An intelligent thermodynamic/economic approach based on artificial neural network combined with MOGWO algorithm to study a novel polygeneration scheme using a modified dual-flash geothermal cycle
Flash-based geothermal cycles correspond to environmentally friendly and cost-effective processes in a renewable framework and provide an opportunity for combined cycles. However, these cycles are characterized by significant energy losses and their waste stream’s low/medium operational temperature...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Haghghi, Maghsoud Abdollahi [verfasserIn] Hasanzadeh, Amirhossein [verfasserIn] Nadimi, Ebrahim [verfasserIn] Rosato, Antonio [verfasserIn] Athari, Hassan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Process safety and environmental protection - Amsterdam : Elsevier, 1990, 173, Seite 859-880 |
|---|---|
| Übergeordnetes Werk: |
volume:173 ; pages:859-880 |
| DOI / URN: |
10.1016/j.psep.2023.03.056 |
|---|
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ELV009590935 |
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| 245 | 1 | 0 | |a An intelligent thermodynamic/economic approach based on artificial neural network combined with MOGWO algorithm to study a novel polygeneration scheme using a modified dual-flash geothermal cycle |
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| 520 | |a Flash-based geothermal cycles correspond to environmentally friendly and cost-effective processes in a renewable framework and provide an opportunity for combined cycles. However, these cycles are characterized by significant energy losses and their waste stream’s low/medium operational temperature is the principal defect for managing multiple generation arrangements without assisting other energy resources. Hence, the main aim of this study is to propose a novel polygeneration scheme, integrated with a dual-flash geothermal cycle equipped with self-superheaters, able to mitigate the discussed defect. A new coupled series and parallel design of energy recovery is established, allowing to increase the compatibility of combined cycles and enable a larger production. This design encompasses a single-effect refrigeration cycle, a modified transcritical CO2 cycle, a polymer electrolyte membrane electrolyzer, and a thermal desalination cycle. The proposed process is examined from thermodynamic, sustainability, and economic (exergoeconomic and net present value analyses) points of view. Besides, a detailed sensitivity study is conducted by which the trend of performance variables in response to the increasing five main decision parameters is viewed. Afterward, an intelligent approach relying on an artificial neural network is built to learn and validate the behavior of defined objective functions (exergetic efficiency and products’ levelized cost). Moreover, a multi-objective grey wolf optimization (MOGWO) procedure endeavors to optimize the operation of the system. According to the results of this study, flash tank 2′s inlet pressure is the effective parameter, and its mean sensitivity index equals 0.289. Besides, the aforementioned objectives are gauged at 37.45% and 0.0625 $/kWh, respectively. | ||
| 650 | 4 | |a Multi-stage waste management | |
| 650 | 4 | |a Dual-flash geothermal cycle | |
| 650 | 4 | |a Transcritical CO | |
| 650 | 4 | |a Polygeneration | |
| 650 | 4 | |a Multi-objective grey wolf optimization | |
| 650 | 4 | |a Artificial neural network | |
| 700 | 1 | |a Hasanzadeh, Amirhossein |e verfasserin |0 (orcid)0000-0001-7815-3988 |4 aut | |
| 700 | 1 | |a Nadimi, Ebrahim |e verfasserin |0 (orcid)0000-0003-3338-5288 |4 aut | |
| 700 | 1 | |a Rosato, Antonio |e verfasserin |4 aut | |
| 700 | 1 | |a Athari, Hassan |e verfasserin |4 aut | |
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10.1016/j.psep.2023.03.056 doi (DE-627)ELV009590935 (ELSEVIER)S0957-5820(23)00259-8 DE-627 ger DE-627 rda eng 660 540 333.7 VZ 58.18 bkl Haghghi, Maghsoud Abdollahi verfasserin aut An intelligent thermodynamic/economic approach based on artificial neural network combined with MOGWO algorithm to study a novel polygeneration scheme using a modified dual-flash geothermal cycle 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Flash-based geothermal cycles correspond to environmentally friendly and cost-effective processes in a renewable framework and provide an opportunity for combined cycles. However, these cycles are characterized by significant energy losses and their waste