Today’s chemical process industry is faced with pressing challenges to sustain the increasingly competitive global market with rising concerns about energy, water, food, and the environment. Process intensification (PI) offers many promising opportunities to address these challenges by realizing step changes in process economics, energy efficiency, and environmental impacts through the development of novel process schemes and equipment. However, PI is typically regarded as a standalone toolbox comprising specific technology examples developed via Edisonian efforts and engineering expertise (e.g., membrane reactor, dividing wall column). The full potential of process intensification is yet to be exploited toward a systematic strategy driving process design innovation.
In this talk, we will present our recent efforts toward a unified theory, methodology framework, and software prototype for computer-aided process intensification. We will focus on two key open questions in this field: (i) how to develop a model-based understanding of the PI principles such as combined reaction/separation, miniaturization, etc.? and (ii) how to systematically generate innovative and intensified equipment/flowsheet designs which may be non-intuitive or even outside the box of current industrial practice?
To address the first question, we will explore a novel representation approach going beyond the traditional unit operation concept. Herein, generic mass and heat transfer building blocks will be utilized to describe chemical 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. We will further extend the proposed approach to a process synthesis framework 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 discussed to expedite the intelligent search of the combinatorial design space.
Yuhe Tian is an assistant professor in the Department of Chemical and Biomedical Engineering at West Virginia University. She holds Ph.D. 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.