Using neural network optimized by imperialist competition method and genetic algorithm to predict water productivity of a nanofluid-based solar still equipped with thermoelectric modules-Reference-Cited by-同舟云学术

Using neural network optimized by imperialist competition method and genetic algorithm to predict water productivity of a nanofluid-based solar still equipped with thermoelectric modules

Published:2020-04 Issue: Volume:366 Page:571-586
ISSN:0032-5910
Container-title:Powder Technology
language:en
Short-container-title:Powder Technology

Author:

Bahiraei Mehdi^ORCID,Nazari Saeed,Moayedi Hossein^ORCID,Safarzadeh Habibollah

Publisher

Elsevier BV

Subject

General Chemical Engineering

Reference79 articles.

1. Water Desalination Cost Literature: Review and Assessment;Karagiannis,2008

2. Solar thermal desalination technologies;Qiblawey;Desalination,2008

3. Solar Distillation Practice for Water Desalination Systems;Tiwari,2008

4. Experimental study and evaluation of single slope solar still combined with flat plate collector, parabolic trough and packed bed;Madiouli;Sol. Energy,2020

5. Effects of tray shape of a multi-stage solar still coupled to a parabolic concentrating solar collector in Algeria;Abdessemed;Renew. Energy,2019

Cited by 80 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. An integral and multidimensional review on multi-layer perceptron as an emerging tool in the field of water treatment and desalination processes;Desalination;2024-10

2. A review of the current situation and prospects for nanofluids to improve solar still performance;Journal of Thermal Analysis and Calorimetry;2024-08-17

3. Hybrid photovoltaic/thermal performance prediction based on machine learning algorithms with hyper-parameter tuning;International Journal of Sustainable Energy;2024-06-28

4. Innovative solar distillation system with prismatic absorber basin: Experimental analysis and LSTM machine learning modeling coupled with great wall construction algorithm;Process Safety and Environmental Protection;2024-06

5. Effects of temperature and nanoparticle mixing ratio on the thermophysical properties of GNP–Fe2O3 hybrid nanofluids: an experimental study with RSM and ANN modeling;Journal of Thermal Analysis and Calorimetry;2024-05