stream’s low/medium operational temperature is the principal defect for managing multiple generation arrangements without assisting other energy resources. Hence, the main aim of this study is to propose a novel polygeneration scheme, integrated with a dual-flash geothermal cycle equipped with self-superheaters, able to mitigate the discussed defect. A new coupled series and parallel design of energy recovery is established, allowing to increase the compatibility of combined cycles and enable a larger production. This design encompasses a single-effect refrigeration cycle, a modified transcritical CO2 cycle, a polymer electrolyte membrane electrolyzer, and a thermal desalination cycle. The proposed process is examined from thermodynamic, sustainability, and economic (exergoeconomic and net present value analyses) points of view. Besides, a detailed sensitivity study is conducted by which the trend of performance variables in response to the increasing five main decision parameters is viewed. Afterward, an intelligent approach relying on an artificial neural network is built to learn and validate the behavior of defined objective functions (exergetic efficiency and products’ levelized cost). Moreover, a multi-objective grey wolf optimization (MOGWO) procedure endeavors to optimize the operation of the system. According to the results of this study, flash tank 2′s inlet pressure is the effective parameter, and its mean sensitivity index equals 0.289. Besides, the aforementioned objectives are gauged at 37.45% and 0.0625 $/kWh, respectively. Multi-stage waste management Dual-flash geothermal cycle Transcritical CO Polygeneration Multi-objective grey wolf optimization Artificial neural network Hasanzadeh, Amirhossein verfasserin (orcid)0000-0001-7815-3988 aut Nadimi, Ebrahim verfasserin (orcid)0000-0003-3338-5288 aut Rosato, Antonio verfasserin aut Athari, Hassan verfasserin aut Enthalten in Process safety and environmental protection Amsterdam : Elsevier, 1990 173, Seite 859-880 Online-Ressource (DE-627)318710420 (DE-600)2008004-9 (DE-576)284747785 nnns volume:173 pages:859-880 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 58.18 Chemische Betriebstechnik VZ AR 173 859-880 |
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10.1016/j.psep.2023.03.056 doi (DE-627)ELV009590935 (ELSEVIER)S0957-5820(23)00259-8 DE-627 ger DE-627 rda eng 660 540 333.7 VZ 58.18 bkl Haghghi, Maghsoud Abdollahi verfasserin aut An intelligent thermodynamic/economic approach based on artificial neural network combined with MOGWO algorithm to study a novel polygeneration scheme using a modified dual-flash geothermal cycle 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Flash-based geothermal cycles correspond to environmentally friendly and cost-effective processes in a renewable framework and provide an opportunity for combined cycles. However, these cycles are characterized by significant energy losses and their waste stream’s low/medium operational temperature is the principal defect for managing multiple generation arrangements without assisting other energy resources. Hence, the main aim of this study is to propose a novel polygeneration scheme, integrated with a dual-flash geothermal cycle equipped with self-superheaters, able to mitigate the discussed defect. A new coupled series and parallel design of energy recovery is established, allowing to increase the compatibility of combined cycles and enable a larger production. This design encompasses a single-effect refrigeration cycle, a modified transcritical CO2 cycle, a polymer electrolyte membrane electrolyzer, and a thermal desalination cycle. The proposed process is examined from thermodynamic, sustainability, and economic (exergoeconomic and net present value analyses) points of view. Besides, a detailed sensitivity study is conducted by which the trend of performance variables in response to the increasing five main decision parameters is viewed. Afterward, an intelligent approach relying on an artificial neural network is built to learn and validate the behavior of defined objective functions (exergetic efficiency and products’ levelized cost). Moreover, a multi-objective grey wolf optimization (MOGWO) procedure endeavors to optimize the operation of the system. According to the results of this study, flash tank 2′s inlet pressure is the effective parameter, and its mean sensitivity index equals 0.289. Besides, the aforementioned objectives are gauged at 37.45% and 0.0625 $/kWh, respectively. Multi-stage waste management Dual-flash geothermal cycle Transcritical CO Polygeneration Multi-objective grey wolf optimization Artificial neural network Hasanzadeh, Amirhossein verfasserin (orcid)0000-0001-7815-3988 aut Nadimi, Ebrahim verfasserin (orcid)0000-0003-3338-5288 aut Rosato, Antonio verfasserin aut Athari, Hassan verfasserin aut Enthalten in Process safety and environmental protection Amsterdam : Elsevier, 1990 173, Seite 859-880 Online-Ressource (DE-627)318710420 (DE-600)2008004-9 (DE-576)284747785 nnns volume:173 pages:859-880 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 58.18 Chemische Betriebstechnik VZ AR 173 859-880 |
