A Novel Electricity and Freshwater Production System: Performance Analysis from Reliability and Exergoeconomic Viewpoints with Multi-Objective Optimization
Based on the benefits of integrated gasification combined cycles (IGCCs), a cogeneration plant for providing electricity and freshwater is proposed. The main novelties of the devised system are the integration of biomass gasification and a regenerative gas turbine with intercooling and a syngas comb...
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doaj-6ec7c331df814b12a283730d8382e0a22021-06-30T23:26:03ZengMDPI AGSustainability2071-10502021-06-01136448644810.3390/su13116448A Novel Electricity and Freshwater Production System: Performance Analysis from Reliability and Exergoeconomic Viewpoints with Multi-Objective OptimizationFarzad Hamrang0S. M. Seyed Mahmoudi1Marc A. Rosen2Department of Mechanical Engineering, University of Tabriz, Tabriz 51666-14766, IranDepartment of Mechanical Engineering, University of Tabriz, Tabriz 51666-14766, IranFaculty of Engineering and Applied Science, University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa, ON L1G 0C5, CanadaBased on the benefits of integrated gasification combined cycles (IGCCs), a cogeneration plant for providing electricity and freshwater is proposed. The main novelties of the devised system are the integration of biomass gasification and a regenerative gas turbine with intercooling and a syngas combustor, where the syngas produced in the gasifier is burned in the combustion chamber and fed to a gas turbine directly. The energy discharged from the gas turbine is utilized for further electricity and freshwater generation via Kalina and MED hybridization. The proposed system is analyzed from energy, exergy, exergoeconomic, and reliability–availability viewpoints. The optimal operating condition and optimum performance criteria are obtained by hybridizing an artificial neural network (ANN), the multi-objective particle swarm optimization (MOPSO) algorithm. According to results obtained, for the fourth scenario of the optimization process, optimal values of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>45.10</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>14.27</mn><mrow><mtext> </mtext><mi>kg</mi></mrow><mo>·</mo><msup><mi mathvariant="normal">s</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>12.95</mn><mrow><mtext> </mtext><mi>USD</mi></mrow><mo>·</mo><msup><mrow><mi>GJ</mi></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>8141</mn><mrow><mtext> </mtext><mi>kW</mi></mrow></mrow></semantics></math></inline-formula> are obtained for the exergy efficiency, freshwater production rate, sum unit cost of products, and net output power, respectively. According to reliability and availability assessment, the probability of the healthy working state of all components and subsystems is <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>88.4403</mn><mo>%</mo><mo>;</mo></mrow></semantics></math></inline-formula> the system is shown to be <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>87.74</mn><mo>%</mo></mrow></semantics></math></inline-formula> available of the time over the 20-year lifetime.https://www.mdpi.com/2071-1050/13/11/6448electricity and freshwater cogenerationavailability and reliabilityexergoeconomic assessmentmulti-objective optimizationartificial neural network |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Farzad Hamrang S. M. Seyed Mahmoudi Marc A. Rosen |
spellingShingle |
Farzad Hamrang S. M. Seyed Mahmoudi Marc A. Rosen A Novel Electricity and Freshwater Production System: Performance Analysis from Reliability and Exergoeconomic Viewpoints with Multi-Objective Optimization Sustainability electricity and freshwater cogeneration availability and reliability exergoeconomic assessment multi-objective optimization artificial neural network |
author_facet |
Farzad Hamrang S. M. Seyed Mahmoudi Marc A. Rosen |
author_sort |
Farzad Hamrang |
title |
A Novel Electricity and Freshwater Production System: Performance Analysis from Reliability and Exergoeconomic Viewpoints with Multi-Objective Optimization |
title_short |
A Novel Electricity and Freshwater Production System: Performance Analysis from Reliability and Exergoeconomic Viewpoints with Multi-Objective Optimization |
title_full |
A Novel Electricity and Freshwater Production System: Performance Analysis from Reliability and Exergoeconomic Viewpoints with Multi-Objective Optimization |
title_fullStr |
A Novel Electricity and Freshwater Production System: Performance Analysis from Reliability and Exergoeconomic Viewpoints with Multi-Objective Optimization |
title_full_unstemmed |
A Novel Electricity and Freshwater Production System: Performance Analysis from Reliability and Exergoeconomic Viewpoints with Multi-Objective Optimization |
title_sort |
novel electricity and freshwater production system: performance analysis from reliability and exergoeconomic viewpoints with multi-objective optimization |
publisher |
MDPI AG |
series |
Sustainability |
issn |
2071-1050 |
publishDate |
2021-06-01 |
description |
Based on the benefits of integrated gasification combined cycles (IGCCs), a cogeneration plant for providing electricity and freshwater is proposed. The main novelties of the devised system are the integration of biomass gasification and a regenerative gas turbine with intercooling and a syngas combustor, where the syngas produced in the gasifier is burned in the combustion chamber and fed to a gas turbine directly. The energy discharged from the gas turbine is utilized for further electricity and freshwater generation via Kalina and MED hybridization. The proposed system is analyzed from energy, exergy, exergoeconomic, and reliability–availability viewpoints. The optimal operating condition and optimum performance criteria are obtained by hybridizing an artificial neural network (ANN), the multi-objective particle swarm optimization (MOPSO) algorithm. According to results obtained, for the fourth scenario of the optimization process, optimal values of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>45.10</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>14.27</mn><mrow><mtext> </mtext><mi>kg</mi></mrow><mo>·</mo><msup><mi mathvariant="normal">s</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>12.95</mn><mrow><mtext> </mtext><mi>USD</mi></mrow><mo>·</mo><msup><mrow><mi>GJ</mi></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>8141</mn><mrow><mtext> </mtext><mi>kW</mi></mrow></mrow></semantics></math></inline-formula> are obtained for the exergy efficiency, freshwater production rate, sum unit cost of products, and net output power, respectively. According to reliability and availability assessment, the probability of the healthy working state of all components and subsystems is <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>88.4403</mn><mo>%</mo><mo>;</mo></mrow></semantics></math></inline-formula> the system is shown to be <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>87.74</mn><mo>%</mo></mrow></semantics></math></inline-formula> available of the time over the 20-year lifetime. |
topic |
electricity and freshwater cogeneration availability and reliability exergoeconomic assessment multi-objective optimization artificial neural network |
url |
https://www.mdpi.com/2071-1050/13/11/6448 |
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