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
Abstract
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.
Topics & Concepts
MCPADicambaAdsorptionArtificial neural networkMetal-organic frameworkResponse surface methodologyMetalChemistryBiological systemChromatographyComputer scienceArtificial intelligenceOrganic chemistryBiologyPesticideEcologyWeed controlMetal-Organic Frameworks: Synthesis and ApplicationsX-ray Diffraction in CrystallographyAdsorption and biosorption for pollutant removal