Optimizations and artificial neural network validation studies for naphthalene and phenanthrene adsorption onto NH2-UiO-66(Zr) metal-organic framework

Zango, Z.U. and Jumbri, K. and Zaid, H.F.M. and Sambudi, N.S. and Matmin, J. (2021) Optimizations and artificial neural network validation studies for naphthalene and phenanthrene adsorption onto NH2-UiO-66(Zr) metal-organic framework. In: UNSPECIFIED.

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Abstract

Adsorptive removal of naphthalene (NAP) and phenanthrene (PHE) was reported using NH2-UiO-66(Zr) metal-organic frameworks. The process was optimized by response surface methodology (RSM) using central composite design (CCD). The fitting of the model was described by the analysis of variance (ANOVA) with significant Fischer test (F-value) of 85.46 and 30.56 for NAP and PHE, respectively. Validation of the adsorption process was performed by artificial neural network (ANN), achieving good prediction performance at node 6 for both NAP and PHE with good agreement between the actual and predicted ANN adsorption efficiencies. The good reusability of the MOF was discovered for 7 consecutive cycles and achieving adsorption efficiency of 89.1 and 87.2 for the NAP and PHE, respectively. The performance of the MOF in a binary adsorption system was also analyzed and the adsorption efficiency achieved was 97.7 and 96.9 for the NAP and PHE, respectively. © Published under licence by IOP Publishing Ltd.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: cited By 4; Conference of 3rd International Conference on Tropical Resources and Sustainable Sciences, CTReSS 2021 ; Conference Date: 14 July 2021 Through 15 July 2021; Conference Code:171582
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 10 Nov 2023 03:29
Last Modified: 10 Nov 2023 03:29
URI: https://khub.utp.edu.my/scholars/id/eprint/14475

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