural dichotomy
Challenge: differences between metropolitan, urban, sub-urban, and rural areas can compromise AD projects. Segregation by infrastructure and income for example could affect waste collection and limit access to feedstock. Opportunity: prospects for rural development with public utilities, services, and infrastructure (roads, power, water, etc.) These would facilitate logistics for waste collection, AD processes, and digestate handling/evacuation.
8.14. Contamination of agricultural land
Challenge: most of the digestate produced in AD is used for soil amendment and as biofertilizer. There are risks of spreading animal pathogens, heavy metals, and other pollutants on soils due to the presence of these hazards in animal by-products used in AD. Sulfadiazine and oxytetracycline are antibiotics found in manure of medicated animals that affect soil quality. Twenty five percent of 70 digestate and compost samples assessed in Switzerland contained polycyclic aromatic hydrocarbons (PAHs) concentrations beyond the regulated threshold value of 4000 μg/kgdw [75]. Opportunity: digestate is a sustainable fertilizer and soil improver; thus, necessary to assure its safety. The potential to contaminate soils with pollutants from digestate application beacons vigilance and chances to develop technical and monitoring strategies that sequester and purge the digestates of polluting hazards before their use.
8.15. Air pollution
Challenge: digestate has potential to emit substances and gasses that contaminate the air and influence global warming [11]. Challenges also exist due to lack of practical tools to monitor primary air pollutants [76]. Opportunity: advanced methods of digestate management and reutilization to minimize emissions of air pollutants (ammonia: NH3, nitrous oxide: NO2) and greenhouse gases (methane: CH4, nitrogen dioxide: N2O). Strategies may include processing (composting, curing, dewatering); alternative applications (in construction, aquaculture, regeneration activities); and storage. Development of software tools that enable quantitative monitoring of emissions from digestate soil applications on a routine basis is another prospect area.
8.16. Bad odors
Challenge: compared to raw manure slurry, digestate has fewer bad odors. However, this may not be true when compared to chemical fertilizer. There have been complaints of nuisance odors associated with land-spreading of digestate [77], and at landfills and composting plants [78]. Opportunity: this problem could be due to spreading practice and/or the spreading of unstable digestates. Application of good timing and spreading techniques (trailing-shoes, injection), and use of stabilized digestates (sufficient HRT, aerobic composting) would minimize odor issues.
8.17. Bad legacies
Challenge: there are challenges associated with bad reputation of AD systems and biogas plants around the world. A study in 2006 found that 60% of 600–700 domestic biogas plants in Ethiopia was not functioning [79]. During the 7 years period from 2009, more than 3600 biogas plants were installed in the Tigray region of Ethiopia; and a 2017 study reported that 58.1% of the installations was not operational [80]. The 21 biogas plants installed by Pakistan council for appropriate technology (PCAT) in the 1970s were reported to have failed to perform [81]. In 1986, a survey of the status of 25 biogas plants in Kenya found 36% to be alive, functional and maintained. Another 36% was described as dead, not functional, and not maintained. Unfinished projects accounted for 8%; while remaining plants were reported in disrepair, with varied patterns of being alive, dead, not functional, and not maintained [82]. The regional bioenergy program of the Latin American energy organization (OLADE), catalogs biogas technology projects in Latin American countries. Experience began in 1953 and by 1986 at least 22 countries including Bolivia, Colombia, Costa Rica, Dominican Republic, Ecuador, Grenada, Guatemala, Guyana, Haiti, Honduras, Nicaragua, Jamaica, and Peru had projects at varying levels of implementation. Out of the 3950 biodigesters inventoried, 60% was found operable and 40% was either shut down or functioning irregularly or completely abandoned [83]. Though China rebounded and emerged as a major reference on household digesters, about 50% of biogas tanks installed from 1958 into the 1970s were abandoned in the 1980s. By 1988 the seven million rural digesters existing in 1980 dropped to 4.7 million [84]. In 1986, a survey of biogas plants in Sri Lanka indicated that 61% was functional. By 1996 only 28.5% of completely surveyed 365 biogas systems was reported functional. At this point 16 units had been abandoned and the success rate for biogas systems implementation was reported as 32.9% [74]. In the Netherlands, for a period of over 30 years beginning in the 1970s, many AD projects using biomass were considerably delayed, suspended, abandoned and out rightly never realized. [85, 86]. These failures and circumstances taken together portrayed negative images and bad legacies for biogas plants. Opportunity: reasons adduced for failures included economic, social, technical, and policy components such as high investment and maintenance costs, urbanization and socio-cultural constraints, poor dissemination strategy, complicated permit regulations, shortage of feedstocks, lack of or inadequate training, poor digester design, etc. These reasons provide opportunities to create circumstances, provisions and tools that would promote and sustain biogas systems. Some examples are mobilization of local and external funds, more business-friendly policies and rules, appropriate and sustainable technologies, technical training, warranties for plant performance. Also, public dissemination of information and follow-up on successful programs could help.
8.18. Low diffusion rate
Challenge: in Latin America, the number of rural biogas plants installed yearly from mid-1985 to 1992 was less than 15% of that installed from 1982 to mid-1985. Challenges included technology adoption, technical manpower and materials of construction. However, non-technical reasons for biogas adoption failures accounted for up to 69%, 50% and 25%, respectively, in Cote d’Ivoire (Ivory Coast), Costa Rica and Tanzania [84]. Unstable institutional environment, lack of network and lobby activities, lack of initiatives between academia, research institutes, private sector entrepreneurs and stakeholders were cited nontechnical reasons. For the Netherlands, apart from technological problems; limited economic feasibility, fragmented support from the government, decreases in energy prices, and lack of financial support which made return on investment uncertain contributed to inadequate AD diffusion. Opportunity: cooperation between academia, government, industry and other stakeholders (farmers, energy sector, municipalities). Cooperative efforts that landed mutually beneficial outcomes should be highlighted, applauded and replicated. Well planned long-term, clear and supportive arrangements would facilitate continuity. Government policy that guide search for solutions, market formation and resources mobilization. Ease of technology adoption would also require reliable and sustainable infrastructure (technical assistance, manpower, cohesive farming approach with biogas and digestate, integration and dissemination of societal and cultural values and norms).
8.19. Inhibition of microalgae
Challenge: it has been shown that the green alga (Raphidocelis subcapitata) is sensitive to digestate, with ecotoxicity index; EC50 of 0.77% [87]. Similarly, Scenedesmus bijuga; and oil-rich Chlorella sp., including C. minutissima and C. sorokiniana were found sensitive to digestate. Also, the dark color of liquid digestate of algal biomass inhibited the growth of Chroococcus sp. Therefore, cultivation of algae for value added products recovery could be minimized in the presence of digestate. Opportunity: because algae are exploited for biofuels, and various other useful biotechnological metabolites production by valorization of digestate, the inhibitory effect of digestate on algae cultivation is of practical interest. Therefore, digestate pretreatment or at least its dilution before use [88], would aid good algal productivity.
8.20. Nomadic and free-range culture
Challenge: many developing nations have nomadic animal husbandry architecture and free-range culture. These make the gathering of animal manure as feedstock for digesters a major challenge. In Pakistan, for example, livestock farmers from time to time relocate to weather conditions more benign to their livestock. However, current digester designs used by rural populations such as the fixed dome and floating drum are sedentary and cannot be readily moved by the farmers with their livestock. Opportunity: perhaps this challenge creates opportunity for a mobile biogas system such as the portable biogas plant reported in the year 2016 [81].
8.21. Disparity between developed and developing countries
Challenge: the economic, political and technological mismatches and divides between industrialized and industrially developing countries are challenging local, regional and international waste management systems. Environmental and health dangers do not know or respect boundaries (local, regional, or international) by land, sea, air or space. Planet earth is perhaps at the cusp of the axiomatic global village and economy. Sooner than later, pollution and instability at one corner of the earth would reverberate and affect other parts (Plastics in the oceans? Heavy metals in food, aquatic and terrestrial biota? Ebola in America? Flood events in Zimbabwe, Mozambique, Puerto Rico and U.S. Virgin Islands? Wildfires in Australia, Brazil, Portugal and USA? Coronavirus (COVID–19) in Japan, Singapore, and USA?). Opportunity: cooperation and support are needed to enable developing nations to leapfrog and shorten the learning curve and development timescales. Developing nations need guidance and assistance to cope with technological demands and challenges, and eschew reinventing the wheel. Waste management offers an opportunity for cooperation among nations for the betterment of humanity and planet earth.
Table 4.
Challenges and opportunities of the waste, AD and digestate system.
9. Cassava peeling residue (CPR) digestate
N, P, and K are critical macro nutrients for crops production. N is considered the limiting nutrient in growth and yield [89]. P is required for energy transfer, signal transduction, photosynthesis, and macromolecular respiration [90]. K is responsible for metabolism of cell division, enzymatic reactions of amide formation, and amino acid activation during proteins biosynthesis and substrate phosphorylation [91]. To be a credible mineral fertilizer substitute, digestate must have the capacity to deliver the necessities and requirements of N, P, and K.
Table 1 presented a broad gamut of materials used in biogas and digestate creation. The table covered energy crops, agricultural byproducts, food processing residues, livestock effluents, organic fraction of municipal solid wastes, and pharmaceutical industry sludge. However, cassava peeling residue (CPR) was not represented in the table. There is a published report on ammonium, potassium, total nitrogen, and total phosphorus contents of digestate generated from co-digestion of human urine, cow dung, and cassava effluent (a mixture of peeled cassava wash water and crushed cassava juice) [92]. CPR is a solid substrate abundantly generated during production of cassava root-based food systems such as gari and starch [93]. The present author is not aware of any report on nutrients value of digestate generated from the AD of CPR as sole feedstock. Therefore, a technical experiment was conducted to secure an overview assessment of N, P, and K compositions of liquid fraction of CPR digestate.
Some results of the research work on CPR as sole substrate for AD were reported earlier. These included proximate properties (e.g., moisture content, total solids, volatile solids), digester performance characteristics (methane content of biogas, pH, discharge effluent COD), feedstock materials, sampling procedures, analyses [94]. Presented in Table 5 are results of nutrient values of liquid fraction of CPR digestate. Table 5 results appear to be within the range of some published nutrients values for liquid digestates derived from other feedstocks such as algal biomass (Chroococcus sp.) [88], starch processing wastewater [95], source separated household waste [96], as well as liquid and solid manure slurries [97].
S/N
Nutrient
Value [mg/L]
1
Ammonia nitrogen
561
2
Ortho-phosphorus
20
3
Potassium
1066
4
Total Kjeldahl nitrogen
573
5
Total phosphorus
31
Table 5.
Nutrients values of liquid fraction of cassava peeling residue (CPR) digestate.
10. Conclusions
Cassava (Manihot esculenta Crantz) is perhaps third largest source of food energy for humans. Cassava supports the nutrition and subsistence of up to one billion persons in over 100 countries. Also, cassava is gluten free and could thus assuage medical complications for individuals with celiac disease. Cassava root processing byproduct such as CPR has organic matter content with applications in biogas and digestate production. This is a welcome development in views of biorefinery platform and the emergent circular economy. CPR digestate may be applied directly for agronomic uses or treated to generate products with varied applications and utilities. Treatment technologies may be biological, chemical, physical, or some combinations. Global benefits would include carbon sequestration, energy recovery, resource sustainability and recycling, waste reduction, profitability of AD process, biogas facilities, and agricultural systems in general. End effects of climate change mitigation, enhanced energy and food security, environmental and ecological protection, and sustainable development are good news for humanity and planet earth. These outcomes should motivate and provide consumers, farmers, regulators, managers, and other stakeholders in the emergent circular economy with insights to integrate and apply quality, safety, marketing, handling, storage, transportation, compliance with environmental regulations, and cost considerations and requirements strategies for digestate; into a renewable and sustainable energy production and waste management system.
Acknowledgments
All currency conversions to US$ were based on exchange rate taken at different times and days, during the period of last quarter of the year 2019, from the Foreign Exchange Converter Site: https://www1.oanda.com/currency/converter/
Conflict of interest
There is no conflict of interest (private or public) associated with this work.
