IntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
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IntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
With the desire to make book publishing more relevant for the digital age and offer innovative Open Access publishing options, we are thrilled to announce the launch of our new publishing format: IntechOpen Book Series.
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Designed to cover fast-moving research fields in rapidly expanding areas, our Book Series feature a Topic structure allowing us to present the most relevant sub-disciplines. Book Series are headed by Series Editors, and a team of Topic Editors supported by international Editorial Board members. Topics are always open for submissions, with an Annual Volume published each calendar year.
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After a robust peer-review process, accepted works are published quickly, thanks to Online First, ensuring research is made available to the scientific community without delay.
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Our innovative Book Series format brings you:
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Topic Focused Publications - Each topic showcases high impact subject areas
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Renowned Editorial Expertise - Series Editors, Topic Editors, and a team of international Board Members that permanently support each Book Series
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Fast Publishing - quick turnaround which is unique for book publishing
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The benefit of ISSN and ISBN for increased citation and indexing possibilities
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IntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\n\n
IntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
We invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
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Note: Edited in October 2021
\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:"6348",leadTitle:null,fullTitle:"Advanced Electronic Circuits - Principles, Architectures and Applications on Emerging Technologies",title:"Advanced Electronic Circuits",subtitle:"Principles, Architectures and Applications on Emerging Technologies",reviewType:"peer-reviewed",abstract:"This research book volume offers an important learning opportunity with insights into a variety of emerging electronic circuit aspects, such as new materials, energy harvesting architectures, and compressive sensing technique. Advanced circuit technologies are extremely powerful and developed rapidly. They change industry. They change lives. And we know they can change the world. The exhibition on these new and exciting topics will benefit readers in related fields.",isbn:"978-1-78923-207-3",printIsbn:"978-1-78923-206-6",pdfIsbn:"978-1-83881-420-5",doi:"10.5772/intechopen.69787",price:119,priceEur:129,priceUsd:155,slug:"advanced-electronic-circuits-principles-architectures-and-applications-on-emerging-technologies",numberOfPages:194,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"c5a1bb3da69158c572f9983972ae97d0",bookSignature:"Mingbo Niu",publishedDate:"June 13th 2018",coverURL:"https://cdn.intechopen.com/books/images_new/6348.jpg",numberOfDownloads:13187,numberOfWosCitations:8,numberOfCrossrefCitations:13,numberOfCrossrefCitationsByBook:1,numberOfDimensionsCitations:18,numberOfDimensionsCitationsByBook:1,hasAltmetrics:1,numberOfTotalCitations:39,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 22nd 2017",dateEndSecondStepPublish:"June 12th 2017",dateEndThirdStepPublish:"September 8th 2017",dateEndFourthStepPublish:"December 7th 2017",dateEndFifthStepPublish:"February 5th 2018",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"141595",title:"Dr.",name:"Mingbo",middleName:null,surname:"Niu",slug:"mingbo-niu",fullName:"Mingbo Niu",profilePictureURL:"https://mts.intechopen.com/storage/users/141595/images/system/141595.jpg",biography:"Dr. Niu received a BEng in Electronic Engineering from Northwestern Polytechnical University, China, and an MSc (Eng.) (first-class) in Communication and Information Systems from the same university. Prior to his Ph.D., Dr. Niu worked at a State Key Laboratory on underwater information and signal processing. He received his Ph.D. in Electrical and Computer Engineering from the University of British Columbia, Canada. From 2008 to 2012, he was a research assistant at Optical Wireless Communications Laboratory and Integrated Optics Laboratory where he contributed to the development of ultra-high-speed optical data transmission links. Dr. Niu held a postdoctoral fellowship at Queen’s University, Canada for two years. He also worked for Public Works at Calian Tech. Ltd., where he contributed to highly efficient statistical evaluation models of MIMO compressive sensing projects. Dr. Niu has co-authored more than thirty Institute of Electrical and Electronics Engineers (IEEE) and Optical Society of America (OSA) papers and supervised numerous student projects. Currently, he serves as a Lead Guest Editor for Wireless Communications and Mobile Computing and an Academic Editor for IntechOpen. He is a member of the Internet of Things (IoT) Committee at the China Institute of Communications (CIC). Dr. Niu received numerous scholarships during his undergraduate and graduate studies, including a Chinese Government Award, two University of British Columbia University Graduate Fellowships (UGFs), and a Huawei Tech. Ltd. Special Fellowship. His current research and teaching interests include the Internet of Vehicles (IoV), vehicle-to-road (V2R) infrastructure, cooperative microgrids, massive multiple input, multiple outputs (MIMO), image signal processing, low-carbon smart cities, energy harvesting, and electronic circuit theory. Dr. Niu is a licensed professional engineer in British Columbia.",institutionString:"Chang'an University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Chang'an University",institutionURL:null,country:{name:"China"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"739",title:"Electronic Circuits",slug:"electrical-and-electronic-engineering-electronic-circuits"}],chapters:[{id:"58662",title:"Self-Oscillatory DC-DC Converter Circuits for Energy Harvesting in Extreme Environments",doi:"10.5772/intechopen.72718",slug:"self-oscillatory-dc-dc-converter-circuits-for-energy-harvesting-in-extreme-environments",totalDownloads:1235,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"A novel self-starting converter circuit technology is described for energy harvesting and powering wireless sensor nodes, constructed from silicon carbide devices and proprietary high temperature passives for deployment in hostile environments. After a brief review of the advantages using Silicon Carbide (SiC) over other semiconductors in extreme environments, the chapter will describe the advantages and principles when designing circuitry and architectures using SiC for power electronics. The practical results from a novel self-starting DC-DC converter are reported, which is designed to supply power to a WSN for deployment in high temperature environments. The converter operates in the boundary between continuous and discontinuous mode of operation and has a Voltage Conversion Ratio (VCR) of 3 at 300°C. This topology is able to self-start and so requires no external control circuitry, making it ideal for energy harvesting applications, where the energy supply may be intermittent. Experimental results for the self-starting converter operating from room temperature up to 300°C are presented. The converter output voltage, switching frequency, total power loss and efficiency were presented at temperatures up to 300°C.",signatures:"Ming-Hung Weng, Daniel Brennan, Nick Wright and Alton Horsfall",downloadPdfUrl:"/chapter/pdf-download/58662",previewPdfUrl:"/chapter/pdf-preview/58662",authors:[{id:"175070",title:"Dr.",name:"Ming-Hung",surname:"Weng",slug:"ming-hung-weng",fullName:"Ming-Hung Weng"},{id:"215269",title:"Prof.",name:"Nick",surname:"Wright",slug:"nick-wright",fullName:"Nick Wright"},{id:"215271",title:"Dr.",name:"Alton",surname:"Horsfall",slug:"alton-horsfall",fullName:"Alton Horsfall"},{id:"222660",title:"Dr.",name:"Daniel",surname:"Brennan",slug:"daniel-brennan",fullName:"Daniel Brennan"}],corrections:null},{id:"58795",title:"New Energy Harvesting Systems Based on New Materials",doi:"10.5772/intechopen.72613",slug:"new-energy-harvesting-systems-based-on-new-materials",totalDownloads:1140,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"This study starts with the ZnO nanostructured materials used for improve the efficiency of polycrystalline solar cells operation under low solar radiation conditions. The ZnO nanowires were prepared using the hydrothermal method of deposition on the seed layer by a new and complex process, with controllable morphological and optical properties. The analysis of the XRD patterns, scanning electron microscopy images (SEM) of the ZnO nanowires and a lot of tests made Pasan Meyer Burger HighLight 3 solar simulator, confirm the advantages of using the ZnO nanowires in solar cells applications for antireflection coatings. Then, piezoelectric structures based on new modified PZT zirconate titanate designed for energy harvesting applications is presented. Based on their piezoelectric characteristics, modified PZT zirconate titanate ceramics made of Pb(Zr0.53Ti0.47)0.99Nb0.01O3 ceramic have efficient applications in energy harvesting devices. A piezoelectric transducer, consisting of a thin plate of this piezoceramic material, with dimensions (34 mm × 14 mm × 1 mm), is illustrated. A multiphysics numerical simulation further illustrates such piezoelectric transducer operation. Finally, the miniature planar transformer with circular spiral winding and hybrid core—ferrite and magnetic nanofluid, designed for new energy harvesting systems is presented. We purpose now that the magnetic nanofluid be used both as a coolant and as part of the hybrid magnetic core.",signatures:"Lucian Pîslaru-Dănescu and Lipan Laurențiu Constantin",downloadPdfUrl:"/chapter/pdf-download/58795",previewPdfUrl:"/chapter/pdf-preview/58795",authors:[{id:"187612",title:"Dr.",name:"Lucian",surname:"Pîslaru-Dănescu",slug:"lucian-pislaru-danescu",fullName:"Lucian Pîslaru-Dănescu"},{id:"196151",title:"Dr.",name:"Laurentiu Constantin",surname:"Lipan",slug:"laurentiu-constantin-lipan",fullName:"Laurentiu Constantin Lipan"}],corrections:null},{id:"58619",title:"Nanoarchitecture of Quantum-Dot Cellular Automata (QCA) Using Small Area for Digital Circuits",doi:"10.5772/intechopen.72691",slug:"nanoarchitecture-of-quantum-dot-cellular-automata-qca-using-small-area-for-digital-circuits",totalDownloads:1655,totalCrossrefCites:7,totalDimensionsCites:11,hasAltmetrics:1,abstract:"Novel digital technologies always lead to high density and very low power consumption. One of these concepts—quantum-dot cellular automata (QCA), which is one of the new emerging nanotechnologies, is based on Coulomb repulsion. This chapter presents a novel design of 2-input Exclusive-NOR (XNOR)/Exclusive-OR (XOR) gates with 3-input Exclusive-NOR (XNOR) gates which are composed of 10 cells on 0.006 μm2 of area. A novel architecture of 3-input Exclusive-OR (XOR) gate is defined by 12 cells on 0.008 μm2 of area. The proposed design of 2-input XOR/XNOR gate structures provide less area and low complexity than the best reported design. The simulation results of proposed designs have been achieved using QCA Designer tool version 2.0.3.",signatures:"Radhouane Laajimi",downloadPdfUrl:"/chapter/pdf-download/58619",previewPdfUrl:"/chapter/pdf-preview/58619",authors:[{id:"218855",title:"Dr.",name:"Radhouane",surname:"Laajimi",slug:"radhouane-laajimi",fullName:"Radhouane Laajimi"}],corrections:null},{id:"58442",title:"Millimeter-Wave Multi-Port Front-End Receivers: Design Considerations and Implementation",doi:"10.5772/intechopen.72715",slug:"millimeter-wave-multi-port-front-end-receivers-design-considerations-and-implementation",totalDownloads:1547,totalCrossrefCites:3,totalDimensionsCites:4,hasAltmetrics:0,abstract:"This chapter covers recent achievements on the integrated 60 GHz millimeter-wave front-end receiver based on the multi-port (six-port) concept. For this purpose, the design procedure of a fully integrated 60 GHz multi-port (six-port) front-end receiver implemented on a thin ceramic substrate (εr = 9.9, h = 127 μm) using an miniature hybrid microwave integrated circuit (MHMIC) fabrication process is presented in detail. All components constituting the proposed front-end receiver including an 8 × 2 antenna array, a low-noise amplifier (LNA), a six-port circuit, and the RF power detectors are presented and characterized separately before they are integrated into the final front-end receiver prototype. The performance of the latter has been experimentally evaluated in terms of various M-PSK/M-QAM demodulations. The obtained demodulation results are very satisfactory (the constellation points for all considered M-PSK/M-QAM schemes are very close to the ideal locations), demonstrating and confirming the high ability of the proposed 60 GHz millimeter-wave six-port front-end receiver to operate as a high-performance quadrature demodulator, without any calibration, for modulation schemes up to 32 symbols.",signatures:"Chaouki Hannachi and Serioja Ovidiu Tatu",downloadPdfUrl:"/chapter/pdf-download/58442",previewPdfUrl:"/chapter/pdf-preview/58442",authors:[{id:"34160",title:"Prof.",name:"Serioja O.",surname:"Tatu",slug:"serioja-o.-tatu",fullName:"Serioja O. Tatu"},{id:"212045",title:"Ph.D.",name:"Chaouki",surname:"Hannachi",slug:"chaouki-hannachi",fullName:"Chaouki Hannachi"}],corrections:null},{id:"59972",title:"Applications of Compressive Sampling Technique to Radar and Localization",doi:"10.5772/intechopen.75072",slug:"applications-of-compressive-sampling-technique-to-radar-and-localization",totalDownloads:1089,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"During the last decade, the emerging technique of compressive sampling (CS) has become a popular subject in signal processing and sensor systems. In particular, CS breaks through the limits imposed by the Nyquist sampling theory and is able to substantially reduce the huge amount of data generated by different sources. The technique of CS has been successfully applied in signal acquisition, image compression, and data reduction. Although the theory of CS has been investigated for some radar and localization problems, several important questions have not been answered yet. For example, the performance of CS radar in a cluttered environment has not been comprehensively studied. Applying CS to passive radars and electronic warfare receivers is another concern that needs more attention. Also, it is well known that applying this strategy leads to extra computational costs which might be prohibitive in large-sized localization networks. In this chapter, we first discuss the practical issues in the process of implementing CS radars and localization systems. Then, we present some promising and efficient solutions to overcome the arising problems.",signatures:"Soheil Salari, Francois Chan and Yiu-Tong Chan",downloadPdfUrl:"/chapter/pdf-download/59972",previewPdfUrl:"/chapter/pdf-preview/59972",authors:[{id:"214787",title:"Dr.",name:"Francois",surname:"Chan",slug:"francois-chan",fullName:"Francois Chan"},{id:"214788",title:"Dr.",name:"Soheil",surname:"Salari",slug:"soheil-salari",fullName:"Soheil Salari"},{id:"214789",title:"Dr.",name:"Yiu-Tong",surname:"Chan",slug:"yiu-tong-chan",fullName:"Yiu-Tong Chan"}],corrections:null},{id:"60655",title:"High-Speed Electronic Memories and Memory Subsystems",doi:"10.5772/intechopen.76257",slug:"high-speed-electronic-memories-and-memory-subsystems",totalDownloads:885,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Memories have played a vital role in embedded system architectures over the years. A need for high-speed memory to be embedded with state-of-the-art embedded system to improve its performance is essential. This chapter focuses on the development of high-speed memories. The traditional static random access memory (SRAM) is first analyzed with its different variant in terms of static noise margin (SNM); these cells occupy a larger area as compared to dynamic random access memory (DRAM) cell, and hence, a comprehensive analysis of DRAM cell is then carried out in terms of power consumption, read and write access time, and retention time. A faster new design of P-3T1D DRAM cell is proposed which has about 50% faster reading time as compared to the traditional three-transistor DRAM cell. A complete layout of the structure is drawn along with its implementation in a practical 16-bit memory subsystem.",signatures:"Prateek Asthana and Loveneet Mishra",downloadPdfUrl:"/chapter/pdf-download/60655",previewPdfUrl:"/chapter/pdf-preview/60655",authors:[{id:"218477",title:"Mr.",name:"Prateek",surname:"Asthana",slug:"prateek-asthana",fullName:"Prateek Asthana"},{id:"221356",title:"Mr.",name:"Loveneet",surname:"Mishra",slug:"loveneet-mishra",fullName:"Loveneet Mishra"}],corrections:null},{id:"58744",title:"High Voltage Energy Harvesters",doi:"10.5772/intechopen.72959",slug:"high-voltage-energy-harvesters",totalDownloads:4677,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Green energy helps in reducing carbon emission from fossil fuel, harvesting energy from natural resources like wind to power consumer appliances. To date, many researches have been focusing on designing circuits that harvest energy from electromagnetic signals wirelessly. While it could be designed to be efficient, the generated power however is insufficient to drive large loads. Wind energy is highly available environmentally but development of small-scale energy harvesting apparatus aiming to extract significant power from miniature brushless fan has received limited attention. The aim of this chapter is to give audience an insight of different voltage multipliers used in energy harvester and knowledge on various circuit techniques to configure voltage multipliers for use in different high voltage applications. These include AC-DC converter, AC-AC converter and variable AC-DC converter.",signatures:"Xi Sung Loo, Kiat Seng Yeo, Joel Yang, Chee Huei Lee, Rong Zhao\nand Moe Z. Win",downloadPdfUrl:"/chapter/pdf-download/58744",previewPdfUrl:"/chapter/pdf-preview/58744",authors:[{id:"189098",title:"Dr.",name:"Xi Sung",surname:"Loo",slug:"xi-sung-loo",fullName:"Xi Sung Loo"},{id:"189214",title:"Prof.",name:"Kiat Seng",surname:"Yeo",slug:"kiat-seng-yeo",fullName:"Kiat Seng Yeo"},{id:"215816",title:"Prof.",name:"Joel",surname:"Yang",slug:"joel-yang",fullName:"Joel Yang"},{id:"215817",title:"Dr.",name:"Chee Huei",surname:"Lee",slug:"chee-huei-lee",fullName:"Chee Huei Lee"},{id:"215818",title:"Prof.",name:"Moe Z.",surname:"Win",slug:"moe-z.-win",fullName:"Moe Z. Win"},{id:"221473",title:"Prof.",name:"Rong",surname:"Zhao",slug:"rong-zhao",fullName:"Rong Zhao"}],corrections:null},{id:"60585",title:"Experimental Studies of the Electrical Nonlinear Bimodal Transmission Line",doi:"10.5772/intechopen.76204",slug:"experimental-studies-of-the-electrical-nonlinear-bimodal-transmission-line",totalDownloads:959,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"After a few years of calm, the investigations on the dynamic, especially nonlinear, systems returned to the front of the research in non-linear physics. We propose, in this chapter, a study of an electrical nonlinear transmission line, realized in a previous work, to use the latter to highlight certain properties (modulation instability—MI, Fermi-Pasta-Ulam (FPU) recurrence, fragmentation of solitons in wave trains, multiplication(increase) and division of frequencies, etc.), which are observed in several domains in applied physics: hydraulic, artificial neuronal, network physical appearance (physics) of the plasma, and the circulation.",signatures:"Abdou Karim Farota, Mouhamadou Mansour Faye, Bouya Diop,\nDiène Ndiaye and Mary Teuw Niane",downloadPdfUrl:"/chapter/pdf-download/60585",previewPdfUrl:"/chapter/pdf-preview/60585",authors:[{id:"107261",title:"Dr.",name:"Diene",surname:"Ndiaye",slug:"diene-ndiaye",fullName:"Diene Ndiaye"},{id:"214425",title:"Dr.",name:"Abdou Karim",surname:"Farota",slug:"abdou-karim-farota",fullName:"Abdou Karim Farota"},{id:"214426",title:"Prof.",name:"Bouya",surname:"Diop",slug:"bouya-diop",fullName:"Bouya Diop"},{id:"214427",title:"Prof.",name:"Mouhamadou Mansour",surname:"Faye",slug:"mouhamadou-mansour-faye",fullName:"Mouhamadou Mansour Faye"},{id:"214429",title:"Prof.",name:"Mary Teuw",surname:"Niane",slug:"mary-teuw-niane",fullName:"Mary Teuw Niane"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:[{id:"65",label:"highly cited contributor"}]},relatedBooks:[{type:"book",id:"3576",title:"Solid State Circuits Technologies",subtitle:null,isOpenForSubmission:!1,hash:"a14e0865ac126e0234df9b53a5943ebf",slug:"solid-state-circuits-technologies",bookSignature:"Jacobus W. 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1. Introduction
Research about speech sound disorders in children with Down syndrome (DS) and children with specific language impairment (SLI) suggests similar linguistic profiles with weakness in phonology skills [1, 2]. The question is if these similarities are superficial or share deficits in different levels and underlying skills to their speech disorders.
