LAPSE:2020.0640
Published Article
LAPSE:2020.0640
Mesoporous Mn-Doped Fe Nanoparticle-Modified Reduced Graphene Oxide for Ethyl Violet Elimination: Modeling and Optimization Using Artificial Intelligence
Yu Hou, Jimei Qi, Jiwei Hu, Yiqiu Xiang, Ling Xin, Xionghui Wei
June 23, 2020
Mesoporous Mn-doped Fe nanoparticle-modified reduced graphene oxide (Mn-doped Fe/rGO) was prepared through a one-step co-precipitation method, which was then used to eliminate ethyl violet (EV) in wastewater. The prepared Mn-doped Fe/rGO was characterized by X-ray diffraction, X-ray photoelectron spectroscopy, Raman spectroscopy, high-resolution transmission electron microscopy, scanning electron microscopy, energy dispersive spectroscopy, N2-sorption, small angle X-ray diffraction and superconducting quantum interference device. The Brunauer−Emmett−Teller specific surface area of Mn-doped Fe/rGO composites was 104.088 m2/g. The EV elimination by Mn-doped Fe/rGO was modeled and optimized by artificial intelligence (AI) models (i.e., radial basis function network, random forest, artificial neural network genetic algorithm (ANN-GA) and particle swarm optimization). Among these AI models, ANN-GA is considered as the best model for predicting the removal efficiency of EV by Mn-doped Fe/rGO. The evaluation of variables shows that dosage gives the maximum importance to Mn-doped Fe/rGO removal of EV. The experimental data were fitted to kinetics and adsorption isotherm models. The results indicated that the process of EV removal by Mn-doped Fe/rGO obeyed the pseudo-second-order kinetics model and Langmuir isotherm, and the maximum adsorption capacity was 1000.00 mg/g. This study provides a possibility for synthesis of Mn-doped Fe/rGO by co-precipitation as an excellent material for EV removal from the aqueous phase.
Keywords
Artificial Intelligence, ethyl violet, gradient boosted regression trees, mesoporous materials, Mn-doped Fe/rGO nanocomposites
Subject
Suggested Citation
Hou Y, Qi J, Hu J, Xiang Y, Xin L, Wei X. Mesoporous Mn-Doped Fe Nanoparticle-Modified Reduced Graphene Oxide for Ethyl Violet Elimination: Modeling and Optimization Using Artificial Intelligence. (2020). LAPSE:2020.0640
Author Affiliations
Hou Y: Guizhou Provincial Key Laboratory for Information Systems of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, Guiyang 550001, China
Qi J: Guizhou Provincial Key Laboratory for Information Systems of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, Guiyang 550001, China
Hu J: Guizhou Provincial Key Laboratory for Information Systems of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, Guiyang 550001, China; Cultivation Base of Guizhou National Key Laboratory of Mountainous Karst Eco-Environ [ORCID]
Xiang Y: Guizhou Provincial Key Laboratory for Information Systems of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, Guiyang 550001, China
Xin L: Guizhou Provincial Key Laboratory for Information Systems of Mountainous Areas and Protection of Ecological Environment, Guizhou Normal University, Guiyang 550001, China
Wei X: Department of Applied Chemistry, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
Journal Name
Processes
Volume
8
Issue
4
Article Number
E488
Year
2020
Publication Date
2020-04-22
Published Version
ISSN
2227-9717
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PII: pr8040488, Publication Type: Journal Article
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LAPSE:2020.0640
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doi:10.3390/pr8040488
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Jun 23, 2020
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