\n',keywords:"anaerobic digestion, biofuel, biogas, cassava, cassava peeling residue, CPR, circular economy, digestate, management options, renewability, sustainability",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/72107.pdf",chapterXML:"https://mts.intechopen.com/source/xml/72107.xml",downloadPdfUrl:"/chapter/pdf-download/72107",previewPdfUrl:"/chapter/pdf-preview/72107",totalDownloads:1065,totalViews:0,totalCrossrefCites:3,totalDimensionsCites:6,totalAltmetricsMentions:3,impactScore:2,impactScorePercentile:72,impactScoreQuartile:3,hasAltmetrics:1,dateSubmitted:"September 12th 2019",dateReviewed:"January 23rd 2020",datePrePublished:"May 11th 2020",datePublished:"February 17th 2021",dateFinished:"May 9th 2020",readingETA:"0",abstract:"Circular economic paradigm applies residue from one process as input material for another, fostering sustainable benefits for humanity. Anaerobic digestion (AD) is an attractive technology for biogas production in a circular economy. Digestate is the residual organic matter generated as coproduct of biogas. Because digestate is nutrient rich and largely stabilized, it has varied management options. Digestate is suitable for direct use as bio-fertilizer and is a good amendment material to improve soil physical properties. However, the quality, safety, and utility of digestate are dependent upon the characteristics of feedstock, digester process, pre- and post- digestion treatments. Digestates emanating from AD of animal manure, energy crops, food processing residues, and other feedstocks have been reported in published literature. On the other hand, there is dearth of reports on digestate emanating from AD process that utilized cassava peeling residue (CPR) as sole feedstock. This chapter presents relevant information on digestates including production, feedstock, quality and safety requirements, processing and treatment technologies, regulatory aspects, applications management options, cost implications, as well as challenges and opportunities. In addition, new results of nitrogen (N), phosphorus (P), and potassium (K) compositions of liquid fraction of CPR digestate are reported.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/72107",risUrl:"/chapter/ris/72107",book:{id:"9385",slug:"renewable-energy-technologies-and-applications"},signatures:"Sammy N. Aso",authors:[{id:"219927",title:"Ph.D.",name:"Sammy N.",middleName:null,surname:"Aso",fullName:"Sammy N. Aso",slug:"sammy-n.-aso",email:"sammyasso@yahoo.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Anaerobic digestion (AD)",level:"1"},{id:"sec_3",title:"3. Digestate",level:"1"},{id:"sec_4",title:"4. Regulations, quality, and safety requirements",level:"1"},{id:"sec_5",title:"5. Treatment technology options",level:"1"},{id:"sec_6",title:"6. Applications management options for digestate",level:"1"},{id:"sec_6_2",title:"6.1 Biofertilizer and soil amendment",level:"2"},{id:"sec_7_2",title:"6.2 Nutrients recovery",level:"2"},{id:"sec_8_2",title:"6.3 Energy production",level:"2"},{id:"sec_9_2",title:"6.4 Other applications",level:"2"},{id:"sec_9_3",title:"6.4.1 Biochar",level:"3"},{id:"sec_10_3",title:"6.4.2 Gardening and horticulture",level:"3"},{id:"sec_11_3",title:"6.4.3 Building materials",level:"3"},{id:"sec_12_3",title:"6.4.4 Aquaculture",level:"3"},{id:"sec_13_3",title:"6.4.5 Bio-adsorbents and bedding",level:"3"},{id:"sec_16",title:"7. Cost implications",level:"1"},{id:"sec_17",title:"8. Challenges and opportunities",level:"1"},{id:"sec_18",title:"9. Cassava peeling residue (CPR) digestate",level:"1"},{id:"sec_19",title:"10. 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Characterization of digestates from anaerobic co-digestion of manioc effluent, human urine and cow dung. Journal of Water Resource and Protection. 2019;11(06):777-788. DOI: 10.4236/jwarp.2019.116047'},{id:"B93",body:'Aso SN. Food engineering stratagem to protect the environment and improve the income opportunities of gari processors. Journal of Nigerian Environmental Society (JNES). 2004;2(1):31-36'},{id:"B94",body:'Aso SN, Pullammanappallil PC, Teixeira AA, Welt BA. Biogasification of cassava residue for on-site biofuel generation for food production with potential cost minimization, health and environmental safety dividends. Environmental Progress & Sustainable Energy. 2019. DOI: 10.1002/ep.13138'},{id:"B95",body:'Tan X, Chu H, Zhang Y, Yang L, Zhao F, Zhou X. Chlorella pyrenoidosa cultivation using anaerobic digested starch processing wastewater in an airlift circulation photobioreactor. Bioresource Technology. 2014;170:538-548. DOI: 10.1016/j.biortech.2014.07.086'},{id:"B96",body:'Haraldsen TK, Andersen U, Krogstad T, Sørheim R. Liquid digestate from anaerobic treatment of source-separated household waste as fertiliser to barley. Waste Management & Research. 2017;29:1271-1276. DOI: 10.1177/0734242X11411975'},{id:"B97",body:'Pirelli T, Rossi A, Miller C. Sustainability of biogas and cassava-based ethanol value chains in Viet Nam: Results and recommendations from the implementation of the Global Bioenergy Partnership indicators. In: FAO Environment and Natural Resources Management Working Paper 69. Rome: FAO; 2018. Available from: http://www.fao.org/3/i9181en/I9181EN.pdf'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Sammy N. Aso",address:"sammyasso@yahoo.com",affiliation:'
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1. Introduction
Functionally graded materials (FGMs) are new generation of advanced composites that have gained interest in several engineering applications such as spacecraft heat shields, high-performance structural elements, and critical engine components [1, 2]. They are formed of two or more constituent phases with a continuously variable composition producing properties that change spatially within a structure. FGMs possess a number of advantages that make them attractive in improving structural performance, such as maximized torsional rigidity of composite shafts [3], improved residual stress distribution and enhanced thermal properties [4], higher natural frequencies of composite beams [5], and broader aeroelastic stability boundaries of aircraft wings [6]. Actually, the concept of FGMs was originated in Japan in 1984 during a space project, in the form of proposed thermal barrier material capable of withstanding high-temperature gradients.
Figure 1 shows variation of the volume fraction through the thickness of a plate fabricated from ceramic and metal. Ceramic provides high-temperature resistance because of its low thermal conductivity, while metal secures the necessary strength and stiffness. FGMs may also be developed using fiber-reinforced layers with a volume fraction of fibers that is coordinate dependent, rather than constant, producing favorable properties or response [6]. In this chapter, much attention is given to fibrous-type and their constitutive relationships.
Figure 1.
FGM ceramic/metal particulate composite with volume fraction graded in the vertical direction [1].
An excellent review paper dealing with the basic knowledge and various aspects on the use of FGMs and their wide applications is given by Birman and Byrd [7], who presented comprehensive discussions of the development related to stability and dynamic of FGM structures. Closed-form expressions for calculating the natural frequencies of an axially graded beam were derived in [8], where the modulus of elasticity was taken as a polynomial of the axial coordinate along the beam’s length. An inverse problem was solved to find the stiffness and mass distributions so that the chosen polynomial serves as an exact mode shape. Qian and Batra [9] considered frequency optimization of a cantilevered plate with variable volume fraction according to simple power laws. Genetic algorithm was implemented to find the optimum values of the power exponents, which maximize the natural frequencies. They concluded that the volume fraction needs to be varied in the longitudinal direction of the plate rather than in the thickness direction. Another work presented an analytical approach for designing efficient patterns of FGM bars having maximized frequencies while maintaining the total mass at a constant value [10]. The distribution of the volume fractions of the material constituents was optimized using either discrete or continuous variations along the bar length.
In the context of structural stability, Elishakoff and Endres [11] considered buckling of an axially graded cantilevered column and derived closed form solution for the mode shape and the critical load. A semi-inverse method was employed to obtain the spatial distribution of the elastic modulus in the axial direction. In Ref. [12], the buckling of simply supported three-layer circular cylindrical shell under axial compressive load was analyzed. The middle layer sandwiched with two isotropic layers was made of an isotropic FGM whose Young’s modulus varies parabolically in the thickness direction. Classical shell theory was implemented under the assumption of very small thickness/radius and very large length/radius ratios. Numerical results showed that the buckling load increases with an increase in the average value of Young’s modulus of the middle layer. An exact method was given in [13] for obtaining column’s designs with the maximum possible critical buckling load under equality mass constraint. Both material and wall thickness grading in the axial direction have been applied to determine the required optimal solutions. Case studies and detailed results were given for the cases of simply supported and cantilevered columns. Another work by Maalawi [14] presented a mathematical model for enhancing the buckling stability of composite, thin-walled rings/long cylinders under external pressure using radial material grading. The main structure is made of multiangle fibrous laminated layups having different volume fractions within the individual plies. This produced a piecewise grading of the material and thickness in the radial direction. The critical buckling contours are plotted for different types of materials, showing significant improvement in the overall stability limits of the structure under the imposed mass constraint.
Considering dynamic aeroelasticity of FGM structures, Shin-Yao [15] investigated the effect of variable fiber spacing on the supersonic flutter of composite laminates using the finite element method and quasi-steady aerodynamic theory. The formulation of the location-dependent stiffness and mass matrices due to nonhomogeneous material properties was derived. This study first demonstrates the flutter analysis of composite laminates with variable fiber spacing. Numerical results show that the sequence of the natural mode may be altered, and the two natural frequencies may be close to each other because the fiber distribution may change the distributed stiffness and mass of the plate. Therefore, it may change the flutter coalescent modes, and the flutter boundary may be increased or decreased due to the variable fiber spacing. More detailed discussions on stability, dynamic, and aeroelasticity of FGM structures are outlined in Ref. [16]. The attained optimal solutions were determined by applying the global search techniques [17, 18], which construct a number of starting points and use a local solver, such as “fmincon” routine in the MatLab optimization toolbox [19]. Global search technique is distinguished with fast converging to the global optima even if it starts with a design point far from the optimum. The local solver “fmincon” uses the method of sequential quadratic programming (SQP), which has a theoretical basis related to the solution of a set of nonlinear equations using Newton’s method and applies Kuhn-Tucker conditions to the Lagrangian of the constrained optimization problem.
It is the main intend of this chapter to present some fundamental issues concerning design optimization of different types of functionally graded composite structures. Practical realistic optimization models using different strategies for enhancing structural dynamics, stability, as well as aeroelastic performance are presented and discussed. Case studies include frequency optimization of thin-walled box beams, optimal design of drive shafts against torsional buckling and whirling, and aeroelastic optimization of subsonic aircraft wings. Design of pipelines against flow-induced flutter and/or divergence has also been addressed. Several design charts that are useful for direct determination of the optimal values of the design variables are introduced. In all, the given mathematical models can be regarded as useful design tools, which may save designers from having to choose the values of some of their variables arbitrarily.
2. Mathematical modeling of material grading
There are different scenarios in modeling the spatial variation of material properties of a functionally graded structure. For example, Chen and Gibson [20] considered distributions represented by polynomial functions and applied Galerkin’s method to calculate the required polynomial coefficients from the resulting algebraic equations. They found that the linear variation of the volume fraction is a best fit with that predicted experimentally for selected composite beam specimens. Chi and Chung [21] studied the mechanical behavior of FGM plates under transverse loading, where a constant Poisson’s ratio and variable moduli of elasticity throughout the plate thickness were assumed. The volume fraction of the constituent materials was defined by simple power laws, and closed form solutions using Fourier series were given for the case of simply supported plates. In general, the distribution of the material properties in functionally graded structures may be designed by either continuous or piecewise variation of the volume fraction in a specified direction. The most commonly utilized distributions are summarized in what follows.
2.1. Thickness grading
The first early model of volume fraction variation through the thickness of a plate fabricated from ceramic and metal was considered in [20]. This volume fraction is based on the mixture of metal and ceramic and is an indicator of the material composition (volumetric wise) at any given location in the thickness. If the volume fraction of ceramic is defined as “v” then the volume fraction of metal is the remainder of the material, or (1 − v), assuming no voids are present. A typical example, which was considered by numerous researchers in the field [1- 4] assumed that the volume fraction “v” can be varied through the thickness coordinate z by the power law (refer to Figure 1):
vz=12+zhp−1/2≤z/h≤1/2,p≥0E1
where h is the plate thickness and p is a volume fraction exponent, which dictates the amount and distribution of ceramic in the plate. With higher values of p, the plate tends toward metal while lower values tend toward ceramic (p = 0.0: fully ceramic, p = ∞: fully metal). Accordingly, the distribution of the mechanical and physical properties of FGM can be defined in terms of the material constants of the constituent phases based on a selected power-law model. Designers can vary the p-value to tailor the FGM to specific applications. In case of fibrous composites, Eq. (1) ought to be modified to account for the limits imposed on the fiber volume fractions at ẑ=zlh=±1/2 for consideration of other strength requirements and/or manufacturing restrictions. The modified form can be expressed as follows [5]:
vfẑ=vf−0.5+vf0.5−vf−0.5ẑ+0.5P,E2
Another type of the power-law expression was utilized by Bedjilili et al. [22], who considered vibration of fibrous composite beams with a variable volume fraction through the thickness of the cross section, as shown in Figure 2. It was concluded that by varying the fiber volume fraction within the beam thickness to create a FGM, certain vibration characteristics are significantly affected. The utilized formula was given as:
vfẑ=vf0+vf0.5−vf02ẑP,−0.5≤ẑ=zh≤0.5,p≥0E3
Figure 2.
Thickness distribution of the fiber volume fraction in FGM beam, vf (0) = 40%, vf (1/2) = 60% [22].