The widespread problem of language disorder in children with SLI, according to some researchers, can be explained by the deficit in speech processing skills (perceptual skills, phonological memory, praxis, or motor programming), which hinders the phonological development, vocabulary learning, morphosyntactic processing, and production of words [3, 4]. It is the main handicap that may be interfering with articulatory accuracy, revealing a slowing of the typical development which can also present different evolutionary patterns with idiosyncratic itineraries. Van der Lely [5] proposed that the phonological deficit could attend with other grammatical alterations (morphological, syntactic), although it does not mean a causal relationship between them, although they can act in a reciprocal manner. Other studies suggest that not all children with SLI presented a phonological deficit [6, 7].
On the other hand, various factors that affect speech and development of the language of people with DS have been described in the literature. In addition to cognitive deficit that is the main factor, hearing loss, anatomical alterations, and failures in speech processing, among others, have an effect on speech. However, it is difficult to determine the influence needed for each factor, also can vary from one person to another, but it is known that difficulties in speech are not highly correlated with language or cognition, which may indicate that these problems are rooted in other factors [8]. The speech of children with DS often presents inconsistent errors, both developmental and devious, which reduce the intelligibility producing negative effects on social and labor activities [9–13]. This issue requires several levels of analysis.
1.1. Phonological short‐term memory
The first level of study is focused toward phonological short‐term memory (PM) which plays a crucial role in the segmentation of speech and further construction of accurate phonological representations, which will have implications in speech and in development of other areas of language (e.g. vocabulary) in the acquisition of phonological awareness and literacy development [14–16].
Traditionally, this cognitive function has been evaluated with pseudo-words repetition tasks (PWR), but it is debated whether it is really a unique and reliable measure of phonological memory [6], since, in addition, its execution is influenced by input processing, phonological awareness, and vocabulary skills and motor programs [17]. Recent research notes that high variability in speech errors is associated with low scores in RPW tasks [18].
Numerous studies conclude that children with SLI have low performance in these tasks in comparison with children of same chronological age and, in some cases, their own linguistic age, which is interpreted as a deficit in PM that seems to persist through time [16, 19–21]. This limitation may influence the quantity and / or quality of stored phonological information which, in turn, can affect language development [16, 22].
Although difficulties may be due to linguistic skills usually reduced, poor performance can be found even in children with SLI that have reached levels close to normal language, suggesting it is a good phenotypic marker of SLI in ages ranging from 4 to 6 years [23–26]. The reason for this age range is because in preschool the PM seems to be more related to oral language ability. However, after 6 years of age, this ability can be more tied to development of literacy [27, 28].
The evidence shows that alterations in memory have their greatest effect with longer stimulus [29–31]. In addition, some studies have found that phonological complexity and lexical and sublexical phonological awareness mediate on accuracy of repetitions [17, 20, 32].
An alternative explanation for the low yields obtained in PWR tasks is that children with SLI have a deficit in phonological processing beyond a specific limitation in memory, at the stage of coding, storage, or retrieval of phonological stimuli [3, 15, 33]. Consequently, representation of any pseudo‐word will be low in quality, increasing difficulties with the length of stimuli.
On the other hand, research has exposed that the deficit in the PM is also one of the characteristics of people with DS and therefore presented a speech widely variable [34–36]. The findings of some studies suggest that it is a specific deficit for verbal information, since they do not seem to show poor performance on tests of visuospatial short‐term memory, and is not caused, mainly, by hearing loss or speech production difficulties [37–40].
Effect of stimulus length has also been found in the population with DS, even when performance is compared to children matched in linguistic age [34, 37]. Other works have not observed this effect so clearly [36, 38], but we have to think about some limitations that could be masking the results: in one of the studies, only stimuli of one or two syllables were compared, and on the other, the control group was of preschool age, where it is common to find it hard to repeat pseudo‐words of four syllables. Moreover, some researchers have found that the lexical effects influence either the population with DS and preschoolers with typical development, both of them benefiting from the linguistic knowledge. The difference is that people with DS need to lean more on the lexical knowledge, even though it does not mean that they benefit to a greater extent than the control group.
1.2. Coordination motor skills
Oral motor skills are another factor that could explain the articulatory accuracy difficulties, since there are evidences which connect neuromotor maturation with phonological development [41]. One way that has been suggested to evaluate it is oral‐diadochokinetic rates tasks (oral‐DDK) that measure the speed with which a subject is capable of producing, repeatedly and with precision, sequences of nonsense syllables that are alternating movements of articulators’ different organs [42–44].
The majority of studies on motor coordination skills assessed through oral‐DDK tasks have included children with verbal dyspraxia. In this condition, it is characteristic to find a deficit which is reflected in low yields obtained in oral‐DDK (reduced rates, sequencing and precision errors), both compared to children with typical development [45, 46] and children with speech and language disorders [45]. Other works have studied children with phonological and articulation disorders, noting difficulties in sequencing and accuracy of sounds during the repetition of syllabic scripts (e.g. replacement of /k/ for /t/), but not in fluency and intelligibility [41, 47, 48]. Finally, a study compared children with SLI with two control groups matched for chronological age and linguistic age, wherein the SLI group always showed poor performance, suggesting it is a marker of SLI in combination with others [49].
Most of the researches, conducted on people with DS, have found decreased rates with higher accuracy errors, less consistency of production, and a greater number of attempts when oral‐DDK tasks are carried out [50–52]. McCann and Wrench [53] observed similar rates to those obtained in children with typical development, although productions were more inaccurate. This suggests that motor speech disorder is not only the difficulty in execution (dysarthria) but also the difficulty in planning or programming of spatial‐temporal parameters in sequences of movement (dyspraxia).
1.3. Articulatory muscular system
The effector organs of articulation are dependent on muscle function. Research on aspects such as endurance and strength of lips and tongue in children with speech disorders is scarce, mainly, due to doubts about reliability of measurements of performance in children and lack of comparative data in children with typical development.
Potter and Short [54] examined tongue strength in 150 children and teenagers (between 3 and 16 years of age), no history of speech disorders, using Iowa Oral Pressure Instrument (IOPI). In their study they concluded that strength of tongue increases with age and in men is slightly higher than women between 14 and 16 years of age.
Some works do not find alterations in children with phonological disorder, but find them in children with verbal dyspraxia, concluding that oral performance variable is a differentiator between these two groups [55, 56]. There is no reason to think about a possible disturbance in this level of speech production in children with SLI, as it is suggested by the data provided in this research.
Differences in anatomy and physiology of the organs of people with DS (hypotonia, reduced range of motion, etc.) are well‐known factors that could be the basis of reduced intelligibility [52, 57–59]. However, there are no clear conclusions about its impact on the specific speech difficulties [8]. Thus, some authors point out that these differences do not explain the entire speech disorder [2, 60].
In our research, we have analyzed variables related to articulatory accuracy in the population with SLI and DS: phonological processing skills, motor coordination, and physiological variables, using Medical IOPI device that allows obtaining objective measures of the strength and endurance of lips and tongue.
2. Method
2.1. Participants
The sample was 24 participants divided into four groups: (1) six children with SLI matched in chronological age (p = 0.29) and nonverbal intelligence with a control group of six children with typical development (Typical Development Control 2); (2), six teenagers with DS matched in chronological age with another control group of six subjects with typical development (Typical Development Control 1) (p = 0.87). This decision was taken because the research includes the variables of physiological and motor coordination which are related to age-dependent maduration factors.
SLI group presented a severity level of mild‐moderate speech disorder (percentage of consonants correct, PCC = 0.80) and DS group moderate‐severe (PCC = 0.65), compared to their controls groups that reached the highest articulatory accuracy. Groups’ characteristics are described in Table 1.
N
Age (months)
Age range (months)
IQ nonverbal
PCC
M
SD
Min.
Max.
M
SD
M
SD
S.SD1
SLI
6
72
13.06
52
86
109.17
12.64
0.80
0.13
MM
CG 2
6
68
4.85
60
73
103.67
13.79
1
0.01
–
DS
6
173
23.2
135
208
65
0
0.65
0.20
MS
CG 1
6
170.33
13.64
148
184
102.17
13.18
1
0
–
Table 1.
Participants.
Notes: M, media; SD, standard deviation; PCC, percentage of consonants correct; S.SD, severity speech disorder; MM, mild‐moderate; MS, moderate‐severe. 1Shriberg y Kwiatkowski, 1982.
It was established as common inclusion criteria that participants had Spanish as their first language and they used it at school. The control groups were matched in chronological age to two study groups and they had no history of language disorders or learning disabilities.
2.2. Instruments
To assess PM, a PWR task was used [61]. It consists of repeating two lists of 40 pseudo‐words with high‐frequency and low‐frequency syllables. Each list contained four groups of 10 pseudo‐words for two, three, four, and five syllables. Each pseudo‐word was equal to another pseudo‐word of the other list in number of syllables, syllabic structure, accentuation pattern, and order in which syllables were placed with their different structures.
Oral‐DDK: they were used to assess oral motor coordination skills. They consist of issuing a number of nonsense syllables involving opposing movement patterns, accurately and quickly. One syllable ([pa], [ta], and [ka]), two syllables ([pata], [paka], [taka]), and three syllables ([pataka]) were used.
Oral performance measurement: Medical IOPI device (Iowa Oral Performance Instrument, model 2.3) has been used to objectively measure lingual and labial resistance (maximum pressure in kilopascals, kPa, time in seconds that a pressure equal to 50% of the maximum force can be sustained).
Pronunciation task: Stimuli AF125 composed by a set of 125 images designed to induce a representative sample of the Spanish phonological system and to find the percentage of correct consonants (PCC) was used with the Ánfora software [62–64]. It contains a comprehensive repertoire of syllabic types in Spanish, repeated at least four times, with words of syllabic structures common in the language. All language phonemes appear at least three times in each position and in the most common phonetic environments.
Raven’s progressive matrices test [65]: It is a nonverbal intelligence test applied to control this variable and perform the pairing of SLI group.
3. Procedures
All participants in this study have been subjected to all assessment protocol that was applied in three sessions of 30 minutes: in the first, PWR together with oral‐DDK were applied; in the second session, AF125 pronunciation task and oral performance was measured with Medical IOPI device. Finally, Raven’s progressive Matrices test was applied to obtain IQ.
The application of PWR task was divided into two parts because it is a fairly long task and requires sustained attention. Each pseudo‐word was read by the evaluator twice, slowly, clearly, and respecting accentuation. The instruction given to child was: I’ll say a few words that mean nothing. You should pay attention because you will have to repeat them as I do. The scoring method used was that of whole words, that is, each repeated item is evaluated as a whole and noted down as right or wrong production compared to the target, regardless of the number of phonological errors and without penalizing accentuation and/or articulation errors. Productions were recorded to listen to them carefully and to record the percentage of successful responses.
The measurement of oral‐DDK consisted of two phases: First of all came the training, where the examiner showed a child how to do repetitions and they practiced together. After that, the child was able to produce oral‐diadochokinetic without help, with precision and speed, until the evaluator indicated him to stop. The order given was: Quiero que digas unos sonidos lo más rápido posible. Primero lo haré yo y luego tú. El primer sonido es… (I want you to say sounds as soon as possible. I will do it first, then you. The first sound is…). If the Item was annulled after several attempts or stopped before the indication of the evaluator, is because the child could not make changes of articulation place. The Time‐by‐count method has been used to record the time each subject takes to produce 20 repetitions for each isolated syllable ([pa], [ta], and [ka]), 15 for two syllables([pata], [paká], [taka]), and finally, 10 repetitions for three syllables ([pataka]). The productions were recorded to count the number of syllables and to write down exact time using a wave’s editor.
Oral performance measurement also had a first phase of training in which a child was acquainted with Medical IOPI device. Once the participants were prepared and their maximum tongue strengths were measured, in kPa, by placing the balloon on the top of the tongue and pressing it against the palate with the greatest possible strength for approximately 2 seconds. Then, orbicular muscle strength was measured by positioning the balloon of IOPI device on front side of the mouth, between teeth and lips, to exercise force. Finally, tongue endurance was assessed by quantifying time in seconds that each participant was able to maintain a pressure equal to 50% of its maximum value in tongue strength, placing the balloon in the same position as in the first measurement. Three measures of each valued appearance were taken at periods between 30 and 60 seconds, and the maximum value obtained was recorded. If a decreasing trend was observed in the values obtained in these three measurements, the rest time had to be increased.
