Estimation of the effect of catalyst physical characteristics on Fenton-like oxidation efficiency using adaptive neuro-fuzzy computing technique

• Catalyst size as one of the factors in catalytic performance.
• Response surface methodology (RSM).
• Adaptive neuro-fuzzy inference system (ANFIS) application.
• To quantify the effects of physical characteristics of magnetite.
• Fenton-like oxidation efficiency of methylene blue.

Catalyst size, which determines surface area, is one of the major factors in catalytic performance. In this study, response surface methodology (RSM) and an adaptive neuro-fuzzy inference system (ANFIS) were applied to quantify the effects of physical characteristics of magnetite on Fenton-like oxidation efficiency of methylene blue. For this purpose, two magnetite samples (M and N) were used and characterized by XRD, BET surface area, particle size analyzer and FE-SEM. Central composite design (CCD) was applied to design the experiments, develop regression models, optimize and evaluate the individual and interactive effects of five independent variables: H2O2 and catalyst concentrations, pH, reaction time (numeric factors) and the type of catalyst (categorical factor). For each categorical factor, three quadratic models were developed regarding target responses: decolorization (YMB), COD (YCOD) and TOC (YTOC) removal efficiencies (%). The quadratic models were estimated by CCD and ANFIS methodologies. ANFIS was implemented using Matlab/Simulink and the performances were investigated. ANFIS models performed better for catalyst N compared to catalyst M, for color, COD and TOC separately. On contrary, it performed better for catalyst M compared to catalyst N, for combinations of color, COD and TOC. The obtained RMSE and R2 for the ANFIS networks show the effectiveness of catalyst N compared to catalyst M in Fenton oxidation process.