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10.1016/j.psep.2023.03.056 doi (DE-627)ELV009590935 (ELSEVIER)S0957-5820(23)00259-8 DE-627 ger DE-627 rda eng 660 540 333.7 VZ 58.18 bkl Haghghi, Maghsoud Abdollahi verfasserin aut An intelligent thermodynamic/economic approach based on artificial neural network combined with MOGWO algorithm to study a novel polygeneration scheme using a modified dual-flash geothermal cycle 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Flash-based geothermal cycles correspond to environmentally friendly and cost-effective processes in a renewable framework and provide an opportunity for combined cycles. However, these cycles are characterized by significant energy losses and their waste stream’s low/medium operational temperature is the principal defect for managing multiple generation arrangements without assisting other energy resources. Hence, the main aim of this study is to propose a novel polygeneration scheme, integrated with a dual-flash geothermal cycle equipped with self-superheaters, able to mitigate the discussed defect. A new coupled series and parallel design of energy recovery is established, allowing to increase the compatibility of combined cycles and enable a larger production. This design encompasses a single-effect refrigeration cycle, a modified transcritical CO2 cycle, a polymer electrolyte membrane electrolyzer, and a thermal desalination cycle. The proposed process is examined from thermodynamic, sustainability, and economic (exergoeconomic and net present value analyses) points of view. Besides, a detailed sensitivity study is conducted by which the trend of performance variables in response to the increasing five main decision parameters is viewed. Afterward, an intelligent approach relying on an artificial neural network is built to learn and validate the behavior of defined objective functions (exergetic efficiency and products’ levelized cost). Moreover, a multi-objective grey wolf optimization (MOGWO) procedure endeavors to optimize the operation of the system. According to the results of this study, flash tank 2′s inlet pressure is the effective parameter, and its mean sensitivity index equals 0.289. Besides, the aforementioned objectives are gauged at 37.45% and 0.0625 $/kWh, respectively. Multi-stage waste management Dual-flash geothermal cycle Transcritical CO Polygeneration Multi-objective grey wolf optimization Artificial neural network Hasanzadeh, Amirhossein verfasserin (orcid)0000-0001-7815-3988 aut Nadimi, Ebrahim verfasserin (orcid)0000-0003-3338-5288 aut Rosato, Antonio verfasserin aut Athari, Hassan verfasserin aut Enthalten in Process safety and environmental protection Amsterdam : Elsevier, 1990 173, Seite 859-880 Online-Ressource (DE-627)318710420 (DE-600)2008004-9 (DE-576)284747785 nnns volume:173 pages:859-880 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 58.18 Chemische Betriebstechnik VZ AR 173 859-880 |
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10.1016/j.psep.2023.03.056 doi (DE-627)ELV009590935 (ELSEVIER)S0957-5820(23)00259-8 DE-627 ger DE-627 rda eng 660 540 333.7 VZ 58.18 bkl Haghghi, Maghsoud Abdollahi verfasserin aut An intelligent thermodynamic/economic approach based on artificial neural network combined with MOGWO algorithm to study a novel polygeneration scheme using a modified dual-flash geothermal cycle 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Flash-based geothermal cycles correspond to environmentally friendly and cost-effective processes in a renewable framework and provide an opportunity for combined cycles. However, these cycles are characterized by significant energy losses and their waste stream’s low/medium operational temperature is the principal defect for managing multiple generation arrangements without assisting