AF125 pronunciation test was administered in a single application. This task is to present images under the general order: Dime qué es esto o cómo se llama (Tell me what this is or what this is called). If the child did not respond, the examiner had to tell him the right word and he would ask him later. The evaluation continued during two more items, and then the examiner was retreated to retrieve the words that the child had not acted upon. If this was not possible after the third attempt, this word had to be ruled out of the sample. Productions were transcribed to software Ánfora. If the pronunciation was distorted, but it was intelligible, it was noted. If the pronunciation was distorted, but it was intelligible, it was noted. If the pronunciation was unintelligible, the option “nonparsable” appeared marked in the program. From the analysis of speaking sample, software Ánfora calculated the percentage of consonants correct (PCC = consonants pronounced error‐free/total sample consonants). It means consonant pronounced error‐free and in correct position. Values are included between 1, perfect pronunciation, and 0.
Finally, Raven test‐scale Color was applied (series A, Ab, and B) to children from 4 to 10 years of age. General scaling, series A, B, C, D, and E, was applied to older participants.
4. Results
Two objectives of comparison have been raised: SLI group and CG2 (younger); SD group and CG1 (older). To find the differences between the study groups, the data was analyzed through Mann‐Whitney U contrasts for independent samples. In addition, the range test with Wilcoxon sign for related samples was applied to check differences in the PWR task between high‐frequency and low‐frequency syllables.
Homoscedasticity condition is met in most of the variables examined in the study group SLI‐CG2 (tongue strength, p = 0.31; lips strength, p = 0.76; tongue endurance, p = 0.94; repetitions [ta], p = 0.08; repetitions [ka], p = 0.14; repetitions [taka], p = 0.28; repetitions [pataka], p = 0.18; PWR with high‐frequency syllables, p = 0.11), and also in the DS‐CG1 group (IQ, p = 0.84; tongue strength, p = 0.62; lips strength, p = 0.82; tongue endurance, p = 0.68; repetitions [pa], p = 0.89; repetitions [ta], p = 0.28; repetitions [ka], p = 0.47; repetitions [pata], p = 0.94; repetitions [paka], p = 0.70; repetitions [taka], p = 0.69; repetitions [pataka], p = 0.85; PWR with high‐frequency syllables, p = 0.45; PWR with low‐frequency syllables, p = 0.22).
In objective 1, averages obtained in oral performance variable are similar between the two groups, except in the force of tongue where scores are more distant (Figure 1). Results of the comparative analysis (Table 2) prove absence of significant differences in the measures taken. This indicates that participants with SLI do not present alterations in peripheral component of speech, at least in the three studied variables.
Figure 1.
Mean scores in physiological variables (SLI group vs control).
In oral motor coordination variable, SLI children tend to spend more time in oral‐DDK, both repeat isolated syllables as in two and three syllables (Table 2 and Figure 2). Results also reflect a progressive times increase as repetitions require more number of opposing movements of lips and tongue in both groups. Statistical analysis (Table 2) shows that differences are not significant; therefore, pronunciation errors cannot be explained by an affectation of general motor coordination.
Figure 2.
Mean scores in oral‐diadochokinetic tasks (SLI group vs control).
Finally, you can see in Figure 3 that the averages obtained by the SLI group in RPW task are lower than that of the control group. The contrast of hypotheses (Table 2) shows significant differences (p = 0.02) with a large effect size (r = 0.83) in both lists: They obtained lower percentages in low‐frequency syllables. This significant difference is more pronounced in SLI group (p = 0.02; r = 0.63). The data suggest that deficits in PM justify an important part of pronunciation errors.
Figure 3.
Percentage of hits in repetition pseudo‐words task (PWR) (SLI group vs control).
During the Objective 2, the SD group reached an average score in oral performances lower than the control scores of the control group (Figure 4). Mann‐Whitney U (Table 3) confirms that the differences are statistically significant in tongue (p = 0.00) and lips (p = 0.04) strenght.
Figure 4.
Mean scores in physiological variables (Down syndrome group vs control).
Average times obtained by the SD group were greater than the CG1 in oral‐DDK tasks (Figure 5); statistical analysis (Table 3) shows significant differences in all repetitions with elevated effect sizes. This suggests that motor maturation is not expected for age and can be a variable that is interfering in the articulatory accuracy.
Figure 5.
Mean scores in oral‐diadochokinetic tasks (Down syndrome group vs control).
Finally, success rate of the SD group in PWR does not exceed 50% compared to almost 100% of the CG1 group (Figure 6): Comparative analysis (Table 3) tested hypothesis of difference in our list of high‐frequency and low‐frequency syllables with large effect sizes. Wilcoxon test was applied in the same way as it was applied in the objective 1, obtaining significant differences (p = 0.04) with high effect size (r = 0.58). Success rate in PWR with high‐frequency syllables was significantly higher than with the low‐frequency syllables list. Therefore, deficits in PM constitute another factor that interferes with correct pronunciation in the SD group.
Figure 6.
Percentage of hits in repetition pseudo‐words task (RPW) (Down syndrome group vs control).
5. Discussion
Results confirm the existence of phonological short‐term memory deficit with effects of use frequency, according to investigations conducted with SLI children [16, 19–21] and DS [34–38]. These results indicate that PM test cannot be used as a single measure of phonological short‐term memory, because other levels of speech processing and phonological awareness are also involved; therefore, it is difficult to determine direct influences on articulatory accuracy as well as design tasks that only evaluate phonological memory.
The groups studied are not affected alike in PM test. This raises the question of whether it is a matter of severity, because it seems that poor performance has repercussions on the level of speech disorder severity or, on the contrary, there are qualitative differences in speech processing or even other variables that influence phonological memory, for example vocabulary size [66], perhaps because it influences the constant improvement of phonological categories. It would also be interesting to examine whether the limitation in the phonological memory occurs due to issues of quantity and/or quality of the information stored.
Literature found about neuromotor maturation in SLI population is sparse. We have recorded oral‐diadochokinetic rates similar to typical development group, although with difficulties in sequencing and precision of sounds, as other works of articulation or phonological disorders [41, 47]. Buiza et al. [49] proposed that low yields obtained in this variable is a good marker of SLI, but our results do not allow to support this idea because although SLI children execution is qualitatively different from typical development children, oral‐diadochokinetic rates are similar. However, measures and analyses used in our study are different, therefore comparisons should be made with caution. More conclusive data have been obtained in children with verbal dyspraxia in which motor coordination skills deficit is primary [45, 46].
Publications aimed at this level in people with Down syndrome come to similar results: reduced oral‐diadochokinetic rates with more attempts, less consistency of production, and more sequencing errors [50–52]. Data confirm that there is a difficulty in motor programming and sequencing of speech which could explain low scores in articulatory precision. This agrees with the contribution by Wertzner et al. [41] that found an interrelation between neuromotor maturation and phonological development.
Finally, we have not found involvement of peripheral variable in participants with SLI, which confirms an aspect apparently clear among professionals, but that does not translate into clinical practice. Intervention programs, that have an impact on this level of production, are still applied [67] when there is insufficient evidence supporting its use to produce effects in speech [68].
Data concerning population with DS are consistent with previous research [52, 57–59]: there are significant differences in values of oral performance, but it is discussed to what extent it interferes in articulatory accuracy [8]. Studies with different experimental designs and other kind of analysis are required to determine this with certainty.
In summary, in children with SLI, deficits in phonological short‐term memory could explain many of the articulatory accuracy errors, since significant differences were not found in other analyzed variables. It would be necessary to clarify the type of specific difficulties in speech processing to design specific psycholinguistic intervention programs for each child. That is the only variable that differs with respect to control group, it suggests that PWR task could be a useful language disorders screening measure, as proposed by other authors [69]. In subjects with DS, articulatory difficulties are not explained by a specific involvement, since there have been significant differences in all analyzed variables. As a result, intervention programs that are designed should address all levels, not only linguistic but also physiological and motor coordination. However, in both populations it is difficult to determine how much each variable affects the pronunciation.
6. Conclusions
While in children with DS seems that the phonological memory, the motor coordination, and the physiological variables could be factors associated with articulatory difficulties, in children with SLI would be involved the first of them. We cannot ensure that variables behave the same way in children with severe speech disorders, since our participants ranged between mild and moderate levels.
These findings have implications in clinical practice. In children with SLI, nonspeech oral motor treatments are not justified to improve speech disorder, because it is clear that there is no involvement at this level, and scientific evidence does not support its use as standard treatment. We suggest the need to clearly evaluate where the difficulties are in the speech processing level and to design programs that affect specific deficits of each person.
On the other hand, children with DS seem to need a broader treatment, that is, treatment not only for speech deficits but also for oral motor and coordination skills. But, before intervening in this last aspect, clinicians must determine if physiological deficiencies are sufficient to interfere with speech and find scientific support for programs that work in this level, so it is recommended to follow principles of practice based on evidence.
Intervention programs cannot be designed depending on the severity of the articulatory disorder because the same symptoms may be due to alterations in different levels of processing. It is necessary to further research in these two population groups to define processes, mechanisms, and skills underlying speech disorders.
\n',keywords:"speech sound disorder, specific language impairment, Down syndrome, oral‐diadochokinetic, tongue strength",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/56331.pdf",chapterXML:"https://mts.intechopen.com/source/xml/56331.xml",downloadPdfUrl:"/chapter/pdf-download/56331",previewPdfUrl:"/chapter/pdf-preview/56331",totalDownloads:1532,totalViews:508,totalCrossrefCites:1,totalDimensionsCites:2,totalAltmetricsMentions:0,introChapter:null,impactScore:2,impactScorePercentile:77,impactScoreQuartile:4,hasAltmetrics:0,dateSubmitted:"December 21st 2016",dateReviewed:"May 30th 2017",datePrePublished:null,datePublished:"September 13th 2017",dateFinished:"July 5th 2017",readingETA:"0",abstract:"Research about speech sound disorders (SSD) in children with Down syndrome (DS) and children with specific language impairment (SLI) suggests similar linguistic profiles with weakness in phonology skills. The question is if these similarities are superficial or share deficits in levels and underlying skills to its speech disorders: phonological memory (PM), coordination motor skills, and articulatory muscular system. Our research involved 24 children divided into four groups: SLI, DS, and two groups of typical development. SLI group presented a mild‐moderate speech disorder and DS group moderate‐severe. Following skills were evaluated: nonverbal intelligence, PM, and oral motor coordination (oral‐DDK). The Iowa Oral Performance Instrument (IOPI) was used for the measurement of physiological variables (strength and endurance of tongue and lips). Percentage of consonants correct (PCC) was found. Phonological memory, motor coordination, and physiological variables are factors associated with SSD in teenagers with DS. However, SSD in children with SLI only are associated to phonological memory. Motor coordination and physiological variables are not involved in their SSD of mild and moderate‐severe levels. We have objectively measured the strength and endurance of tongue and lips. This may have clinical implications. It is necessary to assess objectively all the variables affecting articulatory accuracy to design intervention programs in SSD.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/56331",risUrl:"/chapter/ris/56331",book:{id:"5957",slug:"advances-in-speech-language-pathology"},signatures:"Miriam Zarzo-Benlloch, José F. Cervera-Mérida and Amparo Ygual-\nFernández",authors:[{id:"204358",title:"Dr.",name:"Amparo",middleName:null,surname:"Ygual Fernández",fullName:"Amparo Ygual Fernández",slug:"amparo-ygual-fernandez",email:"amparo.ygual@uv.es",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"University of Valencia",institutionURL:null,country:{name:"Spain"}}},{id:"210247",title:"Dr.",name:"José F",middleName:null,surname:"Cervera-Mérida",fullName:"José F Cervera-Mérida",slug:"jose-f-cervera-merida",email:"josefran.cervera@ucv.es",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Valencia Catholic University Saint Vincent Martyr",institutionURL:null,country:{name:"Spain"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_1_2",title:"1.1. Phonological short‐term memory",level:"2"},{id:"sec_2_2",title:"1.2. Coordination motor skills",level:"2"},{id:"sec_3_2",title:"1.3. Articulatory muscular system",level:"2"},{id:"sec_5",title:"2. Method",level:"1"},{id:"sec_5_2",title:"2.1. Participants",level:"2"},{id:"sec_6_2",title:"2.2. Instruments",level:"2"},{id:"sec_8",title:"3. Procedures",level:"1"},{id:"sec_9",title:"4. Results",level:"1"},{id:"sec_10",title:"5. Discussion",level:"1"},{id:"sec_11",title:"6. Conclusions",level:"1"}],chapterReferences:[{id:"B1",body:'Laws G, Bishop D. A comparison of language abilities in adolescents with Down syndrome and children with specific language impairment. Journal of Speech Language and Hearing Research. 2003;46(6):1324‐1339. DOI: 10.1044/1092‐4388(2003/103)'},{id:"B2",body:'Laws G, Bishop D. Verbal deficits in Down’s syndrome and specific language impairment: A comparison. International Journal of Language & Communication Disorders. 2004;39(4):423‐451. DOI: 10.1080/13682820410001681207'},{id:"B3",body:'Chiat S. Mapping theories of developmental language impairment: Premises, predictions and evidence. 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Test de Matrices Progresivas Raven. Madrid: Ediciones TEA; 2003'},{id:"B66",body:'Munson B, Kurtz BA, Windsor J. The influence of vocabulary size, phonotactic probability, and wordlikeness on nonword repetitions of children with and without specific language impairment. Journal of Speech Language and Hearing Research. 2005;48(5):1033‐1047. DOI: 10.1044/1092‐4388(2005/072)'},{id:"B67",body:'Lof GL, Watson MM. A nationwide survey of nonspeech oral motor exercise use: Implications for evidence‐based practice. Language Speech and Hearing Services in Schools. 2008;39(3):392‐407. DOI: 10.1044/0161‐1461(2008/037)'},{id:"B68",body:'Ygual A, Cervera JF. Eficacia de los programas de ejercicios de motricidad oral para el tratamiento logopédico de las dificultades de habla. Revista de Neurologia. 2016;62(1):59‐64'},{id:"B69",body:'Guiberson M, Rodríguez BL. Classification accuracy of nonword repetition when used with preschool-age Spanish-speaking children. Language Speech and Hearing Services in Schools. 2013;44(2):121‐132. DOI: 10.1044/0161‐1461(2012/12‐0009)'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Miriam Zarzo-Benlloch",address:null,affiliation:'
Universitat de Valencia, Valencia, Spain
'},{corresp:null,contributorFullName:"José F. Cervera-Mérida",address:null,affiliation:'
Universidad Católica de Valencia San Vicente Mártir, Valencia, Spain
'}],corrections:null},book:{id:"5957",type:"book",title:"Advances in Speech-language Pathology",subtitle:null,fullTitle:"Advances in Speech-language Pathology",slug:"advances-in-speech-language-pathology",publishedDate:"September 13th 2017",bookSignature:"Fernanda Dreux M. 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\n
1. Introduction
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Resource depletion and carbon emissions caused by using fossil fuels have increased interest in alternative fuel sources. Utilization of biomass resources is one option to meet the energy requirements for rapid industrialization and population growth with potential environmental and economic benefits. Energy could be derived from a variety of terrestrial, renewable, bio-based feedstocks like sugar-based biomass (e.g. corn, sugarcane, sugarbeet) and lignocellulosic biomass (e.g. wheat straw, corn stover, sugarcane bagasse, forestry residues, switchgrass, energy cane, sorghum, short rotation woody crops). However, production and conversion of these feedstocks could entail risks associated with disruption of the food chain and biodiversity, depletion of freshwater resources and eutrophication.
