Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
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This achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
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We are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
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Thank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
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\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{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"}]},book:{item:{type:"book",id:"7181",leadTitle:null,fullTitle:"Erythrocyte",title:"Erythrocyte",subtitle:null,reviewType:"peer-reviewed",abstract:'Red blood cells constitute approximately 40% of the total amount of blood and 99% of shaped elements of blood. Their major function is oxygen transportation and this makes erythrocytes "the basis of life." However, as readers will see in this book, erythrocytes have a lot of different, important functions in our body. With this book, it is planned to collect current information related to "erythrocytes." The book has been divided into two sections. The first section includes information about the roles of erythrocytes in the physiological and pathophysiological processes. The second section includes information on the future perspectives of erythrocytes like their therapeutic applications in medicine. This book will be a stepping stone for scientists who are rapidly advancing their science journey.',isbn:"978-1-78984-210-4",printIsbn:"978-1-78984-209-8",pdfIsbn:"978-1-78984-693-5",doi:"10.5772/intechopen.73981",price:119,priceEur:129,priceUsd:155,slug:"erythrocyte",numberOfPages:176,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"267d215004c995048557176978208b15",bookSignature:"Anil Tombak",publishedDate:"October 23rd 2019",coverURL:"https://cdn.intechopen.com/books/images_new/7181.jpg",numberOfDownloads:12375,numberOfWosCitations:8,numberOfCrossrefCitations:19,numberOfCrossrefCitationsByBook:2,numberOfDimensionsCitations:32,numberOfDimensionsCitationsByBook:2,hasAltmetrics:1,numberOfTotalCitations:59,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 20th 2018",dateEndSecondStepPublish:"November 7th 2018",dateEndThirdStepPublish:"January 6th 2019",dateEndFourthStepPublish:"March 27th 2019",dateEndFifthStepPublish:"May 26th 2019",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"202814",title:"Associate Prof.",name:"Anil",middleName:null,surname:"Tombak",slug:"anil-tombak",fullName:"Anil Tombak",profilePictureURL:"https://mts.intechopen.com/storage/users/202814/images/system/202814.jpeg",biography:"Assoc. Prof. Dr. Anil Tombak, MD, was born on April 25, 1976.\nHe graduated from Gazi Anatolian High School in 1994, and\nthen from Çukurova University Medical Faculty in 2000. Subsequently, he was trained at Mersin University Medical Faculty\nInternal Medicine Department, and after internal medicine\nspecialization became a fellow of hematology at the same university. He became a hematologist in 2013 while still working\nat Mersin University Medical Faculty, Department of Internal Medicine—Hematology, Mersin, Turkey. He has performed research in several fields, and has more\nthan 50 publications in (inter)national journals and numerous papers in scientific\nconferences. He has received several awards and is a member of the Turkish Society\nof Hematology. Dr. Tombak is married and has two children.",institutionString:"Mersin University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"Mersin University",institutionURL:null,country:{name:"Turkey"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"183",title:"Hematology",slug:"hematology"}],chapters:[{id:"68721",title:"Introductory Chapter: Erythrocytes - Basis of Life",doi:"10.5772/intechopen.88847",slug:"introductory-chapter-erythrocytes-basis-of-life",totalDownloads:838,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:null,signatures:"Anil Tombak",downloadPdfUrl:"/chapter/pdf-download/68721",previewPdfUrl:"/chapter/pdf-preview/68721",authors:[{id:"202814",title:"Associate Prof.",name:"Anil",surname:"Tombak",slug:"anil-tombak",fullName:"Anil Tombak"}],corrections:null},{id:"67667",title:"Erythrocyte Morphology and Its Disorders",doi:"10.5772/intechopen.86112",slug:"erythrocyte-morphology-and-its-disorders",totalDownloads:1919,totalCrossrefCites:3,totalDimensionsCites:4,hasAltmetrics:0,abstract:"Blood cell morphology is a key tool in laboratory haematology. Erythrocyte morphology points to possible aetiopathogenetic events in several primary and secondary haemopathies. Despite advances in medical technology and laboratory automation, red cell morphology remains a basic aspect of haematological evaluation. The human erythrocytes are discoid (bi-concave), about 7–8 μm (size of the nucleus of a small lymphocyte) in diameter, with a central area of pallor (which occupies a third of the red cell diameter) and is well haemoglobinised in the outer two thirds of the red cell diameters, without any inclusions. Deviations from the normal in terms of size, shape, colour, distribution or presence of inclusion bodies suggests possible disease processes. This chapter is therefore dedicated to morphologic description of the human erythrocytes, a study of possible abnormalities, its underlying pathophysiology and the associated differential diagnosis in humans.",signatures:"Ademola Samson Adewoyin, Oluwafemi Adeyemi, Nosimot Omolola Davies and Ann Abiola Ogbenna",downloadPdfUrl:"/chapter/pdf-download/67667",previewPdfUrl:"/chapter/pdf-preview/67667",authors:[{id:"281974",title:"Dr.",name:"Ademola Samson",surname:"Adewoyin",slug:"ademola-samson-adewoyin",fullName:"Ademola Samson Adewoyin"},{id:"304907",title:"Dr.",name:"Oluwafemi",surname:"Adeyemi",slug:"oluwafemi-adeyemi",fullName:"Oluwafemi Adeyemi"},{id:"304908",title:"Dr.",name:"Nosimot Omolola",surname:"Davies",slug:"nosimot-omolola-davies",fullName:"Nosimot Omolola Davies"},{id:"304909",title:"Dr.",name:"Ann",surname:"Abiola Ogbenna",slug:"ann-abiola-ogbenna",fullName:"Ann Abiola Ogbenna"}],corrections:null},{id:"67065",title:"Red Blood Cells Actively Contribute to Blood Coagulation and Thrombus Formation",doi:"10.5772/intechopen.86152",slug:"red-blood-cells-actively-contribute-to-blood-coagulation-and-thrombus-formation",totalDownloads:1040,totalCrossrefCites:4,totalDimensionsCites:5,hasAltmetrics:0,abstract:"The chapter describes the likely molecular mechanisms leading to the aggregation of human red blood cells (RBCs) under conditions of physiological coagulation when prostaglandin E2 (PGE2) or lysophosphatidic acid (LPA) is released from activated platelets and under pathophysiological conditions, in particular thrombi formation in sickle cell disease when patients are in a vaso-occlusive crisis. In both scenarios cation channels are activated. This leads to an increase of the free intracellular Ca2+ concentration resulting in an activation of the lipid scramblase, which in turn mediates a movement of phosphatidylserine (PS) from the inner to the outer membrane leaflet. In addition, the increased Ca2+ concentration leads to the activation of the Gardos channel. Experiments suggesting this mechanism have been performed with fluorescence microscopy, flow cytometry as well as single-cell force spectroscopy. The Ca2+-triggered RBC aggregation force has been identified to be close to 100 pN, a value large enough to play a significant role during thrombus formation or in pathological situations.",signatures:"Ingolf Bernhardt, Mauro C. Wesseling, Duc Bach Nguyen and Lars Kaestner",downloadPdfUrl:"/chapter/pdf-download/67065",previewPdfUrl:"/chapter/pdf-preview/67065",authors:[{id:"205405",title:"Prof.",name:"Ingolf",surname:"Bernhardt",slug:"ingolf-bernhardt",fullName:"Ingolf Bernhardt"},{id:"299027",title:"Dr.",name:"Lars",surname:"Kaestner",slug:"lars-kaestner",fullName:"Lars Kaestner"},{id:"303082",title:"Dr.",name:"Duc Bach",surname:"Nguyen",slug:"duc-bach-nguyen",fullName:"Duc Bach Nguyen"},{id:"303083",title:"Dr.",name:"Mauro C.",surname:"Wesseling",slug:"mauro-c.-wesseling",fullName:"Mauro C. Wesseling"}],corrections:null},{id:"67465",title:"Who Is Balancing: Is It RBC or Acid-Base Status?",doi:"10.5772/intechopen.84768",slug:"who-is-balancing-is-it-rbc-or-acid-base-status-",totalDownloads:1079,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Hemoglobin is an important intracellular protein buffer present inside the red blood cells (RBC). When the partial pressure of carbon dioxide (pCO2) is increased, it freely diffuses into the RBC where it reacts with water molecules to form carbonic acid which dissociates to form bicarbonate and hydrogen ions by the enzyme carbonic anhydrase. Hydrogen ions liberated in this reaction are buffered by hemoglobin. Oxyhemoglobin is a stronger acid than deoxyhemoglobin. Oxygenation of hemoglobin causes an increase in net titratable hydrogen ion due to the Haldane effect. As the oxygen saturation of hemoglobin (sO2) increases, the base excess is changed in the acidic direction, or as the sO2 decreases, the base excess is changed in alkaline direction. The changes in the level of the enzyme carbonic anhydrase in RBC are related to the changes in pH, pCO2, and bicarbonate levels in the blood. The understanding of the acid-base balance is a challenging task, but at the same time, it has immense clinical value. The relationship of carbonic anhydrase enzyme present inside the RBC in maintaining the acid-base balance to the commonly employed arterial blood gas (ABG) parameters like pH, pCO2 bicarbonate, and base excess may help us for better understanding.",signatures:"T. Rajini Samuel",downloadPdfUrl:"/chapter/pdf-download/67465",previewPdfUrl:"/chapter/pdf-preview/67465",authors:[{id:"279821",title:"Dr.",name:"T. Rajini",surname:"Samuel",slug:"t.-rajini-samuel",fullName:"T. Rajini Samuel"}],corrections:null},{id:"65673",title:"Red Blood Cells as Redox Modulators in Hemolytic Anemia",doi:"10.5772/intechopen.84498",slug:"red-blood-cells-as-redox-modulators-in-hemolytic-anemia",totalDownloads:1135,totalCrossrefCites:2,totalDimensionsCites:5,hasAltmetrics:0,abstract:"The oxidative status of cells, representing the balance between prooxidants and antioxidants, is involved in their normal physiological functioning, such as signal transduction, proliferation, and differentiation. When the prooxidant activity overrides the antioxidative capacity oxidative stress occurs. Chronic oxidative stress causes cytotoxicity and organ failure. As such, it is believed to play a role in various pathologies, including the hemolytic anemias. In this review, we suggest that red blood cells (RBC), in addition to their primary role as oxygen carriers, function as redox modulators. In the RBC, various systems afford it with antioxidative capacity that, in addition to balancing its own redox state, can provide antioxidative protection to the cellular and intracellular milieus throughout the body. Their vast number, mobility, occurrence throughout the body, and renewability make them good candidates for this function. A decrease in their number (anemia) or function due to oxidative stress may exacerbate the symptoms of many diseases by failing to neutralize oxidative stress. However, correcting anemia, e.g., by repeated RBC transfusions or iron supplementation, may increase the iron load, which, in turn, causes oxidative stress. This situation suggests that the status of both iron and redox should be monitored during treatment, using RBC as bioindicators.",signatures:"Eitan Fibach and Mutaz Dana",downloadPdfUrl:"/chapter/pdf-download/65673",previewPdfUrl:"/chapter/pdf-preview/65673",authors:[{id:"83134",title:"Prof.",name:"Eitan",surname:"Fibach",slug:"eitan-fibach",fullName:"Eitan Fibach"},{id:"280928",title:"Dr.",name:"Mutaz",surname:"Dana",slug:"mutaz-dana",fullName:"Mutaz Dana"}],corrections:null},{id:"65133",title:"Interplay between Erythrocyte Peroxidases and Membrane",doi:"10.5772/intechopen.83590",slug:"interplay-between-erythrocyte-peroxidases-and-membrane",totalDownloads:1396,totalCrossrefCites:1,totalDimensionsCites:3,hasAltmetrics:0,abstract:"Red blood cells (RBCs) are continuously exposed to oxidative stress (OS), mainly due to their primary function as oxygen carriers. Since RBC is a unique cell, without nucleus or other organelles, it presents a very special metabolism and a highly efficient antioxidant system to face OS conditions. Hemoglobin and RBC membrane are the major targets of oxidative modifications when RBC antioxidant capacity is overwhelmed. Fortunately, non-enzymatic agents, such as glutathione, and enzymatic agents, namely, several peroxidases, such as catalase, glutathione peroxidase and peroxiredoxin 2, are able to prevent OS damage. Although these peroxidases are mainly cytosolic enzymes, evidence exists about their association to the RBC membrane. So far, it appears that the relative importance of the three enzymes is related to hydrogen peroxide levels within the RBC. In this chapter, we will focus on the importance of these peroxidases in the RBC’s defense against OS mainly in the RBC cytosol and also the interplay between them and the RBC membrane. The potential role of their binding to the membrane will also be addressed.",signatures:"Daniela Melo, Susana Rocha, Susana Coimbra and Alice Santos Silva",downloadPdfUrl:"/chapter/pdf-download/65133",previewPdfUrl:"/chapter/pdf-preview/65133",authors:[{id:"56251",title:"Prof.",name:"Alice",surname:"Santos Silva",slug:"alice-santos-silva",fullName:"Alice Santos Silva"},{id:"66774",title:"Prof.",name:"Susana",surname:"Coimbra",slug:"susana-coimbra",fullName:"Susana Coimbra"},{id:"280442",title:"MSc.",name:"Daniela",surname:"Melo",slug:"daniela-melo",fullName:"Daniela Melo"},{id:"280443",title:"Dr.",name:"Susana",surname:"Rocha",slug:"susana-rocha",fullName:"Susana Rocha"}],corrections:null},{id:"67156",title:"Effects of Therapeutic and Toxic Agents on Erythrocytes of Different Species of Animals",doi:"10.5772/intechopen.85865",slug:"effects-of-therapeutic-and-toxic-agents-on-erythrocytes-of-different-species-of-animals",totalDownloads:1238,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:"A preponderance of therapeutic and toxic agents that affect erythrocytes is being used in preclinical and clinical settings. Such agents are responsible for wrong diagnosis of a myriad of diseases and poor prognosis of some therapeutic interventions. In view of this, literature search was carried out with a view to investigate morphometry of erythrocytes in various diseased conditions and species of animals. Findings have shown that erythrocyte size, area, and volume vary in different species of animals under different diseased conditions. Environmental factors, toxicants, toxins, therapeutics, and management system, among others, can cause erythrocyte deformation, leading to anemia. Erythrocyte-related diseases include but not limited to sickle cell anemia, malaria, cancer, psychiatric illness, and chronic inflammation. Hence the principal source of our survival is erythrocyte, because it transports oxygen needed for metabolism of cell nutrients.",signatures:"Saganuwan Alhaji Saganuwan",downloadPdfUrl:"/chapter/pdf-download/67156",previewPdfUrl:"/chapter/pdf-preview/67156",authors:[{id:"266889",title:"Prof.",name:"Saganuwan",surname:"Alhaji Saganuwan",slug:"saganuwan-alhaji-saganuwan",fullName:"Saganuwan Alhaji Saganuwan"}],corrections:[{id:"81168",title:"Corrigendum to: Effects of Therapeutic and Toxic Agents on Erythrocytes of Different Species of Animals",doi:null,slug:"corrigendum-to-effects-of-therapeutic-and-toxic-agents-on-erythrocytes-of-different-species-of-anima",totalDownloads:null,totalCrossrefCites:null,correctionPdfUrl:null}]},{id:"64230",title:"The Biology and Therapeutic Applications of Red Blood Cell Extracellular Vesicles",doi:"10.5772/intechopen.81758",slug:"the-biology-and-therapeutic-applications-of-red-blood-cell-extracellular-vesicles",totalDownloads:1447,totalCrossrefCites:7,totalDimensionsCites:11,hasAltmetrics:1,abstract:"This chapter focuses on the biology of red blood cell extracellular vesicles (RBCEVs) in normal and diseased conditions, and the potential application of RBCEVs in treatment. Extracellular vesicles (EVs) refer to membranous vesicles secreted by cells into the extracellular environment. EV biology belongs to a rapidly developing field in biomedical sciences. EVs represent a natural mode of cell-to-cell communication, which makes them suitable for delivery of therapeutic agents, such as nucleic acids and proteins, in the body. In particular, RBCEVs feature a wide range of benefits in drug delivery as compared to extracellular vesicles derived from other cell types. In comparison to other delivery systems currently available, RBCEVs are nontoxic, low immunogenic, conveniently obtainable, and easy to use and store. Therefore, RBCEVs boast promising and exceptional advantages in overcoming various limitations of conventional therapeutics.",signatures:"Daniel Xin Zhang, Theodoros Kiomourtzis, Chun Kuen Lam and Minh T.N. Le",downloadPdfUrl:"/chapter/pdf-download/64230",previewPdfUrl:"/chapter/pdf-preview/64230",authors:[{id:"267051",title:"Dr.",name:"Minh",surname:"Le",slug:"minh-le",fullName:"Minh Le"},{id:"276896",title:"Dr.",name:"Daniel Xin",surname:"Zhang",slug:"daniel-xin-zhang",fullName:"Daniel Xin Zhang"},{id:"276897",title:"Mr.",name:"Theodoros",surname:"Kiomourtzis",slug:"theodoros-kiomourtzis",fullName:"Theodoros Kiomourtzis"},{id:"276898",title:"Mr.",name:"Chun Kuen",surname:"Lam",slug:"chun-kuen-lam",fullName:"Chun Kuen Lam"}],corrections:null},{id:"65464",title:"Application of Red Cell Membrane in Nanobiotechnology",doi:"10.5772/intechopen.84274",slug:"application-of-red-cell-membrane-in-nanobiotechnology",totalDownloads:1161,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Red cells are full of unique biological properties such as immune evasion and molecular-specific permeability. These properties originate from various membrane proteins on the surface of the cell membrane. For this reason, red cell membrane is coated on nanomaterials or sensors to bestow the functionalities of the membrane proteins. In this chapter, various types of membrane proteins of red cell and its functions are described. Also, the following two experimental procedures are summarized: (I) the extraction of red cell membrane containing membrane proteins and (II) coating of the extracted cell membrane onto the nanoparticles and solid surface of sensors. Finally, the applications of red cell membrane in drug delivery system and biosensor are discussed.",signatures:"Insu Kim, Gyudo Lee and Dae Sung Yoon",downloadPdfUrl:"/chapter/pdf-download/65464",previewPdfUrl:"/chapter/pdf-preview/65464",authors:[{id:"180553",title:"Prof.",name:"Dae Sung",surname:"Yoon",slug:"dae-sung-yoon",fullName:"Dae Sung Yoon"},{id:"280027",title:"Dr.",name:"Gyudo",surname:"Lee",slug:"gyudo-lee",fullName:"Gyudo Lee"},{id:"280035",title:"Mr.",name:"Insu",surname:"Kim",slug:"insu-kim",fullName:"Insu Kim"}],corrections:null},{id:"65276",title:"The Influence of the Golden Ratio on the Erythrocyte",doi:"10.5772/intechopen.83682",slug:"the-influence-of-the-golden-ratio-on-the-erythrocyte",totalDownloads:1125,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Erythrocytes must maintain a biconcave discoid shape in order to efficiently operate and serve an important physiological role in an organism. The erythrocyte can be viewed as a toroidal dielectrophoretic (DEP) electromagnetic field (EMF)-driven cell that maintains its zeta potential via a dielectric constant (chloride anion) that resides between a negatively charged membrane surface and a positively charged Stern layer. There are ferromagnetic (iron) and ferroelectric (chloride anion) influences that may be crucial to the maintenance of this zeta potential. We hypothesize that within this uniquely shaped cell resides an interesting geometric mathematical measure, the Golden Ratio, that houses a DEP EMF may be driven/fueled by the zeta potential and may be critical for the efficient recycling of CO2 and the delivery of O2 to organisms.",signatures:"Marcy C. Purnell and Risa D. Ramsey",downloadPdfUrl:"/chapter/pdf-download/65276",previewPdfUrl:"/chapter/pdf-preview/65276",authors:[{id:"278362",title:"Ph.D.",name:"Marcy C.",surname:"Purnell",slug:"marcy-c.-purnell",fullName:"Marcy C. Purnell"},{id:"288727",title:"Prof.",name:"Risa D.",surname:"Ramsey",slug:"risa-d.-ramsey",fullName:"Risa D. Ramsey"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"6905",title:"Blood Groups",subtitle:null,isOpenForSubmission:!1,hash:"545ab2a5b402edec6332c7d632eba398",slug:"blood-groups",bookSignature:"Anil Tombak",coverURL:"https://cdn.intechopen.com/books/images_new/6905.jpg",editedByType:"Edited by",editors:[{id:"202814",title:"Associate Prof.",name:"Anil",surname:"Tombak",slug:"anil-tombak",fullName:"Anil Tombak"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6634",title:"Homeostasis",subtitle:"An Integrated Vision",isOpenForSubmission:!1,hash:"3731dfa513781db054545963a4394938",slug:"homeostasis-an-integrated-vision",bookSignature:"Fernanda Lasakosvitsch and Sergio Dos Anjos Garnes",coverURL:"https://cdn.intechopen.com/books/images_new/6634.jpg",editedByType:"Edited by",editors:[{id:"117630",title:"Dr.",name:"Fernanda",surname:"Lasakosvitsch Castanho",slug:"fernanda-lasakosvitsch-castanho",fullName:"Fernanda Lasakosvitsch Castanho"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6273",title:"Thrombocytopenia",subtitle:null,isOpenForSubmission:!1,hash:"182f67f8c83b1d8897447f05207feae9",slug:"thrombocytopenia",bookSignature:"Pankaj Abrol",coverURL:"https://cdn.intechopen.com/books/images_new/6273.jpg",editedByType:"Edited by",editors:[{id:"90782",title:"Dr.",name:"Pankaj",surname:"Abrol",slug:"pankaj-abrol",fullName:"Pankaj Abrol"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7845",title:"Platelets",subtitle:null,isOpenForSubmission:!1,hash:"d33b20516d6ff3a5b7446a882109ba26",slug:"platelets",bookSignature:"Steve W. 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\n
