{"title":"改进的基于分数阶的未来搜索算法,用于增强基于pemfc的CCHP的性能","authors":"Biao Lu, Navid Razmjooy","doi":"10.1080/15567036.2023.2276385","DOIUrl":null,"url":null,"abstract":"ABSTRACTLoad control and cost optimization are considered to be crucial in tri-generation or combined cooling, heating, and power (CCHP) systems. In this study, an inventive CCHP system employs an FC system as its first mover and includes a heat exchanger, a heat recovery, as well as an auxiliary boiler, an electric chiller, and an absorption chiller. The electrical grid is linked to this system. The idea here is to maximize the system’s performance from a financial perspective and to make the annual expenditure of the system minimum over a 20–year period that is considered as the cycle life-span. It is a multi-objective optimization problem which is optimized using a newly introduced metaheuristic optimization method and a Fractional-order future search optimizer. The findings of this study are used to divine an ideal configuration of the CCHP. Finally, to demonstrate the higher efficiency of the suggested method, a comparison should be conducted among the optimization results of the fractional-order-based future search algorithm, the results of Non-dominated Sorting Genetic Algorithm II (NSGA-II), and standard future search algorithms in previous studies. Based on the results presented, the proposed Fractional-order Future Search Algorithm (FOFSA) was able to optimize the performance of a PEMFC-based CCHP system more effectively than conventional methods. The system’s exergy efficiency was found to decrease from 52% at 793 mA/cm2 current density to 36% at 1000 mA/cm2 current density. However, with the application of FOFSA, the suggested optimal system had a higher exergy efficiency of 41.6% and a yearly cost of $2765, resulting in the maximum annual greenhouse gas (GHG) reduction of 4.48E6 g. Therefore, in summary, the proposed FOFSA yielded an optimized CCHP system configuration that had higher energy efficiency, lower annual cost, and reduced GHG emissions. These findings highlight the effectiveness of the FOFSA method in optimizing the performance of PEMFC-based CCHP systems.KEYWORDS: Combined heatingcoolingand power cycle; proton exchange membranefuel cell; economic performanceannual cost; fractional-order future search algorithm Nomenclature Symbol=ExplanationCCHP=Combined cooling, heating, and powerNSGA-II=Non-dominated Sorting Genetic Algorithm IIFOFSA=Fractional-order Future Search AlgorithmGHG=Greenhouse gasIMPO=Improved marine predators optimizerPROX=Preferential oxidationPCM=Phase change materialDAC=Desiccant air conditioningHX=Heat-exchangerMEA=Membrane-electrode assemblyNs=The connected cells’ quantityEN=The open-circuit Nernst relation (V)Vloss=The overall voltage loss (V)Vcon=Concentration loss (V)Vact=Activation loss (V)VΩ=Ohmic loss (V)EN=The stack output voltage (V)E0=The open-circuit voltage of the cell (V)F=The Faraday’s constant (C/mol)R=The universal gas constant (J/mol.K)T=The operating temperaturePO2=The partial pressure of O2 (Pa)PH2=The partial pressure of H2 (Pa)PH2Oc=The partial pressure of steam (Pa)Rhc=The vapor relative humidity in cathodeRha=The vapor relative humidity in anodeI=The FC’s current operating (A)A=The FC’s membrane active area (m2)PC=The inlet partial pressure in electrodes for cathode (Pc)PA=The inlet partial pressure in electrodes for anode (Pa)Rc=The resistance of connections (kΩ)Rm=The resistance of membrane (kΩ)ρm=The resistivity of the membrane (Ω.m)l=The thickness of the membrane (m)λ=A changeable variableI=The current of fuel cell stack (A)I0=The limiting current (A)n=The charge transfer coefficientbm=The mass transfer voltage (V)ηex=The system’s exergy efficiencyPCCHP=The produced electric energy in the systemExe=The provided cooling exergyExhw=The provided hot water exergyExCCHP=The consumed fuel exergy in the systemSH2=The H2 stoichiometryRH2=The molar rate of fuel consumption (mol.s−1)ExH2=The exergy of the standard chemical for 1 mol hydrogen Tc=The temperature of chiller’s cooling waterT0=The surrounding temperature Thw=The temperature of hot waterCostinv=The CCHP system’s original investment costCostf=Overall fuel costCostmt=Maintenance costCostf=Total fuel cost of the proposed systemMcH2=hydrogen’s molar capacity (kg/mol)CostH2=The hydrogen generation unit cost ($/kg)Trt=The overall functioning duration of the system (year)Coav=The CCHP system’s average yearly costPER=Pollution-related emission reductionEmst=The station’s air pollutant emissionsEmGHG=The formed greenhouse gas emissions during the production of energyEeq=In the two systems, all of the energy kinds were translated into equal electric powerEhw=The heated water’s transformed electricityEFC=The electricity of the fuel cellEc=The altered cooling