TY  - JOUR
ID  - scholars12475
KW  - Adsorption; Benzoic acid; Forecasting; Herbicides; Mean square error; Metal-Organic Frameworks; Organometallics; Predictive analytics; Reusability; Surface properties
KW  -  Adsorption capacities; Artificial neural network modeling; Central composite designs; Experimental conditions; Hydrothermally synthesized; Non-linear relationships; Response surface methodology; Root mean square errors
KW  -  Neural networks
N2  - 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 (�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. This journal is © The Royal Society of Chemistry.
IS  - 70
Y1  - 2020///
VL  - 10
JF  - RSC Advances
A1  - Isiyaka, H.A.
A1  - Jumbri, K.
A1  - Sambudi, N.S.
A1  - Zango, Z.U.
A1  - Fathihah Abdullah, N.A.
A1  - Saad, B.
A1  - Mustapha, A.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097835532&doi=10.1039%2fd0ra07969c&partnerID=40&md5=e49c7c45052022f192dbacadbdf0d459
AV  - none
SP  - 43213
TI  - Adsorption of dicamba and MCPA onto MIL-53(Al) metal-organic framework: Response surface methodology and artificial neural network model studies
N1  - cited By 14
PB  - Royal Society of Chemistry
SN  - 20462069
EP  - 43224
ER  -