Design and Optimization of Catalysts with Multi-Objective Optimization Algorithms Based on Artificial Intelligence

This study presents a comprehensive approach for the optimization and design of Cu-Mn-Ce three-component catalysts for hydrogenation reactions using artificial intelligence (AI) techniques. By leveraging machine learning models, our focus is on predicting and enhancing key catalytic properties such as activity, stability, thermal resistance, and environmental impact. The data-driven framework combines Random Forest (RF) and Gradient Boosting (GB) models to predict the performance of various catalyst formulations based on experimental data. In the optimization phase, the multi-objective optimization algorithm NSGA-II (Non-dominated Sorting Genetic Algorithm II) is employed to balance conflicting objectives, including maximizing catalytic activity, minimizing costs, and reducing environmental impacts. The results show that the Gradient Boosting model outperforms the Random Forest model in predicting catalytic behavior and is more suitable for optimizing large-scale catalysts. Optimization using NSGA-II identifies efficient Pareto solutions that provide promising trade-offs for industrial applications. Furthermore, the performance of the optimized catalysts was evaluated using various experimental methods, and the results demonstrated significant improvements in catalytic activity and stability. This study also provides insights into how machine learning can accelerate the discovery and development of novel, high-performance catalysts with minimal environmental impact. This work showcases the potential for integrating AI with catalyst design and provides a pathway for the development of sustainable and efficient catalytic systems for industrial applications. The approach presented here is not limited to the design of Cu-Mn-Ce catalysts and can be extended to other materials and catalytic processes as well.

Keywords:

Artificial Intelligence , Machine Learning , Cu-Mn-Ce Three-Component Catalyst , Predictive Models , Hydrogenation , Catalytic Activity