Research
B. Wayne Bequette

Research Interests: From Biomedicine & Healthcare to Energy & Sustainability

Process and biomedical systems engineering topics, including (i) glucose monitoring and control for diabetes, (ii) bioreactor modeling and control, (iii) pharmaceutical process analytical technology, (iv) drug infusion control, (v) nonlinear control, (vi) chromatography, (vii) fuel cell modeling and control, (viii) alternative fuels (e.g. biodiesel), (ix) thermochemical cycles for hydrogen production, and (x) application of systems engineering techniques to disaster relief.

Example publications are included in the descriptions that follow.

Fuel Cell Modeling and Control

Modeling and control of both high temperature and low temperature (PEM) fuels cells is being considered. An initial focus is on "balance of plant" control issues, including the regulation of temperature in the post-stack combustion of excess hydrogen.

Horgan, T. and B.W. Bequette "Integrated Reformer/Combuster Models for High Temperature Fuel Cell Systems," in preprints of the 2007 Fuel Cell Seminar, paper 239, San Antonio (2007).

Kuure-Kinsey, M., H. Leister, J. O'Rourke and B.W. Bequette," Model Predictive Control of Integrated Fuel Cell Systems," in preprints of the 2007 Fuel Cell Seminar, paper 409, San Antonio (2007).

Zhao, F., D. Rohr, C.-J. Tang, A. Feitelberg and B.W. Bequette "A Dynamic Model of a Fuel Cell Anode Tail-bas Catalytic Burner," 2006 Fuel Cell Seminar, paper 396, Hawaii (2006).

Kuure-Kinsey, K.A. Schnebele, R. Cutright and B.W. Bequette "Multiple Model Predictive Control of Hydrogen Reforming for Fuel Cell Applications," in preprints of the 2006 Fuel Cell Seminar, paper 658, Hawaii.

Modeling and Control of Integrated Gasification and Combined Cycle (IGCC) Power Plants

Integrated coal gasification combined cycle (IGCC) power plants have the potential for increased efficiency compared with classical coal-fired generating plants. IGCC power plants are composed of a number of units, including coal gasifier, synthesis gas cleanup and char combustor sections, an air separation unit, and the combined cycle section (gas turbine, waste heat exchange, steam generation and steam turbine). These systems clearly interact, with streams passing back and forth between subsystems, but important differences in time scales are not captured by steady-state design and analysis. In this research project a dynamic system model will be developed to understand how process design imposes inherent limitations on the operability characteristics of IGCC power plants, independent of the control system design. The dynamic model will also be used to predict the transient behavior during startup and shutdown (planned and unplanned) procedures. Model predictive control strategies will be developed to handle constraints and multivariable interactions, and to operate the processes over a variety of operating conditions.

Thermochemical Cycles for Hydrogen Production

Current methods of producing hydrogen from hydrocarbon sources are not sustainable. Thermochemical cycles can be used to produce hydrogen and oxygen from water, using a sequence of intermediate reactions, and heat sources and sinks. In this project, the ASPEN Engineering Suite will be used to generate flowsheet simulations, provide thermophysical property data, and perform sensitivity, cost, and performance analyses for alternative thermochemical cycles. Our initial focus is on an iron-chloride cycle, but a long-term goal is to develop automated procedures to synthesize alternative cycles.

Andress, R.J., X. Huang, B.W. Bequette and L.L. Martin "A Systematic Methodology for the Evaluation of Thermochemical Cycles for Hydrogen Production," Int. J. Hydrogen Energy 34(9), 4146-4154 (2009).

Closed-loop artificial pancreas

To obtain tight control of their blood glucose levels, individuals with type 1 diabetes must monitor their blood glucose concentration through frequent fingerstick measurements, and give themselves insulin shots, or make adjustments to external insulin delivery pumps. The long term goal of this project is to develop a closed-loop artificial pancreas, consisting of a glucose sensor, insulin infusion pump and feedback controller. Our initial effort has focussed on developing a monitoring system to warn an individual that they are in danger of becoming hypoglycemic (low blood sugar).

In related work, we are developing algorithms for the closed-loop control of blood glucose in intensive care units (ICU).

Lee, H., B.A. Buckingham, D.M. Wilson and B.W. Bequette "A Closed-loop Artificial Pancreas Using Model Predictive Control and a Sliding Meal Size Estimator," J. Diabetes Sci. Tech., 3(5), 1082-1090 (2009).

Lee, H. and B.W. Bequette "A Closed-loop Artificial Pancreas based on MPC: human-friendly identification and automatic meal disturbance rejection," Biomed. Signal Proc. Cont., 4(4),347-354 (2009).

Bequette, B.W. "Analysis of Algorithms for Intensive Care Unit Blood Glucose Control," J. Diabetes Science and Technology, 1(6), 813-824 (2007).

