Multi-objective performance optimization of regenerative S-CO2 Brayton cycle based on neural network prediction
The regenerative supercritical CO2 Brayton cycle (RSCBC) has great development potential in waste heat recovery and utilization, and it is necessary to carry out performance analysis and optimization. This article first applies the theory of finite time thermodynamics to establish a RSCBC model with...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-05-01
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| Series: | Energy Conversion and Management: X |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590174522000265 |
| _version_ | 1828825051171913728 |
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| author | Qinglong Jin Shaojun Xia Penglei Li Tianchao Xie |
| author_facet | Qinglong Jin Shaojun Xia Penglei Li Tianchao Xie |
| author_sort | Qinglong Jin |
| collection | DOAJ |
| description | The regenerative supercritical CO2 Brayton cycle (RSCBC) has great development potential in waste heat recovery and utilization, and it is necessary to carry out performance analysis and optimization. This article first applies the theory of finite time thermodynamics to establish a RSCBC model with finite temperature difference heat transfer, irreversible compression, irreversible expansion and other irreversible factors under variable temperature heat source conditions, and then uses the data samples to construct the corresponding neural network model. Based on the NSGA-Ⅱ algorithm, the working fluid mass flow rate, the pressure ratio, the heat conductance distribution ratios of the regenerator and the heater are chosen as optimization variables, multi-objective optimization is carried out with the goals of cycle thermal efficiency, net power output, ecological function and exergy efficiency. The results show that the use of neural network models to predict cycle performance can save a lot of calculation time compared to traditional calculation methods; after optimization, the positive ideal point is not on the Pareto front, which shows that the four optimization objectives are mutually restricted and affect each other. The results by using Shannon Entropy method for decision-making have a lower deviation index, and those by using TOPSIS and LINMAP methods for decision-making are consistent with each other; for the results by using Shannon Entropy method for decision-making, the cycle thermal efficiency and net power output can reach 38.4% and 12.047 MW respectively, which can be increased by 31.15% and 43.29% compared to those for the initial design point respectively, and the ecological function and exergy efficiency can reach 7.6274 MW and 73.2% respectively, which are 4.2588 times and 33.75% higher than those for the initial design point respectively. The obtained results can provide some guidance for the optimal design of the RSCBC in real engineering. |
| first_indexed | 2024-12-12T14:09:53Z |
| format | Article |
| id | doaj.art-ad69564a2a144b94af0fece4a261176a |
| institution | Directory Open Access Journal |
| issn | 2590-1745 |
| language | English |
| last_indexed | 2024-12-12T14:09:53Z |
| publishDate | 2022-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Conversion and Management: X |
| spelling | doaj.art-ad69564a2a144b94af0fece4a261176a2022-12-22T00:22:07ZengElsevierEnergy Conversion and Management: X2590-17452022-05-0114100203Multi-objective performance optimization of regenerative S-CO2 Brayton cycle based on neural network predictionQinglong Jin0Shaojun Xia1Penglei Li2Tianchao Xie3College of Power Engineering, Naval University of Engineering, Wuhan 430033, PR ChinaCorresponding author.; College of Power Engineering, Naval University of Engineering, Wuhan 430033, PR ChinaCollege of Power Engineering, Naval University of Engineering, Wuhan 430033, PR ChinaCollege of Power Engineering, Naval University of Engineering, Wuhan 430033, PR ChinaThe regenerative supercritical CO2 Brayton cycle (RSCBC) has great development potential in waste heat recovery and utilization, and it is necessary to carry out performance analysis and optimization. This article first applies the theory of finite time thermodynamics to establish a RSCBC model with finite temperature difference heat transfer, irreversible compression, irreversible expansion and other irreversible factors under variable temperature heat source conditions, and then uses the data samples to construct the corresponding neural network model. Based on the NSGA-Ⅱ algorithm, the working fluid mass flow rate, the pressure ratio, the heat conductance distribution ratios of the regenerator and the heater are chosen as optimization variables, multi-objective optimization is carried out with the goals of cycle thermal efficiency, net power output, ecological function and exergy efficiency. The results show that the use of neural network models to predict cycle performance can save a lot of calculation time compared to traditional calculation methods; after optimization, the positive ideal point is not on the Pareto front, which shows that the four optimization objectives are mutually restricted and affect each other. The results by using Shannon Entropy method for decision-making have a lower deviation index, and those by using TOPSIS and LINMAP methods for decision-making are consistent with each other; for the results by using Shannon Entropy method for decision-making, the cycle thermal efficiency and net power output can reach 38.4% and 12.047 MW respectively, which can be increased by 31.15% and 43.29% compared to those for the initial design point respectively, and the ecological function and exergy efficiency can reach 7.6274 MW and 73.2% respectively, which are 4.2588 times and 33.75% higher than those for the initial design point respectively. The obtained results can provide some guidance for the optimal design of the RSCBC in real engineering.http://www.sciencedirect.com/science/article/pii/S2590174522000265Regenerative supercritical carbon-dioxide Brayton cycleFinite time thermodynamicsNeural networksMulti-objective performance optimization |
| spellingShingle | Qinglong Jin Shaojun Xia Penglei Li Tianchao Xie Multi-objective performance optimization of regenerative S-CO2 Brayton cycle based on neural network prediction Energy Conversion and Management: X Regenerative supercritical carbon-dioxide Brayton cycle Finite time thermodynamics Neural networks Multi-objective performance optimization |
| title | Multi-objective performance optimization of regenerative S-CO2 Brayton cycle based on neural network prediction |
| title_full | Multi-objective performance optimization of regenerative S-CO2 Brayton cycle based on neural network prediction |
| title_fullStr | Multi-objective performance optimization of regenerative S-CO2 Brayton cycle based on neural network prediction |
| title_full_unstemmed | Multi-objective performance optimization of regenerative S-CO2 Brayton cycle based on neural network prediction |
| title_short | Multi-objective performance optimization of regenerative S-CO2 Brayton cycle based on neural network prediction |
| title_sort | multi objective performance optimization of regenerative s co2 brayton cycle based on neural network prediction |
| topic | Regenerative supercritical carbon-dioxide Brayton cycle Finite time thermodynamics Neural networks Multi-objective performance optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2590174522000265 |
| work_keys_str_mv | AT qinglongjin multiobjectiveperformanceoptimizationofregenerativesco2braytoncyclebasedonneuralnetworkprediction AT shaojunxia multiobjectiveperformanceoptimizationofregenerativesco2braytoncyclebasedonneuralnetworkprediction AT pengleili multiobjectiveperformanceoptimizationofregenerativesco2braytoncyclebasedonneuralnetworkprediction AT tianchaoxie multiobjectiveperformanceoptimizationofregenerativesco2braytoncyclebasedonneuralnetworkprediction |