Ezra's Round Table / Systems Seminar: Yuhe Tian (West Virginia)


Frank H. T. Rhodes Hall 253


Can't make it in person? Watch it on Zoom.

Systematic Process Design, Intensification, and Innovation of Chemical and Energy Systems

Process intensification (PI) strives to achieve step-change improvements in process cost, energy consumption, and sustainability by innovating equipment and flowsheet designs based on key principles to exploit multi-functional synergies, enhance mass/heat transfer, etc. However, PI is typically regarded as a standalone toolbox comprising specific technology examples developed via Edisonian efforts and engineering expertise, the full potential of which is yet to be realized as a systematic strategy driving process design innovation. In this presentation, we will discuss our recent efforts toward a unified theory, methodology framework, and software prototype for computer-aided process intensification. We will first introduce a novel representation approach going beyond the traditional unit operation concept. Herein, generic mass and heat exchange building blocks will be utilized to describe process systems in a bottom-up manner. We will take a close look at the physicochemical driving forces which can unveil the fundamental impact of various PI principles. On this basis, superstructure-based process synthesis will be performed using mixed-integer nonlinear programming to identify the optimal, intensified, and sustainable process designs. Recent advances in reinforcement learning-driven process synthesis will also be presented to expedite the intelligent search of the combinatorial design space.

Dr. Yuhe Tian is an assistant professor in the Department of Chemical and Biomedical Engineering at West Virginia University. She holds Ph.D. degree in chemical engineering from Texas A&M University (2021). She received bachelor’s degrees in chemical engineering and mathematics from Tsinghua University, China (2016). She is currently an NSF EPSCoR Research Fellow. Her research focuses on developing process systems approaches for modular process intensification, sustainable energy systems, and explicit model predictive control.