Integrating Artificial Intelligence and Computational Models for Efficient Electrochemical Systems in Chemical Manufacturing

Abstract

As the global transition to sustainable chemical manufacturing accelerates, electrochemical processes powered by renewable electricity are emerging as key enablers of decarbonized, efficient, and modular production systems. However, optimizing electrochemical reactions and designing robust, economically viable processes present significant challenges. This talk will explore how artificial intelligence (AI) and advanced computational modeling are driving the next generation of electrochemical systems for chemical manufacturing, offering powerful tools for overcoming these challenges. AI and machine learning techniques are being employed to predict and optimize the performance of electrocatalysts, electrolytes, and membranes, vastly reducing the time required for material discovery and process design. Computational models, such as density functional theory (DFT) and reaction network simulations, are being used to design more efficient electrochemical cells for key processes, including water electrolysis, organic electrolysis, and electrochemical separations. By simulating reaction pathways, AI can help identify bottlenecks, accelerate material development, and predict system behavior under different operational conditions. In addition to material and reaction optimization, computational models are also critical for system-level design and process intensification. The integration of AI-driven simulations with techno-economic and life cycle analyses provides a holistic framework for evaluating the sustainability and economic feasibility of electrochemical manufacturing processes. These integrated approaches are essential for the development of modular, decentralized production units capable of utilizing alternative feedstocks such as biomass, CO2, and waste streams, offering substantial potential for recycling and upcycling. This talk will showcase the potential of AI and computational modeling to advance electrochemical manufacturing, discussing theoretical insights and future directions for AI-driven innovations. The aim is to highlight how these emerging technologies can accelerate the realization of sustainable, efficient, and economically feasible electrochemical processes that will play a critical role in shaping the future of chemical manufacturing.