other energy resources. Hence, the main aim of this study is to propose a novel polygeneration scheme, integrated with a dual-flash geothermal cycle equipped with self-superheaters, able to mitigate the discussed defect. A new coupled series and parallel design of energy recovery is established, allowing to increase the compatibility of combined cycles and enable a larger production. This design encompasses a single-effect refrigeration cycle, a modified transcritical CO2 cycle, a polymer electrolyte membrane electrolyzer, and a thermal desalination cycle. The proposed process is examined from thermodynamic, sustainability, and economic (exergoeconomic and net present value analyses) points of view. Besides, a detailed sensitivity study is conducted by which the trend of performance variables in response to the increasing five main decision parameters is viewed. Afterward, an intelligent approach relying on an artificial neural network is built to learn and validate the behavior of defined objective functions (exergetic efficiency and products’ levelized cost). Moreover, a multi-objective grey wolf optimization (MOGWO) procedure endeavors to optimize the operation of the system. According to the results of this study, flash tank 2′s inlet pressure is the effective parameter, and its mean sensitivity index equals 0.289. Besides, the aforementioned objectives are gauged at 37.45% and 0.0625 $/kWh, respectively. Multi-stage waste management Dual-flash geothermal cycle Transcritical CO Polygeneration Multi-objective grey wolf optimization Artificial neural network Hasanzadeh, Amirhossein verfasserin (orcid)0000-0001-7815-3988 aut Nadimi, Ebrahim verfasserin (orcid)0000-0003-3338-5288 aut Rosato, Antonio verfasserin aut Athari, Hassan verfasserin aut Enthalten in Process safety and environmental protection Amsterdam : Elsevier, 1990 173, Seite 859-880 Online-Ressource (DE-627)318710420 (DE-600)2008004-9 (DE-576)284747785 nnns volume:173 pages:859-880 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 58.18 Chemische Betriebstechnik VZ AR 173 859-880 |
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10.1016/j.psep.2023.03.056 doi (DE-627)ELV009590935 (ELSEVIER)S0957-5820(23)00259-8 DE-627 ger DE-627 rda eng 660 540 333.7 VZ 58.18 bkl Haghghi, Maghsoud Abdollahi verfasserin aut An intelligent thermodynamic/economic approach based on artificial neural network combined with MOGWO algorithm to study a novel polygeneration scheme using a modified dual-flash geothermal cycle 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Flash-based geothermal cycles correspond to environmentally friendly and cost-effective processes in a renewable framework and provide an opportunity for combined cycles. However, these cycles are characterized by significant energy losses and their waste stream’s low/medium operational temperature is the principal defect for managing multiple generation arrangements without assisting other energy resources. Hence, the main aim of this study is to propose a novel polygeneration scheme, integrated with a dual-flash geothermal cycle equipped with self-superheaters, able to mitigate the discussed defect. A new coupled series and parallel design of energy recovery is established, allowing to increase the compatibility of combined cycles and enable a larger production. This design encompasses a single-effect refrigeration cycle, a modified transcritical CO2 cycle, a polymer electrolyte membrane electrolyzer, and a thermal desalination cycle. The proposed process is examined from thermodynamic, sustainability, and economic (exergoeconomic and net present value analyses) points of view. Besides, a detailed sensitivity study is conducted by which the trend of performance variables in response to the increasing five main decision parameters is viewed. Afterward, an intelligent approach relying on an artificial neural network is built to learn and validate the behavior of defined objective functions (exergetic efficiency and products’ levelized cost). Moreover, a multi-objective grey wolf optimization (MOGWO) procedure endeavors to optimize the operation of the system. According to the results of this study, flash tank 2′s inlet pressure is the effective parameter, and its mean sensitivity index equals 0.289. Besides, the aforementioned objectives are gauged at 37.45% and 0.0625 $/kWh, respectively. Multi-stage waste management Dual-flash geothermal cycle Transcritical CO Polygeneration Multi-objective grey wolf optimization Artificial neural network Hasanzadeh, Amirhossein verfasserin (orcid)0000-0001-7815-3988 aut Nadimi, Ebrahim verfasserin (orcid)0000-0003-3338-5288 aut Rosato, Antonio verfasserin aut Athari, Hassan verfasserin aut Enthalten in Process safety and environmental protection Amsterdam : Elsevier, 1990 173, Seite 859-880 Online-Ressource (DE-627)318710420 (DE-600)2008004-9 (DE-576)284747785 nnns volume:173 pages:859-880 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 58.18 Chemische Betriebstechnik VZ AR 173 859-880 |
