Prediction of tubular solar still performance by machine learning integrated with Bayesian optimization algorithm-Reference-Cited by-同舟云学术

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Prediction of tubular solar still performance by machine learning integrated with Bayesian optimization algorithm

Published:2021-02 Issue: Volume:184 Page:116233
ISSN:1359-4311
Container-title:Applied Thermal Engineering
language:en
Short-container-title:Applied Thermal Engineering

Author:

Wang Yunpeng,Kandeal A.W.,Swidan Ahmed,Sharshir Swellam W.,Abdelaziz Gamal B.,Halim M.A.,Kabeel A.E.,Yang Nuo

Funder

National Key Research and Development Program of China

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Energy Engineering and Power Technology

Reference60 articles.

1. Productivity modelling of a developed inclined stepped solar still system based on actual performance and using a cascaded forward neural network model;Abujazar;J. Cleaner Prod.,2018

2. Novel multi-stage flash (MSF) desalination plant driven by parabolic trough collectors and a solar pond: a simulation study in UAE;Al-Othman;Desalination,2018

3. Performance optimization of an integrated solar combined cycle power plant equipped with a brine circulation MSF desalination unit;Shaaban;Energy Convers. Manage.,2019

4. On the feasibility of small communities wellhead RO treatment for nitrate removal and salinity reduction;Choi;J. Environ. Manage.,2019

5. Cold-cathode X-ray irradiation pre-treatment for fouling control of reverse osmosis (RO) in shale gas produced water (SGPW) treatment;Kim;Chem. Eng. J.,2019

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