1. Introduction
\n
The exhaustion of fossil‐fuel reserves, stricter environmental regulations and the world\'s rising energy needs have led to the deployment of renewable and sustainable energy sources. Among these alternatives, wind energy is a promising technology and recorded the fastest growing installed alternative‐energy production according to reference [1]. It is expected that by the year 2030, at least 20% of the United States energy will be supplied by onshore and offshore wind farms [1]. In the next decade and half, it is vital that authorities record a significant increase in wind turbine installations and operability. Nevertheless, the prime conflict will continue to be the ongoing challenge to maintain a profitable and competitive cost of energy with fossil‐fuel sources. Throughout the last 30 years, wind turbines have grown in size in order to reduce the cost of energy typically expressed in $/kWh. As a result, structural performance, durability requirements, safety hazards, transportation complications, noise and aesthetic pollutions all become issues that are more challenging for designers. Moreover, energy policies, international treaties, legislations and regulations set by governments have to be respected. For this reason, resolving the complex design problem of wind turbine design can be only achieved by optimization where an optimal solution is located. Many objective functions, design constraints, algorithms, tools and models have been proposed as will be discussed.
\n
Figure 1.
Number of published documents on wind turbine design in the last 40 years (reproduced from Scopus database).
\n
The rapid growth in the number of research papers on wind turbine design and optimization (Figure 1) during the last two decades highlights the status of the field of wind turbine optimization. In the past, some authors have compared the impact of different optimization objectives on the quality of the solution, others have reviewed the optimization algorithms, energy policies, economics, environmental impacts of wind turbines, but numerous researchers have proposed different optimization methodologies and resolution strategies. According to a study conducted by Chehouri et al. [2] in 2014, it was identified that less than 25% of the surveyed wind turbine optimization problems were solved using a multi‐objective algorithm. In fact, solving such problems is not a straightforward task and often requires innovative techniques and algorithms. However, the main benefit is the ability to draw trade‐off curves that reveal weaknesses and anomalies in the wind turbine design. For instance, it is very useful to pursue both minimum cost of energy and maximum annual energy production in the early stage of the design process.
\n\n
Many authors carried a multidisciplinary study [3–12], where many objectives are considered in the design of wind turbines. The most common technique to combine conflicting functions (such as annual energy production and cost of energy) is by means of an appropriate set of weights. The variations that exist among these contradictory functions are essential for designers and therefore pursue to sketch the Pareto fronts.
\n
In order to undertake the design of a horizontal wind turbine under multi‐objective optimization (MOOP), there are numerous issues to be considered. The motif of this chapter is to present the fundamental principles of multi‐objective optimization in the design of wind turbines. At the outset of this chapter in Section 2, we briefly discuss the fundamentals and terminology of multi‐objective optimization. Section 3 highlights the objective functions that are used by designers. The design constraints that are enforced by wind turbine designers are enumerated in Section 4. We list the most relevant multi‐objective optimization applied in wind turbine design in Section 5. The most common optimization algorithms used to solve multi‐objective wind turbine optimization problems are examined in Section 6. Finally, a numerical example that demonstrates the resolution of a multi‐objective design problem using a genetic algorithm (GA) is presented in Section 7.
\n
\n
\n
2. Multi‐objective optimization: state of art
\n
\n
2.1. Prologue
\n
The term optimization refers to the finding of one or more feasible solutions, which correspond to extreme values of one or multiple objectives. Optimization methods are important in scientific experiments, particularly in engineering design and decision‐making. When the problem is to find the optimal solution of one objective, the task is called single‐objective optimization. There exist many algorithms that are gradient‐based and heuristic‐based that solves single‐objective optimization problems. Beside deterministic search techniques, the field of optimization has evolved by the introduction of stochastic search algorithms that seek to find the global optimal solution with more ease. Among them, evolutionary algorithms (EAs) mimic nature\'s evolutionary principles and are now emerging as popular algorithms to solve complex optimization problems.
\n
In engineering optimization, the designers are sometimes interested in finding one or more optimum solutions when dealing with two or more objective functions. This is known as multi‐objective optimization and in fact, most real‐world optimization problems involve multiple objectives. In this case, different solutions produce trade‐offs or conflicting situations among the different objectives. Not enough emphasis is usually given to multi‐objective optimization and there is a reasonable explanation for that. The majority of the methods that solve multi‐objective optimization problems transform multiple objectives into a single function. Therefore, most of the efforts have been invested in improving the single‐objective optimization algorithms. The studies concentrate on the conversion of multi‐objective into single‐objective problems, the convergence, constraint‐handling approaches and the speed of these single‐objective techniques.
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Let us discuss the fundamental difference between single‐ and multi‐objective optimization by taking two conflicting objective functions as examples. Obviously, each objective function possesses a unique and different optimal solution. For instance, if one is interested in buying a house, the decision‐making has to take into consideration the cost and the comfort. If the buyer is willing to sacrifice comfort, they will get a house with the lowest price. However, if money is not an issue, then the buyer is able to afford a house with the best comfort. Between these two extremes, there exist many house choices at various costs and comfort. Now the big question is among these trade‐offs, which solution is the best with respect to both objectives? Ironically, no house among the trade‐off choices is the best with respect to both cost and comfort. Without any further information about these solutions (in our case example the houses), no solution from the set of trade‐off can be said to be better than any other. This is the fundamental difference between a multi‐objective and a single‐objective optimization problem. From a practical standpoint, after a set of trade‐off solutions are found, the user will use higher‐level information to determine the convenient solution.
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2.2. Multi‐objective evolutionary algorithms
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Figure 2.
Number of published documents on multi‐objective evolutionary algorithms.
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The classical way to solve multi‐objective problems is to scalarize multiples objectives with a relative preference vector. Since only a single optimized solution can be found in one simulation, evolutionary algorithms shined as interesting methods to solve MOOP. The main reason is, unlike classical methods, EAs use a population of solutions in each iteration and therefore the outcome of an EA is a population of solutions. This ability to find multiple solutions in one single run made EAs an ideal approach to solve multi‐objective optimization problems.
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According to the available literature, the first real application of evolutionary algorithms in the determination of trade‐off solutions for a MOOP was proposed in the doctoral dissertation of David Schaffer [13]. He developed the vector‐evaluated genetic algorithm (VEGA), which demonstrated the ability of genetic algorithm to capture multiple trade‐off solutions. Not much attention was given until another half a decade when David E. Goldberg published his book in 1989 [14] on a multi‐objective evolutionary algorithm (MOEA) using the concept of dominance. From the latter derived many MOEAs such as Srinivas and Deb\'s non‐dominated sorting (NSGA) [15] and the niched Pareto GA by Horn et al. [16]. Other techniques different from the domination‐based MOEAs were proposed by Kursawe in 1990 (Kursawe\'s diploidy approach [17]) and Hajela and Lin\'s weighted‐based approach [18] just to name a few. It can be easily seen from Figure 2 that the number of studies conducted on the topic of MOEA has increased well over the last two decades. In less than 10 years, the number of year‐wise papers has tripled and it can be expected that the growth will continue as new studies, books, surveys, research papers and dissertations will be published.
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2.3. Multi‐objective optimization problems
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A multi‐objective optimization problem is composed of a number of objective functions, which are to be maximized or minimized. Similar to single‐objective problems, the MOOP is subjected to a set of design constraints, which any optimal solution must satisfy. We can state the general form of a multi‐objective optimization problem as follows:\n
The solution x→ is a vector of n variables x→=(x1,x2,…xn)T. Often, the user will restrict the design variables between lower and upper bounds x→iL and x→iU, respectively. In the above problem, there is J inequality and K equality constraints that can be linear and/or non‐linear functions. A solution x→ is said to be feasible when all the constraints (J + K + 2N) are satisfied. Because of the presence of M objective functions that need to be minimized and/or maximized, it is regularly convenient to apply the duality principle. The latter suggests that we can convert a maximization problem into a minimization one by multiplying the objective function by -1. This is a practical method because many optimization algorithms are developed to solve one type, for example, minimization problems. A major difficulty arises when any of the objective or constraint functions are non‐linear, then the resulting MOOP becomes a non‐linear multi‐objective problem. Until now, the techniques to solve such problems do not have convergence proofs. Unfortunately, most real‐world MOOPs are non‐linear in nature and thus create a major challenge for scholars.
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As stated earlier, the task in multi‐objective optimization problems is to find a set of solution called the Pareto‐optimal solution set, in which any two solutions must be non‐dominated with respect to each other. In addition, any solution in the search space must be dominated by at least one point in the Pareto set. Therefore, the ultimate goal in multi‐objection optimization is to find a set of solutions as close as possible to the Pareto‐optimal front and as diverse as possible. The concept of domination is used in most MOOP algorithms. Without going into deep details, a solution x→1 is said to dominate x→2 if both conditions are satisfied:
The solution x→1 is no worse than x→2 in all objectives.
The solution x→1 is strictly better than x→2 in at least one objective.
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To gain more knowledge on the procedures to find the non‐dominated set in a given set P of size N, the reader is referred to [8].
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3. Objective functions
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The objective function that wind turbine engineers have used in their designs has evolved in the last few decades. In the early 1980s, the focus was towards the maximization of the power coefficient (CP), which represents the theoretical fraction of power in the wind that can be extracted by the wind turbine. The blade shapes achieved with this strategy presented blades with large root chords and high twists. Moreover, since the maximization of the power coefficient occurs for a particular tip‐speed ratio, the designer\'s interests shifted towards an alternative optimization metric—maximization of the annual energy production (AEP). Unlike the previous objective function, the AEP is obtained from a range of time (e.g. 1 year) and the given wind‐speed spectrum of the wind turbine site. As wind energy is unable to compete with fossil fuel, the main objective has progressed to the minimization of the cost of energy (COE). The difficulty is the definition of the COE, mainly outlining the total costs of the wind turbine components. An earlier approach to decrease the COE is to limit the blade weight by restricting the chord lengths. Nevertheless, the stability of the blade is strongly affected by the decrease of the weight. Consequently, a proper balance between mass and stability must be ensured during the design of the wind turbine blade.
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3.1. Maximization of the annual energy production
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The annual energy is obtained by the integration of the wind turbine power curve with a wind‐speed distribution (e.g. Weibull) over the wind‐speed spectrum from the cut‐in to cut‐out speed [Eq. (2)]. One of the reasons why this metric is chosen by designers is due to the absence of a reliable structural and cost model. On the other hand, if the cost of energy is insignificant to the manufacturers and consumers, the maximum energy production is assumed as\n
AEP=∫VminVmaxP(V)f(V)dVE2
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P(V) is the power curve of the wind turbine, f(V) is the wind‐speed distribution, Vmin is the cut‐in speed and Vmax is the cut‐out speed.
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3.2. Minimization of the weight
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Jureczko et al. [19] developed a numerical model of the wind turbine blade to perform a multi‐criteria discrete‐continuous optimization of wind turbine blades with the blade mass as the main objective function and the criteria\'s translated into constraints. Liao et al. [20] developed a multi‐criteria‐constrained design model with respect to minimum blade mass integrating a particle swarm optimization (PSO) algorithm using Federation Against Software Theft (FAST) [21]. Ning et al. [22] inspects the minimization of the turbine mass to AEP ratio as one of three examined objective functions. In a recent journal, Chen et al. [23] argue that a lighter blade mass will be beneficial to improve fatigue life based on requirements of the blade\'s strength and stiffness. Therefore, the minimum mass of the wind turbine blade was chosen as objective function.
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4. Design constraints
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Most engineering optimization problems include a set of equality and inequality constraints consisting of both linear and/or non‐linear types. Generally, solving constrained optimization problems is more challenging than unconstrained systems. We have identified the most relevant constraints imposed by wind turbine blade designers as follows:
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4.1. Ground clearance
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A simple condition is set to prevent the collision of the blade with the ground.
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4.2. Tip deflection
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A constraint for the maximum tip deflection was included to ensure the local and global stability of the blade.
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4.3. Shell and airfoil thickness
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Some designers include a feasibility condition on the shell thickness and the surface of the airfoil to guarantee a proper trailing‐edge separation.
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4.4. Airfoil characteristics
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In order to control the aerodynamic behaviour of the airfoil near stall, various constraints can be applied. For example, the absolute value of the slope beyond the stall angle can be regulated. Similarly, the coefficient of moment Cmc/4 and the ratio of the coefficient of lift and drag CL/CD can be constrained to limit the blade torsion and undesirable separation behaviour, respectively. Another technique to avoid abrupt stall is to enforce a transition condition on each of the suction and pressure sides.
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4.5. Noise pollution
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A major objection for wind turbines is the noise that it generates as the blades rotate. Most of the aerodynamic noise models are semi‐empirical and origin from the tip‐vortex/trailing‐edge interaction, turbulent inflow or the trailing‐edge thickness.
