Catalytic System Improvement Through Computational Approaches

Abstract

This research aims to develop a predictive model for hydrogen production using a titanium dioxide-based (TiO2) photocatalyst through an artificial neural network (ANN). The primary objective is to enhance photocatalyst design for efficient hydrogen production, supporting the transition from fossil fuels to clean hydrogen energy while reducing carbon dioxide emissions. The project consists of two parts: machine learning and experimental studies, with a primary focus on the first. In Part 1, machine learning is used to build a predictive model for hydrogen production, utilizing TiO2-based photocatalysts. Pearson correlation is applied to select direct and indirect parameters that significantly impact hydrogen production. Data normalization is performed to minimize variations, and the dataset (122 direct parameter samples and 169 indirect parameter samples) is split by using k-fold cross validation and after that into training (80%), testing (10%), and validation (10%) sets before model training begins. The accuracy of the model is evaluated using R squared and Root Mean Square Error (RMSE). Part 2 involves experimental work focusing on methane-to-ethane conversion using the same TiO2-based photocatalyst. The study compares different silver concentrations to determine the most efficient composition for ethane production. While both parts are crucial, the primary emphasis remains on hydrogen production and predictive modeling, as machine learning plays a key role in optimizing photocatalyst design and improving hydrogen yield predictions.