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.
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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.Modeling and prediction of copper removal from aqueous solutions by nZVI/rGO magnetic nanocomposites using ANN-GA and ANN-PSO.Artificial Intelligence Based Optimization for the Se(IV) Removal from Aqueous Solution by Reduced Graphene Oxide-Supported Nanoscale Zero-Valent Iron Composites.Removal of Crystal Violet by Using Reduced-Graphene-Oxide-Supported Bimetallic Fe/Ni Nanoparticles (rGO/Fe/Ni): Application of Artificial Intelligence Modeling for the Optimization Process.
P2860
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.
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2017 nî lūn-bûn
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2017年の論文
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2017年学术文章
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2017年学术文章
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2017年学术文章
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2017年学术文章
@zh-my
2017年学术文章
@zh-sg
2017年學術文章
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2017年學術文章
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2017年學術文章
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name
Artificial Neural Network Mode ...... nt Iron (nZVI/rGO) Composites.
@en
type
label
Artificial Neural Network Mode ...... nt Iron (nZVI/rGO) Composites.
@en
prefLabel
Artificial Neural Network Mode ...... nt Iron (nZVI/rGO) Composites.
@en
P2093
P2860
P356
P1433
P1476
Artificial Neural Network Mode ...... nt Iron (nZVI/rGO) Composites.
@en
P2093
Rensheng Cao
Tongjun Li
Wenqian Ruan
Xionghui Wei
Xuedan Shi
P2860
P356
10.3390/MA10050544
P50
P577
2017-05-17T00:00:00Z