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4.6. Stress
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The wind turbine is subjected to a large number of loads and therefore its components will be exposed to high stresses. To constrain these stresses, particularly on the wind blades and gearbox, the designers add inequality constraints that relate the generated stresses and the ultimate permissible stresses.
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4.7. Natural frequency
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In order to prevent the occurrence of resonance, the natural frequency of the blade must be separated from the rotor\'s rotation harmonics. Therefore, many designers limit some natural frequencies ω between an admissible bound [ωlow; ωupper]. Another method is to assume a safety gap factor between the rotor\'s rated speed and the natural frequency. The reader is referred to references [24, 25] where the optimal design is pursued with respect to the maximum frequency design criterion.
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5. Wind turbine MOOP
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Researchers began examining multi‐objective optimization algorithms in the design of wind turbines only two decades ago. In 1996, Selig and Coverstone‐Carroll [5] examined the maximization of the AEP with no or few constraints on the loads. A short year later, Giguère and Selig [3] presented a multidisciplinary optimization (MDO) for the blade geometry of horizontal axis wind turbines (HAWTs). A sharing function [26] is used to obtain the trade‐off curve between cost and energy. Only the structure of the blades is considered; however, the effects of the rotor on the wind turbine components are accounted in the cost model. The form of the cost model is indicated as follows (Eq. (3)):\n
Ci=Cib(ci+(1−ci)(PiPib))E3
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where C is the cost, i is the component, c is the cost factor (fixed portion of the total cost—cost factor of 20 % was used for the blades), P is the design parameters of importance for component i and subscripts ending with a b are baseline values.
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Benini et al. [6] apply an MOEA for the design optimization of stall‐regulated wind HAWT with a trade‐off between the ratio of AEP per wind park area (AEPdensity to maximize) and the cost of energy (minimize). The idea behind using the first metric is that the number of turbines that can be installed in a given area is inversely proportional to the square of the turbine radius. The MOEA handles the design parameters and searches for the optimal solutions following a set of Pareto concepts and basic principles of genetic programming [27, 28]. The authors choose the tip‐speed, hub/tip ratio, and chord and twist distributions as their design variables. An assumption is made that the total turbine cost is reconstructed from the cost of the turbine blade alone, using the cost model from Eq. (4):\n
CoE=TC+BOSAEPFCR+OME4
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where TC is the turbine cost, BOS the balance of station, FCR the fixed charge rate and OM the operation and maintenance costs.
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In 2010, Grujicic et al. [10] developed a two‐level optimization scheme to solve the MOOP. In the inner level, for a given aerodynamic design, the blade mass is minimized. In the outer level, a cost‐assessment analysis is performed. This procedure is repeated until minimums are found for both the outer‐ and inner‐level loops.
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Kusiak et al. [9] introduced a data‐driven approach to study the impact of turbine control on vibration. The authors developed a vibration prediction model using neural networks. Three objectives were included in the study (two vibrations and the power output), and a weighted sum of these objectives is minimized and set as follows (Eq. (5)):\n
min(w1y1(t)+w2y2(t)+w31y3(t))E5
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where y1(t) is the estimated vibration of the drive train, y2(t) the tower vibration model and y3(t) the estimated power output model.
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In reference [29], Kusiak and Zheng present an approach to optimize the power factor and the power output of the wind turbine (bi‐objective problem established by weights), using data‐mining and evolutionary computation. The proposed approach generated optimized settings of the generator torque and the blade‐pitch angle.
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In the same year, Bottasso et al. [7] presented a thorough description of a multidisciplinary design optimization procedure. The authors assume that the weight is correlated to the cost. Since a reliable cost model is not offered to the public, no particular cost model was used. Instead of formulating a Pareto‐optimal design problem, a combined cost was defined as the ratio of the annual energy production to the total weight. A two‐stage sequential‐constrained optimization algorithm was used to solve the constrained problem.
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A year later, Wang et al. [11] presented a multi‐objective algorithm. The power coefficient CP at the design wind speed of 9 m/s and the blade mass are chosen as the optimization objectives. The two objectives can be formulated as follows (Eq. (6)):\n
f1=max(Cp|V=9m.s−1)f2=min∫RhubRmidrE6
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Where Cp is the power coefficient, mi is the radial mass distribution of the wind turbine blade, and Rhub and R are the hub radius and the blade length, respectively.
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In the design of HAWTs, the most obvious feature is the rapid growth in the size of HAWT blades [30]; therefore, it is insufficient to perform an airfoil shape optimization by itself. Rather, the entire blade geometry must be taken into consideration. According to reference [12], a wind turbine blade airfoil should satisfy the following aerodynamic requirements:
High lift‐to‐drag ratio (CL/CD).
High lift coefficient CL.
Good performance during the stochastic behaviour of the wind flow.
Low sensitivity to leading‐edge roughness.
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The reader is referred to references [31–33] for the design of more efficient wind turbine blade airfoils. Ju et al. [12] developed a robust design optimization (RDO) for the design of a new series of wind turbine airfoils by maximizing both the CL/CD and CL. They also completed a sensitivity analysis of the roughness at the leading edge associated with the geometry profile uncertainty.
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Multidisciplinary optimization was far and widely recognized of having the potential of becoming the cutting edge of the future [34, 35]. Kim [36, 37] and Michelena [38] apply maximum length sequence (MLS) algorithms called the target‐cascading methodology method, where multiple levels and interfacing between the levels are defined.
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For offshore wind turbines, the environmental conditions are more severe and more considerations have to be taken into account. Designers aim for a more proficient use of the capacity of the very expensive electrical cables, foundation, installation and erection costs.
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6. Algorithms
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6.1. Introduction
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The selection of the appropriate optimization algorithm is a critical undertaking in any engineering optimization problem that relies on the attributes of the design space and on the nature of the problem. The final results depend on the algorithm in terms of accuracy and local minima sensitivity. Throughout the years, the algorithms used to solve wind turbine design problems have matured. At the outset, most of the methods derived directly from the blade‐element momentum (BEM) theory, typically from Wilson and Lissaman [39]. In the 1990s, Selig and Coverstone‐Carroll [5] were one of the originals to suggest a method based on GA for their wind turbine blade design tool. With the need to carry a multidisciplinary or multi‐objective optimization design, Wood [40] and Sale et al. [41] simplified the MOOP into a single‐objective question using a classical‐weighted method. The approaches for solving conventional multi‐objective design problems include:
objective‐weighted method
hierarchical optimization method
ε‐constraint method
goal‐programming method.
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It is important to highlight that all of the above algorithms convert the multi‐objective problem into a single‐objective problem. According to Ribeiro et al. [42], optimization algorithms can be categorized into two groups: gradient‐based approaches (GBAs) and heuristic algorithms, whereas Endo [43] separates the optimization methods between genetic and non‐genetic algorithms. In the last decades, in order to solve complicated optimization problems, evolutionary algorithms have been suggested such as:
Meta‐heuristics are algorithms often inspired from nature, designed to replace or assist conventional solvers. This is a growing research field since the last few decades as meta‐heuristics are now emerging as alternatives to the classical approaches.
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An interesting fact can be drawn from the progress of the field of meta‐heuristics and wind turbine optimization. In the recent years, wind energy showed an increase in the use of optimization methods such as linear programming, Lagrangian relaxation, quadratic programming and heuristic optimization (precisely genetic algorithm and particle swarm optimization) to name a few. However, it can be said that the gradient‐based approaches and genetic algorithm are the two most popular optimization algorithms that have been applied in wind turbine design. The reason is quite simple, in the case of blade‐geometry optimization, there is a large number of design variables, which are continuous (e.g. chord and twist distributions, blade pitch, etc.) and discrete (e.g. airfoil family, number of blades, etc.) at the same time. Moreover, some of these design variables are dependent from one another (e.g. chord and twist), as well as competing objectives within the definition of the objective function (e.g. cost of energy).
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GAs are the most popular evolutionary algorithms because of their robustness and reliability in wind turbine design problems. A genetic algorithm is an optimization method that mimics Darwin\'s principle of ‘survival of the fittest’ over a population of solutions (individuals) that evolves from one generation to another. It was originally proposed by Holland in 1975 [48]. Individuals with a large ‘fitness’ value have a superior probability to ‘reproduce’ in forming the new generation. Similar to a DNA chain, each individual is coded in one string and uses reproduction, crossover and mutation operators to direct the search over the generations. The usefulness of a GA is due to its robustness in multimodal design spaces. Likewise, GA explores non‐linear, non‐derivable, non‐continuous domains and they are less sensitive to the initial domain.
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7. Numerical example
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In this section, we will solve a numerical example for the design of a wind turbine blade using a GA multi‐objective optimization algorithm. The objective functions are the blade mass and the annual energy production. In order to calculate the mass, a structural model must be constructed. For the purpose of this study, a preliminary tool called Co‐Blade [49] is used. As for the annual energy, WT‐Perf [50] is introduced in the multi‐objective platform to generate the AEP.
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Co‐Blade is a tool that helps designers to compute the structural properties of a wind turbine blade. It uses a combination of classical lamination theory (CLT) with an Euler‐Bernoulli theory, and a shear‐flow theory applied to composite beams is used to perform its analysis. This approach allows for a direct computation of the structural properties of a given blade, within several seconds of execution. The fitness function that Co‐Blade minimizes is the blade mass penalitized by the maximum stress, buckling, deflection and the natural frequency. The design variables are the chord‐wise width of the spar cap at the inboard and outboard locations, the thickness of the ‘blade‐root’ material and the thicknesses of the laminas within the leading‐edge panel (LEP), trailing‐edge panel (TEP), spar cap and shear webs along the length of the blade. They are listed in Table 1.
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At first, the blade is represented as a cantilever beam under flap‐wise and edge‐wise bendings, axial deflection and elastic twist. Additional coupling between bending, extension and torsion is accounted for, due to the offsets between the beam‐shear centre, tension centre and centre of mass from the blade‐pitch axis (Figure 3). The beam cross sections are assumed to be thin‐walled, closed and single‐ or multicellular, and the periphery of each beam cross section is discretized as a connection of flat composite laminates.
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Parameter
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DESCRIPTION
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w_cap_inb, w_cap_oub
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Width of the spar cap normalized by the chord length at the INB_STN and OUB_STN blade stations
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t_blade_root
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Thickness of the ‘blade‐root’ material at the INB_STN blade station
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t_blade_skin1 …t_blade_skinN
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Thickness of ‘blade‐shell’ material at control points 1 through NUM_CP. The control points are equally spaced along the blade between the TRAN_STN and OUB_STN blade stations
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t_cap_uni1 …t_cap_uniN
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Thickness of ‘spar‐uni’ material at control points 1 through NUM_CP
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t_cap_core1 …t_cap_coreN
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Thickness of ‘spar‐core’ material at control points 1 through NUM_CP
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t_lep_core1 …t_lep_coreN
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Thickness of ‘LEP‐core’ material at control points 1 through NUM_CP
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t_tep_core1 …t_tep_coreN
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Thickness of ‘TEP‐core’ material at control points 1 through NUM_CP
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t_web_skin1, t_web_skin2
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Thickness of ‘web‐shell’ material at the two control points located at INB_STN and OUB_STN
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t_web_core1, t_web_core2
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Thickness of ‘web‐core’ material at the two control points located at INB_STN and OUB_STN.
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w_cap_inb, w_cap_oub
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Width of the spar cap normalized by the chord length at the INB_STN and OUB_STN blade stations
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t_blade_root
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Thickness of the ‘blade‐root’ material at the INB_STN blade station
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t_blade_skin1 …t_blade_skinN
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Thickness of ‘blade‐shell’ material at control points 1 through NUM_CP. The control points are equally spaced along the blade between the TRAN_STN and OUB_STN blade stations
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t_cap_uni1 …t_cap_uniN
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Thickness of ‘spar‐uni’ material at control points 1 through NUM_CP
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t_cap_core1 …t_cap_coreN
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Thickness of ‘spar‐core’ material at control points 1 through NUM_CP
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t_lep_core1 …t_lep_coreN
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Thickness of ‘LEP‐core’ material at control points 1 through NUM_CP
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Table 1.
Design variable for Co‐Blade.
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Figure 3.
Orientation of the blade‐axe systems [49].
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In regard to Euler‐Bernoulli beam theory, the beam cross sections are considered as heterogeneous and each of the material properties depends on the location in each cross section. The structural analysis at each discrete portion of the composite beam characterizes effective mechanical properties computed via classical lamination theory. Each discrete portion of the cross section then contributes to the global section properties of the composite beam (described further in references [51, 52]). Once the global cross‐sectional properties are calculated, the deflections and effective beam axial stress (σzz) and the effective beam‐shear stress (τzs) can be now computed under the assumptions of an Euler‐Bernoulli beam (refer to references [51–53]). The calculation of τzs, the prediction of shear centre and torsional stiffness are based on a shear‐flow approach, which is discussed in detail in reference [53]. Finally, by converting the distribution of effective beam stresses σZZ and τzs into equivalent in‐plane loads, the lamina‐level strains and stresses in the principal fibre directions (ε11, ε22, γ12, σ11, σ22 and τ12) can be evaluated using CLT.
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As mentioned earlier, Co‐Blade applies a penalized blade mass defined as the following (Eq. (7)):\n
Before we describe our fitness function, let us briefly discuss the second half of the multi‐objective algorithm, the aerodynamic tool that calculates the AEP.
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WT‐Perf uses blade‐element momentum theory to predict the performance of wind turbines with good accuracy. Users must build an appropriate input file that consists of the following set of data (Table 2):
Model configuration
WT‐Perf algorithm configuration
Cavitation model
Turbine data
Aerodynamic data
Input/output settings
Analysis settings.
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E11
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E22
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G12
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υ12
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ρ
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Material Name
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(Pa)
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(Pa)
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(Pa)
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(‐)
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(kg/m3)
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(‐)
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2.80E + 10
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1.40E + 10
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7.00E + 09
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0.4
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1850
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(blade‐root)
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2.80E + 10
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1.40E + 10
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7.00E + 09
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0.4
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1850
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(blade‐shell)
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4.20E + 10
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1.40E + 10
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3.00E + 09
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0.28
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1920
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(spar‐uni)
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2.60E + 08
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2.60E + 08
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2.00E + 07
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0.3
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200
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(spar‐core)
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2.60E + 08
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2.60E + 08
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2.00E + 07
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0.3
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200
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(LEP‐core)
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2.60E + 08
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2.60E + 08
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2.00E + 07
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0.3
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200
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(TEP‐core)
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1.40E + 10
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1.40E + 10
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1.20E + 10
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0.5
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1780
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(web‐shell)
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2.60E + 08
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2.60E + 08
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2.00E + 07
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0.3
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200
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(web‐core)
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Table 2.
Design variables for Co‐Blade.
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We have now defined two conflicting objective functions, the blade mass and the annual energy. Solving such MOOP can be achieved by the method of scalarizing. It consists of formulating a single‐objective optimization problem such that optimal solutions to the single‐objective optimization problem are Pareto‐optimal solutions to the MOOP. A general formulation for a scalarization of a multi‐objective optimization is given as (Eq. (8)):\n
min∑i=1Mwifi(x→)E8
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where the weights of the objectives wi>0 are the parameters of the scalarization.
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We propose to use the following fitness function to minimize the mass and maximize the annual energy production (Eq. (9)):\n
min(αM(x→)M0+(α−1)AEP(x→)AEP0)E9
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For a value of alpha near zero, the mass ratio is eliminated and the fitness function becomes min((α−1)AEP(x→)AEP0), which translates into the maximization of the normalized energy. Probably, for an alpha value close to 1, the energy ratio disappears and the problem is now a minimization of the mass. If we run the optimization problem for different values of alpha between 0 and 1, we can find Pareto‐optimal solution to the MOOP. The reference mass and energy are taken, respectively, from the case study of alpha equals 0. The complete Pareto front is displayed in Figure 4.
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Let us consider the following mechanical properties during the structural analysis. In our study, these properties are derived from Sandia 100‐m blade SNL‐100 [54]. Table 3 lists the mechanical properties utilized in the structural design of the blade. Likewise, in Table 2, we list the configurations (input, model, turbine data and algorithm) for the input file required by the WT‐Perf solver. The general flow chart of multi‐objective optimization algorithm can be summarized in Figure 5. The complete inputs for the multi‐objective optimization algorithm are listed in Table 4.
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Figure 4.
Pareto front for the given numerical example in Section 7.
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Input configuration
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False
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Echo:
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Echo input parameters to ‘<rootname>.ech’?
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True
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DimenInp:
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Turbine parameters are dimensional?
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True
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Metric:
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Turbine parameters are Metric (MKS vs FPS)?
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Model configuration
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1
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NumSect:
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Number of circumferential sectors.
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1000
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MaxIter:
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Maximum number of iterations for induction factor.
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\n
\n
1.00E + 06
\n
ATol:
\n
Error tolerance for induction iteration.
\n
\n
\n
1.00E + 06
\n
SWTol:
\n
Error tolerance for skewed‐wake iteration.
\n
\n
\n
Algorithm configuration
\n
\n
\n
True
\n
TipLoss:
\n
Use the Prandtl tip‐loss model?
\n
\n
\n
True
\n
HubLoss:
\n
Use the Prandtl hub‐loss model?
