Prediction and optimization of isentropic efficiency of vortex pump under full operating conditions in Organic Rankine Cycle waste heat recovery system based on deep learning and intelligent algorithm - 文章详情页

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EI SCIE

摘要：

The　isentropic　efficiency　of　vortex　pump　in　Organic　Rankine　Cycle　(ORC)　system　has　an　important　influence　on　the　performance　of　the　system.　In　this　paper,　vortex　pump　experimental　data　and　deep　learning　are　combined　to　construct　an　experimental　data-driven　isentropic　efficiency　prediction　model　of　vortex　pump　under　full　operating　conditions.　The　activation　function　and　the　number　of　hidden　layer　nodes　in　the　model　are　filtered　by　nested　screening　technique.　Through　bilinear　interpolation　algorithm,　the　influence　of　vortex　pump　operation　parameters　on　the　isentropic　efficiency　is　analyzed.　In　addition,　the　optimization　boundary　is　selected　in　the　four-dimensional　space.　Finally,　the　deep　learning　prediction　model　is　combined　with　Linear　Decreasing　Inertia　Weight　Particle　Swarm　Optimization　(LDIWPSO)　to　predict　and　optimize　the　maximum　isentropic　efficiency　of　vortex　pump　under　full　operating　conditions.　Mean　Absolute　Error　(MAE),　Mean　Absolute　Percentage　Error　(MAPE),　Root　Mean　Square　Error　(RMSE)　and　Coefficient　of　Determination　(R-square)　are　combined　to　evaluate　the　prediction　accuracy　of　the　model.　The　prediction　and　optimization　results　show　that　the　maximum　isentropic　efficiency　of　vortex　pump　can　reach　22.89%.　The　combination　of　deep　learning　and　LDIWPSO　can　predict　and　optimize　the　maximum　isentropic　efficiency　for　vortex　pump　with　high　precision.　It　also　provides　a　reference　for　the　maximum　value　of　vortex　pump　isentropic　efficiency　in　theoretical　analysis　and　numerical　simulation.　©　2020　Elsevier　Ltd

关键词：

Deep learning Efficiency Errors Forecasting Learning systems Mean square error Particle swarm optimization (PSO) Predictive analytics Pumps Rankine cycle Vortex flow Waste heat Waste heat utilization

作者机构：

[ 1 ] [Ping, Xu]MOE Key Laboratory of Enhanced Heat Transfer and Energy Conservation, Beijing Key Laboratory of Heat Transfer and Energy Conversion, College of Environmental and Energy Engineering, Beijing University of Technology, Beijing; 100124, China
[ 2 ] [Yang, Fubin]MOE Key Laboratory of Enhanced Heat Transfer and Energy Conservation, Beijing Key Laboratory of Heat Transfer and Energy Conversion, College of Environmental and Energy Engineering, Beijing University of Technology, Beijing; 100124, China
[ 3 ] [Yang, Fubin]Key Laboratory for Thermal Science and Power Engineering of MOE, Beijing Key Laboratory for CO2 Utilization and Reduction Technology, Tsinghua University, Beijing; 100084, China
[ 4 ] [Zhang, Hongguang]MOE Key Laboratory of Enhanced Heat Transfer and Energy Conservation, Beijing Key Laboratory of Heat Transfer and Energy Conversion, College of Environmental and Energy Engineering, Beijing University of Technology, Beijing; 100124, China
[ 5 ] [Zhang, Wujie]MOE Key Laboratory of Enhanced Heat Transfer and Energy Conservation, Beijing Key Laboratory of Heat Transfer and Energy Conversion, College of Environmental and Energy Engineering, Beijing University of Technology, Beijing; 100124, China
[ 6 ] [Song, Gege]MOE Key Laboratory of Enhanced Heat Transfer and Energy Conservation, Beijing Key Laboratory of Heat Transfer and Energy Conversion, College of Environmental and Energy Engineering, Beijing University of Technology, Beijing; 100124, China
[ 7 ] [Yang, Yuxin]MOE Key Laboratory of Enhanced Heat Transfer and Energy Conservation, Beijing Key Laboratory of Heat Transfer and Energy Conversion, College of Environmental and Energy Engineering, Beijing University of Technology, Beijing; 100124, China
[ 8 ] [Yang, Yuxin]School of Architecture, Harbin Institute of Technology, Harbin; 150006, China

通讯作者信息：

[yang, fubin]moe key laboratory of enhanced heat transfer and energy conservation, beijing key laboratory of heat transfer and energy conversion, college of environmental and energy engineering, beijing university of technology, beijing; 100124, china;;[yang, fubin]key laboratory for thermal science and power engineering of moe, beijing key laboratory for co2 utilization and reduction technology, tsinghua university, beijing; 100084, china

电子邮件地址：

yangfubin@bjut.edu.cn

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