Kinetic Analysis and Artificial Neural Network (ANN) Modeling of Phenol Degradation from Industrial Wastewater Using Cold DBD Plasma-Based Advanced Oxidation Process: A Multi-Objective Optimization Approach via Particle Swarm Optimization (PSO)

The persistent presence of refractory organic pollutants, particularly phenol and its derivatives, in industrial wastewater represents a major environmental concern due to their toxicity and resistance to conventional treatment processes. Among emerging technologies, cold plasma based advanced oxidation processes, especially dielectric barrier discharge (DBD) systems, have attracted increasing interest for their ability to produce highly reactive species under ambient conditions without requiring chemical additives. Because of the complex and nonlinear behavior of plasma driven oxidation reactions, advanced modeling tools are essential for accurate prediction and optimization of system performance. Artificial neural networks (ANN) have demonstrated strong capability in approximating complex relationships between operational variables and treatment efficiency. In parallel, intelligent optimization techniques such as particle swarm optimization (PSO) provide effective solutions for identifying optimal process conditions under multiple performance criteria, including pollutant removal rate, energy consumption, and process stability. This review explores recent developments in the integration of ANN based modeling and PSO driven optimization for enhancing phenol removal using cold DBD plasma reactors. The paper discusses kinetic pathways, predictive modeling frameworks, and optimization strategies, and further identifies critical challenges and future research directions to support industrial scale implementation of plasma based wastewater treatment technologies.

Keywords:

Cold plasma , phenol degradation , artificial neural networks , particle swarm optimization , multi objective optimization