volumeCOPtc=The electric air-conditioningCOPWH=Co-efficient performance of Water heaterEmred=The yearly reduction in green-house gas emissionEmH2=The annual green-house gas emissions from H2 generationEGHG=The greenhouse gas emissions created by the wind-based H2 generation systemHVH2=The heat value of the system’s annual hydrogen consumptionDisclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsBiao LuBiao Lu obtained a master's degree in computer science and a master's degree in computer application from Nanjing University of Posts and telecommunications in Nanjing, China. She is a professor and her main research interests are artificial intelligence and software engineering Dr. Navid Razmjooy is a Postdoc researcher at the industrial college of the Ankara Yıldırım Beyazıt Üniversitesi. He is also a part-time assistant professor at the Islamic Azad University, Ardabil, Iran. His main areas of research are the Renewable Energies, Machine Vision, Soft Computing, Data Mining, Evolutionary Algorithms, Interval Analysis, and System Control.Navid RazmjooyNavid Razmjooy studied his Ph.D. in the field of Electrical Engineering (Control and Automation) from Tafresh University, Iran (2018). He is a senior member of IEEE/USA and YRC in IAU/Iran. He has been ranked among the world's top 2% scientists in the world based on the Stanford University/Scopus database. He published more than 200 papers and 6 books in English and Persian in peer-reviewed journals and conferences and is now Editor and reviewer in several national and international journals and conferences.","PeriodicalId":11580,"journal":{"name":"Energy Sources, Part A: Recovery, Utilization, and Environmental Effects","volume":"21 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A modified fractional‑order-based future search algorithm for performance enhancement of a PEMFC-based CCHP\",\"authors\":\"Biao Lu, Navid Razmjooy\",\"doi\":\"10.1080/15567036.2023.2276385\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACTLoad control and cost optimization are considered to be crucial in tri-generation or combined cooling, heating, and power (CCHP) systems. In this study, an inventive CCHP system employs an FC system as its first mover and includes a heat exchanger, a heat recovery, as well as an auxiliary boiler, an electric chiller, and an absorption chiller. The electrical grid is linked to this system. The idea here is to maximize the system’s performance from a financial perspective and to make the annual expenditure of the system minimum over a 20–year period that is considered as the cycle life-span. It is a multi-objective optimization problem which is optimized using a newly introduced metaheuristic optimization method and a Fractional-order future search optimizer. The findings of this study are used to divine an ideal configuration of the CCHP. Finally, to demonstrate the higher efficiency of the suggested method, a comparison should be conducted among the optimization results of the fractional-order-based future search algorithm, the results of Non-dominated Sorting Genetic Algorithm II (NSGA-II), and standard future search algorithms in previous studies. Based on the results presented, the proposed Fractional-order Future Search Algorithm (FOFSA) was able to optimize the performance of a PEMFC-based CCHP system more effectively than conventional methods. The system’s exergy efficiency was found to decrease from 52% at 793 mA/cm2 current density to 36% at 1000 mA/cm2 current density. However, with the application of FOFSA, the suggested optimal system had a higher exergy efficiency of 41.6% and a yearly cost of $2765, resulting in the maximum annual greenhouse gas (GHG) reduction of 4.48E6 g. Therefore, in summary, the proposed FOFSA yielded an optimized CCHP system configuration that had higher energy efficiency, lower annual cost, and reduced GHG emissions. These findings highlight the effectiveness of the FOFSA method in optimizing the performance of PEMFC-based CCHP systems.KEYWORDS: Combined heatingcoolingand power cycle; proton exchange membranefuel cell; economic performanceannual cost; fractional-order future search algorithm Nomenclature Symbol=ExplanationCCHP=Combined cooling, heating, and powerNSGA-II=Non-dominated Sorting Genetic Algorithm IIFOFSA=Fractional-order Future Search AlgorithmGHG=Greenhouse gasIMPO=Improved marine predators optimizerPROX=Preferential oxidationPCM=Phase change materialDAC=Desiccant air conditioningHX=Heat-exchangerMEA=Membrane-electrode assemblyNs=The connected cells’ quantityEN=The open-circuit Nernst relation (V)Vloss=The overall voltage loss (V)Vcon=Concentration loss (V)Vact=Activation loss (V)VΩ=Ohmic loss (V)EN=The stack output voltage (V)E0=The open-circuit voltage of the cell (V)F=The Faraday’s constant (C/mol)R=The universal gas constant (J/mol.K)T=The operating temperaturePO2=The partial pressure of O2 (Pa)PH2=The partial pressure of H2 (Pa)PH2Oc=The partial pressure of steam (Pa)Rhc=The vapor relative humidity in cathodeRha=The vapor relative humidity in anodeI=The FC’s current operating (A)A=The FC’s membrane active area (m2)PC=The inlet partial pressure in electrodes for cathode (Pc)PA=The inlet partial pressure in electrodes for anode (Pa)Rc=The resistance of connections (kΩ)Rm=The resistance of membrane (kΩ)ρm=The resistivity of the membrane (Ω.m)l=The thickness of the membrane (m)λ=A changeable variableI=The current of fuel cell stack (A)I0=The limiting current (A)n=The charge transfer coefficientbm=The mass transfer voltage (V)ηex=The system’s exergy efficiencyPCCHP=The produced electric energy in the systemExe=The provided cooling exergyExhw=The provided hot water exergyExCCHP=The consumed fuel exergy in the systemSH2=The H2 stoichiometryRH2=The molar rate of fuel consumption (mol.s−1)ExH2=The exergy of the standard chemical for 1 mol hydrogen Tc=The temperature of chiller’s cooling waterT0=The surrounding temperature Thw=The temperature of hot waterCostinv=The CCHP system’s original investment costCostf=Overall fuel costCostmt=Maintenance costCostf=Total fuel cost of the proposed systemMcH2=hydrogen’s molar capacity (kg/mol)CostH2=The hydrogen generation unit cost ($/kg)Trt=The overall functioning duration of the system (year)Coav=The CCHP system’s average yearly costPER=Pollution-related emission reductionEmst=The station’s air pollutant emissionsEmGHG=The formed greenhouse gas emissions during the production of energyEeq=In the two systems, all of the energy kinds were translated into equal electric powerEhw=The heated water’s transformed electricityEFC=The electricity of the fuel cellEc=The altered cooling volumeCOPtc=The electric air-conditioningCOPWH=Co-efficient performance of Water heaterEmred=The yearly reduction in green-house gas emissionEmH2=The annual green-house gas emissions from H2 generationEGHG=The greenhouse gas emissions created by the wind-based H2 generation systemHVH2=The heat value of the system’s annual hydrogen consumptionDisclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsBiao LuBiao Lu obtained a master's degree in computer science and a master's degree in computer application from Nanjing University of Posts and telecommunications in Nanjing, China. She is a professor and her main research interests are artificial intelligence and software engineering Dr. Navid Razmjooy is a Postdoc researcher at the industrial college of the Ankara Yıldırım Beyazıt Üniversitesi. He is also a part-time assistant professor at the Islamic Azad University, Ardabil, Iran. His main areas of research are the Renewable Energies, Machine Vision, Soft Computing, Data Mining, Evolutionary Algorithms, Interval Analysis, and System Control.Navid RazmjooyNavid Razmjooy studied his Ph.D. in the field of Electrical Engineering (Control and Automation) from Tafresh University, Iran (2018). He is a senior member of IEEE/USA and YRC in IAU/Iran. He has been ranked among the world's top 2% scientists in the world based on the Stanford University/Scopus database. 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A modified fractional‑order-based future search algorithm for performance enhancement of a PEMFC-based CCHP
ABSTRACTLoad control and cost optimization are considered to be crucial in tri-generation or combined cooling, heating, and power (CCHP) systems. In this study, an inventive CCHP system employs an FC system as its first mover and includes a heat exchanger, a heat recovery, as well as an auxiliary boiler, an electric chiller, and an absorption chiller. The electrical grid is linked to this system. The idea here is to maximize the system’s performance from a financial perspective and to make the annual expenditure of the system minimum over a 20–year period that is considered as the cycle life-span. It is a multi-objective optimization problem which is optimized using a newly introduced metaheuristic optimization method and a Fractional-order future search optimizer. The findings of this study are used to divine an ideal configuration of the CCHP. Finally, to demonstrate the higher efficiency of the suggested method, a comparison should be conducted among the optimization results of the fractional-order-based future search algorithm, the results of Non-dominated Sorting Genetic Algorithm II (NSGA-II), and standard future search algorithms in previous studies. Based on the results presented, the proposed Fractional-order Future Search Algorithm (FOFSA) was able to optimize the performance of a PEMFC-based CCHP system more effectively than conventional methods. The system’s exergy efficiency was found to