\n
\n
\n
True
\n
Swirl:
\n
Include Swirl effects?
\n
\n
\n
True
\n
SkewWake:
\n
Apply skewed‐wake correction?
\n
\n
\n
True
\n
AdvBrake:
\n
Use the advanced brake‐state model?
\n
\n
\n
True
\n
IndProp:
\n
Use PROP‐PC instead of PROPX induction algorithm?
\n
\n
\n
True
\n
AIDrag:
\n
Use the drag term in the axial induction calculation?
\n
\n
\n
True
\n
TIDrag:
\n
Use the drag term in the tangential induction calculation?
\n
\n
\n
Turbine data
\n
\n
\n
3
\n
NumBlade:
\n
Number of blades.
\n
\n
\n
10
\n
RotorRad:
\n
Rotor radius (length).
\n
\n
\n
0.5
\n
HubRad:
\n
Hub radius (length or div by radius).
\n
\n
\n
0
\n
PreCone:
\n
Precone angle, positive downstream (deg).
\n
\n
\n
0
\n
Tilt:
\n
Shaft tilt (deg).
\n
\n
\n
0
\n
Yaw:
\n
Yaw error (deg).
\n
\n
\n
30
\n
HubHt:
\n
Hub height (length or div by radius).
\n
\n
\n
30
\n
NumSeg:
\n
Number of blade segments (entire rotor radius).
\n
\n\n
Table 3.
Input file for WT‐Perf.
\n
Figure 5.
Flow chart of the multi‐objective optimization algorithm.
\n
\n\n
\n
WT‐Perf settings
\n
\n
\n
1000
\n
MaxIter:
\n
Maximum number of iterations for induction factor.
\n
\n
\n
1.00E‐06
\n
ATol:
\n
Error tolerance for induction iteration.
\n
\n
\n
1.00E‐06
\n
SWTol:
\n
Error tolerance for skewed‐wake iteration.
\n
\n
\n
True
\n
TipLoss:
\n
Use the Prandtl tip‐loss model?
\n
\n
\n
True
\n
HubLoss:
\n
Use the Prandtl hub‐loss model?
\n
\n
\n
True
\n
Swirl:
\n
Include Swirl effects?
\n
\n
\n
True
\n
SkewWake:
\n
Apply skewed‐wake correction?
\n
\n
\n
True
\n
AdvBrake:
\n
Use the advanced brake‐state model?
\n
\n
\n
True
\n
IndProp:
\n
Use PROP‐PC instead of PROPX induction algorithm?
\n
\n
\n
True
\n
AIDrag:
\n
Use the drag term in the axial induction calculation?
\n
\n
\n
True
\n
TIDrag:
\n
Use the drag term in the tangential induction calculation?
\n
\n
\n
3
\n
NumBlade:
\n
Number of blades.
\n
\n
\n
0
\n
Yaw:
\n
Yaw error (deg).
\n
\n
\n
30
\n
HubHt:
\n
Hub height (length or div by radius).
\n
\n
\n
0.00001464
\n
KinVisc:
\n
Kinematic air viscosity
\n
\n
\n
0
\n
ShearExp:
\n
Wind‐shear exponent (1/7 law = 0.143).
\n
\n
\n
False
\n
UseCm:
\n
Are Cm data included in the airfoil tables?
\n
\n
\n
True
\n
TabDel:
\n
Make output tab‐delimited (fixed‐width otherwise).
\n
\n
\n
True
\n
KFact:
\n
Output dimensional parameters in K (e.g. kN instead on N)
\n
\n
\n
True
\n
WriteBED:
\n
Write out blade‐element data to ‘<rootname>.bed\'?
\n
\n
\n
True
\n
InputTSR:
\n
Input speeds as TSRs?
\n
\n
\n
\'mps\'
\n
SpdUnits:
\n
Wind‐speed units (mps, fps, mph)
\n
\n
\n
0
\n
NumCases:
\n
Number of cases to run. Enter zero for parametric analysis.
\n
\n
\n
WS or TSR
\n
RotSpd Pitch
\n
Remove following block of lines if NumCases is zero.
\n
\n
\n
3
\n
ParRow:
\n
Row parameter (1‐rpm, 2‐pitch, 3‐tsr/speed).
\n
\n
\n
1
\n
ParCol:
\n
Column parameter (1‐rpm, 2‐pitch, 3‐tsr/speed).
\n
\n
\n
2
\n
ParTab:
\n
Table parameter (1‐rpm, 2‐pitch, 3‐tsr/speed).
\n
\n
\n
True
\n
OutPwr:
\n
Request output of rotor power?
\n
\n
\n
True
\n
OutCp:
\n
Request output of Cp?
\n
\n
\n
True
\n
OutTrq:
\n
Request output of shaft torque?
\n
\n
\n
True
\n
OutFlp:
\n
Request output of flap‐bending moment?
\n
\n
\n
True
\n
OutThr:
\n
Request output of rotor thrust?
\n
\n
\n
0.0 0.0 0.0
\n
PitSt, PitEnd, PitDel:
\n
First, last, delta blade pitch (deg).
\n
\n
\n
80 80 0.00
\n
OmgSt, OmgEnd, OmgDel:
\n
First, last, delta rotor speed (rpm).
\n
\n
\n
Analysis options
\n
\n
\n
t
\n
SELF_WEIGHT:
\n
Include self‐weight as a body force?
\n
\n
\n
t
\n
BUOYANCY:
\n
Include buoyancy as a body force?
\n
\n
\n
true
\n
CENTRIF:
\n
Include centrifugal force as a body force?
\n
\n
\n
true
\n
DISP_CF:
\n
Apply correction factors to the beam displacements?
\n
\n
\n
0
\n
N_MODES:
\n
Number of modes to be computed
\n
\n
\n
50
\n
N_ELEMS:
\n
Number of blade finite elements to be used in the modal analysis
\n
\n
\n
Optimization options
\n
\n
\n
t
\n
OPTIMIZE:
\n
Perform optimization of composite layup?
\n
\n
\n
GS
\n
OPT_METHOD:
\n
Optimization algorithm for the optimization of composite layup
\n
\n
\n
false
\n
OPT_PITAXIS:
\n
Optimize the pitch axis?
\n
\n
\n
0.375
\n
PITAXIS_VAL:
\n
Pitch axis value outboard of max chord (ignored if OPT_PITAXIS = false)
\n
\n
\n
3
\n
INB_STN:
\n
Inboard station where the leading‐ and trailing‐edge panels, spar caps and shear webs begin
\n
\n
\n
8
\n
TRAN_STN:
\n
Station where the root transition ends
\n
\n
\n
28
\n
OUB_STN:
\n
Outboard station where the leading‐ and trailing‐edge panels, spar caps and shear webs end
\n
\n
\n
4
\n
NUM_CP:
\n
Number of control points between INB_STN and OUB_STN
\n
\n
\n
false
\n
READ_INITX:
\n
Read the initial values for the design variables from INITX_FILE?
\n
\n
\n
none
\n
INITX_FILE:
\n
Input file for the initial values of the design variables.
\n
\n
\n
false
\n
WRITE_STR:
\n
Write structural input files at each function evaluation?
\n
\n
\n
f
\n
WRIT E_F_ALL:
\n
Write the fitness value and penalty factors at each function evaluation?
\n
\n
\n
f
\n
WRIT E_X_ALL:
\n
Write the design variables at each function evaluation?
\n
\n
\n
f
\n
WRITE_X_ITER:
\n
Write the design variables at each iteration?
\n
\n
\n
100
\n
NumGens
\n
Maximum number of generations for GA iterations
\n
\n
\n
100
\n
PopSize
\n
Number of individuals per generation
\n
\n
\n
1
\n
EliteCount
\n
Number of elite individuals per generation
\n
\n
\n
0.5
\n
CrossFrc
\n
Fraction of individuals created by crossover
\n
\n
\n
1.00E‐06
\n
GATol
\n
Error tolerance for the GA fitness value
\n
\n
\n
Environmental data
\n
\n
\n
1.225
\n
FLUID_DEN:
\n
Fluid density (kg/m3)
\n
\n
\n
9.81
\n
GRAV:
\n
Gravitational acceleration (m/s2)
\n
\n
\n
6.03
\n
U_mean:
\n
Long‐term mean flow (m/s)
\n
\n
\n
1.91
\n
Weib_k:
\n
Shape factor
\n
\n
\n
6.8
\n
Weib_c:
\n
Scale factor
\n
\n
\n
Blade data
\n
\n
\n
30
\n
NUM_SEC:
\n
Number of blade cross sections
\n
\n
\n
10
\n
BLD_LENGTH:
\n
Blade length (m)
\n
\n
\n
0.5
\n
HUB_RAD:
\n
Hub radius (m)
\n
\n
\n
0
\n
SHAFT_TILT:
\n
Shaft tilt angle (deg)
\n
\n
\n
0
\n
PRE_CONE:
\n
Precone angle (deg)
\n
\n
\n
180
\n
AZIM:
\n
Azimuth angle (deg)
\n
\n
\n
100
\n
MAX_ROT
\n
Maximum rotational speed (rpm)
\n
\n
\n
10
\n
MIN_ROT
\n
Minimum rotational speed (rpm)
\n
\n
\n
cosine
\n
INTERP_AF:
\n
Interpolate airfoil coordinates? (choose "none", "cosine", or "equal" with no quotation marks)
\n
\n
\n
1
\n
ElmSpc
\n
Blade‐element radial spacing (0 equal, 1 cosinus)
\n
\n
\n
60
\n
N_AF:
\n
Number of points in interpolated airfoil coordinates (ignored
\n
\n
\n
mats‐ Wind.inp
\n
MATS_FILE:
\n
Input file for material properties
\n
\n
\n
0.13
\n
RootTranSt
\n
Start of root transition region
\n
\n
\n
3
\n
RootTranSt_index
\n
Index of start of root transition region
\n
\n
\n
0.288
\n
RootTranEnd
\n
End of root transition region
\n
\n
\n
8
\n
RootTranEnd_index
\n
Index of end of root transition region
\n
\n
\n
3 9 19 26 30
\n
CP_Index
\n
Index of control points (chord and twist)
\n
\n\n
Table 4.
Input file for the multi‐objective algorithm.
\n\n
\n
\n
8. Conclusion
\n
Within the last 20 years, wind energy conversion systems have reached maturity. The obvious growing worldwide wind energy market will culminate to further improvements. The continuous effort for the advancement in horizontal wind turbine performance strategies and techniques will result in additional cost reductions. The ultimate aim of any wind turbine manufacture is to design a wind turbine able to compete with fossil fuel. The number of research paper that applies optimization techniques in the attempt to reach an optimal blade design has demonstrated a significant increase in the recent decade alone. Despite the fact that a minimal cost of energy was chosen as the single main objective in most of the research papers, many have argued that it is more stimulating to evaluate the wind turbine design as an optimization problem consisting of more than one objective. Using multi‐objective optimization algorithms, the designers are able to identify a trade‐off curve called Pareto front that reveals the weaknesses, anomalies and rewards of certain targets. We can anticipate that future optimization problems will be set as multidisciplinary formulations. Consequently, solving such difficult optimization problem will require further developments in the optimization algorithm itself. Since traditional optimization techniques cannot overcome many of their drawbacks such as rapid divergence and sensitivity to the initial solution, population‐based and nature‐inspired algorithms will continue to emerge as worthy alternatives.
\n\n
In this chapter, we presented the fundamental principles of multi‐objective optimization in wind turbine design. We have identified the constraints and objective functions mostly targeted by designers. We briefly discussed the fundamentals and terminology of multi‐objective optimization. The most common optimization algorithms used to solve multi‐objective wind turbine optimization problems were presented. We highlighted the emergence of population‐based techniques, particularly genetic algorithms. Finally, we showed the steps to solve a classic multi‐objective wind turbine design problem using a genetic algorithm. The reader is referred to the following publications for further details [2, 55] concerning wind turbine optimization.
\n
\n\n',keywords:"wind turbine design, optimization, multi‐objective, genetic algorithm, Pareto front",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/50998.pdf",chapterXML:"https://mts.intechopen.com/source/xml/50998.xml",downloadPdfUrl:"/chapter/pdf-download/50998",previewPdfUrl:"/chapter/pdf-preview/50998",totalDownloads:1711,totalViews:502,totalCrossrefCites:7,totalDimensionsCites:9,totalAltmetricsMentions:1,introChapter:null,impactScore:3,impactScorePercentile:84,impactScoreQuartile:4,hasAltmetrics:1,dateSubmitted:"October 30th 2015",dateReviewed:"April 5th 2016",datePrePublished:null,datePublished:"July 27th 2016",dateFinished:"June 7th 2016",readingETA:"0",abstract:"Within the last 20 years, wind turbines have reached matured and the growing worldwide wind energy market will allow further improvements. In the recent decades, the numbers of research papers that have applied optimization techniques in the attempt to obtain an optimal design have increased. The main target of manufacturers has been to minimize the cost of energy of wind turbines in order to compete with fossil‐fuel sources. Therefore, it has been argued that it is more stimulating to evaluate the wind turbine design as an optimization problem consisting of more than one objective. Using multi‐objective optimization algorithms, the designers are able to identify a trade‐off curve called Pareto front that reveals the weaknesses, anomalies and rewards of certain targets. In this chapter, we present the fundamental principles of multi‐objective optimization in wind turbine design and solve a classic multi‐objective wind turbine optimization problem using a genetic algorithm.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/50998",risUrl:"/chapter/ris/50998",book:{id:"5244",slug:"wind-turbines-design-control-and-applications"},signatures:"Adam Chehouri, Rafic Younes, Adrian Ilinca and Jean Perron",authors:[{id:"180827",title:"Dr.",name:"Adam",middleName:null,surname:"Chehouri",fullName:"Adam Chehouri",slug:"adam-chehouri",email:"adam.chehouri1@uqac.ca",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Université du Québec à Chicoutimi",institutionURL:null,country:{name:"Canada"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Multi‐objective optimization: state of art",level:"1"},{id:"sec_2_2",title:"2.1. Prologue",level:"2"},{id:"sec_3_2",title:"2.2. Multi‐objective evolutionary algorithms",level:"2"},{id:"sec_4_2",title:"2.3. Multi‐objective optimization problems",level:"2"},{id:"sec_6",title:"3. Objective functions",level:"1"},{id:"sec_6_2",title:"3.1. Maximization of the annual energy production",level:"2"},{id:"sec_7_2",title:"3.2. Minimization of the weight",level:"2"},{id:"sec_9",title:"4. Design constraints",level:"1"},{id:"sec_9_2",title:"4.1. Ground clearance",level:"2"},{id:"sec_10_2",title:"4.2. Tip deflection",level:"2"},{id:"sec_11_2",title:"4.3. Shell and airfoil thickness",level:"2"},{id:"sec_12_2",title:"4.4. Airfoil characteristics",level:"2"},{id:"sec_13_2",title:"4.5. Noise pollution",level:"2"},{id:"sec_14_2",title:"4.6. Stress",level:"2"},{id:"sec_15_2",title:"4.7. Natural frequency",level:"2"},{id:"sec_17",title:"5. Wind turbine MOOP",level:"1"},{id:"sec_18",title:"6. Algorithms",level:"1"},{id:"sec_18_2",title:"6.1. Introduction",level:"2"},{id:"sec_19_2",title:"6.2. Meta‐heuristic algorithms",level:"2"},{id:"sec_21",title:"7. Numerical example",level:"1"},{id:"sec_22",title:"8. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'Lindenberg, S., B. Smith, and K. O\'Dell, 20% wind energy by 2030. 2008. National Renewable Energy Laboratory (NREL), US Department of Energy, Renewable Energy Consulting Services, Energetics Incorporated, United States.\n'},{id:"B2",body:'Chehouri, A., et al., Review of performance optimization techniques applied to wind turbines. Applied Energy, 2015. 142: p. 361–388.\n'},{id:"B3",body:'Giguere, P. and M.S. Selig, Blade geometry optimization for the design of wind turbine rotors. In Proceedings of AIAA/ASME Wind Energy Symposium. 2000. Reno, NV.\n'},{id:"B4",body:'Giguère, P., J. Tangler, and M. Selig, Blade design trade‐offs using low‐lift airfoils for stall‐regulated HAWTs. Journal of Solar Energy Engineering, 1999. 121(4): p. 217–223.\n'},{id:"B5",body:'Selig, M.S. and V.L. Coverstone‐Carroll, Application of a genetic algorithm to wind turbine design. Journal of Energy Resources Technology: Transactions of the Asme, 1996. 118(1): p. 22–28.\n'},{id:"B6",body:'Benini, E. and A. Toffolo, Optimal design of horizontal‐axis wind turbines using blade‐element theory and evolutionary computation. Journal of Solar Energy Engineering: Transactions of the Asme, 2002. 124(4): p. 357–363.\n'},{id:"B7",body:'Bottasso, C. L., Campagnolo, F., & Croce, A. (2012). Multi-disciplinary constrained optimization of wind turbines. Multibody System Dynamics, 27(1), 21–53.\n'},{id:"B8",body:'Deb, K., Multi‐objective optimization using evolutionary algorithms. Vol. 16, 2001. John Wiley & Sons, United States.\n'},{id:"B9",body:'Kusiak, A., Z.J. Zhang, and M.Y. Li, Optimization of wind turbine performance with data‐driven models. IEEE Transactions on Sustainable Energy, 2010. 1(2): p. 66–76.\n'},{id:"B10",body:'Grujicic, M., et al., Multidisciplinary design optimization for glass‐fiber epoxy‐matrix composite 5 MW horizontal‐axis wind‐turbine blades. Journal of Materials Engineering and Performance, 2010. 19(8): p. 1116–1127.\n'},{id:"B11",body:'Wang, L., T.G. Wang, and Y. Luo, Improved non‐dominated sorting genetic algorithm (NSGA)‐II in multi‐objective optimization studies of wind turbine blades. Applied Mathematics and Mechanics: English Edition, 2011. 32(6): p. 739–748.\n'},{id:"B12",body:'Ju, Y.P. and C.H. Zhang, Multi‐point robust design optimization of wind turbine airfoil under geometric uncertainty. Proceedings of the Institution of Mechanical Engineers Part a: Journal of Power and Energy, 2012. 226(A2): p. 245–261.\n'},{id:"B13",body:'Schaffer, J.D., Multiple objective optimization with vector evaluated genetic algorithms. In Proceedings of the 1st international Conference on Genetic Algorithms. 1985. L. Erlbaum Associates Inc.\n'},{id:"B14",body:'Goldberg, D.E., Genetic algorithms in search, optimization, and machine learning. 1989. Addison‐Wesley, Reading, MA.\n'},{id:"B15",body:'Srinivas, N. and K. Deb, Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation, 1994. 2(3): p. 221–248.\n'},{id:"B16",body:'Horn, J., N. Nafpliotis, and D.E. Goldberg. A niched Pareto genetic algorithm for multiobjective optimization. In Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence, Proceedings of the First IEEE Conference on. 1994. IEEE, Orlando, FL.\n'},{id:"B17",body:'Kursawe, F., A variant of evolution strategies for vector optimization, in Parallel Problem Solving from Nature. p. 193–197, 1990. Springer, 1st Workshop, PPSN I Dortmund, FRG.\n'},{id:"B18",body:'Hajela, P. and C.‐Y. Lin, Genetic search strategies in multicriterion optimal design. Structural Optimization, 1992. 4(2): p. 99–107.\n'},{id:"B19",body:'Jureczko, M., M. Pawlak, and A. Mezyk, Optimisation of wind turbine blades. Journal of Materials Processing Technology, 2005. 167(2–3): p. 463–471.\n'},{id:"B20",body:'Liao, C.C., X.L. Zhao, and J.Z. Xu, Blade layers optimization of wind turbines using FAST and improved PSO Algorithm. Renewable Energy, 2012. 42: p. 227–233.\n'},{id:"B21",body:'Jonkman, J.M. and M.L. Buhl Jr, FAST user\'s guide. 2005. National Renewable Energy Laboratory, Golden, CO.\n'},{id:"B22",body:'Ning, A., R. Damiani, and P. Moriarty. Objectives and constraints for wind turbine optimization. In 31st ASME Wind Energy Symposium, United States. 2013.\n'},{id:"B23",body:'Chen, J., et al., Structural optimization study of composite wind turbine blade. Materials & Design, 2013. 46: p. 247–255.\n'},{id:"B24",body:'Maalawi, K.Y. and M.A. Badr, Frequency optimization of a wind turbine blade in pitching motion. Proceedings of the Institution of Mechanical Engineers Part a‐Journal of Power and Energy, 2010. 