Machine learning prediction of ORC performance based on properties of working fluid-Reference-Cited by-同舟云学术

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Machine learning prediction of ORC performance based on properties of working fluid

Published:2021-08 Issue: Volume:195 Page:117184
ISSN:1359-4311
Container-title:Applied Thermal Engineering
language:en
Short-container-title:Applied Thermal Engineering

Author:

Peng Yannan,Lin Xinxing,Liu Jinghang,Su Wen,Zhou Naijun

Funder

China Three Gorges Corporation

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Energy Engineering and Power Technology

Reference39 articles.

1. A hybrid power cycle using an inverted Brayton cycle with an indirect evaporative device for waste-heat recovery;Matsui;Appl. Therm. Eng.,2020

2. Assessment of a Kalina cycle for waste heat recovery in the cement industry;Júnior;Appl. Therm. Eng.,2019

3. Performance analysis and optimization of engine waste heat recovery with an improved transcritical-subcritical parallel organic Rankine cycle based on zeotropic mixtures;Zhi;Appl. Therm. Eng.,2020

4. A review of industrial waste heat recovery system for power generation with Organic Rankine Cycle: Recent Challenges and Future Outlook;Loni;J. Cleaner Prod.,2021

5. Towards novel low temperature thermodynamic cycle: A critical review originated from organic Rankine cycle;Xu;Appl. Energ.,2020

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