Adsorption of U(VI) from aqueous solution by using KMnO4-modified hazelnut shell activated carbon: characterisation and artificial neural network modelling

Zhu, Mijia; Li, Fanxiu; Wu, Chen; Yin, Xia; Yi, Zichuan; Shuyong, Zhang

doi:10.1007/s11356-021-14034-x

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Experimental Studies and Dataset Of Metals Adsorption On Bms1

Standalone Ann Framework1

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2022

2024

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Cited by 8 publications

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“…[55][56][57][58][59][60][61][62] The surface treatment of biomaterials with acids and alkalis improved their interactions with metals, which resulted in faster adsorption rates and increased uptake capacity. [63][64][65][66] The reutilization of saturated biomaterials into value-added products has been reported in detail by authors in their earlier research. 7 The experimental adsorption studies that depict various aspects of metals adsorption relevant for ANN modelling are given below:…”

Section: Experimental Studies and Dataset Of Metals Adsorption On Bmsmentioning

confidence: 94%

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Application of neural network in metal adsorption using biomaterials (BMs): a review

Nighojkar

Zimmermann

Ateia

et al. 2023

Environ. Sci.: Adv.

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Section: Experimental Studies and Dataset Of Metals Adsorption On Bmsmentioning

confidence: 94%

“…Some studies also included the inuence of lingo-cellulosic functional groups, particle size, and calcination temperature used to optimise biomaterial adsorbents' fabrication. 52,65,66,91,92 Table 4 illustrates recent developments of ANN-based optimization methods for modelling biomaterial adsorption systems.…”

Section: Standalone Ann Frameworkmentioning

confidence: 99%