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Aquatic biomass like microalgae is a promising feedstock with many advantages over terrestrial plants. Its use dates to 1940s [1, 2]. To meet an energy shortage during this period, microalgal biomass was proposed to be used as a source for lipids. Microalgae have higher yield from incident solar energy and higher areal productivity. The photosynthetic efficiency of microalgae (around 3–8%) is substantially higher than that of terrestrial plants (typically 0.5%) due to their simple structure and convenient access to nutrients [3, 4, 5, 108]. Therefore, less land area is required and non-arable, non-productive land could be used for their cultivation. Some species could be cultivated using low quality water such as seawater, brackish water, desalination reject water and wastewater. A microalgae production facility could be operated as a closed loop system by allowing for recycling of water, nutrients and energy from downstream production processes [6, 7, 144]. Microalgae are characterized by high lipid/starch/protein content with a lack of lignin, which makes them well-suited for different conversion technologies [8, 9, 10]. Besides, microalgae cultivation has less potential to interfere with food and feed production. With such versatility, microalgae appear to be a promising biorenewable resource that has the potential to completely replace fossil resources [11]. Research in microalgae biotechnology has increased dramatically since 2005 and has been a very active field in recent years, especially to produce biomass and biofuels [12, 110, 111, 117, 118, 136, 143].
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Though microalgae may demonstrate benefits over terrestrial feedstocks, the major challenges for their production include significant utilization of nutrients, high energy input for harvesting and dewatering, and complex downstream conversion processes for usable fuels like ethanol and biodiesel [6, 8, 100, 109, 131]. An alternative which can potentially decrease the energy footprint could be biogas production through anaerobic digestion [122, 125, 127, 137]. Anaerobic digestion (AD) is a biochemical process that mineralizes organic compounds to biogas through the synergistic and concerted action of microorganisms under anaerobic (O2 free) conditions. Dry biogas is primarily a mixture of methane and carbon dioxide with traces of ammonia, volatile organic compounds and hydrogen sulfide. Methane content of dry biogas usually ranges between 50 and 70% (by volume). Methane has a higher heating value on a mass basis when compared to liquid fuels, such as biodiesel and bioethanol [13, 145]. AD has been recognized as a mature technology to treat organic waste streams and is widely practiced due to its high energy output to input ratio, environmental benefits, as well as for its process simplicity—compared to bioethanol/biodiesel processes [13, 14]. It is suitable for organic feedstock with high moisture content [15] and so can directly be applied to wet algae biomass feedstock with perhaps little dewatering. Besides, no harsh pretreatment is necessary for algal biomass due to the negligible lignin content [14]. The algal biorefinery could be engineered to be resource efficient by recycling phosphorus and nitrogen nutrients in the digestate effluent and carbon dioxide from biogas upgrading processes for microalgae cultivation [13, 14, 16, 17].
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In addition to the physical and chemical properties of the fuel as specified by technical standards, the characteristics desired by the stakeholders, distributors and, consumers could also include sustainability indices related to environmental, social and economic performance. Techno-economic analysis (TEA) establishes a capital and operating cost profile to determine the potential economic viability of the production process for realizing its commercial feasibility. It can be an integral tool to direct research during development of specific technology and assist with investment by averting unnecessary expenditures. A number of techno-economic assessments have been completed to evaluate the economic feasibility of biodiesel derived from microalgae [9, 22, 69, 140, 141]. However, there is a lack of techno-economic analysis on anaerobic digestion of microalgae for biogas production, especially full-scale production taking the characteristics of algae species into consideration. In this chapter, the entire production process from algae cultivation to biogas upgrading will be discussed emphasizing the key cost drivers. TEA literature is reviewed for methodology and state of art technologies. An example of TEA was conducted based on the biogas production process from a microalgae/cyanobacteria species Cyanothece BG0011 [82].
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2. Anaerobic digestion
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An anaerobic digestion (AD) process can biochemically convert the whole, wet biomass rather than specific components. The emissions and effluents from the process can be captured for reuse of components like carbon dioxide, ammonia, and phosphorus, and therefore has the potential for economic and environmental benefits. The general biochemical steps in the AD process include: (1) hydrolysis: the breakdown of macromolecules like proteins, lipids, polysaccharides into simpler compounds such as amino acids, sugars, fatty acids and glycerol; (2) acidogenesis and acetogenesis: the hydrolyzed molecules are converted to volatile fatty acids, primarily acetate, hydrogen, and carbon dioxide; (3) methanogenesis: methane production from acetate, hydrogen and carbon dioxide. The hydrolysis step plays a crucial role in determining the successful production of methane [37, 145]. The biochemical processes in AD also occur in nature. AD technology is well established and recognized as a robust technology to convert biomass to bioenergy [146].
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Despite the potential, questions related to the economic feasibility and the net energy output are the main hurdles hampering the development of biogas production from microalgae [14, 18, 19, 20]. For example, due to the specific structure and composition of the microalgae cell wall, the yield of biogas could be low. Pretreatment to disrupt the cell walls could require high energy inputs. The algae productivity could be low and cultivation cost could be high. Thus, the viability of microalgal biogas production may depend on improvements of efficiency and economic performance. Ongoing efforts include developing inexpensive biomass feedstock, maximizing energy return on investment, and minimizing environmental risks. As only a few studies are available in the literature on the economic feasibility of microalgal biogas exploitation [14], the evaluation and analysis of microalgal biogas production cost will be based on conversion efficiency, technological design aspects as well as available cost information.
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3. Key drivers of microalgal biogas production cost
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The production of biogas from microalgae feedstock entails a series of steps starting with algae cultivation. Implementation of each step involves capital and operational expenditures. The key drivers such as algal biomass productivities, harvesting and dewatering techniques, AD designs, biogas utilization options, integration of algal production, and AD with other bioprocesses were addressed. The production cost breakdown was illustrated in a harmonized framework and a dynamic connection between the technological and economic/environmental assessments was established.
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3.1. Microalgae cultivation, harvesting and dewatering
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A photobioreactor is the essential component of an algae cultivation facility. An open raceway pond (ORP) and a closed photobioreactor (PBR) are two major cultivation platforms. These two platforms for algae biomass production have been extensively studied [22, 23, 24, 25, 26, 27, 83, 84, 85, 101]. The main differences are highlighted in Table 1. In addition, the steps from inoculum preparation to obtaining the wet algal paste typically include systems for culture circulation, growth medium supply, air/flue gas supply, culture cooling, culture harvesting, and process monitoring. Heat exchangers, pumps, and a piping network are also required. The location and climate are important factors for algae cultivation.
A comparison of the open raceway and closed bioreactor systems for algae cultivation.
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Due to the high methodological variation of TEA in literature, drawing a generic conclusion over the economic feasibility of microalgal cultivation could be impossible. From the technological and economic perspective, the factors presented below are the ones most prominent in the existing literature and identified as important topics in the development of algae fuels.
Microalgae productivity and culture stability. According to Davis et al. [23], achievable productivity has a strong influence on the economics. Productivity of more than 25 g/m2/day annual average is critical for maintaining a relatively low minimum biomass selling price. A significant increase in productivity has to be achieved to reduce cost substantially [25]. The cultured strain should have high growth rate and a steady biomass composition. GMOs or extremophiles could provide culture robustness [22]. However, due to lack of regulations for managing GMOs, it is unlikely permits could be obtained for commercial cultivation of GMO algae strains. For commercial outdoor systems, uncertainties could be associated with seasons of the year and across multiple locations. Thus, the productivity data should be integrated with meteorological data for geographically and seasonally resolved assessments using a robust strain.
Photobioreactor design, construction, and operating conditions. For the open pond system, pond liners were found to be one of the primary cost contributors [22, 23, 28]. The location of the pond facilities could be selected according to the nature of the soil. For example, ponds built on soil with high native clay content could avoid full liners to reduce the cost. Acién et al. [26] presented a cost analysis of microalgae production using tubular photobioreactors. In these systems, photobioreactors were found to be one of the significant cost contributors. Generally, open raceway ponds are economically advantageous by more than a factor of 2, compared to closed photobioreactors [29]. However, due to increased productivity and culture stability, closed photobioreactors still have the potential for commercial applications.
Energy consumption. Primary energy consumption is due to the energy required for mixing, circulation, aeration and CO2 sparging. The energy consumption for mixing at experimental scales usually exceeded commercial-scale operations requirements and needs to be optimized to determine the minimum energy requirement [27, 28]. Mixing devices such as the paddle wheels are significant capital cost contributors besides the photobioreactors.
Nutrients and carbon dioxide supply. Higher productivity usually involves higher consumption of nutrients. Thus, nutrient input needs to be adjusted to balance the tradeoff against productivity [23]. Carbon dioxide was found to be the most expensive consumable among the raw materials [26]. Siting algae cultivation facilities on land adjacent to industrial CO2 sources like flue gases may be effective in reducing cost. However, the substantial logistical and practical constraints of using flue gases in facilities of varying sizes are still a challenge [23].
Land and water. Even though, microalgae can be cultivated on nonarable land, the soil composition, climate, solar radiation have a substantial influence on their growth. The most suitable location should be warm places or close to the equator where insolation is not less than 3000 h/year [24]. Water is required during algae cultivation to compensate for evaporation or for cooling purposes. Availability of water at low cost is critical for process success. Water reuse, wastewater, seawater, brackish water and reasonable distance to the water source has the potential to reduce costs.
Scaling. It is critical to quantify the economy of scale for algae production to achieve economic viability [23]. However, large uncertainties and unrealistic assumptions will exist in the research where the productivity potential for microalgae at large-scale is being estimated through linear extrapolation for laboratory-based growth data [30]. Data variability and growth modeling considering geographical information should be considered in large-scale assessments.
Labor and depreciation. Tredici et al. [21] performed a TEA of the microalga Tetraselmis suecica production based on a 1-ha plant in Tuscany, Italy. Cost data were collected from manufacturers and suppliers as well as operating data from pilot and commercial facilities. This study found that the major fraction of cost was labor at small scales (1 ha) and when the pilot plant is scaled to 100 ha, capital expenses contribute the most to the production cost. This assessment is site and strain-specific, but still provides valuable insights for the economic evaluation.
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Algae harvesting and dewatering methods include gravity settling, chemical coagulation, flocculation, filtration, centrifuge, and drying. The economic feasibility and energy consumption are two criteria for assessing the performance of unit operations for harvesting and dewatering methods. It was found that the cost of separation takes 20–30% of the biomass production costs [32, 33]. Gravity settling, chemical coagulation, and flocculation usually concentrate the microalgal slurries to 2–7% while filtration and centrifugation concentrate microalgal slurry to 15–25% of total suspended solids [32]. The suitability of microalgal dewatering methods has been investigated for scalability, species flexibility, and downstream processing efficacy [33, 34, 35, 36]. Dewatering methods reaching high biomass concentrations are usually associated with high energy input and cost. Thus, a combination of dewatering methods such as flocculation followed by filtration is generally considered to be economical due to the increased harvest efficiency. For downstream processing, methods such as flocculation using flocculants comprised of cationic and anionic poly-electrolytes, synthetic polyacrylamide polymers and starch-based polymers can be employed. However, the detrimental effect of these flocculants on the subsequent microbial processes need to be considered. For example, anaerobic digester stability and gas production could be affected by metal contamination. Future work should include replacing chemical coagulants with natural and low-cost organic ones for harvesting algal biomass.
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3.2. Anaerobic digestion systems
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3.2.1. Pretreatment
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The efficiency of biogas production has been shown to be species-dependent [39]. One crucial factor is the differences in structure of microalgae cell walls. The role of the cell wall in the microbial degradability of algae biomass is highlighted in many investigations [6, 13, 37, 38, 40, 41, 42, 43]. Many microalgae species (e.g. Chlorella kessleri, Scenedesmus) have recalcitrant cell walls, which make it difficult for anaerobic cultures to hydrolyze microalgal intracellular organic matter. Thus, to improve the biodegradability of microalgal biomass, pretreatments methods have been developed to disrupt or solubilize cell walls [112, 113, 114, 115, 116]. General insights from these studies are: (1) pretreatment methods are species-specific and their success depends on the nature of the cell wall; (2) mechanical pretreatments which consume electricity are more energy intensive than thermal, chemical and enzyme pretreatments; (3) chemical pretreatments usually have a low cost but produce inhibitory substances which could hamper the AD process; (4) for pretreatment mechanisms such as disruption of microalgal cell walls, the synergistic effects of the enzymes need further investigation; (5) combined pretreatments may provide energy and cost-effective options; (6) multi-objective optimization techniques could be used to obtain a high biogas yield with a positive energy balance; (7) enzyme/biological pretreatments have high selectivity, low inhibitory effects and higher probability of positive energy return [147]; (8) research on pilot/demonstration scale pretreatments is rare; (9) thermal pretreatments have been employed widely and proven to be the most efficient in microalgae pretreatment for AD; and (10) a detailed economic/energy analysis of microalgal AD for biogas production with pretreatment is still missing.
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3.2.2. Hydraulic retention time (HRT), organic loading rate (OLR), and reactor configurations
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The capital cost of the anaerobic digester could be reduced by using reactors designed for high OLR and low HRT [37]. The OLRs are typically between 1 and 6 g VS/L/d while the HRT varies between 10 and 30 days [37, 38]. Although high OLR will increase the methane productivity, overloading will decrease the biogas production efficiency due to the accumulation of inhibitors such as ammonia and acids [6, 37, 38]. Also, prolonged HRT could lead to ammonia inhibition due to slow liquid removal rate [41], while a low HRT could cause the washout of the anaerobic bacteria community [6]. Thus, an optimized OLR and HRT should be applied to achieve the expected specific methane yield. Possible solutions could be improving anaerobic digester configurations such as using membrane reactors or upflow anaerobic sludge blanket reactors to decouple the OLR and HRT [37, 119] and on-line control of anaerobic digester operation [124]. These have not been applied for digesting algal biomass. Additional costs for land and infrastructure and energy expenditures for heating the digesters should be included in the economic analysis.
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3.2.3. Temperature, pH, salinity, sulfur, and lipids content
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AD microorganisms can grow in three temperature regimes: (1) psychrophilic (5–20°C); (2) mesophilic (25–45°C); and (3) thermophilic (45–65°C). The temperature effect on AD has been discussed [13, 37, 41]. The beneficial temperature regime for AD operation is anaerobic digester is species-specific [44, 45]. The rate of methane generation can be enhanced under mesophilic and thermophilic conditions. The increased temperature could improve enzymatic activity for degrading microorganisms, and at the same time, the photosynthesis activity of viable microalgae within the digester could be reduced [13, 37]. However, an increase in temperature beyond the tolerable range of each temperature regime could cause inactivation of the microbes. Thermophilic temperature may cause increased hydrolysis of nitrogenous compounds which may increase ammonia levels and in turn can cause inhibition [6]. For large-scale biogas productions, the energy required for heating may be more than 1/3 of the total energy output in the form of biogas [46]. Thus, the net energy production from algae biogas may still be limited due to the high heat input associated with a low concentration of algae substrates.
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The pH needs to be maintained at an appropriate level for efficient conversion of biomass to biogas. The growth of microbes, enzyme activity, and the biogas compositions are influenced by the pH [47]. The optimum pH level depends on each step of AD [41]. Generally, the pH values are maintained between 7 and 8 for single stage anaerobic digesters [13, 41].
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Microalgae grown in a saline environment offer a sustainable alternative to other biomass by utilizing non-arable land and seawater. Marine microalgae can usually grow in a salinity range of 35–125 ppt [48]. However, when a highly saline culture is processed in an anaerobic digester, the high salinity could be inhibitory to the AD process. The effects of salinity and concentration of sodium are discussed in previous studies [6, 38]. Adaptation of anaerobic digester microbial consortiums under different saline conditions was investigated by Mottet et al. [121]. In a promising study, methane production was observed from anaerobically digesting Dunaliella salina biomass at 35 g/L of salinity.
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Sulfide is a required micronutrient for anaerobic microorganisms, but high concentrations of sulfide (200 mg/L) could be toxic [6]. For saline microalgal species, the sulfur inhibition may occur due to the presence of oxidized sulfur compounds in saline algae growth medium. Proper inoculum selection for anaerobic digesters could favor the growth of methanogenic bacteria and limit the growth sulfate-reducing bacteria [49].
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Lipids can also be inhibitory to the AD process [6, 18, 50] although lipids have a high theoretical methane potential. Generally, inhibition would occur when lipids concentrations are higher than 30%. In this case, the high-lipid microalgae are suitable for lipid extraction for production of liquid fuels.
