Welcome to the website for Dr. Jin Wang’s research group at the Chemical Engineering Department at Auburn University. The common theme of our research is to apply systems engineering approaches, control engineering principles and techniques in particular, to understand, predict, and control complex dynamic processes. Our research focuses on two groups of complex dynamic systems: one is large-scale industrial processes, the other is biological systems. Large-scale industrial processes and biological systems share many similarities at the systems level: they both consist of numerous individual components; they both have built-in feedback control/regulation mechanisms; and the properties of the overall systems are determined by the complex interactions among different components. The complex nature makes the integrative systems approaches essential in the understanding, controlling, and optimizing of these systems. 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, and new tools have to be developed. This site provides information on our progress in both research areas, including current research projects and some results obtained in the past. Please feel free to contact Dr. Wang if you have questions regarding the group and our research activities.
For more information on other active projects, please see the research page.
Min’s paper, “A Novel Bioreactor to Study the Dynamics of Co-culture Systems”, is accepted by Biochemical Engineering Journal.
Xiu successfully defended her dissertation. Her work is titled “Variable selection for industrial process modeling and monitoring”.
Dr. Kim, Kyle, Devarshi, and Matt went to Salt Lake City to present their work at the 2015 AIChE Conference. Dr. Wang served as a session chair. All presentations were very well received.
Andy successfully defended his dissertation. His defense is titled “Control Engineering Perspective on Genome-scale Metabolic Modeling”.