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作者:

Ping, Xu (Ping, Xu.) | Yang, Fubin (Yang, Fubin.) | Zhang, Hongguang (Zhang, Hongguang.) (学者:张红光) | Zhang, Wujie (Zhang, Wujie.) | Zhang, Jian (Zhang, Jian.) | Song, Gege (Song, Gege.) | Wang, Chongyao (Wang, Chongyao.) | Yao, Baofeng (Yao, Baofeng.) | Wu, Yuting (Wu, Yuting.) (学者:吴玉庭)

收录:

EI SCIE

摘要:

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

关键词:

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

作者机构:

  • [ 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

通讯作者信息:

  • [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

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来源 :

Applied Thermal Engineering

ISSN: 1359-4311

年份: 2021

卷: 182

6 . 4 0 0

JCR@2022

ESI学科: ENGINEERING;

ESI高被引阀值:9

被引次数:

WoS核心集被引频次: 0

SCOPUS被引频次: 57

ESI高被引论文在榜: 0 展开所有

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