Comparative Studies of RSM, RSM–GA and ANFILS for Modeling and Optimization of Naphthalene Adsorption on Chitosan–CTAB–Sodium Bentonite Clay Matrix
The aim of this article was to compare the predictive abilities of the optimization techniques of response surface methodology (RSM), the hybrid of RSM–genetic algorithm (RSM–GA) and adaptive neuro-fuzzy interference logic system (ANFILS) for design responses of % removal of naphthalene and adsorption capacity of the synthesized composite nanoparticles of chitosan–cetyltrimethylammonium bromide (CTAB)–sodium bentonite clay. The process variables considered were surfactant concentration,1X, activation time, 2X, activation temperature, 3X, and chitosan dosage, 4X. The ANFILS models showed better modeling abilities of the adsorption data on the synthesized composite adsorbent for reason of lower % mean absolute deviation, lower % error value, higher coefficient of determination, 2R, amongst others and lower error functions’ values than those obtained using RSM and RSM-GA for both responses. When applied RSM, the hybrid of RSM–genetic algorithm (RSM–GA) and ANFILS 3–D surface plot optimization technique to determine the optimal conditions for both responses, ANFILS was adjudged the best. The ANFILS predicted optimal conditions were 1X= 116.00 mg/L, 2X= 2.06 h, 3X= 81.2oC and 4X= 5.20 g. Excellent agreements were achieved between the predicted responses of 99.055% removal of naphthalene and 248.6375 mg/g adsorption capacity and their corresponding experimental values of 99.020% and 248.86 mg/g with % errors of -0.0353 and 0.0894 respectively. Hence, in this study, ANFILS has been successfully used to model and optimize the conditions for the treatment of industrial wastewater containing polycyclic aromatic compounds, especially naphthalene and is hereby recommended for such and similar studies.
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