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3.2.4. C/N ratio
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Microalgae biomass generally has a higher composition of protein than terrestrial plants [6, 37]. The degradation of protein will cause ammonia accumulation and inhibit the methanogenesis process. The optimum C/N ratio for AD is between 15 and 30 while this C/N ratio for microalgal AD is generally below 10 [13, 38, 41]. Thus, increasing C/N ratio and reducing the ammonia toxicity are important to enhance the biogas yield and productivity from microalgae. Possible solutions to this issue could be; (1) using ammonia-tolerant inoculum generated either by bioaugmentation or by acclimation [37, 38]; (2) using microalgae biomass that was cultivated under nitrogen-limitation [41, 99, 102, 130]; (3) co-digestion with sludge, oil-greases, waste paper and food wastes [13, 41, 54]; and (4) using a two-stage AD for better control of the anaerobic microbial communities [6]. However, these solutions may add more complexity to the system, in which the economic and energetic performance is still clear. For example, the co-substrate needs to be secured for co-digestion; the digester volume and cost may increase due to the loading of the co-substrate; more environmental burdens may be associated with the shipping of biomass, and nitrogen-limitation cultivation may affect microalgae productivity.
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3.2.5. Other factors
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Many other factors could affect the biogas yield and production of microalgal biomass. For example, the harvesting time influences the composition and biodegradability of algal biomass. Thus, it is essential to harvest algae in the appropriate stage of growth [13]. Storage conditions such as temperature also have an impact on biomass quality like macromolecular distribution and the content of organic compounds. Besides, inoculum to substrate ratio control is instrumental in avoiding inhibition problems such as drop in pH [51].
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3.2.6. Biochemical methane potential (BMP) of microalgae biomass
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The overall biogas yields depend on the chemical composition of the algae strains. The target strain should be highly digestible. The volatile solids/ash-free dry weight of microalgae plays a significant role in predicting theoretical biogas production potential, which is a critical factor in determining biogas productivities. Theoretically, the methane yield from different components of microalgae is as follows: lipids—1 L CH4/g VS, proteins—0.85 L CH4/g VS, carbohydrates—0.42 L CH4/g VS at standard conditions. Although the lipids have a high theoretical methane yield in AD, a high lipid content (more than 40%) will produce inhibitory substances such as long chain fatty acids [6]. Thus, for high-lipid content microalgae, lipid removal for biofuels production may be a better solution than biomass sent directly to AD.
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The impact of the algae cell wall is another critical factor affecting methane yield. Some species either lack cell wall or have cell walls rich in easily-biodegradable proteins as in Dunaliella salina, a halophilic microalgae and Chlamydomonas reinhardtii, a fresh water green microalgae [38]. Even easily degradable cell wall alone does not ensure efficient methanization. Factors such as the presence of methanogenesis inhibitors, anaerobic microbial community, hydraulic retention time, organic loading rates, salinity, carbon to nitrogen ratio, and the concentration of digestible substrate will also affect the final methane yield of microalgae.
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The microalgal strains which have been investigated extensively include Scenedesmus, Chlamydomonas, Chlorella, and Nannochloropsis [12]. The compositions of these four species are shown in Table 2. AD conversion process with biochemical methane potential (BMP) to theoretical methane potential (TMP) ratio ≥ 70% are considered highly efficient. Chlamydomonas reinhardtii could achieve a 74% BMP (405 ml methane/g VS) to TMP (549 ml methane/g VS) ratio without any pretreatment [52]. Schwede et al. [53] achieved high digestibility of Nannochloropsis salina with thermal pretreatment. The methane yield significantly increased from 0.2 to 0.57 m3 kg VS−1 under batch conditions with a BMP to TMP ratio increasing from 31 to 89%. Similarly, Chlorella vulgaris shows a significant increase in BMP after pretreatments: from 54 to 85% BMP/TMP ratio [41, 52] under an enzyme pretreatment; and from 62 to 78% BMP/TMP ratio under a biological pretreatment [55, 123]. Scenedesmus sp. did not show a BMP/TMP ratio higher than 60%, even after enzyme or thermal pretreatments [56, 57]. The BMP varies from species to species, but no significant difference was found between fresh water microalgae and saline microalgae [58].
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Components
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Species
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Scenedesmus sp.
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Chlamydomonas reinhardtii
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Chlorella vulgaris
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Nannochloropsis salina
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Protein % of DW
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33
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65
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64
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19
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Carbohydrates % of DW
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35
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23
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18
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45
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Lipids % of DW
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22
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13
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10
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36
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Table 2.
Approximate compositions of four microalgal species: Scenedesmus sp., Chlamydomonas reinhardtii, Chlorella vulgaris, and Nannochloropsis salina [41].
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4. Techno-economic analysis
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In published TEA works, the process complexity was often simplified in terms of limited pathways, few choices of economic drivers and implicit assumptions regarding the growth conditions, process modeling factors and financing of the production facility. Existing reviews in anaerobic digestion of microalgae biomass such as Ward et al. [6] focus on the integration of anaerobic digestion into biodiesel refineries. Considering that diesel or ethanol are more valuable products, anaerobic digestion was suggested to treat the residual biomass to improve the economic viability and sustainability of overall microalgae biodiesel/ethanol stages. Global research in various pathways is going on towards the sustainable development of algae biofuels. The following sections will review these works, highlight the variability of methods of estimating microalgal biogas production cost, find the key drivers of cost contributors, pointing out the convergence and difference in published results, and give a view of the whole value chain towards scaling-up and commercialization when performing a techno-economic analysis (TEA).
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4.1. TEA framework
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To achieve an optimal facility design, it is necessary to evaluate the tradeoff resulting from the interactions between technical advances and financing parameters. The technical objectives include maximizing microalgal biomass productivity, maximizing biogas yield via AD of biomass, and process stabilization. The economic objectives are to minimize the production cost and maximize the economic benefits. Figure 1 shows the TEA framework for the sustainability analysis of biogas production from microalgal biomass through anaerobic digestion. The whole biomass processing value chain is determined by the technology framework and progress through experimentally validated process specifics. Economic analysis is based on the process design, which includes the cost assessments and investment analysis. A decision-making platform is built for raw material suppliers, producers and stake holders in an economic perspective. Correspondingly, the economic consequences will direct the research & development of new technologies, which could form a dynamic connection and optimization framework.
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Figure 1.
TEA framework for biogas production from algae biomass.
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Environmental TEA (ETEA) extended the TEA framework with an environmental assessment based on a life cycle analysis [70]. The ETEA is based on the technology readiness level, which means the assessments are performed using the available data based on technology maturity. This would avoid a mismatch between the assessment methodology and the technology readiness level. For example, the whole biogas life cycle includes phases from the biomass cultivation to the final usage and end of life. Under current technology maturity, the whole data set is unavailable, which limits the assessments to certain life cycle phases.
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4.2. State of the art: TEA of microalgal biogas
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Biorefinery optimization and full utilization of biomass addressing in the economic viability and environmental sustainability of the production of algae biofuels can be found in [39, 71, 72]. Dutta et al. [72] analyzed the sustainability of microalgae-derived biofuel production by performing a TEA and life-cycle assessment and found that coproducts valorization is more energy efficient than the processes focusing on specific components such as lipids. Biorefineries with coproducts and byproducts could have better utilization of the algal biomass and can increase the revenue, thus show greater possibility of achieving economic feasibility. In microalgae biodiesel and bioethanol productions, anaerobic digestion is usually integrated into the biorefinery to treat the residues for energy and nutrient recovery. Sialve et al. [18] compared the energy recovery ratio for two scenarios: direct AD of the whole algae biomass and AD of residue biomass after lipid extraction. Direct AD of the whole biomass was considered to have a higher energetic recovery when the cell lipid content does not exceed 40%. Also, increased lipids content in microalgae is not generally compensated with increased productivity due to nitrogen limitation. The potential of direct AD of microalgae biomass was addressed in their research, taking into account the energetic recovery and necessary nutrient recycle for large-scale productions. Chia et al. [73] discussed the economic potential of biohydrogen and biogas production in Germany and Spain. Two processes were compared: direct AD of microalgae biomass (DAD) and coupled hydrogen and biogas production (CHB). In the CHB process, hydrogen was first produced by dark fermentation then effluent from hydrogen fermentation was used for biogas production. The CHB was found to have a lower operating cost due to no additional water and nutrients requirements for the bioreactor feed while the DAD process requires algal biomass in combination with other feedstocks. Both cases have production costs 13–16 times higher than the market price for natural gas. A 1/3 higher biogas yield and a 1/2 lower labor cost did not change the economic status of both processes, due to the high cost of fertilizer and building photobioreactors for microalgae cultivation. Milledge and Heaven [74, 129] performed an energy balance of biogas production from microalgae. Their research emphasized a combination of dewatering methods, as well as the efficient exploitation of the heat generated by the combustion of biogas in combined heat and power (CHP) units to show the energetic viability of the whole process.
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Chew et al. [68] assessed the potential of microalgae biorefineries for producing high-value products such as pigments, proteins, lipids, carbohydrates, and vitamins. The high-value products were added to improve the biorefinery economics. Open pond cultivation and medium recycling were mentioned to have better economic performance than other biorefinery structures. Water, land usage and capital cost were challenging for the economic viability of algal biofuels. The high-value products also need to improve aspects such as separations method, energy consumption, and control of product loss. AD was emphasized to recycle a considerable amount of nutrient usage to make microalgal fuels head towards its large-scale production. Several authors [13, 17, 37, 38, 75, 133, 134] synthesized scientific literature on biogas production from algae and suggested integration of the technology with other technologies as well as co-digestion with other substrates for an optimized biorefinery that sustainably produces biogas. Singh and Gu [76] recommended integrated processes that combine algae cultivation and wastewater treatment for methane production, which could offset the higher cost in comparison to methane production from corn and woody biomass.
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Zamalloa et al. [8] evaluated the techno-economic potential of methane production from microalgae. The assessment was carried out using high rate anaerobic digesters (10–20 kg COD/m3/d) and preconcentrated algae biomass from a full-scale open pond. The energy production cost from microalgal biogas was estimated to be 0.087–0.17 euro/kWh with an algae biomass cost of 86–124 euro/tonne. The result was based on a feed-in tariff of 0.133 euro/kWh and a carbon credit of 30 euro/ton of carbon dioxide. This study is one of the limited works that has been done on a comprehensive technological and economic assessment of electrical and thermal energy produced by biogas through AD of microalgae.
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Collet et al. [77] performed a life-cycle assessment (LCA) of biogas production from the microalgae Chlorella vulgaris and found that electricity consumption and the impacts generated by the production of methane from microalgae are strongly correlated. Decreasing mixing and heating cost in different production steps or increasing the efficiency of AD were important to reduce the overall cost.
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The studies surveyed show considerable variability in the calculated fuel cost and identifying the significant cost contributors. The varied results come from different conversion pathways, technical assumptions (productivity, reactor design, process parameters, etc.) and economic factors (interest rate, raw material cost, etc.), diverse environmental and social conditions (consideration of season and location), and validation of sub-process models (lab/pilot plant/commercial scales). Nevertheless, the contributors to the production cost are mainly identified as microalgal strain selection, biomass cultivation and harvesting, AD operating conditions, biogas upgrading methods, waste management, and type of biorefinery. Thomassen et al. [78] evaluated the methodological reason for the wide variation in the results of multiple environmental and economic assessments. They proposed an environmental techno-economic assessment which can help to solve the challenges for a sustainability assessment: framework for methodology, harmonized assumptions, and integration of different dimensions (stages of technological maturity, technological process). This method is based on the dynamic technological process parameters and the same system boundaries for an integrated TEA and LCA.
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4.3. Cost management
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Gnansounou and Dauriat [79] investigated TEAs following different types of cost management systems in value engineering, target costing and a combination of value engineering and target costing. Value engineering includes process design via data collection and process flowsheeting. Process simulators such as Aspen Plus enables the evaluation of the whole process chain based on scale up of the pilot plant, state of art technologies and price quotes. For microalgae to biogas technologies, key issues along the process chain include the suitable choice and operation options of the microalgae species, harvesting/dewatering strategies, pretreatment methods, AD configurations, recycling the digestate, and energy integration. Not all the steps are necessary for technologies with simplified processes and high economic potential. Target costing is a market-oriented method, which means a target selling price was set for the cost evaluation based on market and societal values. Following the target price, the target cost of the final product and each step of the supply process will be estimated, which means the cost allowance will play a key role in the process design. Target costing could integrate with value engineering in the cost management activities, so the cost allowance and cost target could be reconciliated. In the case of biogas production, the target costing evaluation seems unfeasible for the whole process due to the weak financial position of the natural gas market [80].
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Real options analysis framework was employed by Kern et al. [81] for TEA. The model was adapted to accept stochastic price data for energy and agricultural commodities as well as static operating parameters assumptions for the algal biofuel plants. The TEA work was combined with life cycle analysis in a dynamic system—the fluctuations in market prices for energy and agricultural commodities will influence the operation decisions of the biofuels plants and its associated environmental impacts. Areas such as carbon tax, resource shortage and market forces could be investigated for their impact on biofuel plant design and operations in a dynamic system in the future. This gives the stake holders and suppliers more flexibility in making decisions.
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4.4. TEA limitations
\n
The limitations of TEA include the potential competition for resources. For example, the microalgae biomass could have non-energy applications and has the potential for producing high value products besides biofuels. Then the biomass cost for the process will be influenced not only by the biomass production activities but also the market price which is determined by both the suppliers and purchase competitors.
\n
The sustainability of biogas production from microalgae will depend on not only the commercial viability but also environmental improvements such as greenhouse gas emission reduction, lack of direct and indirect impacts on land-use as well as biodiversity and eutrophication. The scope of TEA is limited for the environmental impact assessment, while these impact categories are appropriate for the goals of the overall sustainability analysis. Thus, an ETEA would allow assessing the sustainability of the entire value chain. Besides, TEA is not reflecting social impacts such as social awareness of algal biofuels’ non-food competitive characteristics, rural development, and public recognition.
\n
\n
\n
\n
5. Case study: TEA of anaerobic digestion of Cyanothece BG0011
\n
The microalgae used for this case study is a cyanobacterium, Cyanothece sp. BG0011 isolated from a shallow lake in Florida Keys [82]. Compared to other algal species, this species is endowed with unique features. First, cyanobacterium Cyanothece sp. BG0011 is a saline species and can be adapted to a wide range of salinities (10–70 psu). Second, it fixes dinitrogen in the air, which means it does not require nitrogenous nutrients in the culture water. Third, it produces exopolysaccharide (EPS) which can be converted to a variety of bioproducts. The aim of this case study is to assess the economic feasibility of biogas production using Cyanothece sp. BG0011 as feedstock by conducting a techno-economic assessment. The analysis investigated alternatives to decrease the cost and energy requirement of the cultivation and anaerobic digestion of algae. Utilization of biogas to produce electrical and thermal energy or upgrading to produce pure methane (renewable natural gas) was also considered. A comprehensive TEA was carried out based on experimental data and a set of operational assumptions which could be conceivably achieved in near term. The process flowsheet for biomass to biogas conversion through anaerobic digestion and biogas purification processes was implemented in Aspen Plus V8.8 to obtain mass balance and energy requirement results. The discussion focused on the preliminary exploration of the conceptual design of a microalgae cultivation and bioconversion system as well as an investigation on improvements that could result in the greatest system flexibility, energy yield and cost reductions.
