Research Group Website

Educational Qualifications (Biosketch)

Ph.D., Chemical Engineering, Princeton University (1971)
M.A., Chemical Engineering, Princeton University (1968)
B.S., Chemical Engineering, University of Kansas (1967)

Courses Taught

CHE360 Process Control
CHE359 Energy, Technology, and Policy

Research

• Control system monitoring and diagnosis are important to ensure that plant performance is close to optimal and that process variable data used in control algorithms is accurate. We are researching performance monitoring of feedback control algorithms for linear processes, and have developed methods that can handle single loop PID, model predictive control and multiloop control with or without constraints.We are also investigating the monitoring of process and sensor faults when variations in duration of batch steps occur. Both data-driven and physical models are being employed. Applications to semiconductor manufacturing and bioreactors are being studied.
• Microelectronics manufacturing is an area where process modeling and control are receiving increased attention. We are carrying out a number of projects in cooperation with semiconductor companies. In semiconductor fabs run-to-run behavior can be influenced by reactor aging, first wafer effects, and other non-uniform processing conditions. In one project we are modeling the run-to-run behavior of these processes and utilize that information for improved control. To deal with multiple product/multiple tool control, sequential parameter estimation techniques are used to update the models and perform model-based control, with application to “high-mix” fabs with more than 20 products. Optimal sampling strategies are also being investigated.
• With the recent increase in the demand for oil and the predicted decline in available supply, the ability to obtain oil efficiently and economically has become increasingly important. There is interest in automating decisions regarding secondary recovery techniques, particularly water injection schemes, to yield so-called “smart reservoirs.” Reservoir simulators have traditionally been too large and run times too long to allow for rigorous solution in an optimization algorithm. It has also proven very difficult to marry an optimizer with the large set of nonlinear differential equations required for reservoir simulation. We have used a recently developed inter-well connectivity model that concentrates on the relationship between injection and production wells. Then we can balance and optimize the effects of injections wells to maximize the net present value of oil production from a given reservoir.
• Maximizing energy efficiency is receiving increased attention as a way to reduce the use of fossil fuels and the resulting production of greenhouse gases, thus providing an avenue to address possible future legislation of cap and trade or a carbon tax.  Automation, process control, and optimization are critical technologies to operate plants in the most efficient way and to pursue the smart manufacturing paradigm.  The increased use of renewable energy such as solar or wind power reduces carbon usage but adds a dynamic element to power production in process plants, which may involve interfacing with smart grids.  Time of day pricing of power and use of demand response techniques to flatten load profiles will be important ingredients of smart grids.  Increased usage of thermal and other energy storage systems will also give industrial energy users some additional degrees of freedom to deal with the dynamic power conditions.
• For more details on Dr. Edgar’s research projects visit the Texas-Wisconsin-California Control Consortium (TWCCC) website.  View Dr. Edgar’s CHE360 Process Control course website or CHE359 Energy, Technology, and Policy website for curriculum material.

Awards & Honors

• AIChE: Colburn Award, 1980; Director, 1989-92; Fellow, 1995
• Computing in Chemical Engineering Award, 1995; President, 1997
• Warren K. Lewis Award in Chemical Engineering Education (AIChE), 2006
• Van Antwerpen Award (AIChE), 2010
• ASEE: Meriam-Wiley Distinguished Author Award, 1990; Fellow, 2005
• George Westinghouse Award, 1988; Chemical Engineering Division Lecturer, 1996
• Chair, Council for Chemical Research, 1992-93
• Council for Chemical Research Pruitt Award, 2009
• President, American Automatic Control Council (AACC), 1990-91
• AACC Education Award, 1992
• ISA Eckman Education Award, 1993
• International Federation of Automatic Control Fellow (IFAC), 2008
• IFAC Control Engineering Prize, 2005
• Process Automation Hall of Fame (Control Magazine), 2007

Selected Publications

• J. Davis, and T.F. Edgar.  “Smart Process Manufacturing – A Vision of the Future,” Design for Energy and the Environment (Proc. 7th Int. Conf. FOCAPD 2009), pp. 149-165, Breckenridge, CO, June 2009, Taylor and Francis, Boca Raton, FL, 2009.
• S. Ziaii, S. Cohen, G.T. Rochelle, T.F. Edgar, M.E. Webber.  “Dynamic Operation of Amine Scrubbing in Response to Electricity Demand and Pricing,” Energy Procedia I, pp. 4047-4053, 2009.
• J.S. Kim, J. Byeon, D. Chun, S.W. Sung, J.T. Lee and T.F. Edgar, “Relay Feedback Method for Processes Under Noisy Environments,” AIChE Journal, Vol. 56, No. 2, pp. 560-562, 2010.
• S. Abrol, T.F. Edgar, “A Fast and Versatile Technique for Constrained State Estimation,” J. Process Control 21 (2010) 343-350.
• S. Abrol, M. Lu, D. Hill, A. Herrick, and T.F. Edgar, “Faster Dynamic Process Simulation using In Situ Adaptive Tabulation,” Ind. Eng. Chem. Res. 2010, 49 (17) pp. 7814-7823.
• J. Lee, S.W. Sung, T.F. Edgar, “Area Methods for Relay Feedback Tests,” Ind. Eng. Chem. Res., 2010, 49 (17), pp.7807-7813.
• B. Spivey, J.D. Hedengren, T.F. Edgar, “Constrained Nonlinear Estimation for Industrial Process Fouling,” Ind. Eng. Chem. Res., 2010, 49 (17), pp. 7824-7831.
• J. Lee, S.W. Sung, T.F. Edgar, “Area method for a Biased Relay Feedback System,” Ind. Eng. Chem. Res., 2010, 49 (17), pp. 8016-8020.
• B. Gill, T.F. Edgar, J.D. Stuber, “A Novel Approach to Virtual Metrology Using Kalman Filtering,” Future Fab International, 2010, Issue 35.