Comparative study of artificial intelligence based multi-modelling approach and optimization of photoreactor

This study presents a generic methodology for modelling and optimizing a reactor with complex and poorly understood kinetics. Here, a photoreactor is considered which performs photodegradation of sodium oxalate salt in spent Bayer liquor. Multiple data-driven modelling methods, including artificial neural networks (ANN), genetic programming (GP), hybrid genetic programming-grey wolf optimization (GP-GWO), and multi-gene genetic programming (MGGP), were used to model the reactor's performance based on experimental data. The input parameters considered for modelling were initial solution pH, power of the lamp, total organic carbon, and catalyst loading. The models were evaluated based on their predictability, explainability, complexity, and adherence to the reactor's phenomenology. The MGGP model was found to be the most effective and was used to generate surface plots showing the parity between experimental results and model predictions. Additionally, the MGGP model was optimized using GWO to determine the process conditions that maximize the reaction rate.