relation: https://khub.utp.edu.my/scholars/15799/
title: Adsorption and Artificial Neural Network Modelling of Metolachlor Removal by MIL-53(Al) Metal-Organic Framework
creator: Isiyaka, H.A.
creator: Ramli, A.
creator: Jumbri, K.
creator: Sambudi, N.S.
creator: Zango, Z.U.
creator: Saad, B.
description: This study investigates the removal and prediction of metolachlor from aqueous medium using advanced prediction technique and porous adsorbent material (metal-organic framework (MOF)). The powdered X-ray diffraction (XRD), Field emission scanning electron microscopy (FESEM), and Brunauer, Emmett and Teller (BET) were used to characterize the synthesized MOF. High removal efficiency (97.75) was recorded within short time (30 min) using only 10 mg of the MOF. The variation in the pH and concentration were also studied to understand the adsorption. The experimental data set for the training and validation were designed and predicted using the model of artificial neural network (ANN). The ANN model gave accurate prediction for training (R2 = 0.998; R2adj = 0.991 and RMSE = 0.311) and validation (R2 = 0.996; R2adj = 0.991 and RMSE = 0.441) at node five. The porous nature of the adsorbent (1104 m2 g�1), fast equilibration time,  removal and accurate ANN prediction makes this MOF an effective material for remediating recalcitrant pollutants. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
publisher: Springer Science and Business Media Deutschland GmbH
date: 2021
type: Article
type: PeerReviewed
identifier:   Isiyaka, H.A. and Ramli, A. and Jumbri, K. and Sambudi, N.S. and Zango, Z.U. and Saad, B.  (2021) Adsorption and Artificial Neural Network Modelling of Metolachlor Removal by MIL-53(Al) Metal-Organic Framework.  Advances in Intelligent Systems and Computing, 1350 A.  pp. 245-255.  ISSN 21945357     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104477231&doi=10.1007%2f978-3-030-70917-4_24&partnerID=40&md5=79eb87077fb4063588ce111d47d5b854
relation: 10.1007/978-3-030-70917-4₂₄
identifier: 10.1007/978-3-030-70917-4₂₄