Machine Learning and AI Based Models for Modeling Optimization of Photocatalytic Conversion of CO2

Photocatalytic conversion is a chemical reaction process in which a photocatalyst is activated by an energy light source (sunlight or artificial). The activated photocatalyst can reduce CO2 into different valuable by-products that can be used in pharmaceutical, chemical and fuel industries. The imminent urgency of climate change has caused an increase in the efforts to develop sustainable methods for reducing atmospheric CO2 level, however, the complexity of the photocatalytic process influenced by numerous features and parameters need of the advanced modeling techniques to optimize the reaction conditions and improve the efficiency. This study presents a novel framework that integrates machine learning (ML) and artificial intelligence (AI) models to predict the outcome of photocatalytic conversion of CO2. By levering extensive dataset created by obtaining large amount of data obtained from experiments and peer reviewed articles we employ supervised learning algorithms to model the relationships between input parameters—such as catalyst composition, reaction conditions, and light source characteristics—and output parameters, including type of primary product, yield and productivity. Our approach utilizes models, such as Extreme Gradient Boosting (XGBoost), Artificial Neural Networks (ANNs), and Generative Adversarial Networks (GANs), enhanced with hyperparameter tuning and regularization techniques to achieve high predictive accuracy. The models are further validated using performance metrics like Mean Squared Error (MSE), cross-validation techniques, and R2 score to ensure robust predictions