Adsorption of dicamba and MCPA onto MIL-53(Al) metal-organic framework: response surface methodology and artificial neural network model studies

Hamza Ahmad Isiyaka; Khairulazhar Jumbri; Nonni Soraya Sambudi; Zakariyya Uba Zango; Nor Ain Fathihah Abdullah; Bahruddin Saad; Adamu Mustapha

doi:10.1039/d0ra07969c

Adsorption of dicamba and MCPA onto MIL-53(Al) metal-organic framework: response surface methodology and artificial neural network model studies

RSC Adv. 2020 Nov 27;10(70):43213-43224. doi: 10.1039/d0ra07969c. eCollection 2020 Nov 23.

Authors

Hamza Ahmad Isiyaka¹, Khairulazhar Jumbri¹, Nonni Soraya Sambudi², Zakariyya Uba Zango¹, Nor Ain Fathihah Abdullah¹, Bahruddin Saad¹, Adamu Mustapha³

Affiliations

¹ Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS 32610 Seri Iskandar Perak Malaysia hamza_18001996@utp.edu.my khairulazhar.jumbri@utp.edu.my.
² Chemical Engineering Department, Universiti Teknologi PETRONAS 32610 Seri Iskandar Perak Malaysia.
³ Department of Geography, Faculty of Earth and Environmental Sciences, Kano University of Science and Technology Wudil 3244 Kano State Nigeria.

Abstract

An aluminium-based metal-organic framework ((MOF), MIL-53(Al)), was hydrothermally synthesized, characterized and applied for the remediation of the herbicides dicamba (3,6-dichloro-2-methoxy benzoic acid) and 4-chloro-2-methylphenoxyacetic acid (MCPA) in aqueous medium. Response surface methodology (RSM) and artificial neural network (ANN) were used to design, optimize and predict the non-linear relationships between the independent and dependent variables. The shared interaction of the effects of key response parameters on the adsorption capacity were assessed using the central composite design-RSM and ANN optimization models. The optimum adsorption capacities for dicamba and MCPA are 228.5 and 231.9 mg g^-1, respectively. The RSM ANOVA results showed significant p-values, with coefficients of determination (R ²) = 0.988 and 0.987 and R ² adjusted = 0.974 and 0.976 for dicamba and MCPA, respectively. The ANN prediction model gave R ² = 0.999 and 0.999, R ² adjusted = 0.997 and 0.995 and root mean square errors (RMSEs) of 0.001 and 0.004 for dicamba and MCPA, respectively. In each set of experimental conditions used for the study, the ANN gave better prediction than the RSM, with high accuracy and minimal error. The rapid removal (∼25 min), reusability (5 times) and good agreement between the experimental findings and simulation results suggest the great potential of MIL-53(Al) for the remediation of dicamba and MCPA from water matrices.