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Research Papers: Energy Systems Analysis

Performance Prediction and Optimization of an Organic Rankine Cycle Using Back Propagation Neural Network for Diesel Engine Waste Heat Recovery

[+] Author and Article Information
Fubin Yang

College of Environmental and
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

Hongguang Zhang

College of Environmental and
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 141(6), 062006 (Jan 18, 2019) (9 pages) Paper No: JERT-18-1691; doi: 10.1115/1.4042408 History: Received September 05, 2018; Revised December 03, 2018

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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Figures

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Fig. 1

Schematic diagram of the waste heat recovery system

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Fig. 2

Test bench of the diesel engine

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Fig. 3

Test bench of the ORC system

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Fig. 4

Topology of the BPNN model

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Fig. 5

Effect of hidden layer number on the prediction accuracy of the BPNN model

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Fig. 6

Test results of the BPNN mode

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Fig. 7

Prediction relative errors of the BPNN model

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Fig. 8

Effects of V˙ and Torexp on W˙exp and Texh,out

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Fig. 9

Effects of p exp,in and p exp,out on W˙exp and Texh,out

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Fig. 10

Pareto frontier for multi-objective optimization

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