224(A4): p. 545–554.\n'},{id:"B25",body:'Maalawi, K.Y. and H.M. Negm, Optimal frequency design of wind turbine blades. Journal of Wind Engineering and Industrial Aerodynamics, 2002. 90(8): p. 961–986.\n'},{id:"B26",body:'Goldberg, D.E. and J. Richardson. Genetic algorithms with sharing for multimodal function optimization. in Genetic algorithms and their applications: Proceedings of the Second International Conference on Genetic Algorithms. 1987. Lawrence Erlbaum, Hillsdale, NJ.\n'},{id:"B27",body:'Goldberg, D.E., Genetic algorithms in search, optimization, and machine learning. Vol. 412, 1989. Addison‐Wesley, Reading, Menlo Park, CA.\n'},{id:"B28",body:'Schwefel, H.‐P.P., Evolution and optimum seeking: the sixth generation. 1993. John Wiley & Sons, Inc., United States.\n'},{id:"B29",body:'Kusiak, A. and H.Y. Zheng, Optimization of wind turbine energy and power factor with an evolutionary computation algorithm. Energy, 2010. 35(3): p. 1324–1332.\n'},{id:"B30",body:'Li, J.Y., et al., Aerodynamic optimization of wind turbine airfoils using response surface techniques. Proceedings of the Institution of Mechanical Engineers Part a: Journal of Power and Energy, 2010. 224(A6): p. 827–838.\n'},{id:"B31",body:'Bizzarrini, N., F. Grasso, and D.P. Coiro, Genetic algorithms in wind turbine airfoil design. p. 14–17, 2011. EWEA, EWEC2011, Bruxelles, Belgium.\n'},{id:"B32",body:'Grasso, F., Usage of numerical ptimization in wind turbine airfoil design. Journal of Aircraft, 2011. 48(1): p. 248–255.\n'},{id:"B33",body:'Grasso, F. Hybrid optimization for wind turbine thick airfoils. In Proceedings of the 53rd AIAAJASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, AIAA Paper AIAA, Honolulu, Hawaii. 2012.\n'},{id:"B34",body:'Frank, P., et al. A comparison of optimization and search methods for multidisciplinary design. In American Institute of Aeronautics and Astronautics (AIAA) Symposium on MDO, Cleveland, OH, U.S.A. 1992.\n'},{id:"B35",body:'Alexandrov, N.M. and M.Y. Hussaini, Multidisciplinary design optimization: state of the art. Vol. 80, 1997. SIAM, United States.\n'},{id:"B36",body:'Kim, H.M., et al., Target cascading in optimal system design. Journal of Mechanical Design, 2003. 125(3): p. 474–480.\n'},{id:"B37",body:'Kim, H.M., et al., Analytical target cascading in automotive vehicle design. Journal of Mechanical Design, 2003. 125(3): p. 481–489.\n'},{id:"B38",body:'Michelena, N., et al., Design of an advanced heavy tactical truck: a target cascading case study. SAE Transactions, 2001. 110(2): p. 457–468.\n'},{id:"B39",body:'Wilson, R.E. and P.B. Lissaman, Applied aerodynamics of wind power machines. 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U Michigan Press, Oxford, England.\n'},{id:"B49",body:'Sale, D., User\'s guide to co‐blade: software for structural analysis of composite blades. 2012. Northwest National Marine Renewable Energy Center, United States.\n'},{id:"B50",body:'Buhl, M.L., WT_Perf user\'s guide. 2004. National Renewable Energy Laboratory, United States.\n'},{id:"B51",body:'Rivello, Robert M. Theory and analysis of flight structures. McGraw-Hill College, 1969.\n'},{id:"B52",body:'Allen, D.H. and W.E. Haisler, Introduction to aerospace structural analysis. 1985.\n'},{id:"B53",body:'Bauchau, O.A. and J.I. Craig, Structural analysis: with applications to aerospace structures. Vol. 163. 2009. Springer, Germany.\n'},{id:"B54",body:'Griffith, D.T. and T.D. Ashwill, The Sandia 100‐meter all‐glass baseline wind turbine blade: SNL100‐00. 2011. Informe Técnico, Sandia National Laboratories, United States.\n'},{id:"B55",body:'Chehouri, A., et al., Optimal design for a composite wind turbine blade with fatigue and failure constraints. Transactions of the Canadian Society for Mechanical Engineering, 2015. 39(2): p. 171.\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Adam Chehouri",address:"adam.chehouri1@uqac.ca",affiliation:'
Anti Icing Materials International Laboratory (AMIL), Université du Québec à Chicoutimi, Québec, Canada
Faculty of Engineering, Third Branch, Lebanese University, Rafic Harriri Campus, Hadath, Beirut, Lebanon
Anti Icing Materials International Laboratory (AMIL), Université du Québec à Chicoutimi, Québec, Canada
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1. Introduction
Interest in fuel production from renewable sources has increased significantly in the recent years due to political issues and the economic instability of the oil market. This approach also brings environmental benefits associated with the lower levels of gaseous emissions from the combustion of fuels from non-renewable sources [1, 2, 3].
Thus, many procedures have been proposed for the production of biofuels, particularly biodiesel, which is a non-petroleum fuel produced by applying transesterification to break down the triglyceride present in fatty acids [3, 4]. In this reaction, the chemical structure of triglycerides in oil is broken down via the exchange of alkyl groups between an ester and alcohol, with the alcohol being used as a reactant.
Biodiesel is a promising alternative to conventional diesel fuel due to its renewable nature and the associated reduction in the emission of particles and greenhouse gases [1, 4, 5]. Homogeneous catalysis can be applied in the transesterification reactions involved in biodiesel production to obtain high yields in a relatively short time. However, since the catalysts cannot be reused and must be neutralized after the reaction, biodiesel does not compete favorably with fossil fuels [2]. This, together with environmental concerns, has led to the search for heterogeneous catalysts, which are eco-friendly and effective [2, 3, 4, 5, 6, 7].
The use of a heterogeneous catalyst in the chemical reaction involved in biodiesel production can also reduce the time and cost of the process. In addition, it could be reused, resulting in a decrease in the level of impurities in the reaction products, and would allow continuous operation in a fixed bed reactor [4, 6].
The conversion efficiency is related to the catalytic activity of the heterogeneous catalyst used. In general, basic heterogeneous catalysts are more active than acidic heterogeneous catalysts, requiring shorter reaction times and relatively low reaction temperatures. Therefore, there are many advantages to performing the transesterification of triglycerides using a basic heterogeneous catalyst [2].
The synthesis of biodiesel with a heterogeneous catalyst can enhance the production and an economically feasible process can be achieved, mainly due to the lower costs related to the reuse of the catalyst and reduced levels of impurities in the products and byproducts of the reaction [6].
The methodology developed in this study facilitates a step change in the conceptual design process, offering a tool for the study of reactive distillation (RD) columns, with distillation, extractive distillation, esterification, and transesterification being performed in the same equipment, to allow biodiesel production in a continuous process.
The RD process is based on the process intensification concept, taking advantage of the synergy that occurs when combining the catalysis reaction and separation in a single unit, which allows the concurrent production and removal of products. This improves productivity and selectivity, reduces energy use, eliminates the need for solvents, and leads to highly efficient systems with improved environmental sustainability [8].
The use of RD as a reactive separation technology has attracted considerable attention given its high levels of efficiency [9] and the advantages include energy and capital savings, increased reaction conversion, high selectivity, and the use of the reaction heat [10]. Therefore, RD processes have been previously examined for application in esterification and etherification reactions [11, 12, 13, 14].
A study developed by Kousoulidou et al. [15], based on the tests on the engine and vehicle, concluded that blending of biodiesel in petroleum diesel at 10% (v/v) changes fuel properties to a degree that can affect combustion characteristics, such as the start of ignition and heat-release rate. For the authors, the limited evidence on the effects of such blends on the combustion and emissions of diesel engines was not originally designed to operate on biodiesel blends.
The authors made a number of experiments with 10% (v/v) (B10) biodiesel fuel of palm oil origin performed on a light duty. The measurements included in-cylinder pressure, pollutants emissions, and fuel consumption. Combustion effects were limited but changes in the start of ignition and heat-release rate could be identified. Emission effects included both higher and lower smoke and NOx, depending on the operation point. Others studies developed by others authors cited in the literature show the possibility to increase the biodiesel perceptual in the blend with diesel that the results show the possibility to increase this perceptual without the modification of engine characteristics.
The complexity involved in improving the performance of this process is associated with the multi-feed positions and selection of the reactants, catalysts, and solvents.
In this study, the transesterification reaction was carried out with homogeneous catalysts, in a continuous distillation column reactor, and the operational performance was evaluated in terms of biodiesel production quality. Experiments were also conducted using an RD column, where the reactant was supplied at the feed position.
The process intensification concept was adopted, using an RD column for the transesterification reaction, to recover the reactant (ethanol) used and to break the azeotrope point between ethanol and water, using the extractive distillation being performed in the top section of this equipment, as shown in Figure 1.
Figure 1.
Reactive distillation column proposed herein where (a) processing of raw material with high acid index and (b) processing of raw material with a reduced acid index.
In this case, a homogeneous catalyst was used in the RD column. The same operational conditions were applied in all cases studied, including the residence time, the mass of reactant, and molar mass used in the reaction. The results obtained show good reaction efficiency, which was improved by applying the process intensification principles. This concept can be applied to other synthesis processes carried out in a continuous regime.
2. Experimental
2.1 Material
The use of residual frying oil for bio-fuel production is associated with advantageous economic and environmental factors. In general, when food is processed, the vegetal oil or raw fats are exposed to environments with high temperatures, where the thermal degradation of oil occurs. The residual frying oil produced can be used for biofuel production due to its potential for energy production through a chemical reaction. For this study, the residual frying oil (soybean oil), with an acid value of 0.2 mgKOH/g and average molecular weight of 884.65 g/mole, was used in all experiments.
2.2 Reactive distillation column
An RD column was designed, constructed and installed (Figure 1). It was composed of three modules, which incorporate internally packing to increase the interfacial area for vapor-liquid contact, mass transfer and chemical reaction. The module located in the intermediate part of the column (Figure 2a) receives the limiting reactant (oil) and the excess reactant (ethyl alcohol) with dissolved sulfuric acid, used as a catalyst in the esterification reaction, based on the conversion of free fatty acids into biodiesel. The flow rate of the reagents is associated with the stoichiometric balance. In this module, the mixtures are fed vertically, downward, through the action of gravity, and interact strongly with the ascendant vapor of the alcohol used in excess coming from the reboiler, installed at the base of the equipment.
Figure 2.
Modules that composed of the reactive distillation column: (a) Esterification module and (b) Transesterification module.
After the esterification reaction, the downward mixture flows to the transesterification module (Figure 2b) where feeding with the basic catalyst occurs. In this case, the feed catalyst must neutralize the acid catalyst from the esterification and the remaining mass acts as a catalyst for the transesterification reaction in the bottom module. As previously mentioned, the transesterification reaction produces biodiesel and glycerol, which are collected in a decanting funnel installed at the bottom of the column. In the decanting funnel, the separation of the products into two phases occurs, that is, a light phase of the crude biodiesel and a heavy phase of crude glycerol (Figure 3).
Figure 3.
Decanting funnel installed at the bottom of the RD column.
In the esterification reaction, with the dissolution of the catalysts in excess reagent, water is produced. This reduces the level of purity of the alcohol in the vapor phase and thus the capacity of the chemical reaction. For this, a module for extractive distillation (Figure 4a) is therefore required at the top of the column to break the azeotrope point between ethanol and water and to produce the anhydrous ethyl alcohol.
Figure 4.
Description of streams associated with chemical reaction modules: a) Module of Extractive Distilation used to break the azeotrope point of mixture and; b) Reboiler used to vaporization of alcohol.
In this process, the residual glycerol recovered at the bottom of the RD column is used as a solvent in the extractive distillation, being fed at the top of this equipment and flowing down through the action of gravity. Along its path, the solvent interacts with the ethanol vapor and the interaction between the flows modifies the thermodynamic characteristics of the ethanol, breaking the azeotrope point and increasing the purity of this compound (Figure 4a).
The top products are condensed and flow downward to the reboiler where they are heated to produce the vapor needed for the RD column (Figure 4b). Based on this principle, the alcohol vapor in excess operates in the recirculation circuit, being involved in the esterification and transesterification reactions and the extractive distillation, and in the last step, the crude glycerol is used as a solvent. On the other hand, the steady-state characteristic of this process guarantees continuous feeding and removal of the products at the bottom of this equipment, with the recirculation of ethanol, used in this study as the reactant in excess.
Figures 2 and 4 are compressed into Figure 1, which represents the RD column, with the three modules described herein. There are four feed flows in the RD column. The operations involved in this process occur in the top condenser, the bottom decanting funnel, and the reboiler, used to produce the vapor flow, and vapor is used to allow effective chemical reactions to produce biodiesel and glycerol.
2.3 Experimental procedures
2.3.1 Reactive distillation column operation
To carry out the biodiesel production experiments in the RD column, the procedures involved the following steps:
The limiting reactant was characterized to determine the acidity index. Under these conditions, the operational sequence can be defined with or without the esterification reaction. If the acidity index is lower than that established in the literature, only the transesterification reaction is used (Figure 1b);
After establishing the quality of the raw material, the stoichiometric equation is used to determine the masses of reagents needed for the chemical reaction. The calculated mass is then converted into flow rates;
The appropriate masses of limiting and excess reactants and the catalyst are weighed out. In this case, the catalyst mass was determined from a previous transesterification reaction study performed in a laboratory batch-scale reactor. Under the study conditions, 1.2% catalyst mass in relation to the oil mass was defined;
In the next step, the mass of the limiting reactant was heated to the reaction temperature, in this case until reaching the boiling point of ethanol. The mass of alcohol in excess was heated and used to dissolve of catalyst mass;
The previously heated masses were inserted into the respective feed tanks. The use of heated mass minimizes the thermal barrier due to the temperature difference between the ascendant vapor and the feed flow;
The anhydrous alcohol, previously held in the reboiler tank, produces the vapor that flows into the RD column, based on the recirculation process described above. Thus, the valve of the reactant tank is open, and the flow rate is based on the values established in the stoichiometric balance. Since the acidity index value was below that established in the literature, esterification and transesterification modules were used in the transesterification reaction, as described in Figure 1b and;
The feed flow rates and the operation temperature were adequately controlled, and the products were removed progressively until the end of the reaction.