Palerm, C.C. and B.W. Bequette "Hypoglycemia Detection and Prediction Using Continuous Glucose Monitoring - A Study on Hypoglycemic Clamp Data," J. Diabetes Science and Technology, 1(5), 624-629 (2007).

B.W. Bequette "A critical assessment of algorithms and challenges in the development of an artificial pancreas," Diabetes Technology and Therapeutics, 7(1), 3-14 (2005).

C.C. Palerm, J.P. Willis, J. Desemone and B.W. Bequette "Hypoglycemia prediction and detection using optimal estimation," Diabetes Technology and Therapeutics, 7(1), 28-47 (2005).

Rotating disk bioreactor

A RDC is used to produce a particularly high strength cellulose using bacteria. The incorporation of 'gradients' of solid particles into the growing cellulose film will result in a new type of biomaterial with applications in foods, medicine and bioprocessing.

M. Kuure-Kinsey, D. Weber, H.R. Bungay, J.L. Plawsky and B.W. Bequette "Modeling and predictive control of a rotating disk bioreactor," in Proceedings of the 2005 American Control Conference, Portland, OR, pp. 3259-3264.

Pharmaceutical process analytical technologies (PAT)

Batch processing provides greater flexibility in the production of specialty and pharmaceutical chemicals, because (i) the same equipment can be used to produce different products, (ii) the specifications for each batch can vary depending on the customer, and (iii) different amounts of product can be produced. Our initial focus has been on understanding the effect of batch process design on control system performance.

The process analytical technology (PAT) initiative of the FDA recognizes the potential of process systems engineering techniques to improve the performances of pharmaceutical manufacturing processes. We are developing real-time estimation and control techniques that infer important (but unmeasured) product properties from noisy, but available, measurements.

Bequette, B.W. "From Pilot Plant to Manufacturing: Effect of Scale-up on Operation of Jacketed Reactors," Chapter 5 in the Pharmaceutical Manufacturing Handbook: Production and Processes, S.C. Gad (ed.), Wiley (in press, 2008).

B.W. Bequette, S. Holihan and S. Bacher "Automation and control issues in the design of a pharmaceutical pilot plant," Control Engineering Practice,12(7), 901-908 (2004).

Closed-loop drug infusion control

During surgery an anesthesiologist must regulate blood pressure, cardiac output and anesthetic depth, in addition to monitoring other patient characteristics, by adjusting drug infusion rates. An individual patient may have significantly different drug sensitivities than another patient, and dynamic responses to drugs may vary throughout the operation. We have developed a multiple model predictive control approach that adapts to the changing dynamics and enforces constraints on the drug infusion rates.

Bequette, B.W. "A Tutorial on Biomedical Process Control. III. Modeling and Control of Drug Infusion in Critical Care," J. Process Control 17(7), 582-586 (2007).

R.R. Rao, B. Aufderheide and B.W. Bequette "Experimental studies on multiple-model predictive control for automated regulation of hemodynamic variables," IEEE Trans. Biomed. Eng., 50(3), 277-288 (2003).

Nonlinear control

Nonlinear behavior can be particularly important for specialty chemicals production where there is an incentive to rapidly change operating conditions to produce different products for different consumers. We have developed a number of approaches to nonlinear control, including (i) multiple-model predictive control, (ii) Kalman filter-based model predictive control, and (iii) neural network-based model predictive control.

Bequette, B.W. "Nonlinear Model Predictive Control: A Personal Retrospective," Canadian Journal of Chemical Engineering, 85(4), 408-415 (2007).

Kuure-Kinsey, M., R. Cutright, and B.W. Bequette "Computationally Efficient Neural Predictive Control based on a Feedforward Architecture," Ind. Eng. Chem. Res., 45(25), 8575-8582 (2006).

B. Aufderheide and B.W. Bequette "Extension of dynamic matrix control to multiple models," Comp. Chem. Engng., 27, 1079-1096 (2003).

Displacement and Gradient Elution Chromatography

One objective of this work is to establish efficient optimization techniques for nonlinear chromatographic processes. We have developed a run-to-run approach to change operating conditions as new information is available at the end of each batch cycle.

D. Nagrath, A. Messac, B.W. Bequette and S.M. Cramer "Multiobjective optimization strategies for linear gradient chromatography," AIChE J., 51(2), 511-525 (2005).

Biodiesel

Waste cooking oil from restaurants and bakeries can create a substantial waste disposal load, leading to overfilling landfills and other environmental hazards. We are developing processes to convert the waste cooking oil to biodiesel, an alternative to petroleum-based diesel. Real-time estimation and control techniques are being developed to reduce batch time while meeting product property requirements.