Artificial Intelligence Based Optimization for the Se(IV) Removal from Aqueous Solution by Reduced Graphene Oxide-Supported Nanoscale Zero-Valent Iron Composites
Highly promising artificial intelligence tools, including neural network (ANN), genetic algorithm (GA) and particle swarm optimization (PSO), were applied in the present study to develop an approach for the evaluation of Se(IV) removal from aqueous solutions by reduced graphene oxide-supported nanos...
Main Authors: | Rensheng Cao, Mingyi Fan, Jiwei Hu, Wenqian Ruan, Xianliang Wu, Xionghui Wei |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2018-03-01
|
Series: | Materials |
Subjects: | |
Online Access: | http://www.mdpi.com/1996-1944/11/3/428 |
Similar Items
-
Modeling of Malachite Green Removal from Aqueous Solutions by Nanoscale Zerovalent Zinc Using Artificial Neural Network
by: Wenqian Ruan, et al.
Published: (2017-12-01) -
Artificial Neural Network Modeling and Genetic Algorithm Optimization for Cadmium Removal from Aqueous Solutions by Reduced Graphene Oxide-Supported Nanoscale Zero-Valent Iron (nZVI/rGO) Composites
by: Mingyi Fan, et al.
Published: (2017-05-01) -
Optimizing Low-Concentration Mercury Removal from Aqueous Solutions by Reduced Graphene Oxide-Supported Fe3O4 Composites with the Aid of an Artificial Neural Network and Genetic Algorithm
by: Rensheng Cao, et al.
Published: (2017-11-01) -
Synthesis and Characterization of Reduced Graphene Oxide-Supported Nanoscale Zero-Valent Iron (nZVI/rGO) Composites Used for Pb(II) Removal
by: Mingyi Fan, et al.
Published: (2016-08-01) -
Kinetic determination of Se(IV) in pharmaceutical samples
by: Mitić S.S., et al.
Published: (2000-01-01)