2.3.2 Measuring the process performance
When the reaction was concluded, the products were purified using a batch distillation column to recover the excess alcohol. For this, phase separation into a light phase containing biodiesel and a heavy phase of glycerol occurred. The biodiesel recovered was used to determine the yield, in terms of biodiesel mass-produced in relation to the frying oil mass initially used in the process. The biodiesel was then neutralized and dried by evaporation to ensure appropriate quality for use in internal combustion to produce electricity.
2.3.3 Batch biodiesel production
Before the experiments were performed in the RD column, tests were performed in a batch reactor using the reagents under the same operational conditions, with the following methodology:
The acidity index of the frying oil was determined. As this parameter is below the limit established in the literature, only the transesterification reaction was carried out;
The stoichiometric balance was determined and the masses of reactants for the transesterification reaction were weighed out. For the excess reactant, an amount six times more than the stoichiometric mass was used;
The reagent masses previously weighed out were heated until the reaction temperature and the basic catalyst was dissolved in the heated alcohol;
The mass of alcohol with the catalyst was inserted in the reactor with the previously heated oil, starting the transesterification reaction, which proceeded for 1 h;
At the end of the reaction, the mixture was placed into a distillation flask to recover the alcohol in excess. The remaining mass was then placed in the decanting funnel for the phase separation into biodiesel (light phase) and glycerol (heavy phase).
2.3.4 Characterization of reaction products
The characterization of the biodiesel was carried out through the analysis of the ester content, kinematic viscosity, acid index, and specific mass. The kinematic viscosity was obtained using an Ostwald viscometer based on the ABNT NBR 10441 methodology and, to determine the acidity index, titration with potassium hydroxide was carried out based on the ABNT NBR 14448 method. The specific mass was determined at room temperature (20± 2°C) using a 25-mL pycnometer calibrated with distilled water. These procedures were also used in a study by Barros et al. [1], who experimented using a batch reactor.
3. Results and discussion
3.1 Batch experiments
The experiments in the RD column were carried out based on reference data obtained in a batch reactor, mainly related to the following parameters: (a) reaction time; (b) reagents and catalyst mass; (c) agitation degree; and (d) mass ratio. The transesterification data from the batch reactor were used to close the mass balances and to determine the conversion rates in terms of the biodiesel mass-produced. In this study, frying oil (92.352 g), anhydrous ethanol (88.9 g), and KOH catalyst (1.108 g) were used. The equivalent catalyst mass was 1.2% of the frying oil mass. The crude biodiesel produced was characterized to determine the quality of the biofuel and the results were as follows: density 0.8785 g/mL; kinematic viscosity 1.920 mm2/s; and acidity index 3.1668 mgOH/g. These parameters deviated from the specifications established by the ANP [16], but the biofuel needs to be purified before the final evaluation.
The operational parameters of the batch reactor were used to carry out the experiments in the RD distillation column, with the configuration shown in Figure 1b. Only two modules were used for the transesterification and the purification of the ascending and recycled alcohol vapor by extractive distillation (performed in the top module). The separation of the products from the RD column in the decanting funnel is shown in Figure 2, where biodiesel is the light phase and glycerol is the heavy phase.
3.2 Reactive distillation column operation
The experiments were performed in an RD column with the transesterification reaction carried out using two modules. Extractive distillation was conducted in the top module of the unit. During the experiments, the performance efficiency was monitored, with the ethanol vapor flowing into the column and passing through each module until stabilization (the steady-state) was reached. When the reagents are inside in the tank, the valves of the feed tank is open, there is an interaction between liquid and vapor phases, ensuring the turbulence necessary for effective mass and heat transfer and the chemical reaction to occur. The feed flow was previously heated to minimize the thermal barriers in the column, characterized by the condensation of the vapor flow, due to interaction between the phases. If this phenomenon occurs, there will be a decrease in the vapor flow rate toward the top of the equipment, which can limit the operational quality of the process. During the operation of the RD column, the bottom products are progressively recovery and after distillation, they are placed in the decanter tank where two phases are formed: a light phase (biodiesel) and a heavy phase (glycerol).
To obtain the results 1240.00 g of frying oil, 208.79 g of anhydrous ethanol (99.9%) and 14.888 g of catalyst (equivalent to 1.2% of the frying oil) were used. Based on the performance of the experiments over 1 h, the following feed flow rates were defined: (a) frying oil 20.67 g/min and (b) anhydrous ethanol with dissolved catalyst 3.728 g/min. Using these operational conditions, with the RD column in the steady state, the measured flow rate of the ethanol vapor in excess was 16.42 g/min, corresponding to 4.4 times less than the stoichiometric flow rate. During the experiments, the products were collected from the bottom of the RD column, as shown in Figure 1 (a and b).
3.3 Characterization of products
The physico-chemical characteristics of the residual biodiesel from the batch reactor and the RD column produced based on process intensification principles, were determined and the results were compared with international standards. The values obtained for the density, acidity index, refractive index, and kinematic viscosity can be seen in Table 1.
Reference
Processed mass (g)
Density 20°C (kg/m3)
Refraction index at 20°C
Acidity index (mgKOH/g)
Kinematic viscosity at 40°C (mm2/s)
Yield (%)
Batch reactor
92.352
878.5
1.453
3.1668
1.920
92.306
RD column
1240.0
876.7
1.454
2.0255
1.812
15.441
International standard
—
850.0–900.0
1.45
≤0.5
3.0–6.0
—
Table 1.
Physico-chemical parameters for biodiesel obtained in this study.
The international standard values for the biodiesel parameters were extracted from the Brazilian standards NBR 7148 and NBR 14065 for density at 20°C, NBR 14448 for the acidity index, and NBR 10441 for kinematic viscosity at 40°C. A comparison between the data shows significant deviations, mainly for the acidity index and kinematic viscosity. In this study, purification of the product is required for the biodiesel to meet international standards. In general, the purification process is based on extraction with an acid solution followed by evaporation to dry the biodiesel [17].
In this context, for better qualification of the crude biodiesel, liquid-liquid extraction was carried out with an acid water solution, and the masses used were previously defined based on a titration procedure. An acid water solution was employed in association with the basic catalyst used in the transesterification reaction. In this procedure, the acid mass required for the titration was determined, and the acid solution was prepared, heated up to 60°C, and then mixed with biodiesel under agitation. After this step, the mixture was transferred to a decanting funnel and left to stabilize for approximately 40 min. The phases were separated, and the biodiesel phase was transferred to a distillation flask to eliminate traces of water by drying.
When the purification step was finished, the biodiesel was characterized using the procedures described in the methodology section of this chapter, and the results are given in Table 2.
Reference
Processed mass (g)
Density 20°C (kg/m3)
Refraction index at 20°C
Acidity index (mgKOH/g)
Kinematic viscosity at 40°C (mm2/s)
Yield (%)
Batch reactor
92.352
876.387
1.456
1.1077
1.885
92.306
RD column
1240.0
880.682
1.455
0.5027
1.651
15.441
International standard
—
850.0–900.0
1.450
≤0.5
3.0–6.0
—
Table 2.
Physico-chemical data for purified biodiesel.
Table 2 shows the characteristics of the biodiesel samples obtained from the RD column and batch reactor. The acidity index for the former adheres to the established international standard, but this is not the case for the biodiesel from the batch reactor. For both cases studied, the kinematic viscosity was below the value given in the standard, probably due to the quality of the raw material used in this study. However, the refraction index and density of both samples lie within the ranges set by the international standard.
The results obtained in this study are similar to those reported by Colombo et al. [2, 5], Pitt et al. [6], and Barros et al. [1], who investigated biodiesel production by esterification and transesterification, in recirculation reactors and a batch reactor, using a raw material with a reduced acidity index.
A limitation of this study is related to the rate of conversion of the raw material into biodiesel (approximately 15.441%). This could be due to the reduced residence time in the RD column and/or the turbulence resulting from the interaction between the two phases involved in the process. Thus, further studies need to be conducted to determine the optimum feed reactant flow rate to maximize the residence time and increase the degree of conversion. Alternatively, the length or number of modules in the RD column could be increased to increase the reaction rate.
However, the turbulence phenomenon involves strong interaction between the liquid and vapor phases and this should be further investigated to better understand the relevance in terms of conversion rates. In addition, the use of other types of internal packing may improve the yield or the degree of conversion of the raw material to biodiesel.
4. Conclusions
The results reported herein can be used to conclude that:
The RD column developed and used in this study is an apparatus with high operational flexibility to carry out esterification, transesterification, and conventional and extractive distillation, under different conditions and evaluate the parameters that affect the conversion rate;
The batch reactor can be used to support continuous processes, characterized by the use of RD columns, with high operational flexibility;
The reduced residence time in RD columns limits the application of this technical process, which requires optimization to increase the conversion rate and obtain products with high purity;
The RD column can be characterized as innovative, mainly when the reactant in excess is used as a circulating vapor, which is purified in the top module by extractive distillation and recycled to the bottom tank and;
The RD column promotes a continuous process with the potential to increase the production flow rate to meet industrial demand.
Acknowledgments
The authors wish to thank Prof. Dr. A. A. Chivanga Barros for his dedication to developing this project, mainly for spending particular money to finance this study.
Conflict of interest
The authors declare no conflict of interest.
\n',keywords:"reactive distillation, transesterification, biodiesel, column, process intensification",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/82284.pdf",chapterXML:"https://mts.intechopen.com/source/xml/82284.xml",downloadPdfUrl:"/chapter/pdf-download/82284",previewPdfUrl:"/chapter/pdf-preview/82284",totalDownloads:14,totalViews:0,totalCrossrefCites:0,dateSubmitted:"November 6th 2021",dateReviewed:null,datePrePublished:"June 17th 2022",datePublished:null,dateFinished:"June 17th 2022",readingETA:"0",abstract:"Environment concerns related to the use of fossil fuels are reflected in proposals for new conversion technologies to produce biofuels from biomass. The biofuels produced in this context have the same characteristics as petroleum derivatives, however, with reduced greenhouse gas emissions and with no sulfur in their molecular structures. In this context, a reactive distillation (RD) column was designed, constructed, installed, and operated using process intensification principles. It was applied in the production of biodiesel, using residual frying oil as the raw material, by the transesterification reaction, in a continuous regime. The process started with alcohol in excess in the reboiler, located in the bottom of the RD, which was heated through the combustion of liquefied petroleum gas (LPG) to produce ethanol vapor, which was recirculated in the column until stabilization. In this stage, the reagents were inserted into the feed tanks. Thus, the tank valves were opened for each reactant. The reaction products were recovered during the experiment from the bottom of the column and they were distilled to obtain two phases, biodiesel and glycerol. The results obtained from this study show that the use of an RD column can produce biodiesel in a continuous regime.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/82284",risUrl:"/chapter/ris/82284",signatures:"Ana Kiesse Zeleme and António André Chivanga Barros",book:{id:"11364",type:"book",title:"Soybean - Recent Advances in Research and Applications",subtitle:null,fullTitle:"Soybean - Recent Advances in Research and Applications",slug:null,publishedDate:null,bookSignature:"Prof. Takuji Ohyama, Dr. Yoshihiko Takahashi, Dr. Norikuni Ohtake, Dr. Takashi Sato and Dr. Sayuri Tanabata",coverURL:"https://cdn.intechopen.com/books/images_new/11364.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-80355-700-7",printIsbn:"978-1-80355-699-4",pdfIsbn:"978-1-80355-701-4",isAvailableForWebshopOrdering:!0,editors:[{id:"30061",title:"Prof.",name:"Takuji",middleName:null,surname:"Ohyama",slug:"takuji-ohyama",fullName:"Takuji Ohyama"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Experimental",level:"1"},{id:"sec_2_2",title:"2.1 Material",level:"2"},{id:"sec_3_2",title:"2.2 Reactive distillation column",level:"2"},{id:"sec_4_2",title:"2.3 Experimental procedures",level:"2"},{id:"sec_4_3",title:"2.3.1 Reactive distillation column operation",level:"3"},{id:"sec_5_3",title:"2.3.2 Measuring the process performance",level:"3"},{id:"sec_6_3",title:"2.3.3 Batch biodiesel production",level:"3"},{id:"sec_7_3",title:"2.3.4 Characterization of reaction products",level:"3"},{id:"sec_10",title:"3. Results and discussion",level:"1"},{id:"sec_10_2",title:"3.1 Batch experiments",level:"2"},{id:"sec_11_2",title:"3.2 Reactive distillation column operation",level:"2"},{id:"sec_12_2",title:"3.3 Characterization of products",level:"2"},{id:"sec_14",title:"4. Conclusions",level:"1"},{id:"sec_15",title:"Acknowledgments",level:"1"},{id:"sec_18",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Barros AAC, Wust E, Meier HF. Study of the technical-scientific feasibility of biodiesel production from fatty residues. Engenharia Sanitaria e Ambiental. 2008;13(3):255-262'},{id:"B2",body:'Colombo K, Ender L, Santos MM, Barros AAC. Production of biodiesel from soybean oil and methanol, catalyzed by calcium oxide in a recycle reactor. South African Journal of Chemical Engineering. 2019;28:19-25'},{id:"B3",body:'Chiarello L, Porto T, Barros AAC, Simionatto E, Botton V, Wiggers V. Bosting an oil refinery into a biorefinery. Angolan Mineral, Oil & Gas Journal. 2020;1:1-5'},{id:"B4",body:'Colombo K, Ender L, Barros AAC. The study of biodiesel production using CaO as a heterogeneous catalytic reaction. Egyptian Journal of Petroleum. 2017;26(2):341-349'},{id:"B5",body:'Colombo K. Biodiesel production via transesterification with heterogeneous catalysis using CaO as a catalyst [master’s thesis]. Brazil: Blumenau Regional University; 2013'},{id:"B6",body:'Pitt FD, Domingos AM, Chivanga Barros AA. Purification of residual glycerol recovered from biodiesel production. South African Journal of Chemical Engineering. 2019;29:42-51'},{id:"B7",body:'Semwal S, Arora AK, Badoni RP, Tulli DK. Biodiesel production using heterogeneous catalysts. Bioresource Technology. 2011;102:2151-2161'},{id:"B8",body:'Kiss AA. Novel catalytic reactive distillation processes for a sustainable chemical industry. Topics in Catalysis. 2019;62:1132-1148. DOI: 10.1007/s11244-018-1052-9'},{id:"B9",body:'Stankiewicz AI, Moulijn JA. Process intensification: Transforming chemical engineering. Chemical Engineering Progress. 2000;96:22-34'},{id:"B10",body:'Cárdenas-Guerra JC, LópezArenas T, Lobo-Oehmichen R, Pérez-Cisneros ESA. Reactive distillation process for deep hydrodesulfurization of diesel: Multiplicity and operation aspects. Computers and Chemical Engineering. 2010;34:196-209'},{id:"B11",body:'Subawalla H, Fair JR. Design guidelines for solid-catalyzed reactive distillation systems. Industrial and Engineering Chemistry Research. 1999;38:3696-3709'},{id:"B12",body:'Taylor R, Krishna R. Modeling reactive distillation. Chemical Engineering Science. 2000;55:5183-5229'},{id:"B13",body:'Huss RS, Chen M, Malone MF, Doherty MF. Reactive distillation for methyl acetate production. Computers and Chemical Engineering. 2003;27:1855-1866'},{id:"B14",body:'Huang K, Wang SJ. Design and control of a methyl tertiary butyl ether (MTBE) decomposition reactive distillation column. Industrial & Engineering Chemistry Research. 2007;46:2508-2519'},{id:"B15",body:'Marina K, Georgios F, Leonidas N, Zissis S. Biodiesel blend effects on common-rail diesel combustion and emissions. Fuel. 2010;89(11):3442-3449'},{id:"B16",body:'Agência Nacional do Petróleo, Gás Natural e Biocombustíveis (ANP). Biodiesel [Internet]. Brazil; 2020. Available from: http://www.anp.gov.br/biocombustiveis/biodiesel [Cited: 18 May 2020]'},{id:"B17",body:'Dib FH. Production of biodiesel from recycled waste oil [master’s thesis]. Ilha Solteira (SP), Brazil; 2010'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Ana Kiesse Zeleme",address:null,affiliation:'
Department of Engineering and Technology (DET), Instituto Superior Politecnico de Tecnologias e Ciências (ISPTEC), Talatona, Luanda, Angola
'},{corresp:"yes",contributorFullName:"António André Chivanga Barros",address:"chivanga.barros@isptec.co.ao",affiliation:'
Department of Engineering and Technology (DET), Instituto Superior Politecnico de Tecnologias e Ciências (ISPTEC), Talatona, Luanda, Angola
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These symptoms are mainly connected to advanced gynaecologic malignancies. Patients with locally advanced or recurrent gynaecologic cancers may present with various symptoms. Among those we discuss vaginal bleeding, vaginal discharge and fistulas. Vice versa non-malignant diseases and non-pelvic site diseases such as inflammations, overdose of anticoagulants, wounds or pressure ulcers may have similar clinical manifestation. Some symptoms may result from aggressive cancer treatment (oncological surgery, radiotherapy) with curative intent. Some symptoms get worse on account of the postmenopausal status (natural or artificial following any type of ovarian failure). For advanced gynae-oncological diseases it has been in practise, that the best palliative care is offered and practised simultaneously with curative treatment. The problematics of gynaecologic symptoms in palliative care represents delicate and intimate sphere and it may disturb patient’s autonomy and dignity. The mission of physicians, nurses and caregivers is to consider treatment options thoroughly in context of patient’s quality of life, prognosis and life conditions to fulfil the ideals of the best symptomatic and supportive care.",signatures:"Monika Náležinská and Josef Chovanec",authors:[{id:"302210",title:"Dr.",name:"Monika",surname:"Náležinská",fullName:"Monika Náležinská",slug:"monika-nalezinska",email:"monika.nalezinska@mou.cz"},{id:"346023",title:"Dr.",name:"Josef",surname:"Chovanec",fullName:"Josef Chovanec",slug:"josef-chovanec",email:"josef.chovanec@mou.cz"}],book:{id:"10456",title:"Suggestions for Addressing Clinical and Non-Clinical Issues in Palliative Care",slug:"suggestions-for-addressing-clinical-and-non-clinical-issues-in-palliative-care",productType:{id:"1",title:"Edited Volume"}}}],collaborators:[{id:"91668",title:"Dr.",name:"Mira",surname:"Florea",slug:"mira-florea",fullName:"Mira Florea",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"316853",title:"Dr.",name:"Ekaete",surname:"Asuquo",slug:"ekaete-asuquo",fullName:"Ekaete Asuquo",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"317133",title:"Ph.D.",name:"Aida",surname:"Puia",slug:"aida-puia",fullName:"Aida Puia",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"317134",title:"Dr.",name:"Rodica Sorina",surname:"Pop",slug:"rodica-sorina-pop",fullName:"Rodica Sorina Pop",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"319041",title:"Ph.D.",name:"Akon",surname:"Ndiok",slug:"akon-ndiok",fullName:"Akon Ndiok",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Calabar",institutionURL:null,country:{name:"Nigeria"}}},{id:"319992",title:"Prof.",name:"Busiswe",surname:"Ncama",slug:"busiswe-ncama",fullName:"Busiswe Ncama",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of KwaZulu-Natal",institutionURL:null,country:{name:"South Africa"}}},{id:"320836",title:"Dr.",name:"Emilia",surname:"Oyira",slug:"emilia-oyira",fullName:"Emilia Oyira",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Calabar",institutionURL:null,country:{name:"Nigeria"}}},{id:"326313",title:"Associate Prof.",name:"Prasit",surname:"Mahawongkajit",slug:"prasit-mahawongkajit",fullName:"Prasit Mahawongkajit",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"328982",title:"Dr.",name:"Paulina Ackley",surname:"Akpan-Idiok",slug:"paulina-ackley-akpan-idiok",fullName:"Paulina Ackley Akpan-Idiok",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Calabar",institutionURL:null,country:{name:"Nigeria"}}},{id:"337750",title:"B.Sc.",name:"Rasheed",surname:"Ofosu-Poku",slug:"rasheed-ofosu-poku",fullName:"Rasheed Ofosu-Poku",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null}]},generic:{page:{slug:"OA-publishing-fees",title:"Open Access Publishing Fees",intro:"
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Personal contact and support throughout the publishing process from your dedicated Author Service Manager
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English language copyediting and proofreading, including the correction of grammatical, spelling, and other common errors
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Open Access Funding
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For Authors who are still unable to obtain funding from their institutions or research funding bodies for individual projects, IntechOpen does offer the possibility of applying for a Waiver to offset some or all processing feed. Details regarding our Waiver Policy can be found here.
