Prediction and optimization of power output of single screw expander in organic Rankine cycle (ORC) for diesel engine waste heat recovery - Details

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Abstract：

The　output　characteristics　of　single　screw　expander　has　a　direct　and　crucial　influence　on　the　performance　of　organic　Rankine　cycle　(ORC)　system.　In　this　paper,　a　machine　learning　prediction　model　driven　by　experimental　data　is　developed　and　applied　to　predict　the　power　output　of　single　screw　expander.　After　screening　different　structural　parameters　of　the　model,　genetic　algorithm　(GA)　is　used　to　optimize　the　initial　weights　and　thresholds　of　the　model,　so　as　to　further　improve　the　generalization　ability　of　the　model.　In　addition,　the　generalization　ability　of　the　model　is　compared　with　that　of　the　model　not　optimized　by　GA.　Furthermore,　the　influence　of　operating　parameters　on　the　power　output　of　single　screw　expander　is　analyzed　by　fitting　algorithm　in　three-dimensional　space.　The　optimization　boundary　value　needed　for　prediction　and　optimization　is　determined　by　fitting　algorithm　in　four-dimensional　space.　Finally,　a　prediction　and　optimization　model　is　created　by　coupling　the　machine　learning　prediction　model　with　GA,　and　the　maximum　power　output　and　corresponding　operating　parameters　of　the　single　screw　expander　under　full　operating　conditions　are　predicted　and　optimized.　The　results　show　that　with　the　application　of　machine　learning　and　GA,　the　maximum　power　output　of　single　screw　expander　can　be　predicted　and　optimized　precisely　under　full　operating　conditions.　So　as　to　directly　guide　the　selection　of　relevant　parameters　in　the　process　of　theoretical　analysis　and　experimental　research.　©　2020　Elsevier　Ltd

Keyword：

Turing machines Screws Machine learning Parameter estimation Rankine cycle Waste heat Thermoelectric power Forecasting Genetic algorithms Predictive analytics Waste heat utilization Diesel engines

Author Community：

[ 1 ] [Ping, Xu]Key Laboratory of Enhanced Heat Transfer and Energy Conservation of MOE, 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]Key Laboratory of Enhanced Heat Transfer and Energy Conservation of MOE, 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]Key Laboratory of Enhanced Heat Transfer and Energy Conservation of MOE, 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]Key Laboratory of Enhanced Heat Transfer and Energy Conservation of MOE, Beijing Key Laboratory of Heat Transfer and Energy Conversion, College of Environmental and Energy Engineering, Beijing University of Technology, Beijing; 100124, China
[ 6 ] [Zhang, Jian]Mechanical Engineering, Richard J. Resch School of Engineering, University of Wisconsin Green Bay, Green Bay; WI; 54311, United States
[ 7 ] [Song, Gege]Key Laboratory of Enhanced Heat Transfer and Energy Conservation of MOE, Beijing Key Laboratory of Heat Transfer and Energy Conversion, College of Environmental and Energy Engineering, Beijing University of Technology, Beijing; 100124, China
[ 8 ] [Wang, Chongyao]Key Laboratory of Enhanced Heat Transfer and Energy Conservation of MOE, Beijing Key Laboratory of Heat Transfer and Energy Conversion, College of Environmental and Energy Engineering, Beijing University of Technology, Beijing; 100124, China
[ 9 ] [Yao, Baofeng]Key Laboratory of Enhanced Heat Transfer and Energy Conservation of MOE, Beijing Key Laboratory of Heat Transfer and Energy Conversion, College of Environmental and Energy Engineering, Beijing University of Technology, Beijing; 100124, China
[ 10 ] [Wu, Yuting]Key Laboratory of Enhanced Heat Transfer and Energy Conservation of MOE, Beijing Key Laboratory of Heat Transfer and Energy Conversion, College of Environmental and Energy Engineering, Beijing University of Technology, Beijing; 100124, China

Reprint Author's Address：

杨富斌
[yang, fubin]key laboratory of enhanced heat transfer and energy conservation of moe, 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

Email：

yangfubin@bjut.edu.cn

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Related Keywords：

Effect of organic rankine cycle under automotive diesel engine variable conditions
2014，Chinese Internal Combustion Engine Engineering
Performance prediction and optimization of an organic rankine cycle (ORC) using back propagation neural network for diesel engine waste heat recovery
2018，ASME 2018 12th International Conference on Energy Sustainability, ES 2018, collocated with the ASME 2018 Power Conference and the ASME 2018 Nuclear Forum
Parametric optimization of organic Rankine cycle for vehicle diesel engine based on particle swarm optimization
2015，CIESC Journal
Waste heat organic Rankine cycle of vehicle diesel engine under variable working conditions
2015，CIESC Journal

Source ：

Applied Thermal Engineering

ISSN： 1359-4311

Year： 2021

Volume： 182

6 . 4 0 0

JCR@2022

ESI Discipline： ENGINEERING;

ESI HC Threshold：87

JCR Journal Grade：1

Cited Count：

WoS CC Cited Count： 0

SCOPUS Cited Count： 63

ESI Highly Cited Papers on the List： 0 Unfold All

WanFang Cited Count：

Chinese Cited Count：

30 Days PV： 11

Affiliated Colleges：

化学与生命科学学院本学院/部未明确归属的数据

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