Design parameter modelling of solar power tower system using adaptive neuro-fuzzy inference system optimized with a combination of genetic algorithm and teaching learning-based optimization algorithm-Reference-Cited by-同舟云学术

Design parameter modelling of solar power tower system using adaptive neuro-fuzzy inference system optimized with a combination of genetic algorithm and teaching learning-based optimization algorithm

Published:2020-01 Issue: Volume:244 Page:118904
ISSN:0959-6526
Container-title:Journal of Cleaner Production
language:en
Short-container-title:Journal of Cleaner Production

Author:

Khosravi A.,Malekan M.,Pabon J.J.G.,Zhao X.,Assad M.E.H.

Funder

Aalto University

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Strategy and Management,General Environmental Science,Renewable Energy, Sustainability and the Environment

Reference55 articles.

1. Micro gas-turbine design for small-scale hybrid solar power plants;Aichmayer;J. Eng. Gas Turbines Power,2013

2. Performance comparison of different supercritical carbon dioxide Brayton cycles integrated with a solar power tower;Al-Sulaiman;Energy,2015

3. Prediction of energetic performance of a building integrated photovoltaic/thermal system thorough artificial neural network and hybrid particle swarm optimization models;Alnaqi;Energy Convers. Manag.,2019

4. Design and analysis of a solar tower based integrated system using high temperature electrolyzer for hydrogen production;AlZahrani;Int. J. Hydrogen Energy,2016

5. Influence of thermal energy storage system on flow and performance parameters of solar updraft tower power plant: a three dimensional numerical analysis;Amudam;J. Clean. Prod.,2019

Cited by 43 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Data-driven based financial analysis of concentrated solar power integrating biomass and thermal energy storage: A profitability perspective;Biomass and Bioenergy;2024-09

2. Use of artificial intelligence methods in designing thermal energy storage tanks: A bibliometric analysis;Journal of Energy Storage;2024-09

3. Artificial Intelligence for Thermal Energy Storage Enhancement: A Comprehensive Review;Journal of Energy Resources Technology;2024-04-16

4. Performance analysis and multi-objective optimization of mechanical draft wet cooling towers based on water saving, plume and cooling characteristics;International Journal of Thermal Sciences;2024-02

5. Decarbonization of Sewage Sludge Processing Through Solar Thermal Energy Integration;2024