This paper presents a methodology to predict and optimize performance of an organic Rankine cycle (ORC) using a back propagation neural network (BPNN) for diesel engine waste heat recovery. A test bench of an ORC with a diesel engine is established to collect experimental data. The collected data are used to train and test a BPNN model for performance prediction and optimization. After evaluating different hidden layers, a BPNN model of the ORC system is determined with the consideration of mean squared error (MSE) and correlation coefficient. The effects of key operating parameters on the power output of the ORC system and exhaust temperature at the outlet of the evaporator are evaluated using the proposed model and further discussed. Finally, a multi-objective optimization of the ORC system is conducted for maximizing power output and minimizing exhaust temperature at the outlet of the evaporator based on the proposed BPNN model. The results show that the proposed BPNN model has a high prediction accuracy and the maximum relative error of the power output is less than 5%. It also shows that when the operations are optimized based on the proposed model, the power output of the ORC system can be higher than the experimental results.
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June 2019
Research-Article
Performance Prediction and Optimization of an Organic Rankine Cycle Using Back Propagation Neural Network for Diesel Engine Waste Heat Recovery
Fubin Yang,
Fubin Yang
College of Environmental and
Energy Engineering,
Beijing University of Technology,
Pingleyuan No.100,
Beijing 100124, China
e-mail: yangfubinnuc@163.com
Energy Engineering,
Beijing University of Technology,
Pingleyuan No.100,
Beijing 100124, China
e-mail: yangfubinnuc@163.com
Search for other works by this author on:
Heejin Cho,
Heejin Cho
Department of Mechanical Engineering,
Mississippi State University,
P.O. Box 9552,
Mississippi State, MS 39762
e-mail: cho@me.msstate.edu
Mississippi State University,
P.O. Box 9552,
Mississippi State, MS 39762
e-mail: cho@me.msstate.edu
Search for other works by this author on:
Hongguang Zhang
Hongguang Zhang
College of Environmental and
Energy Engineering,
Beijing University of Technology,
Pingleyuan No.100,
Beijing 100124, China
e-mail: zhanghongguang@bjut.edu.cn
Energy Engineering,
Beijing University of Technology,
Pingleyuan No.100,
Beijing 100124, China
e-mail: zhanghongguang@bjut.edu.cn
Search for other works by this author on:
Fubin Yang
College of Environmental and
Energy Engineering,
Beijing University of Technology,
Pingleyuan No.100,
Beijing 100124, China
e-mail: yangfubinnuc@163.com
Energy Engineering,
Beijing University of Technology,
Pingleyuan No.100,
Beijing 100124, China
e-mail: yangfubinnuc@163.com
Heejin Cho
Department of Mechanical Engineering,
Mississippi State University,
P.O. Box 9552,
Mississippi State, MS 39762
e-mail: cho@me.msstate.edu
Mississippi State University,
P.O. Box 9552,
Mississippi State, MS 39762
e-mail: cho@me.msstate.edu
Hongguang Zhang
College of Environmental and
Energy Engineering,
Beijing University of Technology,
Pingleyuan No.100,
Beijing 100124, China
e-mail: zhanghongguang@bjut.edu.cn
Energy Engineering,
Beijing University of Technology,
Pingleyuan No.100,
Beijing 100124, China
e-mail: zhanghongguang@bjut.edu.cn
1Corresponding authors.
Contributed by the Advanced Energy Systems Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received September 5, 2018; final manuscript received December 3, 2018; published online January 18, 2019. Assoc. Editor: Reza Baghaei Lakeh.
J. Energy Resour. Technol. Jun 2019, 141(6): 062006 (9 pages)
Published Online: January 18, 2019
Article history
Received:
September 5, 2018
Revised:
December 3, 2018
Citation
Yang, F., Cho, H., and Zhang, H. (January 18, 2019). "Performance Prediction and Optimization of an Organic Rankine Cycle Using Back Propagation Neural Network for Diesel Engine Waste Heat Recovery." ASME. J. Energy Resour. Technol. June 2019; 141(6): 062006. https://doi.org/10.1115/1.4042408
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