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Dissemination and Promotion
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Almost all the parts of this plant, that are, fruit, leaves, flower bud, trunk, and pseudo-stem, can be utilized. This chapter deals with the fiber extracted from the pseudo-stem of the banana plant. It discusses the production of banana pseudo-stem fiber, which includes plantation and harvesting; extraction of banana pseudo-stem fiber; retting; and degumming of the fiber. It also deals with the characteristics of the banana pseudo-stem fiber, such as morphological, physical and mechanical, durability, degradability, thermal, chemical, and antibacterial properties. Several potential applications of this fiber are also mentioned, such as the use of this fiber to fabricate rope, place mats, paper cardboard, string thread, tea bags, high-quality textile materials, absorbent, polymer/fiber composites, etc.",book:{id:"7544",slug:"banana-nutrition-function-and-processing-kinetics",title:"Banana Nutrition",fullTitle:"Banana Nutrition - Function and Processing Kinetics"},signatures:"Asmanto Subagyo and Achmad Chafidz",authors:[{id:"257742",title:"M.Sc.",name:"Achmad",middleName:null,surname:"Chafidz",slug:"achmad-chafidz",fullName:"Achmad Chafidz"},{id:"268400",title:"Mr.",name:"Asmanto",middleName:null,surname:"Subagyo",slug:"asmanto-subagyo",fullName:"Asmanto Subagyo"}]},{id:"40180",title:"Plant Tissue Culture: Current Status and Opportunities",slug:"plant-tissue-culture-current-status-and-opportunities",totalDownloads:66561,totalCrossrefCites:45,totalDimensionsCites:96,abstract:null,book:{id:"3568",slug:"recent-advances-in-plant-in-vitro-culture",title:"Recent Advances in Plant in vitro Culture",fullTitle:"Recent Advances in Plant in vitro Culture"},signatures:"Altaf Hussain, Iqbal Ahmed Qarshi, Hummera Nazir and Ikram Ullah",authors:[{id:"147617",title:"Dr.",name:"Altaf",middleName:null,surname:"Hussain",slug:"altaf-hussain",fullName:"Altaf Hussain"}]},{id:"66996",title:"Ethiopian Common Medicinal Plants: Their Parts and Uses in Traditional Medicine - Ecology and Quality Control",slug:"ethiopian-common-medicinal-plants-their-parts-and-uses-in-traditional-medicine-ecology-and-quality-c",totalDownloads:4172,totalCrossrefCites:6,totalDimensionsCites:11,abstract:"The main purpose of this review is to document medicinal plants used for traditional treatments with their parts, use, ecology, and quality control. Accordingly, 80 medicinal plant species were reviewed; leaves and roots are the main parts of the plants used for preparation of traditional medicines. The local practitioners provided various traditional medications to their patients’ diseases such as stomachaches, asthma, dysentery, malaria, evil eyes, cancer, skin diseases, and headaches. The uses of medicinal plants for human and animal treatments are practiced from time immemorial. Stream/riverbanks, cultivated lands, disturbed sites, bushlands, forested areas and their margins, woodlands, grasslands, and home gardens are major habitats of medicinal plants. Generally, medicinal plants used for traditional medicine play a significant role in the healthcare of the majority of the people in Ethiopia. The major threats to medicinal plants are habitat destruction, urbanization, agricultural expansion, investment, road construction, and deforestation. Because of these, medicinal plants are being declined and lost with their habitats. Community- and research-based conservation mechanisms could be an appropriate approach for mitigating the problems pertinent to the loss of medicinal plants and their habitats and for documenting medicinal plants. Chromatography; electrophoretic, macroscopic, and microscopic techniques; and pharmaceutical practice are mainly used for quality control of herbal medicines.",book:{id:"8502",slug:"plant-science-structure-anatomy-and-physiology-in-plants-cultured-in-vivo-and-in-vitro",title:"Plant Science",fullTitle:"Plant Science - Structure, Anatomy and Physiology in Plants Cultured in Vivo and in Vitro"},signatures:"Admasu Moges and Yohannes Moges",authors:[{id:"249746",title:"Ph.D.",name:"Admasu",middleName:null,surname:"Moges",slug:"admasu-moges",fullName:"Admasu Moges"},{id:"297761",title:"MSc.",name:"Yohannes",middleName:null,surname:"Moges",slug:"yohannes-moges",fullName:"Yohannes Moges"}]},{id:"70658",title:"Factors Affecting Yield of Crops",slug:"factors-affecting-yield-of-crops",totalDownloads:4150,totalCrossrefCites:31,totalDimensionsCites:45,abstract:"A good understanding of dynamics involved in food production is critical for the improvement of food security. It has been demonstrated that an increase in crop yields significantly reduces poverty. Yield, the mass of harvest crop product in a specific area, is influenced by several factors. These factors are grouped in three basic categories known as technological (agricultural practices, managerial decision, etc.), biological (diseases, insects, pests, weeds) and environmental (climatic condition, soil fertility, topography, water quality, etc.). These factors account for yield differences from one region to another worldwide. The current chapter will discuss each of these three basic factors as well as providing some recommendations for overcoming them. In addition, it will provide the importance of climate-smart agriculture in the increase of crop yields while facilitating the achievement of crop production in safe environment. This goes in line with the second goal of 2030 Agenda for Sustainable Development of United Nations in transforming our world formulated as end hunger, achieve food security, improve nutrition and promote sustainable agriculture.",book:{id:"8153",slug:"agronomy-climate-change-food-security",title:"Agronomy",fullTitle:"Agronomy - Climate Change & Food Security"},signatures:"Tandzi Ngoune Liliane and Mutengwa Shelton Charles",authors:[{id:"313819",title:"Dr.",name:"Liliane",middleName:null,surname:"Tandzi",slug:"liliane-tandzi",fullName:"Liliane Tandzi"},{id:"314316",title:"Prof.",name:"Charles Shelton",middleName:null,surname:"Mutengwa",slug:"charles-shelton-mutengwa",fullName:"Charles Shelton Mutengwa"}]},{id:"59402",title:"Robotic Harvesting of Fruiting Vegetables: A Simulation Approach in V-REP, ROS and MATLAB",slug:"robotic-harvesting-of-fruiting-vegetables-a-simulation-approach-in-v-rep-ros-and-matlab",totalDownloads:2813,totalCrossrefCites:8,totalDimensionsCites:9,abstract:"In modern agriculture, there is a high demand to move from tedious manual harvesting to a continuously automated operation. This chapter reports on designing a simulation and control platform in V-REP, ROS, and MATLAB for experimenting with sensors and manipulators in robotic harvesting of sweet pepper. The objective was to provide a completely simulated environment for improvement of visual servoing task through easy testing and debugging of control algorithms with zero damage risk to the real robot and to the actual equipment. A simulated workspace, including an exact replica of different robot manipulators, sensing mechanisms, and sweet pepper plant, and fruit system was created in V-REP. Image moment method visual servoing with eye-in-hand configuration was implemented in MATLAB, and was tested on four robotic platforms including Fanuc LR Mate 200iD, NOVABOT, multiple linear actuators, and multiple SCARA arms. Data from simulation experiments were used as inputs of the control algorithm in MATLAB, whose outputs were sent back to the simulated workspace and to the actual robots. ROS was used for exchanging data between the simulated environment and the real workspace via its publish-and-subscribe architecture. Results provided a framework for experimenting with different sensing and acting scenarios, and verified the performance functionality of the simulator.",book:{id:"6265",slug:"automation-in-agriculture-securing-food-supplies-for-future-generations",title:"Automation in Agriculture",fullTitle:"Automation in Agriculture - Securing Food Supplies for Future Generations"},signatures:"Redmond R. Shamshiri, Ibrahim A. Hameed, Manoj Karkee and\nCornelia Weltzien",authors:[{id:"182449",title:"Prof.",name:"Ibrahim",middleName:"A.",surname:"Hameed",slug:"ibrahim-hameed",fullName:"Ibrahim Hameed"},{id:"203413",title:"Dr.",name:"Redmond R.",middleName:null,surname:"Shamshiri",slug:"redmond-r.-shamshiri",fullName:"Redmond R. Shamshiri"},{id:"241193",title:"Dr.",name:"Manoj",middleName:null,surname:"Karkee",slug:"manoj-karkee",fullName:"Manoj Karkee"},{id:"241194",title:"Dr.",name:"Cornelia",middleName:null,surname:"Weltzien",slug:"cornelia-weltzien",fullName:"Cornelia Weltzien"}]}],onlineFirstChaptersFilter:{topicId:"5",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"82757",title:"Seed Dormancy: Induction, Maintenance and Seed Technology Approaches to Break Dormancy",slug:"seed-dormancy-induction-maintenance-and-seed-technology-approaches-to-break-dormancy",totalDownloads:2,totalDimensionsCites:0,doi:"10.5772/intechopen.106153",abstract:"Dormancy is the major cause of erratic germination, patchy emergence and uneven seedling establishment in the field. These traits are exceedingly undesirable in crop production as future phases of growth and development are strongly linked to uniform seedling development at early growth phases. Variations in maturation time, and difficulty in managing abiotic and biotic stresses during pre- and postharvest are common consequences of uneven germination and seedling emergence. Minimizing this negative impact of dormancy in a seed lot is the major concern of all seed production companies. Generally, mature seeds show some considerable dormancy during which embryo growth is halted momentarily because one or more internal and external stimuli for growth resumption is/are absent. If the inhibition of seed germination is solely due to insufficient or complete absence of external signals, then the seed is in a state of quiescence. Otherwise, if linked to internal factors, then the seed is in a state of dormancy. Induction, maintenance, and release of dormancy are therefore related to Seed-dependent factors such as morphology, hormones, state of embryo maturity at seed dispersal and chemical inhibitors. This chapter focuses on species-dependent methods currently used to break dormancy, reduce germination time and improve emergence and seedling establishment.",book:{id:"11322",title:"Seed Biology Updates",coverURL:"https://cdn.intechopen.com/books/images_new/11322.jpg"},signatures:"Tabi Kingsley Mbi, Ntsomboh Godswill Ntsefong and Tatah Eugene Lenzemo"},{id:"79168",title:"Pulses: A Potential Source of Valuable Protein for Human Diet",slug:"pulses-a-potential-source-of-valuable-protein-for-human-diet",totalDownloads:1,totalDimensionsCites:0,doi:"10.5772/intechopen.99980",abstract:"Nutritional profile of pulses has significant importance in human diet with respect to protein and mineral quality and bioavailability. Protein energy malnutrition is widespread throughout the world especially among the developing countries. Pulses being rich in macronutrients such as protein from 20 to 26% and low in calories are most suitable for product development for target-oriented population. During last decade, the demand for pulse-based products with high protein and fiber, low glycemic index, and gluten free with more antioxidant showed increasing trend by the consumers. Drift of end-use application of pulses generated interest for research in all disciplines such as breeding, agronomy, food, and nutrition, etc. A great share of plant protein in human diet may be a critical step for reducing dependence on animal origin protein source. This chapter will review contribution or choice of plant-based protein from legumes or pulses with good-quality protein based on amino acid composition. Additionally, this overview can give insight into the development of new product with balanced nutritional quality and high protein contents as a potential protein supply for malnourished population.",book:{id:"12236",title:"Legumes Research- Volume 2",coverURL:"https://cdn.intechopen.com/books/images_new/12236.jpg"},signatures:"Saima Parveen, Amina Jamil, Imran Pasha and Farah Ahmad"},{id:"83012",title:"Cotton Based Cellulose Nanocomposites: Synthesis and Application",slug:"cotton-based-cellulose-nanocomposites-synthesis-and-application",totalDownloads:0,totalDimensionsCites:0,doi:"10.5772/intechopen.106473",abstract:"Nanocellulose is a renewable natural biomaterial which has risen to prominence due to its biodegradability and physiochemical properties making it a promising candidate to replace non-biodegradable synthetic fibers. Due to its profound qualities, nanocellulose extracted from cotton fibers have tremendous application potential and have been intensively studied particularly in the generation of nanofillers and as reinforcement components in polymer matrixes. Deposition of inorganic nanoparticles on cotton fabric result in antimicrobial textiles with multifunctional use particularly in manufacture of PPE and as filtration devices against environmental pollutants and pathogens. This chapter compiles three main sections. The first section gives an overview of the extent of work done in the creation and application potential of cotton-based nanocomposites. The second section describes the in situ and ex situ methods of nanoparticle deposition and self assembly on cotton fabrics to generate multifunctional cotton-based nanocomposites with antimicrobial potential while the final section describes the incorporation of cotton nanofibers in polymer matrices, their reinforcing properties, as well as surface modification to assist their incorporation. Finally in the conclusion, a summary of the up-to-date challenges and progresses is presented postulating the undiscovered arenas and future undertakings of this venture.",book:{id:"11362",title:"Cotton",coverURL:"https://cdn.intechopen.com/books/images_new/11362.jpg"},signatures:"Patricia Jayshree Samuel Jacob"},{id:"82476",title:"Joint Action of Herbicides on Weeds and Their Risk Assessment on Earthworm (Eisenia fetida L.)",slug:"joint-action-of-herbicides-on-weeds-and-their-risk-assessment-on-earthworm-eisenia-fetida-l",totalDownloads:2,totalDimensionsCites:0,doi:"10.5772/intechopen.105462",abstract:"Frequent and intensive use of similar modes of action herbicides increases selection pressure resulting in nature adapt and a number of herbicide-resistant weeds. The most effective methods to prevent and delay herbicide-resistant weeds are herbicide tank mixture and adjuvant mixed herbicides. This chapter intends to explain the advantages of herbicide tank mixture and adjuvant mixed herbicides. In addition, the models of estimated herbicide mixture interaction response have been explained. Although herbicide mixtures have benefits, they may present risks leading to soil pollution and affecting soil fauna such as earthworms. Therefore, we discussed the negative effect of mixture herbicides on Eisenia fetida. On the other hand, various models to calculate mixture herbicide toxicity on earthworms will be present in this chapter.",book:{id:"11610",title:"New Insights in Herbicide Science",coverURL:"https://cdn.intechopen.com/books/images_new/11610.jpg"},signatures:"Mohammad Taghi Alebrahim, Elham Samadi Kalkhoran and Te-Ming Paul Tseng"},{id:"82937",title:"Abiotic Stress in Plants",slug:"abiotic-stress-in-plants-1",totalDownloads:3,totalDimensionsCites:0,doi:"10.5772/intechopen.105944",abstract:"Stress in plants refers to external conditions, which drastically affect the growth, development, or productivity of plants. Stress triggers a wide range of plant responses, such as altered gene expression, cellular metabolism, changes in growth rates, and crop yields. Some abiotic stresses, such as low or high temperature, deficient water, and ultraviolet radiation, make plant growth and development unfavorable, leading to a fall in crop yield worldwide. The following writeup incorporated the abiotic stress factors related to the growth and development of plants, such as temperature, drought, heat, cold, and many more. Abiotic stress factors are the nonliving factors influencing the metabolism, growth, and development of the plant tissues at that particular time when such abiotic stress affects them. As a result of such abiotic stresses, the plants have generated many stress tolerance factors. Various stress-responsive genes are thus being formulated in response to the abiotic stresses, so the plants can survive even in such extreme conditions as well. Henceforth, it can be concluded that the abiotic stress factors imposed on the plants adversely impact their growth and developmental procedures, and at the same time, they also produce some stress tolerance factors to minimize the damage.",book:{id:"11330",title:"Plant Response Mechanisms to Abiotic Stresses",coverURL:"https://cdn.intechopen.com/books/images_new/11330.jpg"},signatures:"Shubham Dey and Ayan Raichaudhuri"},{id:"82943",title:"Laboratory Diagnosis of Candidiasis",slug:"laboratory-diagnosis-of-candidiasis",totalDownloads:5,totalDimensionsCites:0,doi:"10.5772/intechopen.106359",abstract:"The burden of Candidiasis continues to increase and so does the Candida species. Although Candida species are