2.2. Spanwise grading
Some researchers considered grading of the fiber volume fraction in the spanwise (longitudinal) direction of a composite plate. Librescu and Maalawi [6] investigated optimization of composite wings using the concept of material grading in the spanwise direction. Both continuous and discrete distributions of the fiber volume fractions were considered in the developed optimization models. The following power-law expression was implemented:
vfŷ=vfr1−βfŷP,0≤ŷ=yL≤1βf=1−∆f,∆f=vft/vfrE4
where vft and vfr are the fiber volume fractions at wing tip and root, respectively. Δf is called the tapering ratio of the fiber volume fraction. Figure 3 shows the different patterns of the fiber volume fraction distribution for different values of the power exponent p. Both configurations of fibers aligned in the transverse (chordwise) and in the longitudinal (spanwise) directions are shown. The volume fraction is constrained to lie between 25% and 75% in order not to violate other strength and manufacturing requirements.
Figure 3.
Spanwise grading of fibers in a fibrous composite plate [6]. (a) Fibers aligned in chordwise direction, (b) fibers aligned in spanwise direction, and (c) fiber volume fraction distribution for different power exponents.
A more general distribution, given in Eq. (5), was tried by Shih-Yao [15], who applied it successfully to investigate the effect of grading on the supersonic flutter of rectangular composite plates.
vfŷ=vfrβf1−ŷnp+∆f,n=1,2,3p≥0E5
2.3. Determination of mechanical properties
A variety of approaches have been developed to predict the mechanical properties of fibrous composite materials [23]. The common approaches fall into the following general categories: mechanics of materials; numerical methods; variational approach; semiempirical formulas; experimental measurements. Mechanics of materials approach is based on simplifying assumptions of either uniform strain or uniform stress in the constituents. Its predictions can be adequate only for longitudinal properties of unidirectional continuous fibrous composites. Numerical methods using finite difference, finite element, or boundary element methods yield the best predictions; however, they are time-consuming and do not yield closed-form expressions. Variational methods based on energy principles have been developed to establish bounds (inequality relations) on the effective properties. The bounds are close to each other in the case of longitudinal properties, but they can be far apart in the case of transverse and shear properties. Semiempirical relationships have been developed to avoid the difficulties with the above theoretical approaches and to facilitate computations. The so-called Halpin-Tsai relationships have consistent forms for all properties of fibrous composite materials and can be used to predict the effects of a large number of system variables. Table 1 summarizes the mathematical formulas for determining the equivalent mechanical and physical properties for known type and volume fractions of the fiber (Vf) and matrix (Vm) materials [23]. The 1 and 2 subscripts denote the principal directions of an orthotropic lamina, defined as follows: direction (1) principal fiber direction, also called fiber longitudinal direction; direction (2) in-plane direction perpendicular to fibers, transversal direction. The factor ξ is called the reinforcing efficiency and can be determined experimentally for specified types of fiber and matrix materials. Whitney [24] suggested the range 1 < ξ < 2 depending on the fiber array type, for example, hexagonal, square, etc. Usually, ξ is taken equal to 1.0 for theoretical analysis procedures in the case of carbon or glass fibrous composite laminates.
Property
Mathematical formula*
Young’s modulus in direction (1) E11
Em Vm + E1f Vf
Young’s modulus in direction (2) E22
Em (1 + ξηVf)/(1-ηVf); η = (E2f –Em)/(E2f + ξEm)
Shear modulus G12
Gm (1 + ξηVf)/(1-ηVf); η = (G12f –Gm)/(G12f + ξGm)
Poisson’s ratioϑ12
ϑmVm+ϑ12fVf
Mass density ρ
ρm Vm + ρf Vf
Table 1.
Halpin-Tsai semiempirical relations for calculating composite properties [23].
Subscripts “m” and “f” refer to properties of matrix and fiber materials, respectively.
Assuming no voids are present, then Vm + Vf = 1.
3. Frequency optimization of FGM thin-walled box beams
This section presents a mathematical model for optimizing the dynamic performance of thin-walled FGM box beams with closed cross sections. The objective function is to maximize the natural frequencies and place them at their target values to avoid the occurrence of large amplitudes of vibration. The variables considered include fiber volume fraction, fiber orientation angle, and ply thickness distributions. Various power-law expressions describing the distribution of the fiber volume fraction have been implemented, where the power exponent was taken as the main optimization variable [25]. The mass of the beam is kept equal to that of a known reference beam. Side constraints are also imposed on the design variables in order to avoid having unacceptable optimal solutions. A case study on the optimization of a cantilevered, single-cell spar beam made of carbon/epoxy composite is considered. The results for the basic case of uncoupled bending motion are given.
3.1. Structural dynamic analysis
Figure 4 shows a slender, composite thin-walled beam constructed from uniform segments, each of which has different cross-sectional dimensions, material properties, and length. Tapered shapes of an actual blade or wing spar can be adequately approximated by such a piecewise structural model with a sufficient number of segments. The various parameters and variables are normalized with respect to a reference beam, which is constructed from just one segment with single unidirectional lamina having equal fiber and matrix volume fractions, that is, Vfo = Vmo = 50%. The different quantities are defined in the following:
Figure 4.
General configuration of a multisegment, composite box beam [25]. (a) Circular cross section, (b) rectangular cross section.
Ns = number of segments (panels).
j = subscript for the j-th segment, j = 1, 2,…….Ns.
NL(j) = number of layers in the j-th segment.
k = subscript for the k-th layer, k = 1, 2,…NL(j).
L̂j=(Lj/Lo) = normalized length of the j-th segment.
L̂=L/Lo=∑j=1NsL̂j = normalized total beam length.
Ĥj = Hj/H0 = Ĥj=∑k=1NLjĥkj= normalized total wall thickness of the j-th segment.
ĥkj=hkj/H0 = normalized thickness of the k-th layer in the j-th segment.
θkj = fiber orientation angle in the k-th layer in the j-th segment.
Γ̂j=Γj/Γo=∮Jds/∮ods = normalized circumference of the j-th segment cross section.
Γj = πDj for circular C.S., Γj = 2(aj + bj) for rectangular C.S.
ρ̂kj=ρkj/ρo = normalized density of the k-th layer in the j-th segment.
Vf,kj = fiber volume fraction in the k-th layer in the j-th segment.
ρkj = ρfVf,kj+ρm1−Vf,kj, ρo=0.5(ρf + ρm).
ρf = fiber mass density, ρm = matrix density.
m̂j=mj/mo= normalized mass per unit length of the j-th segment.
mj=Γj∑k=1NLjρkjhkj = mass per unit length of the j-th segment, mo=ΓoρoHo.
Ij = mass polar moment of inertia per unit length of the j-th segment.
= ∑kNLj∮ρkjhkjy2+z2ds.
The normalized total structural mass is given by the expression:
where Mo=moLo=ΓoρoHoLo is the total mass of the uniform baseline design. A quantity with subscript “o” refers to a reference beam parameter.
3.1.1. Constitutive relationships
The displacement field of anisotropic thin-walled closed cross-sectional beams was derived by Dancila and Armanios [26], who used a variational asymptotic approach to obtain the following constitutive equations:
where Fx, Mx, My , and Mz stand for the axial force, torsional, and bending moments, respectively, and Cmn are the cross-sectional stiffness coefficients derived in terms of closed-form integrals of the geometry and material constants. The notations U1, U2, U3, and ϕ are the kinematic variables representing the average displacements and rotation of the cross section. The primes denote differentiation with respect to x.
3.1.2. Equations of motion
The general equations of motion for the free vibration analysis are derived using Hamilton’s principle and expressed in terms of the kinematic variables, where it was shown that a closed form solution is not available [25]. However, particular choices of cross-sectional shape and layup can produce zero coupling coefficients in the equations of motion. Two special layup configurations can be considered, namely circumferentially uniform stiffness (CUS) and circumferentially asymmetric stiffness (CAS). The equations of the CUS type consist of two coupled equations for extension-twist and two uncoupled bending equations. For the CAS type, the extension displacement (U1) is uncoupled, as well as the edgewise bending (U2), while the flapping displacement (U3) is coupled with twist (φ). The general solution can be obtained by separating the space and time variables, where the time dependence is assumed to be harmonic with circular frequency, ω. The solutions for the uncoupled axial and bending equations are straightforward, while those for the coupled equations involve much mathematics [26].
3.1.3. Solution procedure of uncoupled bending motion
The basic important case to be considered first is the uncoupled bending response, which exists in both CUS and CAS layup configurations. Using the multisegment model depicted in Figure 4 and considering flapping motion (U3), the associated eigenvalue problem can be written directly in the form:
C33,jU3′′′′−ω2mjU3=0E8
which must be satisfied over the length Lj of any segment composing the beam structure. Normalizing with respect to the reference beam, we get:
Û3′′′′−β̂j4Û3=0E9
where β̂j=ω̂m̂j/Ĉ33,j1/4, Ĉ33,j=C33,j/C33,0, and ω̂=ωL02m0C33,01/2. Eq. (9) must be satisfied in the interval 0≤x¯≤L̂j, where x¯=x̂−x̂j is a local coordinate of the j-th segment and x̂=x/Lo. The general solution is well known to be:
Expressing the constants ai,i=1,2,3,4 in terms of the state variables vector {S}T = {U3−U3′−C33U3″−C33U3′″}T at both ends of the j-th segment, we get
Sj+1=TjSjE11
where [T(j)] is called the transfer matrix of the j-th segment with its elements given in detail in Ref. [25]. The state variable vectors can be computed progressively along the length of the beam by applying continuity among the interconnecting joints of the different segments composing the beam structure. An overall transfer matrix denoted by [T], which relates the state variables at both ends of the beam, can be obtained from the following matrix multiplication:
T=TNsTNs−1……..T2T1E12
The required frequency equation for determining the natural frequencies can then be obtained by applying the associated boundary conditions and considering only the nontrivial solution of the resulting matrix equation.
3.2. Formulation of the optimization problem
Several design objectives can exist in structural optimization including minimum mass, maximum natural frequencies, minimum manufacturing cost, etc. [17]. Considering the reduction of vibration level, two optimization alternatives can be formulated, namely, frequency placement by separating the natural frequencies from the harmonics of the excitations or direct maximization of the natural frequencies. The latter can ensure a simultaneous balanced improvement in both stiffness and mass distributions of the vibrating structure. The related optimization problems are usually formulated as nonlinear mathematical programming problem where iterative techniques are implemented for finding the optimal solution in the selected design space. Numerous computer programs [18] are available to solve nonlinear optimization models, which can be interacted with structural and eigenvalue analyses routines. The MATLAB toolbox optimization routines can be useful in solving some types of unconstrained and constrained optimization problems. One of the most commonly applied routines that find the constrained optima of a nonlinear merit function of many variables is named “fmincon” [19].
3.2.1. Basic optimization problem
Before performing the necessary mathematics, it is essential to recognize that design optimization is only as meaningful as its core model of structural analysis. Any deficiencies therein will absolutely be affected in the optimization process. Consider the basic problem of a uniform cantilevered, thin-walled, single-cell spar constructed from just one segment with one unidirectional lamina (Ns = 1, NL = 1). The total length and outer cross-sectional dimensions are given preassigned values equal to those of the baseline design. The remaining set of variables is, therefore, X = VfĤθ. The associated frequency equation for such a basic case is:
It is seen that ω̂ is an implicit function of the design variables and can be calculated numerically by any suitable method such as Newton-Raphson or the Bisection method. However, the frequency equation can be solved directly for the whole term ω̂m̂/Ĉ3314L̂ without regard to the specific values of the design variables. The computed roots are:
In Eq. (14), the frequency parameter ω̂i can be imagined as an explicit function of the design variables. So, for prescribed values of the design variables within the domain of side constraints, ω̂i can be obtained directly from the above equation. Therefore, it is possible to place the frequency at its desired value and obtain the corresponding value of any one of the design variables directly from Eq. (14). The selected composite material of construction is made of epoxy-3501-6 and carbon-AS4 (see Table 2), which has favorable properties and is highly recommended in many applications of civil, aerospace, and mechanical engineering [23].
Property
Fiber: Carbon-AS4
Matrix: Epoxy-3501-6
Density (g/cm3)
ρf = 1.81
ρm = 1.27
Modulus of elasticity (GPa)
E1f = 235.0 E2f = 15.0
Em = 4.30
Modulus of rigidity (GPa)
G12f = 27.0
Gm = 1.60
Poisson’s ratio
ν12f = 0.20
νm = 0.35
Table 2.
Material properties of fiber and matrix materials [23].
Figure 5 depicts the functional behavior of the dimensionless fundamental frequency parameter ω̂1 combined with the structural mass constraint (M̂s=1). The imposed side constraints are:
0.250.75−π/2≤VfĤθ≤0.751.25π/2E15
Figure 5.
Level curves of ω̂1 function augmented with the constraint M̂s=1 in Vf−θ design space (Ns = 1, NL = 1,L̂=1).
It is remarked that the function is continuous and well behaved everywhere in (Vf – θ) design space. The contours are symmetric about the horizontal line θ = 0 where the constrained global maxima occurs when the fiber volume fraction reaches its upper limiting value. It can then be concluded that the unidirectional lamina is favorable when considering beam designs with maximum bending frequency. The optimal design point was found to be (Vf,Ĥ, θ) = (0.75, 0.92, 0) at which ω1max=2.02589. This corresponds to an optimization gain of about 8.04% as measured from the reference value 1.8751. Before ending this section, it is interesting to address here the dual optimization problem of minimizing the total structural mass under preserved frequency (ω̂1=1.8751). The optimal solution was calculated to be (Vf,Ĥ, θ) = (0.50, 0.915, 0) and M̂s,min = 0.915, which corresponds to a mass saving of 8.5% as compared to the baseline design.
