In addition to its annual lectures, ChBE hosts a weekly seminar throughout the year with invited lecturers who are prominent in their fields. Unless otherwise noted, all seminars are held on Wednesdays in the Molecular Science and Engineering Building ("M" Building) in G011 (Cherry Logan Emerson Lecture Theater) at 4:00 p.m. Refreshments are served at 3:30 p.m. in the Emerson-Lewis Reception Salon.

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Systems Engineering Approaches to the Study of Industrial Processes and Biological Systems

Large-scale industrial processes and biological systems share many similarities at the systems level: they both consist of many individual components; they both have built-in feedback control/regulation mechanisms; and the properties of the overall systems are determined by the complex interaction among different components. Their complex nature makes the integrative systems approaches essential in understanding, controlling and optimizing these systems. As a result, many process systems engineering principles and techniques have been extended into the emerging field of systems biology.

However, despite their commonalities at the system level, large-scale industrial processes and biological systems have their unique characteristics and challenges that existing systems approaches cannot fully address. In this talk, our most recent progress in both research areas (process systems engineering and systems biology) will be presented. For large-scale industrial processes, one of our focuses is process monitoring. The specific challenge we aim to address is how to effectively handle process nonlinear dynamics, non-Gaussianity, frequent process changes driven by manufacturing on-demand, but without the heavy computational burden of available nonlinear methods. The solution we developed is a new multivariate framework named statistics pattern analysis (SPA) and we use the benchmark Tennessee Eastman Process to demonstrate the effectiveness of the new framework.  For biological systems, one specific challenge we aim to address is how to effectively utilize genome-wide metabolic network models and extract biological meaningful information from them. The solution we developed is a system identification based approach where we use the metabolic models as a high fidelity simulator to conduct carefully designed in silico experiments. We will use scheffersomyces stiptis (the yeast with the strongest native capability to ferment xylose) as the model system to illustrate our developed approach.

Dr. Jin Wang is B. Redd Associate Professor in the Department of Chemical Engineering at Auburn University. She obtained her BS and PhD degrees in chemical engineering (specialized in biochemical engineering) from Tsinghua University in 1994, and 1999 respectively. She then obtained a PhD degree (specialized in control engineering) from the University of Texas at Austin in 2004. From 2002 to 2006 she was a development engineer and senior development engineer at Advanced Micro Devices, Inc. During her tenure at AMD, her R&D yielded 12 patents granted by USPTO. In addition, she received several prestigious corporate awards for being instrumental in developing effective advanced control solutions.

Dr. Wang joined Auburn University in 2006 as B. Redd Assistant Professor. She was promoted to Associate Professor and granted tenure in 2011. The central theme of her current research is to apply systems engineering, in particular, control engineering principles and techniques to understand, predict and control complex dynamic systems. She was the 2008 recipient of the Ralph E. Powe Junior Faculty Enhancement Awards from Oak Ridge Associated Universities (ORAU). Her graduate student also won the inaugural AIChE CAST Director’s Presentation Award in 2011. Her research is funded by various US federal funding agencies including NSF, USDA and DOT as well as private foundations.