closely similar phenotypically, they differ from each other in terms of epidemiology, genetic characteristics, antifungal susceptibility and virulence profile. Therefore, reliable and accurate laboratory methods for identification of Candida species can determine the Candidiasis burden and enable the administration of the most appropriate antifungal drug therapy to reduce fungal mortality rates. Conventional and biochemical methods are often used in identification of Candida species. However, these techniques are specific and sensitive enough in detecting the non albicans candida (NAC) species. Molecular techniques have improved the laboratory diagnosis and management of Candidiasis due to improved sensitivity and specificity threshold. This chapter provides an overview of different laboratory methods for diagnosis of Candidiasis.",book:{id:"11608",title:"Candida and Candidiasis",coverURL:"https://cdn.intechopen.com/books/images_new/11608.jpg"},signatures:"Benson Musinguzi, Obondo J. Sande, Gerald Mboowa, Andrew Baguma, Herbert Itabangi and Beatrice Achan"}],onlineFirstChaptersTotal:323},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:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:139,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:122,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,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:21,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:10,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",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:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"August 2nd, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:33,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",slug:"miroslav-blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"14",title:"Cell and Molecular Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",isOpenForSubmission:!0,editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",slug:"rosa-maria-martinez-espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",biography:"Dr. Rosa María Martínez-Espinosa has been a Spanish Full Professor since 2020 (Biochemistry and Molecular Biology) and is currently Vice-President of International Relations and Cooperation development and leader of the research group 'Applied Biochemistry” (University of Alicante, Spain). Other positions she has held at the university include Vice-Dean of Master Programs, Vice-Dean of the Degree in Biology and Vice-Dean for Mobility and Enterprise and Engagement at the Faculty of Science (University of Alicante). She received her Bachelor in Biology in 1998 (University of Alicante) and her PhD in 2003 (Biochemistry, University of Alicante). She undertook post-doctoral research at the University of East Anglia (Norwich, U.K. 2004-2005; 2007-2008).\nHer multidisciplinary research focuses on investigating archaea and their potential applications in biotechnology. She has an H-index of 21. She has authored one patent and has published more than 70 indexed papers and around 60 book chapters.\nShe has contributed to more than 150 national and international meetings during the last 15 years. Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"15",title:"Chemical Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",isOpenForSubmission:!0,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. He performed post-doctoral studies at Max-Planck Institute, Germany, and University of Florence, Italy in addition to making several scientific visits abroad. He currently works as a Full Professor of Biochemistry in the Faculty of Pharmacy, Anadolu University, Turkey. Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. Dr. Beydemir is also Rector of Bilecik Şeyh Edebali University, Turkey.",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",slug:"deniz-ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",biography:"Dr. Deniz Ekinci obtained a BSc in Chemistry in 2004, MSc in Biochemistry in 2006, and PhD in Biochemistry in 2009 from Atatürk University, Turkey. He studied at Stetson University, USA, in 2007-2008 and at the Max Planck Institute of Molecular Cell Biology and Genetics, Germany, in 2009-2010. Dr. Ekinci currently works as a Full Professor of Biochemistry in the Faculty of Agriculture and is the Head of the Enzyme and Microbial Biotechnology Division, Ondokuz Mayıs University, Turkey. He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. Dr. Ekinci serves as the Editor in Chief of four international books and is involved in the Editorial Board of several international journals.",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null},{id:"17",title:"Metabolism",coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",isOpenForSubmission:!0,editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",slug:"yannis-karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",biography:"Yannis Karamanos, born in Greece in 1953, completed his pre-graduate studies at the Université Pierre et Marie Curie, Paris, then his Masters and Doctoral degree at the Université de Lille (1983). He was associate professor at the University of Limoges (1987) before becoming full professor of biochemistry at the Université d’Artois (1996). He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. His teaching areas are energy metabolism and regulation, integration and organ specialization and metabolic adaptation.",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null},{id:"18",title:"Proteomics",coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",isOpenForSubmission:!0,editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",slug:"paolo-iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",biography:"Paolo Iadarola graduated with a degree in Chemistry from the University of Pavia (Italy) in July 1972. He then worked as an Assistant Professor at the Faculty of Science of the same University until 1984. In 1985, Prof. Iadarola became Associate Professor at the Department of Biology and Biotechnologies of the University of Pavia and retired in October 2017. Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. He is a Consultant Reviewer for several journals, including the Journal of Chromatography A, Journal of Chromatography B, Plos ONE, Proteomes, International Journal of Molecular Science, Biotech, Electrophoresis, and others. He is also Associate Editor of Biotech.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",slug:"simona-viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",biography:"Simona Viglio is an Associate Professor of Biochemistry at the Department of Molecular Medicine at the University of Pavia. She has been working since 1995 on the determination of proteolytic enzymes involved in the degradation process of connective tissue matrix and on the identification of biological markers of lung diseases. She gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. She is an author of about 90 publications (According to Scopus: H-Index: 23; According to WOS: H-Index: 20) on peer-reviewed journals, a member of the “Società Italiana di Biochimica e Biologia Molecolare,“ and a Consultant Reviewer for International Journal of Molecular Science, Journal of Chromatography A, COPD, Plos ONE and Nutritional Neuroscience.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null}]},overviewPageOFChapters:{paginationCount:42,paginationItems:[{id:"82914",title:"Glance on the Critical Role of IL-23 Receptor Gene Variations in Inflammation-Induced Carcinogenesis",doi:"10.5772/intechopen.105049",signatures:"Mohammed El-Gedamy",slug:"glance-on-the-critical-role-of-il-23-receptor-gene-variations-in-inflammation-induced-carcinogenesis",totalDownloads:9,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Chemokines Updates",coverURL:"https://cdn.intechopen.com/books/images_new/11672.jpg",subseries:{id:"18",title:"Proteomics"}}},{id:"82875",title:"Lipidomics as a Tool in the Diagnosis and Clinical Therapy",doi:"10.5772/intechopen.105857",signatures:"María Elizbeth Alvarez Sánchez, Erick Nolasco Ontiveros, Rodrigo Arreola, Adriana Montserrat Espinosa González, Ana María García Bores, Roberto Eduardo López Urrutia, Ignacio Peñalosa Castro, María del Socorro Sánchez Correa and Edgar Antonio Estrella Parra",slug:"lipidomics-as-a-tool-in-the-diagnosis-and-clinical-therapy",totalDownloads:7,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Fatty Acids - Recent Advances",coverURL:"https://cdn.intechopen.com/books/images_new/11669.jpg",subseries:{id:"17",title:"Metabolism"}}},{id:"82440",title:"Lipid Metabolism and Associated Molecular Signaling Events in Autoimmune Disease",doi:"10.5772/intechopen.105746",signatures:"Mohan Vanditha, Sonu Das and Mathew John",slug:"lipid-metabolism-and-associated-molecular-signaling-events-in-autoimmune-disease",totalDownloads:17,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Fatty Acids - Recent Advances",coverURL:"https://cdn.intechopen.com/books/images_new/11669.jpg",subseries:{id:"17",title:"Metabolism"}}},{id:"82483",title:"Oxidative Stress in Cardiovascular Diseases",doi:"10.5772/intechopen.105891",signatures:"Laura Mourino-Alvarez, Tamara Sastre-Oliva, Nerea Corbacho-Alonso and Maria G. 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Waisundara",profilePictureURL:"https://mts.intechopen.com/storage/users/194281/images/system/194281.jpg",biography:"Dr. Viduranga Waisundara obtained her Ph.D. in Food Science\nand Technology from the Department of Chemistry, National\nUniversity of Singapore, in 2010. She was a lecturer at Temasek Polytechnic, Singapore from July 2009 to March 2013.\nShe relocated to her motherland of Sri Lanka and spearheaded the Functional Food Product Development Project at the\nNational Institute of Fundamental Studies from April 2013 to\nOctober 2016. She was a senior lecturer on a temporary basis at the Department of\nFood Technology, Faculty of Technology, Rajarata University of Sri Lanka. She is\ncurrently Deputy Principal of the Australian College of Business and Technology –\nKandy Campus, Sri Lanka. She is also the Global Harmonization Initiative (GHI)",institutionString:"Australian College of Business & Technology",institution:{name:"Kobe College",institutionURL:null,country:{name:"Japan"}}}]},{type:"book",id:"6820",title:"Keratin",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/6820.jpg",slug:"keratin",publishedDate:"December 19th 2018",editedByType:"Edited by",bookSignature:"Miroslav Blumenberg",hash:"6def75cd4b6b5324a02b6dc0359896d0",volumeInSeries:2,fullTitle:"Keratin",editors:[{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",slug:"miroslav-blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}}]},{type:"book",id:"7978",title:"Vitamin A",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7978.jpg",slug:"vitamin-a",publishedDate:"May 15th 2019",editedByType:"Edited by",bookSignature:"Leila Queiroz Zepka, Veridiana Vera de Rosso and Eduardo Jacob-Lopes",hash:"dad04a658ab9e3d851d23705980a688b",volumeInSeries:3,fullTitle:"Vitamin A",editors:[{id:"261969",title:"Dr.",name:"Leila",middleName:null,surname:"Queiroz Zepka",slug:"leila-queiroz-zepka",fullName:"Leila Queiroz Zepka",profilePictureURL:"https://mts.intechopen.com/storage/users/261969/images/system/261969.png",biography:"Prof. Dr. Leila Queiroz Zepka is currently an associate professor in the Department of Food Technology and Science, Federal University of Santa Maria, Brazil. She has more than fifteen years of teaching and research experience. She has published more than 550 scientific publications/communications, including 15 books, 50 book chapters, 100 original research papers, 380 research communications in national and international conferences, and 12 patents. She is a member of the editorial board of five journals and acts as a reviewer for several national and international journals. Her research interests include microalgal biotechnology with an emphasis on microalgae-based products.",institutionString:"Universidade Federal de Santa Maria",institution:{name:"Universidade Federal de Santa Maria",institutionURL:null,country:{name:"Brazil"}}}]},{type:"book",id:"7953",title:"Bioluminescence",subtitle:"Analytical Applications and Basic Biology",coverURL:"https://cdn.intechopen.com/books/images_new/7953.jpg",slug:"bioluminescence-analytical-applications-and-basic-biology",publishedDate:"September 25th 2019",editedByType:"Edited by",bookSignature:"Hirobumi Suzuki",hash:"3a8efa00b71abea11bf01973dc589979",volumeInSeries:4,fullTitle:"Bioluminescence - Analytical Applications and Basic Biology",editors:[{id:"185746",title:"Dr.",name:"Hirobumi",middleName:null,surname:"Suzuki",slug:"hirobumi-suzuki",fullName:"Hirobumi Suzuki",profilePictureURL:"https://mts.intechopen.com/storage/users/185746/images/system/185746.png",biography:"Dr. Hirobumi Suzuki received his Ph.D. in 1997 from Tokyo Metropolitan University, Japan, where he studied firefly phylogeny and the evolution of mating systems. He is especially interested in the genetic differentiation pattern and speciation process that correlate to the flashing pattern and mating behavior of some fireflies in Japan. He then worked for Olympus Corporation, a Japanese manufacturer of optics and imaging products, where he was involved in the development of luminescence technology and produced a bioluminescence microscope that is currently being used for gene expression analysis in chronobiology, neurobiology, and developmental biology. Dr. Suzuki currently serves as a visiting researcher at Kogakuin University, Japan, and also a vice president of the Japan Firefly Society.",institutionString:"Kogakuin University",institution:null}]}]},openForSubmissionBooks:{paginationCount:1,paginationItems:[{id:"11478",title:"Recent Advances in the Study of Dyslexia",coverURL:"https://cdn.intechopen.com/books/images_new/11478.jpg",hash:"26764a18c6b776698823e0e1c3022d2f",secondStepPassed:!0,currentStepOfPublishingProcess:3,submissionDeadline:"June 30th 2022",isOpenForSubmission:!0,editors:[{id:"294281",title:"Prof.",name:"Jonathan",surname:"Glazzard",slug:"jonathan-glazzard",fullName:"Jonathan Glazzard"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null}]},onlineFirstChapters:{paginationCount:10,paginationItems:[{id:"82903",title:"Walking Accessibility to Primary Healthcare Services: An Inequity Factor for Olders in the Lisbon Metropolitan Area (Portugal)",doi:"10.5772/intechopen.106265",signatures:"Eduarda Marques da Costa, Ana Louro, Nuno Marques da Costa, Mariana Dias and Marcela Barata",slug:"walking-accessibility-to-primary-healthcare-services-an-inequity-factor-for-olders-in-the-lisbon-met",totalDownloads:4,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Social Aspects of Ageing - Selected Challenges, Analyses, and Solutions",coverURL:"https://cdn.intechopen.com/books/images_new/11479.jpg",subseries:{id:"90",title:"Human Development"}}},{id:"82622",title:"Contemporary Geographical Gerontology: Reconciling Space and Place in Population Ageing",doi:"10.5772/intechopen.105863",signatures:"Hamish Robertson",slug:"contemporary-geographical-gerontology-reconciling-space-and-place-in-population-ageing",totalDownloads:13,totalCrossrefCites:0,totalDimensionsCites:0,authors:[{name:"Hamish",surname:"Robertson"}],book:{title:"Social Aspects of Ageing - Selected Challenges, Analyses, and Solutions",coverURL:"https://cdn.intechopen.com/books/images_new/11479.jpg",subseries:{id:"90",title:"Human Development"}}},{id:"82610",title:"Perspective Chapter: The Role of Learning Styles in Active Learning",doi:"10.5772/intechopen.105964",signatures:"Armando Lozano-Rodríguez, Fernanda Inez García-Vázquez and José Luis García-Cué",slug:"perspective-chapter-the-role-of-learning-styles-in-active-learning",totalDownloads:11,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Active Learning - Research and Practice",coverURL:"https://cdn.intechopen.com/books/images_new/11481.jpg",subseries:{id:"89",title:"Education"}}},{id:"81909",title:"Educational Paradigm with Ubuntu Mindset: Implications for Sustainable Development Goals in Education",doi:"10.5772/intechopen.104929",signatures:"George Frempong and Raavee Kadam",slug:"educational-paradigm-with-ubuntu-mindset-implications-for-sustainable-development-goals-in-education",totalDownloads:7,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Active Learning - Research and Practice",coverURL:"https://cdn.intechopen.com/books/images_new/11481.jpg",subseries:{id:"89",title:"Education"}}},{id:"82243",title:"The Language that Grade R Students Use to Achieve the Envisaged Mathematics Outcomes, a South African Perspective",doi:"10.5772/intechopen.105446",signatures:"Shakespear M. 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Gharieb",profilePictureURL:"https://mts.intechopen.com/storage/users/225387/images/system/225387.jpg",institutionString:"Assiut University",institution:{name:"Assiut University",institutionURL:null,country:{name:"Egypt"}}}]},{id:"8",title:"Bioinspired Technology and Biomechanics",keywords:"Bioinspired Systems, Biomechanics, Assistive Technology, Rehabilitation",scope:'Bioinspired technologies take advantage of understanding the actual biological system to provide solutions to problems in several areas. Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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