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Enthalten in Process safety and environmental protection 173, Seite 859-880 volume:173 pages:859-880 |
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Haghghi, Maghsoud Abdollahi @@aut@@ Hasanzadeh, Amirhossein @@aut@@ Nadimi, Ebrahim @@aut@@ Rosato, Antonio @@aut@@ Athari, Hassan @@aut@@ |
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Haghghi, Maghsoud Abdollahi |
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Haghghi, Maghsoud Abdollahi ddc 660 bkl 58.18 misc Multi-stage waste management misc Dual-flash geothermal cycle misc Transcritical CO misc Polygeneration misc Multi-objective grey wolf optimization misc Artificial neural network An intelligent thermodynamic/economic approach based on artificial neural network combined with MOGWO algorithm to study a novel polygeneration scheme using a modified dual-flash geothermal cycle |
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660 540 333.7 VZ 58.18 bkl An intelligent thermodynamic/economic approach based on artificial neural network combined with MOGWO algorithm to study a novel polygeneration scheme using a modified dual-flash geothermal cycle Multi-stage waste management Dual-flash geothermal cycle Transcritical CO Polygeneration Multi-objective grey wolf optimization Artificial neural network |
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ddc 660 bkl 58.18 misc Multi-stage waste management misc Dual-flash geothermal cycle misc Transcritical CO misc Polygeneration misc Multi-objective grey wolf optimization misc Artificial neural network |
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ddc 660 bkl 58.18 misc Multi-stage waste management misc Dual-flash geothermal cycle misc Transcritical CO misc Polygeneration misc Multi-objective grey wolf optimization misc Artificial neural network |
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An intelligent thermodynamic/economic approach based on artificial neural network combined with MOGWO algorithm to study a novel polygeneration scheme using a modified dual-flash geothermal cycle |
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an intelligent thermodynamic/economic approach based on artificial neural network combined with mogwo algorithm to study a novel polygeneration scheme using a modified dual-flash geothermal cycle |
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An intelligent thermodynamic/economic approach based on artificial neural network combined with MOGWO algorithm to study a novel polygeneration scheme using a modified dual-flash geothermal cycle |
| abstract |
Flash-based geothermal cycles correspond to environmentally friendly and cost-effective processes in a renewable framework and provide an opportunity for combined cycles. However, these cycles are characterized by significant energy losses and their waste stream’s low/medium operational temperature is the principal defect for managing multiple generation arrangements without assisting other energy resources. Hence, the main aim of this study is to propose a novel polygeneration scheme, integrated with a dual-flash geothermal cycle equipped with self-superheaters, able to mitigate the discussed defect. A new coupled series and parallel design of energy recovery is established, allowing to increase the compatibility of combined cycles and enable a larger production. This design encompasses a single-effect refrigeration cycle, a modified transcritical CO2 cycle, a polymer electrolyte membrane electrolyzer, and a thermal desalination cycle. The proposed process is examined from thermodynamic, sustainability, and economic (exergoeconomic and net present value analyses) points of view. Besides, a detailed sensitivity study is conducted by which the trend of performance variables in response to the increasing five main decision parameters is viewed. Afterward, an intelligent approach relying on an artificial neural network is built to learn and validate the behavior of defined objective functions (exergetic efficiency and products’ levelized cost). Moreover, a multi-objective grey wolf optimization (MOGWO) procedure endeavors to optimize the operation of the system. According to the results of this study, flash tank 2′s inlet pressure is the effective parameter, and its mean sensitivity index equals 0.289. Besides, the aforementioned objectives are gauged at 37.45% and 0.0625 $/kWh, respectively. |
