Abstract

The catalytic oxidation of organic pollutants presents a promising approach for enhancing wastewater treatment efficiency, particularly in decentralized facilities where space, energy, and operational constraints limit conventional processes. This study develops a comprehensive chemical engineering model tailored to optimize catalytic oxidation mechanisms under decentralized conditions. The model integrates mass transfer, reaction kinetics, and catalyst surface dynamics to predict pollutant degradation rates in real-time. By accounting for variations in flow regime, pH, temperature, and contaminant concentration, the model offers a flexible and scalable solution adaptable to diverse wastewater streams including phenolics, pharmaceuticals, and endocrine-disrupting compounds. Transition metal-based catalysts such as iron, manganese, and copper oxides are evaluated within heterogeneous and homogeneous frameworks to understand their performance under low-pressure and ambient temperature conditions. The model incorporates Langmuir-Hinshelwood and Eley-Rideal kinetics to simulate surface-adsorbed species interactions and pollutant transformation pathways, providing detailed mechanistic insights. Furthermore, the incorporation of advanced oxidants such as ozone and hydrogen peroxide into the catalytic system is examined, with emphasis on their synergistic effects and generation of hydroxyl radicals. The decentralized context is addressed through compact reactor design, low-energy requirements, and the use of immobilized catalysts to ensure longevity and minimal maintenance. Model validation is performed using laboratory-scale data from synthetic wastewater matrices, demonstrating high correlation between predicted and observed degradation rates. Sensitivity analysis reveals key operational levers including catalyst dosage, residence time, and oxidant concentration that significantly influence system performance. This model serves as a valuable tool for engineers and decision-makers in designing catalytic systems that balance cost, efficiency, and environmental impact. By bridging reaction engineering principles with real-world wastewater treatment needs, it offers a strategic framework for expanding access to high-efficiency pollutant removal technologies in rural and peri-urban settings. Ongoing work aims to extend the model’s applicability through integration with AI-based controllers and real-time water quality sensors for autonomous system optimization.