Autonomous Discovery of non-Intuitive Process Designs through Multiscale Simulations and Artificial Intelligence

My research group focuses on accelerating process systems design and optimization via autonomous discovery of new designs for sustainable chemical processes through artificial intelligence and multiscale simulation.

An initial direction that I work on is to develop a multiscale modeling and simulation methodology to improve the model-based concurrent design in materials and process systems. Using the proposed multiscale simulation, I develop a framework to use artificial intelligence for process design and optimization. My Group apply the proposed methodology to design non-traditional electrochemical processes (e-chemical), continuous pharmaceutical manufacturing, and other advanced manufacturing that improve sustainability of the chemical industry. We also collaborate with experimentalists to verify the intensified designs via pilot plant experimentation.

These short to medium-term projects will provide my group with the skills and tools to pursue my long-term goal:

“to develop process systems engineering tools that will lead to the discovery of non-intuitive designs that are not obtainable with existing techniques.”

Multiscale Modeling & Simulations

Research Theme #1: Integrated multiscale modeling and simulation of materials and process systems design: “How to quantify the enterprise-wide profit sensitivity of catalyst activity?”

Machine Learning for Process Systems Engineering

Research Theme #2: Autonomous discovery of new designs in the chemical process systems by addressing artificial intelligence (AI)

Advanced Manufacturing & Process

Research Theme #3: Design non-intuitive processes that intensify the sustainability of the chemical industry.