decrease from 52% at 793 mA/cm2 current density to 36% at 1000 mA/cm2 current density. However, with the application of FOFSA, the suggested optimal system had a higher exergy efficiency of 41.6% and a yearly cost of $2765, resulting in the maximum annual greenhouse gas (GHG) reduction of 4.48E6 g. Therefore, in summary, the proposed FOFSA yielded an optimized CCHP system configuration that had higher energy efficiency, lower annual cost, and reduced GHG emissions. These findings highlight the effectiveness of the FOFSA method in optimizing the performance of PEMFC-based CCHP systems.KEYWORDS: Combined heatingcoolingand power cycle; proton exchange membranefuel cell; economic performanceannual cost; fractional-order future search algorithm Nomenclature Symbol=ExplanationCCHP=Combined cooling, heating, and powerNSGA-II=Non-dominated Sorting Genetic Algorithm IIFOFSA=Fractional-order Future Search AlgorithmGHG=Greenhouse gasIMPO=Improved marine predators optimizerPROX=Preferential oxidationPCM=Phase change materialDAC=Desiccant air conditioningHX=Heat-exchangerMEA=Membrane-electrode assemblyNs=The connected cells’ quantityEN=The open-circuit Nernst relation (V)Vloss=The overall voltage loss (V)Vcon=Concentration loss (V)Vact=Activation loss (V)VΩ=Ohmic loss (V)EN=The stack output voltage (V)E0=The open-circuit voltage of the cell (V)F=The Faraday’s constant (C/mol)R=The universal gas constant (J/mol.K)T=The operating temperaturePO2=The partial pressure of O2 (Pa)PH2=The partial pressure of H2 (Pa)PH2Oc=The partial pressure of steam (Pa)Rhc=The vapor relative humidity in cathodeRha=The vapor relative humidity in anodeI=The FC’s current operating (A)A=The FC’s membrane active area (m2)PC=The inlet partial pressure in electrodes for cathode (Pc)PA=The inlet partial pressure in electrodes for anode (Pa)Rc=The resistance of connections (kΩ)Rm=The resistance of membrane (kΩ)ρm=The resistivity of the membrane (Ω.m)l=The thickness of the membrane (m)λ=A changeable variableI=The current of fuel cell stack (A)I0=The limiting current (A)n=The charge transfer coefficientbm=The mass transfer voltage (V)ηex=The system’s exergy efficiencyPCCHP=The produced electric energy in the systemExe=The provided cooling exergyExhw=The provided hot water exergyExCCHP=The consumed fuel exergy in the systemSH2=The H2 stoichiometryRH2=The molar rate of fuel consumption (mol.s−1)ExH2=The exergy of the standard chemical for 1 mol hydrogen Tc=The temperature of chiller’s cooling waterT0=The surrounding temperature Thw=The temperature of hot waterCostinv=The CCHP system’s original investment costCostf=Overall fuel costCostmt=Maintenance costCostf=Total fuel cost of the proposed systemMcH2=hydrogen’s molar capacity (kg/mol)CostH2=The hydrogen generation unit cost ($/kg)Trt=The overall functioning duration of the system (year)Coav=The CCHP system’s average yearly costPER=Pollution-related emission reductionEmst=The station’s air pollutant emissionsEmGHG=The formed greenhouse gas emissions during the production of energyEeq=In the two systems, all of the energy kinds were translated into equal electric powerEhw=The heated water’s transformed electricityEFC=The electricity of the fuel cellEc=The altered cooling volumeCOPtc=The electric air-conditioningCOPWH=Co-efficient performance of Water heaterEmred=The yearly reduction in green-house gas emissionEmH2=The annual green-house gas emissions from H2 generationEGHG=The greenhouse gas emissions created by the wind-based H2 generation systemHVH2=The heat value of the system’s annual hydrogen consumptionDisclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsBiao LuBiao Lu obtained a master's degree in computer science and a master's degree in computer application from Nanjing University of Posts and telecommunications in Nanjing, China. She is a professor and her main research interests are artificial intelligence and software engineering Dr. Navid Razmjooy is a Postdoc researcher at the industrial college of the Ankara Yıldırım Beyazıt Üniversitesi. He is also a part-time assistant professor at the Islamic Azad University, Ardabil, Iran. His main areas of research are the Renewable Energies, Machine Vision, Soft Computing, Data Mining, Evolutionary Algorithms, Interval Analysis, and System Control.Navid RazmjooyNavid Razmjooy studied his Ph.D. in the field of Electrical Engineering (Control and Automation) from Tafresh University, Iran (2018). He is a senior member of IEEE/USA and YRC in IAU/Iran. He has been ranked among the world's top 2% scientists in the world based on the Stanford University/Scopus database. He published more than 200 papers and 6 books in English and Persian in peer-reviewed journals and conferences and is now Editor and reviewer in several national and international journals and conferences.