A couple of words are stated here regarding the side constraints in Eq. (15). First of all, it is reminded that the main focus of the present study is to optimize the fiber volume fraction in order to achieve higher values of the natural frequencies without mass penalty. The optimization is performed with respect to a known baseline design, which is considered to be conservative having reserve strength to withstand severe dynamic loads. The imposed side constraint on the total wall thickness, normalized with respect to that of the baseline design, is included for consideration of strength and stability requirements, which are not considered in the present study. So, the imposed limits with a percentage of 25% below or above that of the baseline can be practically accepted for the given model formulation. On the other hand, appropriate values of the upper and lower bounds imposed on the fiber volume fraction are chosen to avoid having unacceptable designs from the manufacture point of view. For example, the filament winding is usually associated with the highest fiber volume fractions. With careful control of fiber tension and resin content, values of around 75% would be reasonable [27].
3.2.2. Optimization model for discrete grading
A comprehensive analysis and formulation of discrete optimization models for beam structures considering both stability and dynamic performance were formulated in [28], where mathematical programming coupled with finite element analysis procedures was implemented. For the case of a two-segment spar beam, (Ns = 2, NL = 1), the reduced optimization problem can be defined as follows:
Using the equality constraints, two of the design variables can be expressed in terms of the other two variables. Figure 6 shows the functional behavior of the dimensionless frequency combined with the structural mass constraint. It is remarked that the function is well defined in the feasible domain of the selected design space (VA −L̂)1. Two empty regions can be observed at the upper left and right parts of the design space, where violation of the equality mass constraint is indicated. In the left one, the fiber volume fraction is equal to 100%, violating the imposed side constraint. The feasible domain is seen to be split into two distinct zones separated by the baseline contour, which is represented by the vertical line Vf1 = 50%. The constrained optimum is to found to be (Vfj,L̂j)j=1,2 = (0.75, 0.50), (0.25, 0.50) corresponding to (ω̂1)max = 2.0645 with 10.10% optimization gain.
Figure 6.
Level curves of ω̂1 function augmented with M̂s=1 in Vf1L̂1 design space (Ns = 2).
3.2.3. Optimization model for continuous grading
For continuous grading models, the associated optimization problem is cast as follows: find the design variables vector X= (Δf, p), which minimizes the objective function:
FX¯=−ω̂1
subject to the constraints:
M̂s=10.33≤∆f≤3.0P≥0E17
Solutions obtained by applying the power-law model of Eq. (3) have shown that no improvements can be achieved using grading of the fiber volume fraction in the thickness direction. On the other hand, grading in spanwise direction has shown some interesting results. Considering spanwise grading according to Eq. (4), Figure 7 depicts the level curves of the fundamental frequency parameter ω̂1 combined with the mass constraint in the design space ∆fp. It is observed that the feasible domain is bounded from below and above by the constraint curves corresponding to the upper and lower bounds imposed on the fiber volume fractions at tip and root. The horizontal line ∆f = 1.0 (i.e., Vf = 50% at root and tip) split the domain into two zones. The lower zone encompasses the constrained optimum solution: (ω̂1)max = 2.01875 at the design point (∆f, P)opt. = (0.34, 1.01).
Figure 7.
Level curves of ω̂1 function augmented with M̂s=1 in ∆fp design space (Ns = NL = 1) with spanwise grading “Eq. 4.”
Table 3 summarizes the attained optimal solutions for the different grading patterns. It is seen that the highest optimization gain is obtained by using spanwise grading of Eq. (5) with the coordinate exponent n = 3.
Optimal solutions using different grading patterns (M̂s = 1).
4. Optimization of FGM drive shafts against torsional buckling and whirling
One of the important design issues in mechanical industries is the buckling and whirling instabilities that may arise from the loads applied to a power transmission shaft. These instabilities result in a reduced control of the vehicle, undesirable performance, and often cause damage, sometimes catastrophic, to the vehicle structure. Therefore, by incorporating such considerations into an early design optimization [29], the design space of a power transmission shaft will be reduced such that undesirable instability effects can be avoided during the range of the vehicle’s mission profile. Figure 8 shows an idealized structural model of a long, slender composite shaft having circular thin-walled cross section. The main structure is constructed solely of functionally graded, fibrous composite materials. The laminate coordinates are defined by x parallel to the shaft axis, y points to the tangential direction, and z points to the radial direction. Predictions of both torsional buckling and whirling instabilities are based on simplified analytical solutions of equivalent beam and shell structures. The coupling between bending and torsional deformations, introduced by the composite construction, and its influence on such instabilities is considered.
Figure 8.
Shaft model and definition of reference axes.
4.1. Torsional buckling optimization problem
Bert and Kim [30] derived the governing differential equations of torsional buckling in the form:
where Nx and Ny are the normal forces, Nxy and Nyx are shear forces, Mx and My are bending moments, and Mxy and Myx are torsional moments. All are applied to the midsurface and measured per unit wall thickness of the shaft. T is the applied torque, R is the mean radius, and (u, v, w) are the displacements of a generic point on the middle surface of the shaft wall. An iterative process is outlined in Ref. [30] for calculating the buckling torque for specified boundary conditions. There are other simple empirical equations based on experimental studies that can give a reasonable estimate of the buckling torque. The most commonly used formula for the case of simply supported shaft is [31]:
Tcr=2πR2H0.272Ex0.25Ey0.75H/R1.5E19
where Tcr is the critical buckling torque and H is the total wall thickness of the shaft. Expressions of the equivalent modulii of elasticity in the axial (Ex) and hoop (Ey) directions for symmetric and balanced laminates are given in Ref. [31]. The various parameters and variables are normalized with respect to known baseline design, which is constructed from cross-ply laminates [0o/900]N with equal volume fractions of the fibers and matrix materials, that is, Vf = Vm = 50%. Optimized shaft designs shall have the same transmitted power, length, outer diameter, boundary conditions, and material properties of those known for the baseline design. The different dimensionless quantities are defined in Ref. [31]. The optimal torsional buckling problem is to find the design variables vector X→=Vfθĥk=1,2,…NL, which minimizes the objective function:
MinimizeF=−T̂crsubject tomass limitation:M̂−1≤0E20
Side constraints:0.30−π20.015≤Vfθĥk=1,2,…NL≤0.70π20.200.75≤∑k=1NLĥk≤1.25E22
where T̂cr=Tcr/Tcro, Ω̂=Ω∗2π/60ω1,o are the dimensionless critical torque and rotational speed, respectively. The baseline design parameters are denoted by subscript “o.”τmax=Tmax/2πR2H is the maximum shear stress, Tmax is the maximum applied torque, and τallow is the allowable shear stress that can be calculated according to the embedded material properties and volume fraction of the fiber [23]. This optimization problem may be thought as a search in an (3NL) dimensional space for a point corresponding to the minimum value of the objective function and such that it lies within the region bounded by subspaces representing the constraint functions. It must be noted that the outside dimensions (outer diameter and length) of the shaft are restricted by the available interior space of the vehicle and will be considered as preassigned parameters in the present model formulation. The first case study to be examined herein is a shaft with discrete thickness grading constructed from eight plies (±θ ±θ)s with the same properties of carbon/epoxy composites (see Table 2) and same thicknesses. This sequence is applied in filament wound circular shells, as such a process demands adjacent (±θ) layers. Figure 9 depicts the obtained contours in the (Vf1-θ) design space, which are, as seen, monotonic and symmetric about the zero ply angle. A local maximum of T̂cr can be observed near the design point (Vf1,θ) = (0.7, 90o) with T̂cr = 1. This figure illustrates that the maximum critical buckling torque can be achieved when the fiber orientation angle is close to 90o. Other case studies including both discrete and continuous grading with several optimal solutions can be found in Ref. [31]. At the start of the optimization process in each case, the shaft wall was divided into a large number of layers with equal thicknesses, for example, NL = 32. It has been found that the optimization algorithm treats the number of layers as an additional implicit variable. Sometimes the computer discards one or more layers by letting their thicknesses sink to the lower limits and sometimes makes some consecutive layers identical, that is, having the same fiber orientation and volume fraction. Such a situation was repeated for many cases of study. It was found that the appropriate number to be taken for the shaft problem under consideration is NL = 8. This would eliminate much of the numerical effort necessary for performing structural analysis in each optimization cycle and, consequently, reduces the computational time considerably.
Figure 9.
T̂cr—contours in (Vf1-θ) design space under mass constraint M̂=1. (Case of drive shaft with eight symmetric, balanced, carbon/ epoxy layers)
The final attained optimal solution was a cross ply layup [900/00]4 with the fiber volume fraction in the eight layers reached its upper value of 70%. The optimal dimensionless ply thickness was found to be [0.1994, 0.0967, 0.152, 0.019]s at which the shaft torsional buckling capacity was increased by 32.1% above that of the baseline design. However, the total structural mass has reached its baseline value, and whirling constraint became active at the achieved optimum design point.
4.2. Whirling optimization problem
The calculation of the critical speed, also referred to as whirl instability of a rotating shaft, is based on the work given in Refs. [32, 33]. The critical speed is defined as the point at which the spinning shaft reaches its first natural frequency. The shaft is modeled as a Timoshenko beam, which implies that first-order shear deformation theory with rotatory inertia and gyroscopic action was used. The shaft is assumed to be pinned at both ends with a Cartesian coordinate system (x, y, z), where x is measured along the longitudinal axis of the shaft. The displacements in the y and z directions are denoted by v and w, respectively, and Φ is the angle of twist. The cross-sectional area, second moment of area, and polar moment of area are denoted by A, I, and J, respectively. The equations of motion are derived by invoking Hamilton’s principle, with the following results [32]:
The symbol t denotes time and Ω the rotational speed. The ρA, ρI, and ρJ terms account for translational, rotary, and torsional inertias, respectively, while the 2ρI terms account for the gyroscopic inertia effects. It is assumed that the flexural and bending-twisting coupling rigidities (CB and CBT) associated with bending about the y and z axes are identical; likewise for the transverse shear stiffness (Cs) [32]. Bert and Kim [33] considered the case of simply supported shaft and assumed separable solution in space and time to solve the associated eigenvalue problem. The derived frequency equation is given by:
where λ=nπ/L, ω = circular natural frequency, and n = mode number. For each natural frequency of the nonrotating shaft, the rotational speed (Ω) develops gyroscopic moments, which cause the natural frequency to bifurcate into two. The higher of the two increases with Ω and is associated with forward precession, while the lower one decreases with Ω and is associated with backward precession. A critical instability occurs when the rotational speed coincides with the first backward-precision natural frequency, which is termed as the first critical speed. Two alternatives may be considered regarding the whirling optimization problem [34]:
(a) Direct maximization of the critical rotational speed
Find the design variables vector X→=Vfθĥk=1,2,…NL, which minimizes the objective function:
MinimizeF=−Ω̂crSubject toM̂−1≤0E28
τmaxτallow−1.0≤0TmaxTcr,o−T̂cr≤0E29
(b) Placement of the critical speed
The other alternative of the objective function is defined by:
MinimizeF=Ω̂cr−Ω̂∗2E30
The same set of constraints given in Eq. (23) is applied. The notation Ω̂∗ is a dimensionless target rotational speed, which should be greater than the maximum permissible rotational speed by a reasonable margin (e.g., 10–20%). As a case study, a drive shaft with continuous material grading along the shaft axis is optimized considering the following power-law model:
Vfx̂=Vf0.5+Vf0−Vf0.51−2x̂nP,E31
where Vf (0) is the fiber volume fraction at the right or left end of the drive shaft, while Vf (0.5) is the fiber volume fraction at the middle of the shaft length. Figure 10 illustrates the level curves of the normalized critical speed augmented with the mass equality constraint. It is seen that there are four distinct zones separated by the contour lines Ω̂cr=1.0. The upper left zone and lower right zone contain local maximum solutions. The best point (p, ∆f) = (4.53, 0.3), corresponding to Ω̂cr=1.045, is located inside the zone where the fiber taper ratio ∆f=Vf0/Vf0.5 is less than one. The upper empty zone contains infeasible solutions that violate the imposed constraints. Another case study considers the through-thickness grading pattern given by Eq. (3). The corresponding design variable vector is defined by X→=Vf0Vf12pĤ with lower and upper limits X→L = (0.3, 0.3, 0, 0.75) and X→U = (0.7, 0.7, ∞, 1.25). The attained optimal design variable vector was calculated to be X→opt=0.70.35.610.955 at which the maximum critical speed increased by 14% above that of the baseline design with active mass constraint.
Figure 10.
Normalized critical speed Ω̂cr augmented with the mass constraint (M̂=1.0) in (p-∆f) design space.
A last optimization strategy to be addressed here is to combine the two criteria in a single objective function subject to the mass, strength, and side constraints.
