Elman and back propagation neural networks based working fluid side energy level analysis of shell-and-tube evaporator in organic Rankine cycle (ORC) system

Xu Ping; Fubin Yang; Hongguang Zhang; Jian Zhang; Wujie Zhang

Alexandria Engineering Journal (Sep 2022)

Elman and back propagation neural networks based working fluid side energy level analysis of shell-and-tube evaporator in organic Rankine cycle (ORC) system

Xu Ping,
Fubin Yang,
Hongguang Zhang,
Jian Zhang,
Wujie Zhang

Affiliations

Xu Ping: Key Laboratory of Enhanced Heat Transfer and Energy Conservation of MOE, Beijing Key Laboratory of Heat Transfer and Energy Conversion, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China
Fubin Yang: Key Laboratory of Enhanced Heat Transfer and Energy Conservation of MOE, Beijing Key Laboratory of Heat Transfer and Energy Conversion, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; Corresponding authors.
Hongguang Zhang: Key Laboratory of Enhanced Heat Transfer and Energy Conservation of MOE, Beijing Key Laboratory of Heat Transfer and Energy Conversion, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; Corresponding authors.
Jian Zhang: Mechanical Engineering, Richard J. Resch School of Engineering, University of Wisconsin Green Bay, Green Bay, WI 54311, USA
Wujie Zhang: Key Laboratory of Enhanced Heat Transfer and Energy Conservation of MOE, Beijing Key Laboratory of Heat Transfer and Energy Conversion, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China

Journal volume & issue: Vol. 61, no. 9
pp. 7339 – 7352

Abstract

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As the heat exchange component of the organic Rankine cycle (ORC) system, the evaporator directly affects the overall operation performance of the system. In this paper, an analytical method for energy level on the working fluid side of evaporator is proposed based on the energy level and enerty theory. The reliability, validity, and correlation of the proposed analytical method are studied by means of theoretical analysis, experimental evaluation, and Elman neural network (ElmanNN). The bilinear interpolation algorithm is used to analyze the non-linear relationship between the system parameters and the energy level on the working fluid side. In addition, the correlation between the heat exchange efficiency of the evaporator and the operating performance of the system is compared. Based on the back propagation neural network (BPNN), the high energy level area on working fluid side is accurately evaluated and verified, the direction and degree of the non-linear mapping relationship between the working fluid side energy level and the system performance are quantitatively evaluated, and the sensitivity of thermodynamic cycle parameters in the high energy level area on working fluid side is quantitatively evaluated. This study provides a novel approach to evaluate the operation of the evaporator in the ORC system. In addition, this study also provides guidance on how to keep the evaporator operating continuously in the high energy level area throughout the experiment.

Published in Alexandria Engineering Journal

ISSN: 1110-0168 (Print); 2090-2670 (Online)
Publisher: Elsevier
Country of publisher: Egypt
LCC subjects: Technology: Engineering (General). Civil engineering (General)
Website: http://www.journals.elsevier.com/alexandria-engineering-journal/

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