\n
\n
5.1. Cyanothece BG0011 cultivation
\n
Results from many experiments [149] conducted in the Bioprocess Engineering Laboratory, Department of Agricultural and Biological Engineering, University of Florida gave an average growth rate of 67.5 mg afdw/L/day (20.25 g afdw/m2/day) for BG0011 cell biomass and an EPS production rate of 52.5 mg afdw/L/day (15.75 g afdw/m2/day), resulting in cell density of 2.7 g/L and EPS concentration of 2.1 g/L. The areal rates were calculated by assuming that the depth of culture was 30 cm, which is typically the case for open ponds. In the laboratory, the cultures were cultivated under air sparging, a constant illumination of 1200 μmol photons m−2 s−1 light and 13 h to 11 h light-dark cycle. Open raceway ponds are generally used for large-scale commercial production of algal biomass [86]. Productivity in industrial-scale raceway ponds is generally lower than in small experimental reactors. In literature, algae biomass productivity performance claims range from 7 to 35 g afdw/m2/day [23, 87, 88, 89] with corresponding net photosynthetic efficiencies from under 1–4%. Among these, for studies involving techno-economic analyses, the baseline productivity assumed was 20 g/m2/day, with an optimistic value of 25–30 g afdw/m2/day, and a conservative value of 15 g afdw/m2/day. In this study, which assumed that BG0011 is cultivated in current large commercial open ponds, an average productivity of 12.4 g afdw/m2/day (corresponding to a net photosynthetic efficiency of under 1%) was used. Similar growth rates were obtained by [148] when the algae was cultivated by air sparging and exposed to a lower light intensity of 122 μmol photons m−2 s−1 light and 13 h to 11 h light-dark cycle. Here, laboratory-scale BG0011 cell biomass growth rate is comparable to algae cell growth rates reported from other studies, however, in the case of BG0011, it also produces EPS. The average mass ratio between EPS and cell biomass is 0.778: 1 and also EPS production is cell-growth associated, so for this study it is assumed that in the commercial system, in addition to BG0011 cells, EPS would be concomitantly produced at 0.778 × 12.4 g afdw/m2/d = 9.6 g afdw/m2/day. The total algae biomass productivity used was 22 g/m2/d. Henceforth, the term “algae biomass” will include both BG0011 cells and EPS.
\n
The scale of algae cultivation in literature for techno-economic analysis ranges from 200 to 700 ktonne afdw/year [22, 27, 72, 89]. In the present study, the scale of algae cultivation was determined based on a hypothetical 20 million gallons per year ethanol plant. The sugar required for such a plant would be 128 ktonnes afdw/year (assuming yield of around 0.42 g ethanol/g sugar, and 1.1 g sugar/g polysaccharide). Assuming this amount of sugar will be supplied in the form of EPS, the scale of the algae cultivation pond would be 293 ktonnes of algal biomass/year, which also includes BG0011 cell biomass. This scale falls into the range of values found in literature for TEA. To meet a production capacity of 293 ktonnes/year at algal biomass productivity of 22 g afdw/m2/day, land area required would be 3660 hectares (approximately 4 × 4 miles). For a sanity check, this cultivation area was compared to land area required to supplying corn grain for a 20 million gallon per year corn-ethanol plant. Based on annual corn grain yield of 7000 kg/ha with starch content of 72% [150], and assuming a conversion of 0.5 kg ethanol/kg of starch, land required would be 23,700 ha. In this case the total above ground biomass productivity of corn, including corn grain, stover and cobs, is 16,700 kg/ha/year [150] whereas for BG0011 it is anticipated to be 80,300 kg/ha/year.
\n
The BG0011 cultivation cost was estimated based on vendor quotes, literature, or engineering estimates. The installed pond capital cost includes civil work, liner, piping, electrical, other pond costs (such as paddlewheels). In addition, pumps for pumping water from ponds to refinery and for refilling the pond and required land also incur significant capital costs. Plastic lined earthen ponds were chosen for its lower cost compared to concrete ponds. Larger pond sizes would enable economically viable algal biomass production [23]. Here, the installed capital cost was estimated based on “dollars/hectare” of growth ponds for simplicity. The installed pond cost was set to be 80,000 $/ha. Literature value ranges from 46,000 $/ha to more than 150,000 $/ha (value adjusted for inflation) due to different liner scenarios (partial or full) and specific design (e.g. with or without equipment to minimize dead zones) [23, 86] which was not included here. A land cost of 3080 $/acre [90] was used for low-value land. The operation cost for algae cultivation such as utilities, chemicals, labor, overheads, maintenance, insurance tax, etc. were estimated using engineering estimates [91]. BG0011 was assumed to be cultivated in seawater or brackish water. The only fertilizer used for BG 0011 cultivation is phosphorus since it uses dinitrogen in air as a nitrogen source, and seawater would supply rest of micronutrients. From laboratory experiments it was determined that the phosphorous requirement of BG001 is 8.9 mg/L [149], so the annual requirement of phosphorous will be 1186.7 tonnes. Here, triple superphosphate (Ca(H2PO4)2 H2O) which contains 24.6% P is used as phosphorous source with a price of 270 $/tonne (Source: World Bank, 2017). The requirement of triple superphosphate is 4945 tonne/year.
\n
The fixed capital investment was assumed to be borrowed at an interest rate of 10% for 20 years. The plant operates 24 h a day and 360 days annually. The prices were adjusted for Year 2017 using Chemical Engineering Plant Cost Index (CEPCI). These assumptions were also used for the analysis of subsequent biogas production, conversion and upgrading processes. The production cost was calculated as follows:
\n
\n\n Unit production cost\n=\n\n\n(Annual capital charges+Total operating cost)\n\n\n −\nCoproduct credits)\n\n\n/\n\nAnnual production\n\n\nE1
\n
Here, the annual capital charges are calculated as follows:
\n
\n\n Annual capital charges\n =\n\n\nTotal capital cost\n∗\nInterest rate\n∗\n\n\n(1\n +\nInterest rate)\n\n\n^\n\nLoan period\n\n\n\n/\n\n\n\nInterest rate\n\n^\n\nLoan period\n\n−\n1\n\n\n\nE2
\n
* Total capital cost = Total fixed cost + Working capital.
\n
* Working capital is 10% of fixed capital.
\n
\n
\n
5.2. Anaerobic digestion
\n
The anaerobic digester was designed to treat the un-dewatered whole algae culture from the pond. The energy-intensive steps like algae harvesting and dewatering are avoided in this process which is different from most research [8, 22, 23]. The product biogas was analyzed for economic performance in two different applications: biogas purification or electricity production through combined heat and power.
\n
The first step in modeling mass flow rate of reactor outputs and determining energy requirements is to establish the stoichiometry of reactions. The stoichiometry of methane fermentation of algae biomass was developed based on the following assumptions: (1) microbial cells (cyanobacteria and bacteria) can be represented by the empirical formula CH1.8O0.5N0.2 [151]; (2) EPS is pure polysaccharide represented by the empirical formula C6H10O5; (3) algae biomass can be represented by an empirical formula containing the elements C, H, O and N in the mass ratios in which cells and EPS are produced that is 1:1.2; and (4) methane yield from laboratory assays corresponds to complete decomposition of substrate. The empirical formula for algae biomass was CH1.73O0.67N0.1. The stoichiometry for methane formation is written as follows:
Methane yield from algae biomass was measured in the laboratory to be 300 ml at STP (g afdw)−1. This corresponds to 0.35 moles of methane (mole algae biomass)−1, which is equal to value of ‘c’ in the above stoichiometry. The other stoichiometric coefficients can now be solved from elemental balances for C, H, O and N. The stoichiometry is
In the anaerobic digester it was assumed that 98% of the algae biomass is converted. Different scenarios (three anaerobic digester types) were investigated to evaluate the economic and energetic performance. A schematic of biorefinery scenarios are shown in Figure 2.
\n
Figure 2.
Schematic diagram showing biorefinery scenarios.
\n
Case 1. Above ground mesophilic anaerobic digester. In Aspen, the influent to the reactor was 15 ktonne/h. The temperature was maintained at 37°C. It was operated at an HRT of 25 days.
\n
Case 2. Above ground low-temperature anaerobic digester. Anaerobic digestion at low temperatures (LTAD) was applied to improve the energy balance. In this scenario the digester is operated in the psychrophilic range (12–20°C) [92, 93, 94]. However, with the same flow rate, the digester volume is larger to achieve a higher HRT for LTAD than mesophilic and thermophilic anaerobic digestion. Here, the temperature of LTAD is set to 20°C with an HRT of 50 days.
\n
Case 3. Covered anaerobic lagoon. Covered anaerobic lagoon (CAL) does not require additional energy for the biogas production because no heating or mixing processes are involved. Besides, it is economical to construct and operate. The CAL in this research was 6 meters deep and covers an area of 1.5 hectares based on literature data [95]. The HRT was set to 50 days. The cost includes anaerobic lagoon excavation, cut and fill, lagoon liner, inlet and out structures, lagoon cover, ancillaries, pipework & installation, contingencies, design, engineering, etc. Operating costs including utility usage are minimal.
\n
In all three cases above, the capital cost of anaerobic digester was estimated using vendor quotation or literature values. The operating cost was estimated by Aspen Process Economic Analyzer.
\n
\n
\n
5.3. Biogas purification
\n
Several biogas upgrading or purification methods are available such as high-pressure water scrubbing, membrane, pressure swing, gas permeation and chemical scrubbing. High pressure water scrubbing and chemical scrubbing (using amine solutions—MEA) are two of the most commonly used processes.
\n
The MEA scrubbing method uses aqueous monoethanolamine (MEA) for acidic gas removal. The concentration of amine for acidic gas absorption is usually below 30% (by weight). The amine process has two main steps, absorption and stripping [96]. The detailed MEA scrubbing process is shown in Figure 3. Raw biogas goes through a scrubbing column in which MEA is flowing counter-current to biogas. The CO2-rich MEA is collected at the bottom of the scrubbing column and pumped into a stripping column to remove CO2 and regenerate MEA by heating. Similar to MEA scrubbing, high pressure water scrubbing was also employed for biogas upgrading: biogas is fed to the bottom of scrubber after compressing it to 10 bar. At the top of scrubber, pressurized water is fed. CO2-rich water is then transferred to a flash column with a lower pressure of 3 bar to release gases for feed recirculation and minimizing methane loss. Then the CO2-rich water goes through a CO2 desorption process from the water stream by air [97]. Both biogas purifying approaches were simulated in ASPEN Plus to determine the economics of each approach. The technical specification details are shown in Table 3. The table shows high pressure water scrubbing to be a more economical alternative and was chosen for the integrated process.
\n
Figure 3.
MEA scrubbing for biogas upgrading.
\n
\n
\n
\n
\n\n
\n
Specification
\n
MEA
\n
High pressure water scrubbing
\n
\n\n\n
\n
Thermodynamic method
\n
ELECNRTL
\n
PSRK
\n
\n
\n
Scrubbing column
\n
RadFrac, 15 stages, pressure: 1.2 bar
\n
RadFrac, 10 stages, pressure: 10 bar
\n
\n
\n
Stripping column
\n
RadFrac, 15 stages, pressure: 8 bar
\n
RadFrac, 10 stages, pressure: 1 bar
\n
\n
\n
Make up chemicals
\n
Water: 150 kmol/h MAE: 750 kmol/h
\n
Water: 11500 kmol/h
\n
\n
\n
Solvent recycle rate
\n
MEA: 0.99
\n
Water: 0.95
\n
\n
\n
Methane loss
\n
1%
\n
0.3%
\n
\n
\n
Product methane purity
\n
95 wt%
\n
99.2 wt%
\n
\n
\n
Capacity (raw biogas flow rate)
\n
948.5 kmol/h
\n
948.5 kmol/h
\n
\n
\n
Capital cost (million $)
\n
8.2
\n
12
\n
\n
\n
Operating cost (million $/year)
\n
20
\n
4.6
\n
\n
\n
Utility cost (million $/year)
\n
17
\n
2
\n
\n
\n
Purification cost ($/kg of methane)
\n
0.3
\n
0.09
\n
\n\n
Table 3.
Technical and economic aspects of the biogas purifying systems in ASPEN V 8.8.
\n
\n
\n
5.4. Power generation from biogas
\n
While the raw biogas can be purified to obtain biomethane, another option is to use the raw biogas to produce heat and power. Steam and electricity can be generated by burning the raw biogas through a combined heat and power (CHP) system. For reference, the CHP system uses General Electric Jenbacher JGS 420 system which is a 1425 kw generator. The total capital cost is $ 1,150,000 (including installation, tax, etc. 2007), which is 807 $/kw. The working capital is 10% of the total capital. The operating cost includes direct operating cost such as operating labor, supervised labor, maintenance and repairs, as well as indirect operating cost such as overhead, taxed, insurances. It is assumed that 40% biogas energy is for electricity, 50% for steam, 10% loss.
\n
\n
\n
5.5. Techno-economic analysis of integrated system
\n
\n
5.5.1. Biomass cultivation economics
\n
The BG0011 cultivation economics analysis details are shown in Table 4. In the literature algae production costs range from 150 to 6000 $/tonne [19, 22, 27, 72, 89, 142], however, the studies vary from assumptions (production scale, chemical prices, plant life, etc.) to differences in technical specification (photobioreactor design, algal species, etc.). Some of the estimates also account costs for dewatering of algae [22, 27]. Thus, it is difficult to make a direct comparison between different studies. Besides, specific assumptions in each study could be based on different social-economic conditions, which makes comparisons more complicated [98].
\n
\n
\n
\n\n
\n
Parameters
\n
Values
\n
\n\n\n
\n
Production scale
\n
\n
\n
\n
BG0011 cells production (ktonne/year)
\n
165
\n
\n
\n
BG0011 EPS production (ktonne/year)
\n
128
\n
\n
\n
Total algae biomass production (ktonne/year)
\n
293
\n
\n
\n
Capital cost (including fixed, installed and working capital)
\n
\n
\n
\n
Pond (million $)
\n
308
\n
\n
\n
Land (million $)
\n
26.6
\n
\n
\n
Pump (million $)
\n
7.85
\n
\n
\n
Total capital cost (million $)
\n
342.45
\n
\n
\n
Annual capital charges (million $/year)
\n
40.22
\n
\n
\n
Operating cost
\n
\n
\n
\n
Chemicals (P fertilizer: Ca (H2PO4)2 H2O) (million $/year)
\n
1.3
\n
\n
\n
Other operating cost (including utilities, maintenance and repairs, labor etc.) (million $/year)
\n
3.26
\n
\n
\n
Total operating cost (million $/year)
\n
4.56
\n
\n
\n
BG0011 algae biomass production cost ($/tonne)
\n
153
\n
\n\n
Table 4.
Algae cultivation economics.
\n
\n
\n
5.5.2. Economics of anaerobic digestion
\n
Details of the production cost of renewable natural gas for the three anaerobic digestion scenarios are shown in Table 5. Case 2 contains two scenarios: The size of anaerobic digester in Case 2(a) is two times of that in Case 1. This is because the hydraulic retention time is longer under lower temperature, the volume of digester needs to be larger to keep the same production scale (the inflow rate). The size of anaerobic digester in Case 2(b) is the same as Case 1. Keeping the digester volume same as Case 1, because the temperature is lower, the productivity will be lower as well. Thus Case 2(b) has a lower production scale compared to other cases. The effect of temperature was incorporated by using the empirical relationship that for every 10°C rise in temperature the degradation rate is doubled. As the difference between the temperature for Case 1 and Case 2 is 17°C, it is expected that in Case 1, the digester has a processing capacity twice as much as that of the digester in Case 2b. The main contributor to the production cost of biogas is the biomass cost. Considering a carbon credit of 10 $/tonne of CO2, the production cost of biogas only drops 0.5 $/MMBtu. The results are comparable to Zamalloa et al.’s [8] research (the only paper focusing on the economics of renewable energy through AD, to our best knowledge): 32.2–61.5 $/MMBtu with the algae biomass cost of 115.4–166.4 $/tonne (0.087–0.17 euro/kwh with an algae biomass cost of 86–124 euro/tonne, 2011). The methane yield is 0.012 MMBtu/kg of VS biomass, which is in close agreement to our experimental result 0.0124 MMBtu/kg of VS biomass.