Eq. (22) assumes that whirling and torsional buckling instabilities are of equal relative importance. This model resulted in a balanced improvement in both stabilities with active mass constraint. The attained optimal solution was found to have a uniform distribution of the fiber volume fraction with its upper limiting value of 70% and wall thickness = 0.935. The corresponding optimal values of the design objectives were Ω̂cr=1.135 and T̂cr=1.161, representing optimization gains 13.5 and 16.1%, respectively, as measured from the baseline design.
5. Optimization of FGM wings against divergence
The use of the in-plane grading in aeroelastic design was first exploited by Librescu and Maalawi [6], who introduced the underlying concepts of using material grading in optimizing subsonic rectangular wings against torsional instability. Exact mathematical models were developed allowing the material physical and mechanical properties to change in the wing spanwise direction, where both continuous and piecewise structural models were successfully implemented. In this section, analytical solutions are developed for slender tapered composite wings through optimal grading of the material volume fraction in the spanwise direction. The enhancement of the wing torsional stability is measured by maximization of the critical flight speed at which aeroelastic divergence occurs. The total structural mass is maintained at a value equals to that of a known baseline design in order not to violate other performance requirements. Figure 11 depicts a slender wing constructed from Np panels with trapezoidal planform and known airfoil cross section. The wing is considered to be made of unidirectional fiber-reinforced composites with variable fiber volume fraction in the spanwise direction. The flow is taken to be steady and incompressible, and the aspect ratio is assumed to be sufficiently large so that the classical engineering theory of torsion can be applicable and the state of deformation described in terms of one space coordinate.
Figure 11.
Trapezoidal wing planform and cross section geometry. (a) Multipanel, piecewise wing model, (b) airfoil section and applied airloads.
The chord distribution is assumed to have the form:
Cx=Cr1−βcx,βc=1−ΔcE33
The symbol Δc denotes the chord taper ratio (= tip chord Ct/root chord Cr) and x (= x1/L) denotes the dimensionless spanwise coordinate. The equivalent shear modulus G of a unidirectional reinforced composite, thin-walled cross section can be determined from the relation [35]:
G=f1G12E34
where f1 is a function that depends on the geometry and thickness ratio of the cross section (h/C) and the ratio (G12/ G13), where G12 and G13 are the in-plane and out-of-plane shear moduli, respectively (refer to Table 1). C is the chord length and h is the maximum thickness of the cross section. For many types of fibrous composites that are commonly utilized in aerospace industry [23], such as carbon/epoxy and graphite epoxy, both moduli are approximately equal, G12 ≈ G13.
Using the classical elasticity and aerodynamic strip theories, the governing differential equation of torsional stability in dimensionless form is [35]:
ĜĴα′′+V̂2αx=0E35
?The associated boundary conditions of the elastic angle of attack, α, are α(0) = 0 and α′1=0. The symbol Ĝ=G12/G12,o denotes the dimensionless shear modulus, Ĵ=J/Jr denotes the dimensionless torsion constant, and the prime denotes differentiation with respect to the dimensionless coordinate x = x1/L. The dimensionless flight speed is defined by V̂=VCrbρae/2GJr, where (GJ)r is the torsional stiffness of the baseline design at root. The shear modulus G12,0 of the baseline design can be calculated by taking Vfo = 50%.
Considering the K-th panel of the wing as shown in Figure 11a, and using the transformation y = (1-βx), Eq. (25) takes the form:
zα″+3α′+ak2α=0,1‐βxk+1≤y≤1−βxkE36
where ak=V̂/βĥkĜk, ĥk and Ĝk are the normalized wall thickness and shear modulus of the kth wing panel, respectively. The general solution of Eq. (26) is:
αy=A1J22akyy−A2Y22akyyE37
where J2 and Y2 are Bessel’s function of the first and second kind with order 2, respectively [35], and A1 and A2 are the constants of integration. The dimensionless internal torsional moment, T, can be obtained by differentiating Eq. (27) and multiplying by the dimensionless shear rigidity. Applying the boundary conditions at stations (k) and (k + 1), the constants A1 and A2 can be expressed in terms of the state variables at station (k), which can be related to those at station (k + 1) by the transfer matrix relation:
αk+1Tk+1=EkαkTkE38
It is now possible to compute the state variables progressively along the wing span by applying continuity requirements of the variables (α, T) among the interconnecting boundaries of the various wing panels. The divergence speed can be calculated by applying the boundary conditions and considering the nontrivial solution of the resulting equations (similar to the procedure outlined in Section 3.1.3). The associated optimization problem may be cast in the following:
The preassigned parameters that do not change during the optimization process include the wing semispan (b), the chord taper ration (∆c), airfoil type and geometry, and fiber and resin material types. This model has been applied to obtain wing designs with improved torsional stability by maximizing the divergence speed (Vdiv) without weight penalty. The selected material is carbon-AS4/epoxy-3501-6 composite (see Table 2), which has favorable characteristics and is highly desirable in manufacturing aircraft structures. The baseline design has uniform mass and stiffness distributions and is made of uniform unidirectional fibrous composite with equal volume fraction of the matrix and fiber materials, that is, Vfo = 50%.Figures 12 and 13 show the developed level curves of constant divergence speed (also named isodiverts) for two-panel wings with chord tapering ratio, ∆c = 0.5. Actually, these curves represent the dimensionless critical speed, augmented with the equality mass constraint. Examining Figure 12, it is seen that the Vdiv function is well behaved and continuous everywhere in the selected design space except in the empty regions to the upper left and right regions, where the equality mass constraint is violated. The feasible domain is bounded from above by the two curved lines representing the upper and lower limiting constraints imposed on the volume fraction of the outboard blade panel. The contours inside the feasible domain are not allowed to penetrate these borderlines and obliged to turn sharply to be asymptotes to them, in order not to violate the mass constraint. The final attained optimal solutions are summarized in Table 4. It can be observed that good wing patterns shall have the lower limit of the fiber volume fraction at the tip and the upper limit at root. Using material and wall thickness grading together results in a considerable enhancement of the wing torsional stability.
Figure 12.
Isodivert in (Vf1- b1) design space for a two-panel wing model (h1 = h2 = 1.0, Δc = 0.5,M̂s=1).
Figure 13.
Isodivert in (Vf1- b1) design space for a two-panel wing model (h2 = 0.5, Vf2 = 0.3, Δc = 0.5,M̂s=1).
Optimal piecewise wing designs with chord taper = 0.5 (baseline value = 1.9747).
6. Optimization of composite thin-walled pipes conveying fluid
The subject of vibration and stability of thin pipes conveying flowing fluids is of a considerable practical interest. An advanced textbook by Paїdoussis [36] gives an excellent review of the several developments made in this research area. Practical models for enhancing static and dynamic stability characteristics of pipelines constructed from uniform modules were addressed by Maalawi et al. [37, 38], where the relevant design variables were selected to be the mean diameter, wall thickness, and length of each module composing the pipeline. The general case of an elastically supported pipe, covering a variety of boundary conditions, was also investigated. Distinct domains of the flutter instability boundaries were presented for different ratios of the fluid-to-pipe mass, and the variation of the critical flow velocity with support flexibility was examined and discussed. Concerning pipelines made of advanced FGMs, this section presents a mathematical model for enhancing the overall system stability against flutter and/or divergence under mass constraint. Figure 14 shows a FGM pipe conveying flowing fluid with the coordinate system chosen such that the x-axis coincides with the longitudinal centroidal axis in its undeformed position, while the y- and z-axes coincide with the cross section principal axes. The pipe model consists of rigidly connected thin-walled circular tubes made of unidirectional fibrous composite material. Each pipe module has different material properties, wall thickness, and length. Such a configuration results in a piecewise axial grading of either the material of construction or the wall thickness in the direction of the pipe axis. Assuming no voids are present, the distributions of the mass density (ρ) and modulus of elasticity (E) can be determined using the formulas of Table 1.
The associated eigenvalue problem is described by the fourth-order ordinary differential equation [39]:
EIkV′′′′+U2V′′+2iωUβoV′−mkω2V=0E40
where V(x) is the dimensionless mode shape satisfying boundary conditions, and ω is the corresponding dimensionless frequency of oscillation, which will be, in general, a complex number to be determined by the requirement of nontrivial solutions, V(x) ≠ 0. More details for the definition of the various parameters and dimensionless design variables are given in Ref. [39]. Both static and dynamic instability phenomena can be involved for the physical model described by Eq. (30), depending on the type of boundary conditions at the pipe ends. The notation ()′ means total spatial derivative. The general solution can be obtained using standard power series methods:
Vx=∑j=14AjeipjxE41
pj, j = 1,…4 are the four roots of the fourth-order polynomial:
p4−αkU2p2−2ωUβoαkp−mkω2αk=0,αk=1/EkhkE42
An efficient method [38] is successfully implemented to find the complex roots of Eq. (32) by formulating a special companion matrix and finding the associated eigenvalues for any desired values of the variables αk, βo, ω, and U. The transfer matrix [Tk] of the kth pipe module can be obtained by performing the matrix multiplications
Applying the appropriate boundary conditions and considering only the nontrivial solution, the resulting characteristic equation can be solved numerically for the frequency and the critical flow velocity. The system is stable or unstable according to whether the imaginary component of the frequency, ω, is positive or negative, respectively. In case of neutral stability, ω is wholly real. As the flow velocity increases, the system may become unstable in one or more of its normal modes. The critical flow velocity is the greatest velocity for which the system is stable in all its modes. The characteristic equation for cantilevered pipe is:
T33T44−T34T43=0,E46
where Tij are the elements of the overall transfer matrix. For a specific mode number, the proper starting frequency is determined by solving a subsidiary eigenvalue problem corresponding to the case of stationary fluid inside the vibrating pipe. A globally convergent optimization algorithm, known as Levenberg-Marquardt algorithm [38], has been applied to solve the resulting nonlinear equations derived from the consideration of nontrivial solution of characteristic equation. The effect of flow for small velocity is to damp the system in all modes. At higher velocities, some of the modes become less damped and the corresponding branches cross the Re(ω)-axis, indicating the existence of unstable oscillations of the system. If a branch passes through the origin, that i, ω = 0, the case of static instability (called divergence) is reached.
6.1. Flutter solutions
To verify the developed formulation, the classical problem dealing with one-module cantilevered pipe is considered first. The dimensionless wall thickness and length of the pipe are assigned at a value of 1.0, while the volume fraction at 50%. Figure 15 depicts variation of the critical flutter velocity and frequency with the mass density ratio MRo, covering a wide range of pipe and fluid mass densities. It is seen that there are four subdomains with the associated flutter modes defined in the specified intervals of the mass ratio. The upper and lower bounds determine the critical values of the mass ratios at which some of the frequency branches cross each other at the same value of the flutter velocity. The overall flutter mode may be regarded as composed of different quasimodes separated at the shown “jumps” in the Uf-MRo curve. The calculated mass density ratios at the three indicated frequency jumps are 0.4225, 2.29, and 12.33, respectively. They correspond to multiple points of neutral stability, where for a finite incremental increase in the flow velocity, the system becomes unstable, then regains stability, and then once again becomes unstable with a noticeable abrupt increase in the flutter frequency. Next, we consider a baseline design made of carbon/epoxy composites (see Table 2) with mass ratio MRo = 2.0. The calculated values of the dimensionless flutter velocity and frequency are found to be Uf = 10.78 and ωf = 26, respectively. Keeping the total dimensionless mass constant at the value corresponding to the baseline design, the best solution having the greatest flutter velocity was found to be (Vf, h1) = (0.70, 0.9345), which corresponds to Uf = 12.517 and ωf = 31.8615.
Figure 15.
Variation of flutter speed and frequency with mass ratio for a uniform one-module cantilevered pipe (Vf1 = 50%, h1 = 1, L1 = 1).
6.1.1. Solutions for cantilevered two-module pipe with uniform thickness (h1 = h2 = 1.0)
Considering the case of two-module pipe, a direct and fast way for checking out system stability for any desired set of the dimensionless design variables (Vf1, L)k = 1,2 is given here. Lower and upper bounds are imposed on the design variables in order not to violate other strength and manufacturing requirements. The fiber volume fraction is constrained to be within the range 30% up to 70%, while the dimensionless length is between 0.0 and 1.0. The mass ratio MRo is taken to be 2.0.
Dimensionless flutter velocity and flutter frequency are obtained from the frequency and velocity branches at the four modes. The lowest frequency and velocity among the four modes at which Imag(ω) = 0.0 are considered the flutter velocity and frequency. These computed values at different conditions are employed in constructing the flutter velocity and frequency contours as shown in Figure 16. The white regions shown in both figures indicate that the fiber volume fraction of the material of the second segment does not fall in range between 0.3 and 0.7. The maximum flutter velocity (Uf) and its corresponding flutter frequency (ωf) occur in the region colored with dark brown L1 = 0.36 and Vf1 = 0.3. The maximum Uf and its corresponding ωf are 13.67 and 58.4, respectively. Table 5 gives several standard optimal solutions for the two-module case study. The global optimum design point is seen to be (Vf, L)k = 1,2 = (0.35, 0.40), (0.60, 0.60), at which the normalized flutter velocity reached a value of 13.31 corresponding to 23.47% optimization gain.
