IoT-Integrated Photocatalytic Reactor with AI-Based Control for Autonomous Industrial Effluent Treatment

Abstract

The increasing complexity of industrial effluent streams and the demand for sustainable wastewater treatment technologies have accelerated the adoption of intelligent and autonomous treatment systems. This study presents an Internet of Things (IoT)-integrated photocatalytic reactor with artificial intelligence (AI)-based control for autonomous industrial effluent treatment under dynamic operating conditions. The proposed system combines advanced photocatalytic oxidation, real-time environmental sensing, IoT-enabled monitoring infrastructure, and AI-driven process optimization to enhance pollutant degradation efficiency and operational reliability. The photocatalytic reactor utilizes semiconductor-based catalytic materials activated under controlled irradiation conditions to generate reactive oxidative species capable of degrading refractory organic contaminants and toxic industrial pollutants. IoT-enabled sensors continuously monitored critical operational parameters including pH, turbidity, temperature, dissolved oxygen, flow rate, and contaminant concentration, enabling real-time process evaluation and adaptive control. AI-based predictive algorithms were implemented to optimize catalyst activity, irradiation intensity, hydraulic retention time, and reactor operating conditions according to influent variability and treatment performance requirements. Experimental and simulation analyses demonstrated significant improvements in contaminant degradation efficiency, treatment stability, and energy utilization compared to conventional photocatalytic treatment approaches. The autonomous control framework also enabled rapid detection of operational disturbances, reduced chemical consumption, and minimized manual intervention through intelligent decision-making and automated process adjustments.