\n
\n
\n
\n
\n
\n
\n\n
\n
Item
\n
Case 1 (mesophilic anaerobic digester)
\n
Case 2(a) (low-temperature anaerobic digester)
\n
Case 2(b) (low-temperature anaerobic digester
\n
Case 3 (covered anaerobic lagoon)
\n
\n\n\n
\n
Biogas production scale (106 MMBtu/year)
\n
3.7
\n
3.7
\n
1.85
\n
3.7
\n
\n
\n
Fixed capital cost of anaerobic digester (million $)
\n
67.12
\n
102
\n
67.12
\n
7.5
\n
\n
\n
Capital cost except anaerobic digester (million $)
\n
16.3
\n
16.3
\n
12.3
\n
16.4 (including land: $11400)
\n
\n
\n
Annual capital charges (million $/year)
\n
9.8
\n
13.9
\n
9.3
\n
2.8
\n
\n
\n
Total raw materials (algae biomass) cost (million $/year)
\n
44.8
\n
44.8
\n
44.8
\n
44.8
\n
\n
\n
Other operating (labor, utility, indirect, etc.) cost (million $/year)
\n
25.8
\n
7.1
\n
4.4
\n
7.1
\n
\n
\n
Utility cost (million $/year)
\n
21
\n
2.3
\n
1.4
\n
2.3
\n
\n
\n
Renewable natural gas production cost ($/MMBtu)
\n
21.7
\n
17.8
\n
31.6
\n
14.8
\n
\n\n
Table 5.
Process and economic assessment for purified biogas production through anaerobic digestion of Cyanothece BG0011 biomass.
\n
\n
\n
5.5.3. Electricity production cost
\n
On an energy potential basis, 40% of total methane produced per year could support a 50 MW power plant. Current residential electricity price is around 12 cents/kwh, while industrial price is around 7 cents/kwh. As shown in Table 6, the electricity production cost from biogas is 13 cents/kwh. Renewable energy technologies are usually more expensive than fossil fuel technologies. The reasons could be environmental costs associated with fossil fuels that are not paid by the rate payers, mechanical difficulty in bioenergy production, start-up issues and so on. European countries such as Germany and UK governments subsidize the production of renewable energy by introducing feed-in tariffs. These tariffs may be important to make bioenergy industry profitable.
\n
\n
\n
\n\n
\n
Item
\n
Value
\n
\n\n\n
\n
Electricity capacity (million kwh/year)
\n
435
\n
\n
\n
Total capital cost of the CHP system (million $) (including fix capital cost and 10% working capital)
\n
52.4
\n
\n
\n
Capital charges (million $/year)
\n
6.2
\n
\n
\n
Steam credits (million $/year)
\n
3.7
\n
\n
\n
Raw biogas cost (million $/year)
\n
47.7
\n
\n
\n
Other operating cost (million $/year)
\n
9.5
\n
\n
\n
Electricity production cost ($/kwh)
\n
0.13
\n
\n\n
Table 6.
The economics of biogas—electricity and steam system.
\n
\n
\n
\n
\n
6. Cost minimization approaches
\n
\n
6.1. Nutrient recycling and biogas upgrading
\n
Nutrient (mostly nitrogen and phosphorous) recycling such as utilizing the digestate or wastewater for microalgae cultivation was highlighted in various studies [59, 60, 61, 62, 63, 104, 105, 106, 107, 126, 128, 138, 139]. Recycling the effluent from the anaerobic digester for algae cultivation could mitigate the costs associated with supplying nutrient for algal biomass growth and effluent treatment. Erkelens et al. [59] validated that microalgae Tetraselmis sp. could utilize its digested effluent as a growth medium and thus form a closed loop system. Also, Prajapati et al. [60] showed that algal liquid digestate have good potential to be utilized as nutrient supplement (30% concentration) in rural sector wastewater for biomass cultivation. The biomass production level is closer to the case in which conventional medium is used. Although there are still technological obstacles when growing microalgae on digestate such as low growth rate due to poor nutrient ratios, shading, ammonia inhibition and bacteria growth, the performance of the nutrient recycling process could be further developed by scale up/optimizing strategies such as controlling inoculum and substrate concentrations, bacteria growth as well as harvesting strategies [59, 61, 64, 132].
\n
One option to increase algae biomass productivity and its concentration in the culture is to enrich the air with CO2. It has been shown that enriching the air with 1% CO2 increases cell concentration to 3.46 g afdw/L and EPS concentration to 2.91 g afdw/L, giving an algae biomass concentration of at least 6.37 g afdw/L [149], which is 1.33 times more than that used in the case study above. The increased productivity of algae biomass will reduce further the cost for biomass production. The CO2 released from the biogas upgrading process or waste gases from biogas combustion containing CO2 could be recycled to the algae growth ponds for enriching the air. The economic analysis for this scenario was also performed assuming algae biomass concentration is 1.33 times the previous value of 293 ktonne/year. The estimated production cost for Cyanothece BG 0011 algae biomass is 121.6 $/tonne. This was calculated by accounting for the following additional costs: (1) capital cost associated with pipes and pumps to take CO2 from biogas purification system or biogas combustion output to the pond and (2) operating costs resulting from more nutrient addition to maintain higher cell density and power consumption of compressing CO2 for sparging [152]. Only biogas production from covered anaerobic lagoon as in Case 3 was considered here. Algae production cost was lowered by 20%. The estimated cost of renewable natural gas is now reduced to 12.16 $/MMBtu and the electricity production cost from biogas is only 10.98 cents/kwh.
\n
Upgrading biogas by fixation of the CO2 in biogas via photosynthesis by microalgae has been investigated with respect to CO2 removal capability, biomass productivity and O2 desorption minimization [16, 63, 64, 65, 66, 67]. Toledo-Cervantes et al. [16] optimized the biogas upgrading process by studying the influence of the recycling liquid to biogas ratio. The biomethane produced met specification for injection into natural gas grids. However, this technique requires closed photobioreactors. Hydrogen sulfide (H2S) is another contaminant to be removed from the biogas. Hydrogen sulfide removal was realized by the oxidation of H2S to sulfate by sulfur oxidizing bacteria that used the oxygen produced photosynthetically in situ. In this case, the algae-bacteria symbiosis was employed in the photobioreactors [67]. Nutrient recycling and biogas upgrading provides not only the opportunity for AD of microalgal biomass to be cost-effective, but also the potential to reduce the environmental impacts.
\n
To move industrial application of biogas production from microalgal biomass towards commercialization, additional assessment is required regarding large scale operations. These include (1) strain robustness, outdoor productivity, location and seasonal effects, yield from real production systems, and harvesting strategy for algae cultivation (2) for biomass to biogas conversion processes, the conceptual process design needs to take the following factors into consideration: costs associated with digester heating, land, and infrastructure as well as operational parameters such as maintaining pH, temperature, mixing, power consumption, and production of coproducts like fertilizer.
\n
\n
\n
6.2. Dynamic growth models
\n
The uncertainty of large-scale algae cultivation is still a challenge which prevents commercialization; process modeling could provide useful information about the performance of microalgae cultivation systems by estimation and optimization of microalgae productivity under different conditions [103]. A growth kinetic model is critical in a process model simulating microalgae cultivation which has a direct impact on downstream conversion processing systems [135] Lee et al. [31] classified the existing kinetic models into three groups: a single limiting substrate (phosphorus, or dissolved CO2 concentration), a physical limiting factor (light intensity or temperature), and multiple factors (e.g. both substrate and light). Based on their study, there was a tradeoff between the accuracy of the model representation and real-world usability. A future modeling framework should consider along with limiting nutrients, integration of light and temperature, and incorporation of species diversity.
\n
\n
\n
6.3. Biorefinery concepts
\n
AD can be integrated to biorefineries which produce high value products from algae such as chemicals for cosmetics, nutraceuticals and pharmaceuticals. This requires diversified business strategies which benchmark the market potential for the total raw materials and alternative products. In the economic perspective, three approaches could be possible for the development of microalgae AD: (1) implementing AD for biogasification of cell debris or waste streams in microalgal based processes such as biodiesel/bioethanol/high-value bioproducts (e.g. PHA)/fuel cell/hydrothermal liquefaction/hydrogen production [68, 120]; (2) investigation of high-value products from intermediate metabolites produced during AD such as carboxylic acids [37]; (3) electricity production from microalgae derived biogas. In previous sections, the cost of electricity from microalgae derived biogas is comparable with market value while cost of the renewable natural gas from microalgae is much higher than the current market value of natural gas.
\n
\n
\n
\n
7. Conclusion and future work
\n
This chapter reviewed the literature on TEA of biogas production from algae. The key drivers to the overall production cost were identified and possible process improvements to reduce cost were discussed. The need for harmonization of resource, life cycle and techno-economic assessments in the methodology of TEA was highlighted. Modeling efforts, based on well-informed, rigorous engineering-based process models, should be integrated on a baseline framework such that different process technologies, subprocesses and alternative pathways can be directly compared at a system level. TEA model improvements include strategic planning and using reliable input data from simple mass balance calculations to geographically and seasonally specific assessments, as well as risk analysis for large-scale productivity. Nutrient recycling process has the potential to reduce both cost and environmental burdens.
\n
The cultivation of microalgae BG0011 and its economic feasibility as an energy source through anaerobic digestion was evaluated through a techno-economic analysis. The main contribution to the biogas cost is the biomass production cost. The best-case estimate was a biomethane production cost of 14.8 $/MMBtu using covered anaerobic lagoon and high-pressure water scrubbing purification. The cost of electricity production from biogas was estimated to be 13 cents/kwh. Even though these costs are higher than commercial prices in the United States, these are much lower than those costs with production of liquid fuels like ethanol or biodiesel from algae.
\n
Improved algal biomass productivities could be essential for lowering the cost of algae-derived biogas. This could be achieved by recycling the CO2 released during biogas upgrading or combustion for algae cultivation. Algal biogas economics could be further improved by marketing the digester sludge as a soil-amendment product, considering that nitrogen in the sludge was fixed from atmospheric dinitrogen.
\n
\n
Acknowledgments
\n
The authors gratefully acknowledge funding provided by Office of Energy, Florida Department of Agricultural and Consumer Services under contract number 92420 for this project.
\n
Conflict of interest
The authors declare that there are no potential financial or other interests that could be perceived to influence the outcomes of the research.
\n',keywords:"microalgae, anaerobic digestion, biogas, techno-economic analysis, Cyanothece BG0011",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/66930.pdf",chapterXML:"https://mts.intechopen.com/source/xml/66930.xml",downloadPdfUrl:"/chapter/pdf-download/66930",previewPdfUrl:"/chapter/pdf-preview/66930",totalDownloads:2165,totalViews:1,totalCrossrefCites:16,dateSubmitted:"September 11th 2018",dateReviewed:"March 27th 2019",datePrePublished:"April 27th 2019",datePublished:"September 4th 2019",dateFinished:"April 27th 2019",readingETA:"0",abstract:"Microalgae are a promising feedstock for bioenergy due to higher productivity, flexible growing conditions, and high lipid/polysaccharide content compared to terrestrial biomass. Microalgae can be converted to biogas through anaerobic digestion (AD). AD is a mature technology with a high energy return on energy invested. Microalgae AD can bypass energy intensive dewatering operations that are associated with liquid fuel production from algae. A techno-economic assessment of the commercial feasibility of algae-based biogas production was conducted using Cyanothece BG0011 biomass as an example. BG0011 is a naturally occurring, saline cyanobacterium isolated from Florida Keys. It fixes atmospheric nitrogen and produces exopolysaccharide (EPS). Maximum cell density and EPS concentration of 2.7 and 2.1 g afdw1/L (for total algae biomass concentration of 4.8 g afdw/L) were obtained by air sparging. For an areal cell and EPS productivity of 12.4 and 9.6 g afdw/m2/day, respectively, the biomethane production cost was 14.8 $/MMBtu using covered anaerobic lagoon and high-pressure water scrubbing for biogas purification. Electricity production from biogas costs 13 cents/kwh. If areal productivity was increased by 33% from the same system, by sparging air enriched with 1% CO2, then biomethane cost was reduced to 12.16 $/MMBtu and electricity cost to 11 cents/kwh.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/66930",risUrl:"/chapter/ris/66930",signatures:"Na Wu, Cesar M. Moreira, Yingxiu Zhang, Nguyet Doan, Shunchang Yang, Edward J. Phlips, Spyros A. Svoronos and Pratap C. Pullammanappallil",book:{id:"6839",type:"book",title:"Anaerobic Digestion",subtitle:null,fullTitle:"Anaerobic Digestion",slug:"anaerobic-digestion",publishedDate:"September 4th 2019",bookSignature:"J. Rajesh Banu",coverURL:"https://cdn.intechopen.com/books/images_new/6839.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-83881-850-0",printIsbn:"978-1-83881-849-4",pdfIsbn:"978-1-83881-851-7",isAvailableForWebshopOrdering:!0,editors:[{id:"218539",title:"Dr.",name:"Rajesh Banu",middleName:null,surname:"Jeyakumar",slug:"rajesh-banu-jeyakumar",fullName:"Rajesh Banu Jeyakumar"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"157581",title:"Dr.",name:"Pratap",middleName:null,surname:"Pullammanappallil",fullName:"Pratap Pullammanappallil",slug:"pratap-pullammanappallil",email:"pcpratap@ufl.edu",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"University of Florida",institutionURL:null,country:{name:"United States of America"}}},{id:"274349",title:"Ph.D.",name:"Na",middleName:null,surname:"Wu",fullName:"Na Wu",slug:"na-wu",email:"wuna8703@ufl.edu",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"University of Florida",institutionURL:null,country:{name:"United States of America"}}},{id:"276285",title:"Dr.",name:"Yingxiu",middleName:null,surname:"Zhang",fullName:"Yingxiu Zhang",slug:"yingxiu-zhang",email:"yingxiu.zhang88@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Tianjin University",institutionURL:null,country:{name:"China"}}},{id:"276286",title:"Dr.",name:"Shunchang",middleName:null,surname:"Yang",fullName:"Shunchang Yang",slug:"shunchang-yang",email:"jack.shushu87@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"University of Florida",institutionURL:null,country:{name:"United States of America"}}},{id:"276289",title:"Dr.",name:"Doan",middleName:null,surname:"Nguyet",fullName:"Doan Nguyet",slug:"doan-nguyet",email:"dtmnguyetagu@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"University of Florida",institutionURL:null,country:{name:"United States of America"}}},{id:"276290",title:"Dr.",name:"Spyros",middleName:null,surname:"Svoronos",fullName:"Spyros Svoronos",slug:"spyros-svoronos",email:"svoronos@ufl.edu",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"University of Florida",institutionURL:null,country:{name:"United States of America"}}},{id:"289093",title:"Dr.",name:"Edward",middleName:null,surname:"Phlips",fullName:"Edward Phlips",slug:"edward-phlips",email:"phlips@ufl.edu",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"University of Florida",institutionURL:null,country:{name:"United States of America"}}},{id:"299854",title:"Dr.",name:"Cesar",middleName:"M",surname:"Moreira",fullName:"Cesar Moreira",slug:"cesar-moreira",email:"cemoreir@espol.edu.ec",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Escuela Superior Politecnica del Litoral",institutionURL:null,country:{name:"Ecuador"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Anaerobic digestion",level:"1"},{id:"sec_3",title:"3. Key drivers of microalgal biogas production cost",level:"1"},{id:"sec_3_2",title:"3.1. Microalgae cultivation, harvesting and dewatering",level:"2"},{id:"sec_4_2",title:"3.2. Anaerobic digestion systems",level:"2"},{id:"sec_4_3",title:"3.2.1. Pretreatment",level:"3"},{id:"sec_5_3",title:"3.2.2. Hydraulic retention time (HRT), organic loading rate (OLR), and reactor configurations",level:"3"},{id:"sec_6_3",title:"3.2.3. Temperature, pH, salinity, sulfur, and lipids content",level:"3"},{id:"sec_7_3",title:"3.2.4. C/N ratio",level:"3"},{id:"sec_8_3",title:"3.2.5. Other factors",level:"3"},{id:"sec_9_3",title:"Table 2.",level:"3"},{id:"sec_12",title:"4. Techno-economic analysis",level:"1"},{id:"sec_12_2",title:"4.1. TEA framework",level:"2"},{id:"sec_13_2",title:"4.2. State of the art: TEA of microalgal biogas",level:"2"},{id:"sec_14_2",title:"4.3. Cost management",level:"2"},{id:"sec_15_2",title:"4.4. TEA limitations",level:"2"},{id:"sec_17",title:"5. Case study: TEA of anaerobic digestion of Cyanothece BG0011",level:"1"},{id:"sec_17_2",title:"5.1. Cyanothece BG0011 cultivation",level:"2"},{id:"sec_18_2",title:"5.2. Anaerobic digestion",level:"2"},{id:"sec_19_2",title:"5.3. Biogas purification",level:"2"},{id:"sec_20_2",title:"5.4. Power generation from biogas",level:"2"},{id:"sec_21_2",title:"5.5. Techno-economic analysis of integrated system",level:"2"},{id:"sec_21_3",title:"Table 4.",level:"3"},{id:"sec_22_3",title:"Table 5.",level:"3"},{id:"sec_23_3",title:"Table 6.",level:"3"},{id:"sec_26",title:"6. Cost minimization approaches",level:"1"},{id:"sec_26_2",title:"6.1. Nutrient recycling and biogas upgrading",level:"2"},{id:"sec_27_2",title:"6.2. Dynamic growth models",level:"2"},{id:"sec_28_2",title:"6.3. Biorefinery concepts",level:"2"},{id:"sec_30",title:"7. 