Figure 16.
Contour plots of flutter velocity and frequency in (Vf1-L1) design space.
Standard solutions for a cantilevered two-module pipeline with uniform thickness (h1 = h2 = 1.0, M = 1.0).
7. Conclusions
As a major concern in producing efficient structures with enhanced properties and tailored response, this chapter presents appropriate design optimization models for improving performance and operational efficiency of different types of composite structural members. The concept of material grading has been successfully applied by incorporating the distribution of the volume fractions of the composite material constituents in the mathematical model formulation. Various scenarios in modeling the spatial variation of material properties of functionally graded structures are addressed. The associated optimization strategies include frequency maximization of thin-walled composite beams, optimization of drive shafts against torsional buckling and whirling instability, and maximization of the critical flight speed of subsonic aircraft wings. Design variables encompass the distribution of volume fraction, ply angle, and wall thickness as well. Detailed optimization models have been formulated and presented for improving the dynamic performance and increasing the overall stiffness-to-mass level of thin-walled composite beams. The objective functions have been measured by maximizing the natural frequencies and place them far away from the excitation frequencies, while maintaining the total structural mass at a constant value. For discrete models, the optimized beams can be constructed from any arbitrary number of uniform segments where the length of each segment has shown to be an important variable in the optimization process. It has also been proved that expressing all parameters in dimensionless forms results in naturally scaled design variables, constraints, and objective functions, which are favored by a variety of optimization algorithms. The attained optimal solutions using continuous grading depend entirely upon the prescribed power-law expression, which represents additional constraint on the optimization problem. Results show that material grading in the spanwise direction is much more better than grading through the wall thickness of the cross section. Regarding optimization of FGM drive shafts, it was shown that the best model is to combine torsional buckling and whirling in a single objective function subject to mass constraint. This has produced a balanced improvement in both stabilities with active mass constraint at the attained optimal design point.
In the context of aeroelastic stability of aircraft structures, an analytical model has been formulated to optimize subsonic trapezoidal wings against divergence. It was shown that by using material and thickness grading simultaneously, the aeroelastic stability boundary can be broaden by more than 50% above that of a known baseline design having the same total structural mass. Other stability problems concerning fluid-structure interaction have also been addressed. Both flutter and divergence optimization have been considered, and several design charts that are useful for direct determination of the optimal values of the design variables are given. It has been confirmed that the segment length is the most significant design variable in the whole optimization process. Some investigators who apply finite elements have not recognized that the length of each element can be taken as a main design variable in the whole set of optimization variables. The results from the present approach reveal that piecewise grading of the material can be promising in producing truly efficient structural designs with enhanced stability, dynamic, and aeroelastic performance. It is the author’s wish that the results presented in this chapter will be compared and validated through other optimization techniques such as genetic algorithms or any appropriate global optimization algorithm.
Actually, the most economic structural design that will perform its intended function with adequate safety and durability requires much more than the procedures that have been described in this chapter. Further optimization studies must depend on a more accurate analysis of constructional cost. This combined with probability studies of load applications and materials variations should contribute to further efficiency achievement. Much improved and economical designs for the main structural components may be obtained by considering multidisciplinary design optimization, which allows designers to incorporate all relevant design objectives simultaneously. Finally, it is important to mention that, while FGM may serve as an excellent optimization and material tailoring tool, the ability to incorporate optimization techniques and solutions in practical design depend on the capacity to manufacture these materials to required specifications. Conventional techniques are often incapable of adequately addressing this issue. In conclusion, FGMs represent a rapidly developing area of science and engineering with numerous practical applications. The research needs in this area are uniquely numerous and diverse, but FGMs promise significant potential benefits that fully justify the necessary effort.
\n',keywords:"functionally graded materials, composite structures, optimum design, buckling stability, structural dynamics, fluid-structure interaction",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/64535.pdf",chapterXML:"https://mts.intechopen.com/source/xml/64535.xml",downloadPdfUrl:"/chapter/pdf-download/64535",previewPdfUrl:"/chapter/pdf-preview/64535",totalDownloads:1460,totalViews:321,totalCrossrefCites:1,dateSubmitted:"May 28th 2018",dateReviewed:"November 6th 2018",datePrePublished:"November 26th 2018",datePublished:"January 30th 2019",dateFinished:"November 26th 2018",readingETA:"0",abstract:"This chapter focuses on some of the most advances made in the field of stability, dynamic, and aeroelastic optimization of functionally graded composite structures. Practical realistic optimization models using different strategies for measuring structural performance are presented and discussed. The selected design variables include the volume fractions of the composite material constituents as well as geometrical and cross-sectional parameters. The mathematical formulation is based on dimensionless quantities; therefore, the analysis can be valid for different configurations and sizes. Such normalization has led to a naturally scaled optimization model, which is favorable for most optimization techniques. Case studies include structural dynamic optimization of thin-walled beams in bending motion, optimization of drive shafts against torsional buckling and whirling, and aeroelastic optimization of subsonic aircraft wings. Other stability problems concerning fluid-structure interaction has also been addressed. Several design charts that are useful for direct determination of the optimal values of the design variables are introduced. The proposed mathematical models have succeeded in reaching the required optimum solutions, within reasonable computational time, showing significant improvements in the overall structural performance as compared with reference or known baseline designs.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/64535",risUrl:"/chapter/ris/64535",signatures:"Karam Maalawi",book:{id:"7277",type:"book",title:"Optimum Composite Structures",subtitle:null,fullTitle:"Optimum Composite Structures",slug:"optimum-composite-structures",publishedDate:"January 30th 2019",bookSignature:"Karam Y. Maalawi",coverURL:"https://cdn.intechopen.com/books/images_new/7277.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-78985-068-0",printIsbn:"978-1-78985-067-3",pdfIsbn:"978-1-83881-805-0",isAvailableForWebshopOrdering:!0,editors:[{id:"18593",title:"Prof.",name:"Karam",middleName:"Youssef",surname:"Maalawi",slug:"karam-maalawi",fullName:"Karam Maalawi"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"18593",title:"Prof.",name:"Karam",middleName:"Youssef",surname:"Maalawi",fullName:"Karam Maalawi",slug:"karam-maalawi",email:"maalawi@netscape.net",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/18593/images/system/18593.jpeg",institution:{name:"National Research Centre",institutionURL:null,country:{name:"Egypt"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Mathematical modeling of material grading",level:"1"},{id:"sec_2_2",title:"2.1. Thickness grading",level:"2"},{id:"sec_3_2",title:"2.2. Spanwise grading",level:"2"},{id:"sec_4_2",title:"2.3. Determination of mechanical properties",level:"2"},{id:"sec_6",title:"3. Frequency optimization of FGM thin-walled box beams",level:"1"},{id:"sec_6_2",title:"3.1. Structural dynamic analysis",level:"2"},{id:"sec_6_3",title:"3.1.1. Constitutive relationships",level:"3"},{id:"sec_7_3",title:"3.1.2. Equations of motion",level:"3"},{id:"sec_8_3",title:"3.1.3. Solution procedure of uncoupled bending motion",level:"3"},{id:"sec_10_2",title:"3.2. Formulation of the optimization problem",level:"2"},{id:"sec_10_3",title:"Table 2.",level:"3"},{id:"sec_11_3",title:"3.2.2. Optimization model for discrete grading",level:"3"},{id:"sec_12_3",title:"Table 3.",level:"3"},{id:"sec_15",title:"4. Optimization of FGM drive shafts against torsional buckling and whirling",level:"1"},{id:"sec_15_2",title:"4.1. Torsional buckling optimization problem",level:"2"},{id:"sec_16_2",title:"4.2. Whirling optimization problem",level:"2"},{id:"sec_18",title:"5. Optimization of FGM wings against divergence",level:"1"},{id:"sec_19",title:"6. Optimization of composite thin-walled pipes conveying fluid",level:"1"},{id:"sec_19_2",title:"6.1. Flutter solutions",level:"2"},{id:"sec_19_3",title:"Table 5.",level:"3"},{id:"sec_22",title:"7. Conclusions",level:"1"}],chapterReferences:[{id:"B1",body:'Suresh S, Mortensen A. Fundamentals of Functionally Graded Materials. London: IOM Communications Ltd; 1998. 165 p. ISBN: 9781861250636'},{id:"B2",body:'Miyamoto Y, Kaysser WA, Rabin BH, Kawasaki A, Ford RG. Functionally Graded Materials: Design, Processing and Applications. Springer Science & Business Media; 2013. 330 p. ISBN: 9781461553014'},{id:"B3",body:'Lipton R. Design of functionally graded composite structures in the presence of stress constraints. International Journal of Solids and Structures. 2002;39:2575-2586'},{id:"B4",body:'Cho JR, Shin SW. 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International Journal of Solids and Structures. 1998;35(23):3105-3119'},{id:"B27",body:'Kieback B, Neubrand A, Riedal H. Processing techniques for functionally graded materials. Materials Science and Engineering. 2003;362:81-105'},{id:"B28",body:'Maalawi KY, El-Chazly NM. Global optimization of multi-element beam-type structures. In: Proceedings of the Second International Conference on Advances in Structural Engineering and Mechanics (ASEM\'02). Busan, South Korea; August 2002. pp. 21-23'},{id:"B29",body:'Bijagare Anup A, Mehar PG, Mujbaile VN. Design optimization & analysis of drive shaft. VSRD International Journal of Mechanical, Automobile & Production Engineering. 2012;2(6):210-215'},{id:"B30",body:'Bert CW, Chun-Do K. Analysis of buckling of hollow laminated composite drive shafts. Composites Science and Technology. 1995;53:343-351'},{id:"B31",body:'Nasr MF, El-Zoghby AA, Maalawi KY, Azzam BS, Badr MA. Torsional buckling optimization of composite drive shafts. World Applied Sciences Journal. 2015;33(3):517-524'},{id:"B32",body:'Bauchau OA. Optimal design of high speed rotating graphite/epoxy shafts. Journal of Composite Materials. 1983;17(3):170-180'},{id:"B33",body:'Bert CW, Chun-Do K. Whirling of composite-material drive shafts including bending-twisting coupling and transverse shear deformation. Journal of Vibration and Acoustics, ASME. 1995;117:17-21'},{id:"B34",body:'Nasr MF, Maalawi KY, Badr MA. Optimization of composite drive shafts against whirling instability. In: Proceedings of the 8th International Conference on Mechanical and Aerospace Engineering (ICMAE); 22-25 July 2017; Prague. Czech Republic: IEEE; 2017. pp. 816-821. DOI: 10.1109/ICMAE.2017.8038756'},{id:"B35",body:'Librescu L, Maalawi KY. Aeroelastic design optimization of thin-walled subsonic wings against divergence. Thin-Walled Structures. 2009;47:89-97'},{id:"B36",body:'Paїdoussis MP. Fluid Structure Interactions: Volume I; Slender Structures and Axial Flow. 2nd ed. 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CHANGES IN AUTHORSHIP
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AFFILIATION
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Authors are responsible for ensuring all addresses and emails provided are correct. Under affiliation(s) all Authors should indicate where the research was conducted. Please note that no changes to the affiliation(s) can be made after the chapter has been published.
Substantially contribute to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work
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Participate in drafting or revising the work
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Approve the final version of the work to be published.
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All contributors who meet these criteria are listed as Authors. Their exact contributions should be described in the manuscript at the time of submission.
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Conversely, all contributors who do not meet these criteria should be listed in the Acknowledgments section of the manuscript, along with a short description of their specific contributions.
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CHANGES IN AUTHORSHIP
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If it is felt necessary to make changes to the list of Authors after a manuscript has been submitted or published, it is the responsibility of the Author concerned to provide a valid reason to amend the published list. Additionally, all listed Authors must verify and approve the proposed changes in order for any amendments to be made.
\n\n
AFFILIATION
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Authors are responsible for ensuring all addresses and emails provided are correct. Under affiliation(s) all Authors should indicate where the research was conducted. Please note that no changes to the affiliation(s) can be made after the chapter has been published.
