چکیده مقاله
Purpose: This study presents a data driven approach for optimizing the heterogeneous electro Fenton process applied to pharmaceutical wastewater treatment using the CatBoost machine learning model combined with the Non dominated Sorting Genetic Algorithm II NSGA II Methods: Experimental data were obtained from a 1 L electrochemical reactor using MIL 100 Fe as a heterogeneous catalyst and persulfate as an oxidant Results: CatBoost was trained to predict tetracycline TC removal efficiency and electrical energy consumption, achieving reliable performance with RMSE values of 8 51% and 56 63 kWh/kg, MAE values of 7 63% and 50 96 kWh/kg, and R² values of 0 67 and 0 79 for TC removal and energy consumption, respectively The optimization, with weighting factors of 0 7 for TC removal and 0 3 for energy minimization, yielded an optimal trade off point of 79 94% TC removal and 209 85 kWh/kg energy consumption, with an overall weighted score of 0 716 Conclusion: The results confirm the potential of CatBoost based modeling integrated with evolutionary optimization to enhance environmental performance and energy efficiency in advanced oxidation processes
کلیدواژهها
نویسندگان
Soran Ezati
Environmental Engineering Division, Civil & Environmental Engineering Faculty, Tarbiat Modares University
Hossein Ganjidoust
Environmental Engineering Division, Civil & Environmental Engineering Faculty, Tarbiat Modares University
Bita Ayati
Environmental Engineering Division, Civil & Environmental Engineering Faculty, Tarbiat Modares University
شیوه ارجاع
Ezati, Soran and Ganjidoust, Hossein and Ayati, Bita,1404,CatBoost-Based Multi-Objective Optimization of the Heterogeneous Electro-Fenton Process for Tetracycline Removal and Energy Efficiency,The 3rd National Conference on Environmental Challenges: The role of industry, mining, and society in developing green governance,Tehran
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