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Pretreatment of microalgae to improve biogas production: A review. Bioresource Technology. 2014;172:403-412\n'},{id:"B115",body:'Rodriguez C, Alaswad A, Mooney J, Prescott T, Olabi AG. Pre-treatment techniques used for anaerobic digestion of algae. Fuel Processing Technology. 2015;138:765-779\n'},{id:"B116",body:'Bohutskyi P, Betenbaugh MJ, Bouwer EJ. The effects of alternative pretreatment strategies on anaerobic digestion and methane production from different algal strains. Bioresource Technology. 2014;155:366-372\n'},{id:"B117",body:'Giraldo-Calderón ND, Romo-Buchelly RJ, Arbeláez-Pérez AA, Echeverri-Hincapié D, Atehortúa-Garcés L. Microalgae biorefineries: Applications and emerging technologies. DYNA. 2018;85(205):219-233\n'},{id:"B118",body:'Jagadevan S, Banerjee A, Banerjee C, Guria C, Tiwari R, Baweja M, et al. Recent developments in synthetic biology and metabolic engineering in microalgae towards biofuel production. Biotechnology for Biofuels. 2018;11(1):185\n'},{id:"B119",body:'Solé-Bundó M, Salvadó H, Passos F, Garfí M, Ferrer I. Strategies to optimize microalgae conversion to biogas: Co-digestion, pretreatment and hydraulic retention time. Molecules. 2018;23(9):2096\n'},{id:"B120",body:'Sathyaprakasan P, Kannan G. Economics of bio-hydrogen production. International Journal of Environmental Science and Development. 2015;6(5):352\n'},{id:"B121",body:'Mottet A, Habouzit F, Steyer JP. Anaerobic digestion of marine microalgae in different salinity levels. Bioresource Technology. 2014;158:300-306\n'},{id:"B122",body:'Doğan-Subaşı E, Demirer GN. Anaerobic digestion of microalgal (Chlorella vulgaris) biomass as a source of biogas and biofertilizer. Environmental Progress & Sustainable Energy. 2016;35(4):936-941\n'},{id:"B123",body:'Lü F, Ji J, Shao L, He P. Bacterial bioaugmentation for improving methane and hydrogen production from microalgae. Biotechnology for Biofuels. 2013;6(1):92\n'},{id:"B124",body:'Yang S, Liu Y, Wu N, Zhang Y, Svoronos S, Pullammanappallil P. Low-cost, Arduino-based, portable device for measurement of methane composition in biogas. Renewable Energy. 2019;138:224-229\n'},{id:"B125",body:'Perazzoli S, Steinmetz RL, Mezzari MP, Nunes EO, da Silva ML. Biogas production from microalga biomass. In: Embrapa Suínos e Aves-Artigo em anais de congresso (ALICE). Simpósio Internacional Sobre Gerenciamento de Resíduos Agropecuários e Agroindustriais. Vol. 3. São Pedro, SP. Anais… São Pedro, SP: SBERA; 2013\n'},{id:"B126",body:'Passos F, Solé M, García J, Ferrer I. Biogas production from microalgae grown in wastewater: Effect of microwave pretreatment. Applied Energy. 2013;108:168-175\n'},{id:"B127",body:'Bohutskyi P, Bouwer E. Biogas production from algae and cyanobacteria through anaerobic digestion: A review, analysis, and research needs. In: Advanced Biofuels and Bioproducts. New York, NY: Springer; 2013. pp. 873-975\n'},{id:"B128",body:'Xin C, Addy MM, Zhao J, Cheng Y, Cheng S, Mu D, et al. Comprehensive techno-economic analysis of wastewater-based algal biofuel production: A case study. Bioresource Technology. 2016;211:584-593\n'},{id:"B129",body:'Milledge JJ, Heaven S. Energy balance of biogas production from microalgae: Development of an energy and mass balance model. Current Biotechnology. 2015;4(4):554-567\n'},{id:"B130",body:'Klassen V, Blifernez-Klassen O, Wibberg D, Winkler A, Kalinowski J, Posten C, et al. Highly efficient methane generation from untreated microalgae biomass. Biotechnology for Biofuels. 2017;10(1):186\n'},{id:"B131",body:'Juneja A, Murthy GS. Evaluating the potential of renewable diesel production from algae cultured on wastewater: Techno-economic analysis and life cycle assessment. AIMS Energy. 2017;5(2):239-257\n'},{id:"B132",body:'Xiang X, Ozkan A, Kelly C, Radniecki T. Importance of microalgae speciation on biogas production and nutrient recovery from anaerobic digestion of lipid-extracted microalgae biomass. Environmental Engineering Science. 2018;35(4):382-389\n'},{id:"B133",body:'Mussgnug JH, Klassen V, Schlüter A, Kruse O. Microalgae as substrates for fermentative biogas production in a combined biorefinery concept. Journal of Biotechnology. 2010;150(1):51-56\n'},{id:"B134",body:'Wang X, Nordlander E, Thorin E, Yan J. Microalgal biomethane production integrated with an existing biogas plant: A case study in Sweden. Applied energy. 2013;112:478-484\n'},{id:"B135",body:'Darvehei P, Bahri PA, Moheimani NR. Model development for the growth of microalgae: A review. Renewable and Sustainable Energy Reviews. 2018;97:233-258\n'},{id:"B136",body:'Kumar K, Ghosh S, Angelidaki I, Holdt SL, Karakashev DB, Morales MA, et al. Recent developments on biofuels production from microalgae and macroalgae. Renewable and Sustainable Energy Reviews. 2016;65:235-249\n'},{id:"B137",body:'Santos-Ballardo DU, Rossi S, Reyes-Moreno C, Valdez-Ortiz A. Microalgae potential as a biogas source: Current status, restraints and future trends. Reviews in Environmental Science and Bio/Technology. 2016;15(2):243-264\n'},{id:"B138",body:'Wang X, Bao K, Cao W, Zhao Y, Hu CW. Screening of microalgae for integral biogas slurry nutrient removal and biogas upgrading by different microalgae cultivation technology. Scientific Reports. 2017;7(1):5426\n'},{id:"B139",body:'Lu D, Zhang XJ, Liu X, Zhang L, Hines M. Sustainable microalgae cultivation by using anaerobic centrate and biogas from anaerobic digestion. Algal Research. 2018;35:115-124\n'},{id:"B140",body:'Harun R, Davidson M, Doyle M, Gopiraj R, Danquah M, Forde G. Technoeconomic analysis of an integrated microalgae photobioreactor, biodiesel and biogas production facility. Biomass and Bioenergy. 2011;35(1):741-747\n'},{id:"B141",body:'Barlow J, Sims RC, Quinn JC. Techno-economic and life-cycle assessment of an attached growth algal biorefinery. Bioresource Technology. 2016;220:360-368\n'},{id:"B142",body:'Batan LY, Graff GD, Bradley TH. Techno-economic and Monte Carlo probabilistic analysis of microalgae biofuel production system. Bioresource Technology. 2016;219:45-52\n'},{id:"B143",body:'Saharan BS, Sharma D, Sahu R, Sahin O, Warren A. Towards algal biofuel production: A concept of green bio energy development. Innovative Romanian Food Biotechnology. 2013;12:1\n'},{id:"B144",body:'Stiles WA, Styles D, Chapman SP, Esteves S, Bywater A, Melville L, et al. Using microalgae in the circular economy to valorise anaerobic digestate: Challenges and opportunities. Bioresource Technology. 2018;267:732-742\n'},{id:"B145",body:'Kavitha S, Subbulakshmi P, Banu JR, Gobi M, Yeom IT. Enhancement of biogas production from microalgal biomass through cellulolytic bacterial pretreatment. Bioresource Technology. 2017;233:34-43\n'},{id:"B146",body:'Kavitha S, Banu JR, Priya AA, Uan DK, Yeom IT. Liquefaction of food waste and its impacts on anaerobic biodegradability, energy ratio and economic feasibility. Applied Energy. 2017;208:228-238\n'},{id:"B147",body:'Kavitha S, Kannah RY, Banu JR, Kaliappan S, Johnson M. Biological disintegration of microalgae for biomethane recovery-prediction of biodegradability and computation of energy balance. Bioresource Technology. 2017;244:1367-1375\n'},{id:"B148",body:'Doan NT. Assessing and Enhancing Methane Productivity from Anaerobic Digestion Using Cyanothece BG0011 as Feedstock [Doctoral Dissertation]: University of Florida; 2017\n'},{id:"B149",body:'Zhang Y. Cultivation, growth optimization and modeling of a saline Cyanothece species BG0011 for production of biofuels and bioproducts [doctoral dissertation]. University of Florida; 2018\n'},{id:"B150",body:'Brown RC, Brown TR. Biorenewable Resources: Engineering New Products from Agriculture. Hoboken, New Jersey: John Wiley & Sons; 2013\n'},{id:"B151",body:'Doran PM. Bioprocess Engineering Principles. Cambridge, Massachusetts: Academic Press; 1995\n'},{id:"B152",body:'McCollum DL, Ogden JM. Techno-economic models for carbon dioxide compression, transport, and storage & correlations for estimating carbon dioxide density and viscosity. [Internet]. 2006. Available from: https://escholarship.org/uc/item/1zg00532 [Accessed: 2018-12-05]\n'}],footnotes:[{id:"fn1",explanation:"Ash free dry weight."}],contributors:[{corresp:"yes",contributorFullName:"Na Wu",address:"wuna8703@ufl.edu",affiliation:'
Department of Agricultural and Biological Engineering, University of Florida, USA
'},{corresp:null,contributorFullName:"Cesar M. Moreira",address:null,affiliation:'
Department of Agricultural and Biological Engineering, University of Florida, USA
Faculty of Mechanical Engineering and Production Sciences, ESPOL Polytechnic University, Ecuador
Department of Agricultural and Biological Engineering, University of Florida, USA
'},{corresp:null,contributorFullName:"Edward J. Phlips",address:null,affiliation:'
Department of Fisheries and Aquatic Science, University of Florida, USA
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Department of Chemical Engineering, University of Florida, USA
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Department of Agricultural and Biological Engineering, University of Florida, USA
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Semchyshyn graduated from the the Yuriy Fedkovych Chernivtsi State University, Ukraine in 1990 with MSc degree in Analytical \nChemistry. In 1993 she obtained Doctoral Scholarship at the Department of Biochemistry of Ivano-Frankivsk Medical State Academy (Ukraine). After defending her Ph.D. thesis in 2002, she started working as an Assistant Professor and and as an Associate Professor at the Department of Biochemistry and Biotechnology of Vassyl Stefanyk \nPrecarpathian University Ivano-Frankivsk, Ukraine. From 2006 to 2011 she worked as a visiting researcher and a visiting professor at the Jagiellonian University, Poland and at the Lund University, Sweden. Her current research is related to dual role of reactive oxygen and carbonyl species, interplay between different kinds of stresses, and organisms’ \nadaptations to environmental challenges.",institutionString:null,institution:{name:"Vasyl Stefanyk Precarpathian National University",institutionURL:null,country:{name:"Ukraine"}}},{id:"108752",title:"Dr.",name:"Padmini",surname:"Ghugre",slug:"padmini-ghugre",fullName:"Padmini Ghugre",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"128379",title:"Prof.",name:"Knox",surname:"Van Dyke",slug:"knox-van-dyke",fullName:"Knox Van Dyke",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"West Virginia University",institutionURL:null,country:{name:"United States of America"}}}]},generic:{page:{slug:"open-access-funding-funders-list",title:"List of Funders by Country",intro:"
If your research is financed through any of the below-mentioned funders, please consult their Open Access policies or grant ‘terms and conditions’ to explore ways to cover your publication costs (also accessible by clicking on the link in their title).
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IMPORTANT: You must be a member or grantee of the listed funders in order to apply for their Open Access publication funds. Do not attempt to contact the funders if this is not the case.
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UK Research and Innovation (former Research Councils UK (RCUK) - including AHRC, BBSRC, ESRC, EPSRC, MRC, NERC, STFC.) Processing charges for books/book chapters can be covered through RCUK block grants which are allocated to most universities in the UK, which then handle the OA publication funding requests. It is at the discretion of the university whether it will approve the request.)
UK Research and Innovation (former Research Councils UK (RCUK) - including AHRC, BBSRC, ESRC, EPSRC, MRC, NERC, STFC.) Processing charges for books/book chapters can be covered through RCUK block grants which are allocated to most universities in the UK, which then handle the OA publication funding requests. It is at the discretion of the university whether it will approve the request.)
Wellcome Trust (Funding available only to Wellcome-funded researchers/grantees)
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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:{name:"Association for Computing Machinery",country:{name:"United States of America"}}},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"426586",title:"Dr.",name:"Oladunni A.",middleName:null,surname:"Daramola",slug:"oladunni-a.-daramola",fullName:"Oladunni A. Daramola",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Federal University of Technology",country:{name:"Nigeria"}}},{id:"357014",title:"Prof.",name:"Leon",middleName:null,surname:"Bobrowski",slug:"leon-bobrowski",fullName:"Leon Bobrowski",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Bialystok University of Technology",country:{name:"Poland"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"354126",title:"Dr.",name:"Setiawan",middleName:null,surname:"Hadi",slug:"setiawan-hadi",fullName:"Setiawan Hadi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Padjadjaran University",country:{name:"Indonesia"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"332603",title:"Prof.",name:"Kumar S.",middleName:null,surname:"Ray",slug:"kumar-s.-ray",fullName:"Kumar S. Ray",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Statistical Institute",country:{name:"India"}}},{id:"415409",title:"Prof.",name:"Maghsoud",middleName:null,surname:"Amiri",slug:"maghsoud-amiri",fullName:"Maghsoud Amiri",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Allameh Tabataba'i University",country:{name:"Iran"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}}]}},subseries:{item:{id:"22",type:"subseries",title:"Applied Intelligence",keywords:"Machine Learning, Intelligence Algorithms, Data Science, Artificial Intelligence, Applications on Applied Intelligence",scope:"This field is the key in the current industrial revolution (Industry 4.0), where the new models and developments are based on the knowledge generation on applied intelligence. The motor of the society is the industry and the research of this topic has to be empowered in order to increase and improve the quality of our lives.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/22.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11418,editor:{id:"27170",title:"Prof.",name:"Carlos",middleName:"M.",surname:"Travieso-Gonzalez",slug:"carlos-travieso-gonzalez",fullName:"Carlos Travieso-Gonzalez",profilePictureURL:"https://mts.intechopen.com/storage/users/27170/images/system/27170.jpeg",biography:"Carlos M. Travieso-González received his MSc degree in Telecommunication Engineering at Polytechnic University of Catalonia (UPC), Spain in 1997, and his Ph.D. degree in 2002 at the University of Las Palmas de Gran Canaria (ULPGC-Spain). He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). 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