Elman and back propagation neural networks based working fluid side energy level analysis of shell-and-tube evaporator in organic Rankine cycle (ORC) system-Reference-Cited by-同舟云学术

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

Published:2022-09 Issue:9 Volume:61 Page:7339-7352
ISSN:1110-0168
Container-title:Alexandria Engineering Journal
language:en
Short-container-title:Alexandria Engineering Journal

Author:

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

Funder

Natural Science Foundation of Beijing Municipality

State Key Laboratory of Engines

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

General Engineering

Reference48 articles.

1. A review of heat integration approaches for organic rankine cycle with waste heat in production processes;Anastasovski;Energy Convers. Manage.,2020

2. Thermoeconomic multi-objective optimization of a dual loop organic Rankine cycle (ORC) for CNG engine waste heat recovery;Yang;Appl. Energy,2017

3. Analysis of the backpressure effect of an organic Rankine cycle (ORC) evaporator on the exhaust line of a turbocharged heavy duty diesel power generator for marine applications;Michos;Energy Convers. Manage.,2017

4. Thermodynamic, economic, and environmental analysis and multi-objective optimization of a dual loop organic Rankine cycle for CNG engine waste heat recovery;Ping;Appl. Therm. Eng.,2021

5. Parametric design and off-design analysis of organic Rankine cycle (ORC) system;Song;Energy Convers. Manage.,2016

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