Developing an adaptive neuro‐fuzzy inference system based on particle swarm optimization model for forecasting Cr ( VI ) removal by NiO nanoparticles-Reference-Cited by-同舟云学术

Developing an adaptive neuro‐fuzzy inference system based on particle swarm optimization model for forecasting Cr ( VI ) removal by NiO nanoparticles

Published:2021-01-13 Issue:4 Volume:40 Page:
ISSN:1944-7442
Container-title:Environmental Progress & Sustainable Energy
language:en
Short-container-title:Environ Prog Sustainable Energy

Author:

Rajabi Kuyakhi Hossein¹^ORCID,Tahmasebi Boldaji Ramin²

Affiliation:

1. Department of Chemical Engineering University of Guilan Rasht Iran

2. Department of Chemical Engineering College of Engineering, University of Isfahan Isfahan Iran

Publisher

Wiley

Subject

General Environmental Science,Waste Management and Disposal,Water Science and Technology,General Chemical Engineering,Renewable Energy, Sustainability and the Environment,Environmental Chemistry,Environmental Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ep.13597

Reference37 articles.

1. Artificial neural network modelling of Cr(VI) surface adsorption with NiO nanoparticles using the results obtained from optimization of response surface methodology

2. Efficient removal of Cr(VI) from water by the uniform fiber ball loaded with polypyrrole: Static adsorption, dynamic adsorption and mechanism studies

3. Studies on Cr(VI) Removal from Aqueous Solutions by Nanoalumina

4. Cr (VI) removal by a new type of anion exchange resin DEX-Cr: Adsorption affecting factors, isotherms, kinetics, and desorption regeneration

5. Adsorption Separation of Cr(VI) from a Water Phase Using Multiwalled Carbon Nanotube-Immobilized Ionic Liquids

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