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Dr. Vassilios Tzouanas, Assistant Professor
Dr. Vassilios Tzouanas is an Assistant Professor of Control and Instrumentation in the Engineering Technology Department at the University of Houston-Downtown. Dr. Tzouanas earned a Diploma in Chemical Engineering from Aristotle University, the Master of Science degree in Chemical Engineering/Process Control from the University of Alberta, and the Doctor of Philosophy degree in Chemical Engineering/Process Control from Lehigh University. His research interests focus on process control systems, process modeling and simulation, artificial intelligence and expert systems. His professional experience includes management and technical positions with chemicals, refining, and consulting companies. He has published and presented a number of papers on advanced process control, real-time optimization systems, adaptive control, artificial intelligence and expert systems. He is a member of AIChE.
Ph.D. in Chemical Engineering/Process Control, Lehigh University, PA, 1989.
M.Sc. in Chemical Engineering/Process Control, University of Alberta, AB, 1985.
Diploma in Chemical Engineering, Aristotle University, Greece, 1982.
Assistant Professor of Control and Instrumentation Engineering Technology, University of Houston-Downtown,
2010 – present.
Regional Technical Superintendent of Control Systems Engineering, Lyondell Chemical, 2006 - 2009.
Consulting Engineer of Control Systems Engineering, Lyondell Chemical, 1999 - 2006.
Staff Engineer, Project Manager and Technical Leader, AMOCO, 1996 - 1999.
Principal Control Systems Engineer, ARCO Chemical, 1994 - 1996.
Project Engineer to Lead Engineer, Setpoint Inc., 1990 - 1994.
Honors and Awards
Lyondell Corporate Award for Efficiency, 2006.
Amoco Technical Award, 1998.
My broad research interests focus on advanced multivariable process control, adaptive control, process modeling and simulation, artificial intelligence and expert systems. My overarching research goal is not simply to invent new knowledge but to also convert the feasible into practical. The result will be control systems that help optimize process operation (i.e. maximum return on investment) subject to operational excellence (i.e. safety, environmental, reliability, quality) constraints. Process examples are drawn from different industrial sectors such as chemicals, refining, bio-systems, and renewable energy.
Examples of current research efforts include:
- Multivariable and Adaptive Control: the objective is to design robust, multivariable, adaptive control systems and demonstrate their effectiveness on time varying processes such as transitioning of industrial scale polymerization reactors from one product to another or biological processes
- Process Modeling and Simulation: the objective is to design robust product quality inferential methods to predict infrequently measured product qualities when on-line measurements are infrequent or not available. Demonstrate effectiveness on industrial processes such as refining and petrochemicals.
- Artificial Intelligence and Expert Systems: the objective is to develop methods to help identify erroneous sensors used in feedback control loops or in making critical decisions. Use of expert systems, statistical methods, and data mining techniques may be employed. Data mining is the process of extracting valid, previously unknown, comprehensible and actionable information from large databases and using it to make business decisions. Expert system techniques and pattern recognition can be used for fault signal detection. Statistical methods can be employed to screen data for possible errors (e.g. standard deviation, drifting from nominal values per current process operation).
- Semi-Autonomous Systems: the objective is to develop technologies that improve process safety during abnormal process operating conditions and demonstrate their effectiveness during the startup/shutdown of major industrial processes such as polymerization reactors or ethylene plants.
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Last updated or reviewed on 8/13/12