This paper aims to conduct a comprehensive exergoeconomic analysis of a novel zero-carbon-emission multi-generation system and propose a fast optimization method combined with machine learning.The detailed exergoecono...This paper aims to conduct a comprehensive exergoeconomic analysis of a novel zero-carbon-emission multi-generation system and propose a fast optimization method combined with machine learning.The detailed exergoeconomic analysis of a novel combined power,freshwater and cooling multi-generation system is performed in this study.The exergoeconomic analysis model is established by exergy flow theory.A comprehensive exergy,exergoeconomic and environmental analysis is carried out.Five critical decision variables are researched to bring out effects on the multi-generation system exergoeconomic performance.A novel fast optimization method combining genetic algorithm and Bagging neural network is proposed.The advanced nature comparison is made between the proposed system and four similar cases.Results display that increasing the turbine inlet temperature can improve exergy efficiency and decrease the total product unit cost.The multi-generation system exergy destruction directly determines exergy efficiency and total exergy destruction cost rate.The total product unit cost in the cost optimal design case is reduced by 7.7%and 25%,respectively,compared with exergy efficiency optimal design case and basic design case.Compared with four similar cases,the proposed multi-generation system has great advantages in thermodynamic performance and exergoeconomic performance.This paper can provide research methods and ideas for performance analysis and fast optimization of multi-generation system.展开更多
基金financial support from the Jilin provincial Development and Reform Commission(No.2023C032-7)Science Foundation of Jilin province Science and Technology Agency(No.20210203057SF)Science and Technology Development Program of Jilin province Science and Technology Agency(No.20230101211JC)。
文摘This paper aims to conduct a comprehensive exergoeconomic analysis of a novel zero-carbon-emission multi-generation system and propose a fast optimization method combined with machine learning.The detailed exergoeconomic analysis of a novel combined power,freshwater and cooling multi-generation system is performed in this study.The exergoeconomic analysis model is established by exergy flow theory.A comprehensive exergy,exergoeconomic and environmental analysis is carried out.Five critical decision variables are researched to bring out effects on the multi-generation system exergoeconomic performance.A novel fast optimization method combining genetic algorithm and Bagging neural network is proposed.The advanced nature comparison is made between the proposed system and four similar cases.Results display that increasing the turbine inlet temperature can improve exergy efficiency and decrease the total product unit cost.The multi-generation system exergy destruction directly determines exergy efficiency and total exergy destruction cost rate.The total product unit cost in the cost optimal design case is reduced by 7.7%and 25%,respectively,compared with exergy efficiency optimal design case and basic design case.Compared with four similar cases,the proposed multi-generation system has great advantages in thermodynamic performance and exergoeconomic performance.This paper can provide research methods and ideas for performance analysis and fast optimization of multi-generation system.