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Policy last updated: 2017-05-29
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In this study, the contribution of biotechnology is emphasized to define the conservation strategy, for a marble Fountain (Two Dragons, XV century) located in Palermo city center, based on an integrated approach and eco-friendly procedures. Biotechnological protocols are preliminarily applied as an integrated approach, based on microscopy observation, in vitro culture and genomic DNA analysis to recognize and characterize microbial communities. Several biological systems have been identified: green algae (Chlorella) and cyanobacteria (Cyanobium, Oscillatoria); bacteria (Arthrobacter, Bacillus, Micrococcus, Paracoccus); fungi (Alternaria, Aspergillus, Penicillium, Phoma, Fusarium, Cladosporium). In order to address biological colonization, the commercial Tea Tree Oil (Melaleuca alternifolia) and laboratory-distilled (Calamintha nepeta and Allium sativum) EOs, have been assayed by in vitro Agar disc diffusion, Well-plates diffusion, and Micro-dilution methods; the result allows to define the most appropriate EOs concentration to use. In a green conservation prospective, this study highlighted that EOs can potentially replace the traditional biocides, but the activity must be preliminary evaluated by centring the choose specifically on each microbial taxon identified.",book:{id:"6944",slug:"heritage",title:"Heritage",fullTitle:"Heritage"},signatures:"Franco Palla",authors:null},{id:"39099",doi:"10.5772/38850",title:"Japan’s University Education in Social Sciences and Humanities Under Globalization",slug:"education-and-research-of-social-science-and-humanities-in-japan-under-globalization",totalDownloads:1841,totalCrossrefCites:2,totalDimensionsCites:3,abstract:null,book:{id:"2301",slug:"social-sciences-and-cultural-studies-issues-of-language-public-opinion-education-and-welfare",title:"Social Sciences and Cultural Studies",fullTitle:"Social Sciences and Cultural Studies - Issues of Language, Public Opinion, Education and Welfare"},signatures:"Akiyoshi Yonezawa",authors:[{id:"120081",title:"Prof.",name:"Akiyoshi",middleName:null,surname:"Yonezawa",slug:"akiyoshi-yonezawa",fullName:"Akiyoshi Yonezawa"}]}],mostDownloadedChaptersLast30Days:[{id:"71819",title:"Significance in African Heritage",slug:"significance-in-african-heritage",totalDownloads:707,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"Heritage professionals are at all times called upon to make significant judgments about heritage places/objects. There is a supposition therefore that heritage places or objects have intrinsic values that need to be discovered and assigned. This paper, using various examples from Africa, however, argues that values are not intrinsic to heritage but are a construct of heritage professionals/community, and therefore, a heritage place/object can have various values depending on who is making the judgment. It therefore follows that if values vary according to who is assigning them, then the significant/insignificant of a heritage place and object will also vary from one person/community to another. The paper concludes by arguing that significant/insignificant judgments are hegemonic constructions between contending forces, and therefore, it is difficult to have a universally accepted significant or insignificant judgment.",book:{id:"6944",slug:"heritage",title:"Heritage",fullTitle:"Heritage"},signatures:"Herman Ogoti Kiriama and Edith Nyangara Onkoba",authors:null},{id:"39079",title:"Are the Social Sciences Really- and Merely- Sciences?",slug:"are-the-social-sciences-really-and-merely-sciences",totalDownloads:2650,totalCrossrefCites:0,totalDimensionsCites:0,abstract:null,book:{id:"2301",slug:"social-sciences-and-cultural-studies-issues-of-language-public-opinion-education-and-welfare",title:"Social Sciences and Cultural Studies",fullTitle:"Social Sciences and Cultural Studies - Issues of Language, Public Opinion, Education and Welfare"},signatures:"Jeffrey Foss",authors:[{id:"121172",title:"Prof.",name:"Jeffrey",middleName:null,surname:"Foss",slug:"jeffrey-foss",fullName:"Jeffrey Foss"}]},{id:"73006",title:"Ideological Interaction Theory in Critical Discourse Analysis",slug:"ideological-interaction-theory-in-critical-discourse-analysis",totalDownloads:798,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"Cultural and ideological references in critical discourse analysis aim at reframing decisions with exploring the cultural as well as ideological perspectives of the familiar and idiosyncratic styles. The chapter focuses on the development of the ideological interaction theory for connecting the discourse with language and personal thought. The concern criteria are ideology and the other social components like people, status, economy, media, and politics with their connectivity to the global situational trends. Therefore, the chapter surveys the personal traits as psychology, sociological surrounding, and cognitive efforts for the development of social as well as cultural interaction with the perspectives of individual influences.",book:{id:"6944",slug:"heritage",title:"Heritage",fullTitle:"Heritage"},signatures:"Yadu Prasad Gyawali",authors:null},{id:"58144",title:"Losing One’s Culture: The Narrative Identity of Nigerian Catholic Religious Sisters",slug:"losing-one-s-culture-the-narrative-identity-of-nigerian-catholic-religious-sisters",totalDownloads:878,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"This chapter reports on part of the findings of a doctoral research focused on identity construction of Catholic religious sisters in the Church and in the wider Nigerian society. Primarily, the chapter interrogates how Catholic religious sisters negotiate their culture identity within the context of living religious life. Data were collected from 18 sister participants, who were purposefully recruited from two religious congregations across the different states of Nigeria. These included six temporary professed, six final professed and six leaders (including superiors/formators) representing the different categories of sisters that live religious life. The data were thematically analysed using the Dialogical Self Theory I-positions. The second sentence revealed tendencies for the participants to lose their cultural identity in terms of their struggles and sometimes compromises in identifying Western culture as the dominate culture of religious life. In this regard, the participants reported that their Nigerian communitarian culture of love, care and hospitality is regulated to the background. In response, this chapter calls for further research towards exploring the impact of culture on Catholic religious sisters’ expression of identity.",book:{id:"6169",slug:"culture-and-identity",title:"Culture and Identity",fullTitle:"Culture and Identity"},signatures:"Chika Eze, Graham C. Lindegger and Susan Rakoczy",authors:[{id:"216952",title:"Dr.",name:"Chika",middleName:null,surname:"Eze",slug:"chika-eze",fullName:"Chika Eze"}]},{id:"72251",title:"Public Diplomacy: Functions, Functional Boundaries and Measurement Methods",slug:"public-diplomacy-functions-functional-boundaries-and-measurement-methods",totalDownloads:834,totalCrossrefCites:2,totalDimensionsCites:2,abstract:"It is common practice when evaluating the effect of public diplomacy to associate it with soft power. This chapter challenges this view. It holds that, rather than turning soft power resources into soft power, the fundamental function of public diplomacy is to transform a country’s assets into soft power resources that will attract the target country. This chapter also shows that although public diplomacy performs such functions, it is not necessarily effective, especially in ‘high politics’ such as territorial security. But even where its effectiveness can be exerted, public diplomacy is subject to other distractions. Therefore, in order to measure accurately the real effect of public diplomacy, it is necessary to exclude the potential influence of these interfering variables by strengthening the rigor of research design.",book:{id:"6944",slug:"heritage",title:"Heritage",fullTitle:"Heritage"},signatures:"Cao Wei",authors:null}],onlineFirstChaptersFilter:{topicId:"1324",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:87,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:98,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:287,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:139,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:10,numberOfPublishedChapters:103,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:0,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:10,numberOfOpenTopics:4,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"25",title:"Environmental Sciences",doi:"10.5772/intechopen.100362",issn:"2754-6713",scope:"
\r\n\tScientists have long researched to understand the environment and man’s place in it. The search for this knowledge grows in importance as rapid increases in population and economic development intensify humans’ stresses on ecosystems. Fortunately, rapid increases in multiple scientific areas are advancing our understanding of environmental sciences. Breakthroughs in computing, molecular biology, ecology, and sustainability science are enhancing our ability to utilize environmental sciences to address real-world problems. \r\n\tThe four topics of this book series - Pollution; Environmental Resilience and Management; Ecosystems and Biodiversity; and Water Science - will address important areas of advancement in the environmental sciences. They will represent an excellent initial grouping of published works on these critical topics.
",coverUrl:"https://cdn.intechopen.com/series/covers/25.jpg",latestPublicationDate:"April 13th, 2022",hasOnlineFirst:!1,numberOfPublishedBooks:1,editor:{id:"197485",title:"Dr.",name:"J. Kevin",middleName:null,surname:"Summers",slug:"j.-kevin-summers",fullName:"J. Kevin Summers",profilePictureURL:"https://mts.intechopen.com/storage/users/197485/images/system/197485.jpg",biography:"J. Kevin Summers is a Senior Research Ecologist at the Environmental Protection Agency’s (EPA) Gulf Ecosystem Measurement and Modeling Division. He is currently working with colleagues in the Sustainable and Healthy Communities Program to develop an index of community resilience to natural hazards, an index of human well-being that can be linked to changes in the ecosystem, social and economic services, and a community sustainability tool for communities with populations under 40,000. He leads research efforts for indicator and indices development. Dr. Summers is a systems ecologist and began his career at the EPA in 1989 and has worked in various programs and capacities. This includes leading the National Coastal Assessment in collaboration with the Office of Water which culminated in the award-winning National Coastal Condition Report series (four volumes between 2001 and 2012), and which integrates water quality, sediment quality, habitat, and biological data to assess the ecosystem condition of the United States estuaries. He was acting National Program Director for Ecology for the EPA between 2004 and 2006. He has authored approximately 150 peer-reviewed journal articles, book chapters, and reports and has received many awards for technical accomplishments from the EPA and from outside of the agency. Dr. Summers holds a BA in Zoology and Psychology, an MA in Ecology, and Ph.D. in Systems Ecology/Biology.",institutionString:null,institution:{name:"Environmental Protection Agency",institutionURL:null,country:{name:"United States of America"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"38",title:"Pollution",coverUrl:"https://cdn.intechopen.com/series_topics/covers/38.jpg",isOpenForSubmission:!0,editor:{id:"110740",title:"Dr.",name:"Ismail M.M.",middleName:null,surname:"Rahman",slug:"ismail-m.m.-rahman",fullName:"Ismail M.M. Rahman",profilePictureURL:"https://mts.intechopen.com/storage/users/110740/images/2319_n.jpg",biography:"Ismail Md. Mofizur Rahman (Ismail M. M. Rahman) assumed his current responsibilities as an Associate Professor at the Institute of Environmental Radioactivity, Fukushima University, Japan, in Oct 2015. He also has an honorary appointment to serve as a Collaborative Professor at Kanazawa University, Japan, from Mar 2015 to the present. \nFormerly, Dr. Rahman was a faculty member of the University of Chittagong, Bangladesh, affiliated with the Department of Chemistry (Oct 2002 to Mar 2012) and the Department of Applied Chemistry and Chemical Engineering (Mar 2012 to Sep 2015). Dr. Rahman was also adjunctly attached with Kanazawa University, Japan (Visiting Research Professor, Dec 2014 to Mar 2015; JSPS Postdoctoral Research Fellow, Apr 2012 to Mar 2014), and Tokyo Institute of Technology, Japan (TokyoTech-UNESCO Research Fellow, Oct 2004–Sep 2005). \nHe received his Ph.D. degree in Environmental Analytical Chemistry from Kanazawa University, Japan (2011). He also achieved a Diploma in Environment from the Tokyo Institute of Technology, Japan (2005). Besides, he has an M.Sc. degree in Applied Chemistry and a B.Sc. degree in Chemistry, all from the University of Chittagong, Bangladesh. \nDr. Rahman’s research interest includes the study of the fate and behavior of environmental pollutants in the biosphere; design of low energy and low burden environmental improvement (remediation) technology; implementation of sustainable waste management practices for treatment, handling, reuse, and ultimate residual disposition of solid wastes; nature and type of interactions in organic liquid mixtures for process engineering design applications.",institutionString:null,institution:{name:"Fukushima University",institutionURL:null,country:{name:"Japan"}}},editorTwo:{id:"201020",title:"Dr.",name:"Zinnat Ara",middleName:null,surname:"Begum",slug:"zinnat-ara-begum",fullName:"Zinnat Ara Begum",profilePictureURL:"https://mts.intechopen.com/storage/users/201020/images/system/201020.jpeg",biography:"Zinnat A. Begum received her Ph.D. in Environmental Analytical Chemistry from Kanazawa University in 2012. She achieved her Master of Science (M.Sc.) degree with a major in Applied Chemistry and a Bachelor of Science (B.Sc.) in Chemistry, all from the University of Chittagong, Bangladesh. Her work affiliations include Fukushima University, Japan (Visiting Research Fellow, Institute of Environmental Radioactivity: Mar 2016 to present), Southern University Bangladesh (Assistant Professor, Department of Civil Engineering: Jan 2015 to present), and Kanazawa University, Japan (Postdoctoral Fellow, Institute of Science and Engineering: Oct 2012 to Mar 2014; Research fellow, Venture Business Laboratory, Advanced Science and Social Co-Creation Promotion Organization: Apr 2018 to Mar 2021). The research focus of Dr. Zinnat includes the effect of the relative stability of metal-chelator complexes in the environmental remediation process designs and the development of eco-friendly soil washing techniques using biodegradable chelators.",institutionString:null,institution:{name:"Fukushima University",institutionURL:null,country:{name:"Japan"}}},editorThree:null},{id:"39",title:"Environmental Resilience and Management",coverUrl:"https://cdn.intechopen.com/series_topics/covers/39.jpg",isOpenForSubmission:!0,editor:{id:"137040",title:"Prof.",name:"Jose",middleName:null,surname:"Navarro-Pedreño",slug:"jose-navarro-pedreno",fullName:"Jose Navarro-Pedreño",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRAXrQAO/Profile_Picture_2022-03-09T15:50:19.jpg",biography:"Full professor at University Miguel Hernández of Elche, Spain, previously working at the University of Alicante, Autonomous University of Madrid and Polytechnic University of Valencia. Graduate in Sciences (Chemist), graduate in Geography and History (Geography), master in Water Management, Treatment, master in Fertilizers and Environment and master in Environmental Management; Ph.D. in Environmental Sciences. His research is focused on soil-water and waste-environment relations, mainly on soil-water and soil-waste interactions under different management and waste reuse. His work is reflected in more than 230 communications presented in national and international conferences and congresses, 29 invited lectures from universities, associations and government agencies. 