| abstractGer |
Flash-based geothermal cycles correspond to environmentally friendly and cost-effective processes in a renewable framework and provide an opportunity for combined cycles. However, these cycles are characterized by significant energy losses and their waste stream’s low/medium operational temperature is the principal defect for managing multiple generation arrangements without assisting other energy resources. Hence, the main aim of this study is to propose a novel polygeneration scheme, integrated with a dual-flash geothermal cycle equipped with self-superheaters, able to mitigate the discussed defect. A new coupled series and parallel design of energy recovery is established, allowing to increase the compatibility of combined cycles and enable a larger production. This design encompasses a single-effect refrigeration cycle, a modified transcritical CO2 cycle, a polymer electrolyte membrane electrolyzer, and a thermal desalination cycle. The proposed process is examined from thermodynamic, sustainability, and economic (exergoeconomic and net present value analyses) points of view. Besides, a detailed sensitivity study is conducted by which the trend of performance variables in response to the increasing five main decision parameters is viewed. Afterward, an intelligent approach relying on an artificial neural network is built to learn and validate the behavior of defined objective functions (exergetic efficiency and products’ levelized cost). Moreover, a multi-objective grey wolf optimization (MOGWO) procedure endeavors to optimize the operation of the system. According to the results of this study, flash tank 2′s inlet pressure is the effective parameter, and its mean sensitivity index equals 0.289. Besides, the aforementioned objectives are gauged at 37.45% and 0.0625 $/kWh, respectively. |
| abstract_unstemmed |
Flash-based geothermal cycles correspond to environmentally friendly and cost-effective processes in a renewable framework and provide an opportunity for combined cycles. However, these cycles are characterized by significant energy losses and their waste stream’s low/medium operational temperature is the principal defect for managing multiple generation arrangements without assisting other energy resources. Hence, the main aim of this study is to propose a novel polygeneration scheme, integrated with a dual-flash geothermal cycle equipped with self-superheaters, able to mitigate the discussed defect. A new coupled series and parallel design of energy recovery is established, allowing to increase the compatibility of combined cycles and enable a larger production. This design encompasses a single-effect refrigeration cycle, a modified transcritical CO2 cycle, a polymer electrolyte membrane electrolyzer, and a thermal desalination cycle. The proposed process is examined from thermodynamic, sustainability, and economic (exergoeconomic and net present value analyses) points of view. Besides, a detailed sensitivity study is conducted by which the trend of performance variables in response to the increasing five main decision parameters is viewed. Afterward, an intelligent approach relying on an artificial neural network is built to learn and validate the behavior of defined objective functions (exergetic efficiency and products’ levelized cost). Moreover, a multi-objective grey wolf optimization (MOGWO) procedure endeavors to optimize the operation of the system. According to the results of this study, flash tank 2′s inlet pressure is the effective parameter, and its mean sensitivity index equals 0.289. Besides, the aforementioned objectives are gauged at 37.45% and 0.0625 $/kWh, respectively. |
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An intelligent thermodynamic/economic approach based on artificial neural network combined with MOGWO algorithm to study a novel polygeneration scheme using a modified dual-flash geothermal cycle |
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| score |
7.400523 |