TY  - JOUR
EP  - 255
PB  - Springer Science and Business Media Deutschland GmbH
SN  - 21945357
N1  - cited By 0; Conference of 2nd International Conference on Innovative Technology, Engineering and Sciences, iCITES 2020 ; Conference Date: 22 December 2020 Through 22 December 2020; Conference Code:256319
SP  - 245
TI  - Adsorption and Artificial Neural Network Modelling of Metolachlor Removal by MIL-53(Al) Metal-Organic Framework
AV  - none
A1  - Isiyaka, H.A.
A1  - Ramli, A.
A1  - Jumbri, K.
A1  - Sambudi, N.S.
A1  - Zango, Z.U.
A1  - Saad, B.
JF  - Advances in Intelligent Systems and Computing
UR  - 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
VL  - 1350 A
Y1  - 2021///
N2  - 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 gâ??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.
ID  - scholars15799
KW  - Chemicals removal (water treatment); Field emission microscopes; Forecasting; Metal-Organic Frameworks; Organometallics; Scanning electron microscopy
KW  -  Accurate prediction; Aqueous medium; Effective materials; Equilibration time; Field emission scanning electron microscopy; Porous adsorbent; Prediction techniques; Removal efficiencies
KW  -  Neural networks
ER  -