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He has developed his research activity in the fields of fauna and soil ecology, and in the treatment of organic waste, having been the founder and principal investigator of the Environmental Biotechnology Group of the University of Vigo.\r\nHis research activity in the field of Environmental Biotechnology has been focused on the development of novel organic waste treatment systems through composting. The result of this line of work are three invention patents and various scientific and technical publications in prestigious international journals.",institutionString:null,institution:{name:"University of Vigo",institutionURL:null,country:{name:"Spain"}}},editorTwo:{id:"60498",title:"Prof.",name:"Josefina",middleName:null,surname:"Garrido",slug:"josefina-garrido",fullName:"Josefina Garrido",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRj1VQAS/Profile_Picture_2022-03-31T10:06:51.jpg",biography:"Josefina Garrido González (Paradela de Abeleda, Ourense 1959), is a doctor in biology from the University of León and a Professor of Zoology at the Department of Ecology and Animal Biology at the University of Vigo. She has focused her research activity on the taxonomy, fauna and ecology of aquatic beetles, in addition to other lines of research such as the conservation of biodiversity in freshwater ecosystems; conservation of protected areas (Red Natura 2000) and assessment of the effectiveness of wetlands as priority areas for the conservation of aquatic invertebrates; studies of water quality in freshwater ecosystems through biological indicators and physicochemical parameters; surveillance and research of vector arthropods and invasive alien species.",institutionString:null,institution:{name:"University of Vigo",institutionURL:null,country:{name:"Spain"}}},editorThree:{id:"464288",title:"Dr.",name:"Francisco",middleName:null,surname:"Ramil",slug:"francisco-ramil",fullName:"Francisco Ramil",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003RI7lHQAT/Profile_Picture_2022-03-31T10:15:35.png",biography:"Fran Ramil Blanco (Porto de Espasante, A Coruña, 1960), is a doctor in biology from the University of Santiago de Compostela and a Professor of Zoology at the Department of Ecology and Animal Biology at the University of Vigo. His research activity is linked to the taxonomy, fauna and ecology of marine benthic invertebrates and especially the Cnidarian group. Since 2004, he has been part of the EcoAfrik project, aimed at the study, protection and conservation of biodiversity and benthic habitats in West Africa. He also participated in the study of vulnerable marine ecosystems associated with seamounts in the South Atlantic and is involved in training young African researchers in the field of marine research.",institutionString:null,institution:{name:"University of Vigo",institutionURL:null,country:{name:"Spain"}}}},{id:"41",title:"Water Science",coverUrl:"https://cdn.intechopen.com/series_topics/covers/41.jpg",isOpenForSubmission:!0,editor:{id:"349630",title:"Dr.",name:"Yizi",middleName:null,surname:"Shang",slug:"yizi-shang",fullName:"Yizi Shang",profilePictureURL:"https://mts.intechopen.com/storage/users/349630/images/system/349630.jpg",biography:"Prof. Dr. Yizi Shang is a pioneering researcher in hydrology and water resources who has devoted his research career to promoting the conservation and protection of water resources for sustainable development. He is presently associate editor of Water International (official journal of the International Water Resources Association). He was also invited to serve as an associate editor for special issues of the Journal of the American Water Resources Association. He has served as an editorial member for international journals such as Hydrology, Journal of Ecology & Natural Resources, and Hydro Science & Marine Engineering, among others. He has chaired or acted as a technical committee member for twenty-five international forums (conferences). Dr. Shang graduated from Tsinghua University, China, in 2010 with a Ph.D. in Engineering. Prior to that, he worked as a research fellow at Harvard University from 2008 to 2009. Dr. Shang serves as a senior research engineer at the China Institute of Water Resources and Hydropower Research (IWHR) and was awarded as a distinguished researcher at National Taiwan University in 2017.",institutionString:"China Institute of Water Resources and Hydropower Research",institution:{name:"China Institute of Water Resources and Hydropower Research",institutionURL:null,country:{name:"China"}}},editorTwo:null,editorThree:null}]},overviewPageOFChapters:{paginationCount:0,paginationItems:[]},overviewPagePublishedBooks:{paginationCount:1,paginationItems:[{type:"book",id:"10843",title:"Persistent Organic Pollutants (POPs)",subtitle:"Monitoring, Impact and Treatment",coverURL:"https://cdn.intechopen.com/books/images_new/10843.jpg",slug:"persistent-organic-pollutants-pops-monitoring-impact-and-treatment",publishedDate:"April 13th 2022",editedByType:"Edited by",bookSignature:"Mohamed Nageeb Rashed",hash:"f5b1589f0a990b6114fef2dadc735dd9",volumeInSeries:1,fullTitle:"Persistent Organic Pollutants (POPs) - Monitoring, Impact and Treatment",editors:[{id:"63465",title:"Prof.",name:"Mohamed Nageeb",middleName:null,surname:"Rashed",slug:"mohamed-nageeb-rashed",fullName:"Mohamed Nageeb Rashed",profilePictureURL:"https://mts.intechopen.com/storage/users/63465/images/system/63465.gif",biography:"Prof. Mohamed Nageeb Rashed is Professor of Analytical and Environmental Chemistry and former vice-dean for environmental affairs, Faculty of Science, Aswan University, Egypt. He received his Ph.D. in Environmental Analytical Chemistry from Assiut University, Egypt, in 1989. His research interest is in analytical and environmental chemistry with special emphasis on: (1) monitoring and assessing biological trace elements and toxic metals in human blood, urine, water, crops, vegetables, and medicinal plants; (2) relationships between environmental heavy metals and human diseases; (3) uses of biological indicators for monitoring water pollution; (4) environmental chemistry of lakes, rivers, and well water; (5) water and wastewater treatment by adsorption and photocatalysis techniques; (6) soil and water pollution monitoring, control, and treatment; and (7) advanced oxidation treatment. Prof. Rashed has supervised several MSc and Ph.D. theses in the field of analytical and environmental chemistry. He served as an examiner for several Ph.D. theses in analytical chemistry in India, Kazakhstan, and Botswana. He has published about ninety scientific papers in peer-reviewed international journals and several papers in national and international conferences. He participated as an invited speaker at thirty international conferences. Prof. Rashed is the editor-in-chief and an editorial board member for several international journals in the fields of chemistry and environment. He is a member of several national and international societies. He received the Egyptian State Award for Environmental Research in 2001 and the Aswan University Merit Award for Basic Science in 2020. 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He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. 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He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}},{id:"351158",title:"Prof.",name:"David W.",middleName:null,surname:"Anderson",slug:"david-w.-anderson",fullName:"David W. 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\r\n\tIf we aim to prosper as a society and as a species, there is no alternative to sustainability-oriented development and growth. Sustainable development is no longer a choice but a necessity for us all. Ecosystems and preserving ecosystem services and inclusive urban development present promising solutions to environmental problems. Contextually, the emphasis on studying these fields will enable us to identify and define the critical factors for territorial success in the upcoming decades to be considered by the main-actors, decision and policy makers, technicians, and public in general.
\r\n
\r\n\tHolistic urban planning and environmental management are therefore crucial spheres that will define sustainable trajectories for our urbanizing planet. This urban and environmental planning topic aims to attract contributions that address sustainable urban development challenges and solutions, including integrated urban water management, planning for the urban circular economy, monitoring of risks, contingency planning and response to disasters, among several other challenges and solutions.
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Science",numberOfPublishedBooks:10,numberOfPublishedChapters:103,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:0,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:10,numberOfOpenTopics:4,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],subseriesList:[{id:"4",title:"Fungal Infectious Diseases",scope:"Fungi are ubiquitous and there are almost no non-pathogenic fungi. Fungal infectious illness prevalence and prognosis are determined by the exposure between fungi and host, host immunological state, fungal virulence, and early and accurate diagnosis and treatment. \r\nPatients with both congenital and acquired immunodeficiency are more likely to be infected with opportunistic mycosis. Fungal infectious disease outbreaks are common during the post- disaster rebuilding era, which is characterised by high population density, migration, and poor health and medical conditions.\r\nSystemic or local fungal infection is mainly associated with the fungi directly inhaled or inoculated in the environment during the disaster. The most common fungal infection pathways are human to human (anthropophilic), animal to human (zoophilic), and environment to human (soilophile). Diseases are common as a result of widespread exposure to pathogenic fungus dispersed into the environment. \r\nFungi that are both common and emerging are intertwined. In Southeast Asia, for example, Talaromyces marneffei is an important pathogenic thermally dimorphic fungus that causes systemic mycosis. Widespread fungal infections with complicated and variable clinical manifestations, such as Candida auris infection resistant to several antifungal medicines, Covid-19 associated with Trichoderma, and terbinafine resistant dermatophytosis in India, are among the most serious disorders. \r\nInappropriate local or systemic use of glucocorticoids, as well as their immunosuppressive effects, may lead to changes in fungal infection spectrum and clinical characteristics. Hematogenous candidiasis is a worrisome issue that affects people all over the world, particularly ICU patients. CARD9 deficiency and fungal infection have been major issues in recent years. Invasive aspergillosis is associated with a significant death rate. Special attention should be given to endemic fungal infections, identification of important clinical fungal infections advanced in yeasts, filamentous fungal infections, skin mycobiome and fungal genomes, and immunity to fungal infections.\r\nIn addition, endemic fungal diseases or uncommon fungal infections caused by Mucor irregularis, dermatophytosis, Malassezia, cryptococcosis, chromoblastomycosis, coccidiosis, blastomycosis, histoplasmosis, sporotrichosis, and other fungi, should be monitored. \r\nThis topic includes the research progress on the etiology and pathogenesis of fungal infections, new methods of isolation and identification, rapid detection, drug sensitivity testing, new antifungal drugs, schemes and case series reports. It will provide significant opportunities and support for scientists, clinical doctors, mycologists, antifungal drug researchers, public health practitioners, and epidemiologists from all over the world to share new research, ideas and solutions to promote the development and progress of medical mycology.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/4.jpg",keywords:"Emerging Fungal Pathogens, Invasive Infections, Epidemiology, Cell Membrane, Fungal Virulence, Diagnosis, Treatment"},{id:"5",title:"Parasitic Infectious Diseases",scope:"Parasitic diseases have evolved alongside their human hosts. In many cases, these diseases have adapted so well that they have developed efficient resilience methods in the human host and can live in the host for years. Others, particularly some blood parasites, can cause very acute diseases and are responsible for millions of deaths yearly. Many parasitic diseases are classified as neglected tropical diseases because they have received minimal funding over recent years and, in many cases, are under-reported despite the critical role they play in morbidity and mortality among human and animal hosts. The current topic, Parasitic Infectious Diseases, in the Infectious Diseases Series aims to publish studies on the systematics, epidemiology, molecular biology, genomics, pathogenesis, genetics, and clinical significance of parasitic diseases from blood borne to intestinal parasites as well as zoonotic parasites. We hope to cover all aspects of parasitic diseases to provide current and relevant research data on these very important diseases. In the current atmosphere of the Coronavirus pandemic, communities around the world, particularly those in different underdeveloped areas, are faced with the growing challenges of the high burden of parasitic diseases. At the same time, they are faced with the Covid-19 pandemic leading to what some authors have called potential syndemics that might worsen the outcome of such infections. Therefore, it is important to conduct studies that examine parasitic infections in the context of the coronavirus pandemic for the benefit of all communities to help foster more informed decisions for the betterment of human and animal health.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/5.jpg",keywords:"Blood Borne Parasites, Intestinal Parasites, Protozoa, Helminths, Arthropods, Water Born Parasites, Epidemiology, Molecular Biology, Systematics, Genomics, Proteomics, Ecology"},{id:"6",title:"Viral Infectious Diseases",scope:"The Viral Infectious Diseases Book Series aims to provide a comprehensive overview of recent research trends and discoveries in various viral infectious diseases emerging around the globe. The emergence of any viral disease is hard to anticipate, which often contributes to death. A viral disease can be defined as an infectious disease that has recently appeared within a population or exists in nature with the rapid expansion of incident or geographic range. This series will focus on various crucial factors related to emerging viral infectious diseases, including epidemiology, pathogenesis, host immune response, clinical manifestations, diagnosis, treatment, and clinical recommendations for managing viral infectious diseases, highlighting the recent issues with future directions for effective therapeutic strategies.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/6.jpg",keywords:"Novel Viruses, Virus Transmission, Virus Evolution, Molecular Virology, Control and Prevention, Virus-host Interaction"}],annualVolumeBook:{},thematicCollection:[],selectedSeries:null,selectedSubseries:null},seriesLanding:{item:null},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/194142",hash:"",query:{},params:{id:"194142"},fullPath:"/profiles/194142",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()