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.

Featured Projects

System Identification Based Framework for Metabolic Network Analysis

Kinetic Modeling of Co-Culture Systems with a Novel Bioreactor and Pseudo-Continuous Fermentation

Statistics Pattern Analysis-Next Generation Process Monitoring Framework

For more information on other active projects, please see the research page.

Featured News

August 14th
Dr. Wang’s group successfully presented two different posters at the FOSBE Conference in Boston, MA.

August 1st
Min and Tomi has graduated and obtained their degrees! Congratulations Min and Tomi!

June 14th
Andy got his work “Comprehensive evaluation of two genome-scale metabolic network models for Scheffersomyces stipitis” published in the Biotechnology and Bioengineering.

February 26th
Xiu’s work “Comparison of variable selection methods for PLS-based soft sensor modeling” has been published by the Journal of Process Control.