Soft computing based optimization of a novel solar heliostat integrated energy system using artificial neural networks

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dc.authoridPouria Ahmadi / 0000-0001-8829-133X
dc.authorscopusidPouria Ahmadi / 23569183500
dc.authorwosidPouria Ahmadi / G-6879-2013
dc.contributor.authorAlirahmi, S.M.
dc.contributor.authorKhoshnevisan, A.
dc.contributor.authorShirazi, P.
dc.contributor.authorAhmadi, Pouria
dc.contributor.authorKari, D.
dc.date.accessioned2022-01-17T06:22:35Z
dc.date.available2022-01-17T06:22:35Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Biyomedikal Mühendisliği Bölümüen_US
dc.description.abstractThis study proposes and investigates a novel energy system based on biomass and solar energy. This plant is composed of a biomass unit, a solar unit, and a waste-heat recovery unit. This novel proposed integrated system can provide the needs such as electricity, hydrogen, freshwater, heating, and hot water production. For electricity generation, two gas turbines, one steam Rankine cycle, and one organic Rankine cycle are used. In contrast, for utilization of solar energy, a heliostat field, and for biomass conversion, a gasifier is used. In addition, the desalination unit and PEM electrolyzer are utilized to produce fresh water and hydrogen, respectively. Firstly, the present work aims to investigate the developed system from the exergoeconomic and environmental perspective. Multi-objective optimization is conducted to determine the maximum amount of exergetic efficiency and the minimum value of the cost rate. An artificial neural network (ANN) is employed as a mediator tool to accelerate the optimization process. The relation between objective functions and design parameters is studied utilizing ANN to obtain the plant optimal decision variables. Employing the Pareto Envelope-based selection algorithm II (PESA-II) method, the optimum amount for the total cost rate and exergy efficiency is found 224.1 $/h and 26.7%, respectively. In addition, three evolutionary-based optimization algorithms are applied to determine the optimum results of the suggested plant. © 2021 Elsevier Ltden_US
dc.identifier.citationAlirahmi, S. M., Khoshnevisan, A., Shirazi, P., Ahmadi, P., & Kari, D. (2022). Soft computing based optimization of a novel solar heliostat integrated energy system using artificial neural networks. Sustainable Energy Technologies and Assessments, 50en_US
dc.identifier.doi10.1016/j.seta.2021.101850en_US
dc.identifier.issn2213-1388en_US
dc.identifier.scopus2-s2.0-85121604177en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.seta.2021.101850
dc.identifier.urihttps://hdl.handle.net/20.500.12713/2387
dc.identifier.volume50en_US
dc.identifier.wosWOS:000788057800006en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorAhmadi, Pouria
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofSustainable Energy Technologies and Assessmentsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural Networken_US
dc.subjectExergoeconomicen_US
dc.subjectGasifieren_US
dc.subjectMED-TVCen_US
dc.subjectPEM Electrolyzeren_US
dc.titleSoft computing based optimization of a novel solar heliostat integrated energy system using artificial neural networksen_US
dc.typeArticleen_US

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