Tomas B. Co, Associate Professor

PhD, University of Massachusetts, 1988

Contact Information

Department of Chemical Engineering
Michigan Technological University
1400 Townsend Drive
Houghton, MI 49931-1295
Ph: 906/487-2144
Fax: 906/487-3213
E-Mail: tbco@mtu.edu

Process Integrity

As systems undergo operational changes due to equipment degradation, market demands, and other external disturbances, some processes approach failure conditions. To provide appropriate actions to prevent failure, predictive monitoring and control are needed. To this end, we are currently developing a mathematical theory of process integrity. Our study focuses on three major parts: (1) process integrity measure, (2) process integrity control, and (3) plant-wide system integrity. We incorporate existing tools from reliability theory and control theory to assess integrity based on how rapidly recovery can be achieved. 

Process Modeling

Process models are needed to better analyze and design controllers for chemical processes. We are currently investigating methods for parameter estimation of nonlinear continuous-time systems. In particular, we are developing transformation techniques to expand the class of nonlinear systems that can be handled by modulating functions. We have also been developing recursive implementation of the modulating-functions technique to make it applicable on-line for process control and failure diagnosis. 

Plant-Wide Control 

When unit processes are connected together via heat integration and material recycle, the dynamic responses of these units often degrade due to the unanticipated interactions sometimes resulting in unstable plant operation. To reclaim stability and robustness, one can introduce redesigns, such as adding intermediate storage tanks. Alternatively, one can add compensators to tackle undesired interaction effects. Our research in this area is to investigate how to apply both methods optimally to improve plant performance. 

Fuzzy Logic Control 

For processes containing hard nonlinearities, heuristics can yield improved control. Fuzzy logic control is one method that successfully implements heuristics based on fuzzy set theory. Our goal in fuzzy logic includes (1) stability and performance analysis, (2) construction of data-driven fuzzy rules and membership functions, and (3) parametric methods for optimization and tuning of fuzzy logic controllers. 

Selected Publications

1.T. Co and S. Ungarala, Batch Scheme Recursive Estimation of Continuous-Time Systems Using Modulating Functions Method, accepted for publication, Automatica. (1997). 
2.T. Co, Parameter Estimation of Nonlinear Systems Using Modulating Functions Methods, in Identification in Engineering Systems, M. Friswell and J. Mottershead, eds. (1996). 
3.T. Slawinski and T. Co, Neural Network Control of Processes with Recycle, in Proceedings of 1996 IEEE Symposium on Intelligent Control, (1996). 
4.T. Co and B. Ydstie, System Identification Using Modulating Functions and Fast Fourier Transforms, Computers & Chemical Engineering, 14, 1051 (1990).