@article{scholars12475,
          volume = {10},
            note = {cited By 14},
          number = {70},
             doi = {10.1039/d0ra07969c},
           title = {Adsorption of dicamba and MCPA onto MIL-53(Al) metal-organic framework: Response surface methodology and artificial neural network model studies},
            year = {2020},
       publisher = {Royal Society of Chemistry},
         journal = {RSC Advances},
           pages = {43213--43224},
        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 (R2) = 0.988 and 0.987 and R2 adjusted = 0.974 and 0.976 for dicamba and MCPA, respectively. The ANN prediction model gave R2 = 0.999 and 0.999, R2 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 ({\^a}?1/425 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. This journal is {\^A}{\copyright} The Royal Society of Chemistry.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097835532&doi=10.1039\%2fd0ra07969c&partnerID=40&md5=e49c7c45052022f192dbacadbdf0d459},
        keywords = {Adsorption; Benzoic acid; Forecasting; Herbicides; Mean square error; Metal-Organic Frameworks; Organometallics; Predictive analytics; Reusability; Surface properties, Adsorption capacities; Artificial neural network modeling; Central composite designs; Experimental conditions; Hydrothermally synthesized; Non-linear relationships; Response surface methodology; Root mean square errors, Neural networks},
          author = {Isiyaka, H. A. and Jumbri, K. and Sambudi, N. S. and Zango, Z. U. and Fathihah Abdullah, N. A. and Saad, B. and Mustapha, A.},
            issn = {20462069}
}