Integrated AI-driven optimization of Fenton process for the treatment of antibiotic sulfamethoxazole: Insights into mechanistic approach.

Antibiotics, as a class of environmental pollutants, pose a significant challenge due to their persistent nature and resistance to easy degradation. This study delves into modeling and optimizing conventional Fenton degradation of antibiotic sulfamethoxazole (SMX) and total organic carbon (TOC) under varying levels of H2O2, Fe2+ concentration, pH, and temperature using statistical and artificial intelligence techniques including Multiple Regression Analysis (MRA), Support Vector Regression (SVR) and Artificial Neural Network (ANN). In statistical metrics, the ANN model demonstrated superior predictive accuracy compared to its counterparts, with lowest RMSE values of 0.986 and 1.173 for SMX and TOC removal, respectively. Sensitivity showcased H2O2/Fe2+ ratio, time and pH as pivotal for SMX degradation, while in simultaneous SMX and TOC reduction, fine tuning the time, pH, and temperature was essential. Leveraging a Hybrid Genetic Algorithm-Desirability Optimization approach, the trained ANN model revealed an optimal desirability of 0.941 out of 1000 solutions which yielded a 91.18% SMX degradation and 87.90% TOC removal under following specific conditions: treatment time of 48.5 min, Fe2+: 7.05 mg L-1, H2O2: 128.82 mg L-1, pH: 5.1, initial SMX: 97.6 mg L-1, and a temperature: 29.8 °C. LC/MS analysis reveals multiple intermediates with higher m/z (242, 270 and 288) and lower m/z (98, 108, 156 and 173) values identified, however no aliphatic hydrocarbon was isolated, because of the low mineralization performance of Fenton process. Furthermore, some inorganic fragments like NH4+ and NO3- were also determined in solution. This comprehensive research enriches AI modeling for intricate Fenton-based contaminant degradation, advancing sustainable antibiotic removal strategies.

Computational and statistical modeling for parameters optimization of electrochemical decontamination of synozol red dye wastewater.

In this study, computational and statistical models were applied to optimize the inherent parameters of an electrochemical decontamination of synozol red. The effect of various experimental variables such as current density, initial pH and concentration of electrolyte on degradation were assessed at Ti/RuO0·3TiO0·7O2 anode. Response surface methodology (RSM) based central composite design was applied to investigate interdependency of studied variables and train an artificial neural network (ANN) to envisage the experimental training data. The presence of fifteen neurons proved to have optimum performance based on maximum R2, mean absolute error, absolute average deviation and minimum mean square error. In comparison to RSM and empirical kinetics models, better prediction and interpretation of the experimental results were observed by ANN model. The sensitive analysis revealed the comparative significance of experimental variables are pH = 61.03%>current density = 17.29%>molar concentration of NaCl = 12.7%>time = 8.98%. The optimized process parameters obtained from genetic algorithm showed 98.6% discolorization of dye at pH 2.95, current density = 5.95 mA cm-2, NaCl of 0.075 M in 29.83 min of electrolysis. The obtained results revealed that the use of statistical and computational modeling is an adequate approach to optimize the process variables of electrochemical treatment.