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B Wayne Bequette

Publications and source records attributed to B Wayne Bequette.

6 recordsLinked to original sources
PubMed2005

A critical assessment of algorithms and challenges in the development of a closed-loop artificial pancreas.

The development of an artificial pancreas is placed in the context of the history of the field of feedback control systems, beginning with the water clock of ancient Greece, and including a discussion of current efforts in the control of complex systems. The first generation of artificial pancreas devices included two manipulated variables (insulin and glucose infusion) and nonlinear functions of the error (difference between desired and measured glucose concentration) to minimize hyperglycemia while avoiding hypoglycemia. Dynamic lags between insulin infusion and glucose measurement were relatively small for these intravenous-based systems. Advances in continuous glucose sensing, fast-acting insulin analogs, and a mature insulin pump market bring us close to commercial realization of a closed-loop artificial pancreas. Model predictive control is discussed in-depth as an approach that is well suited for a closed-loop artificial pancreas. A major challenge that remains is handling an unknown glucose disturbance (meal), and an approach is proposed to base a current insulin infusion action on the predicted effect of a meal on future glucose values. Better "meal models" are needed, as a limited knowledge of the effect of a meal on the future glucose values limits the performance of any control algorithm.

Algorithms↗
PubMed2005

Hypoglycemia prediction and detection using optimal estimation.

Patients with diabetes play with a double-edged sword when it comes to deciding glucose and A1c target levels. On the one side, tight control has been shown to be crucial in avoiding long-term complications; on the other, tighter control leads to an increased risk of iatrogenic hypoglycemia, which is compounded when hypoglycemia unawareness sets in. Development of continuous glucose monitoring systems has led to the possibility of being able not only to detect hypoglycemic episodes, but to make predictions based on trends that would allow the patient to take preemptive action to entirely avoid the condition. Using an optimal estimation theory approach to hypoglycemia prediction, we demonstrate the effect of measurement sampling frequency, threshold level, and prediction horizon on the sensitivity and specificity of the predictions. We discuss how optimal estimators can be tuned to trade-off the false alarm rate with the rate of missed predicted hypoglycemic episodes. We also suggest the use of different alarm levels as a function of current and future estimates of glucose and the hypoglycemic threshold and prediction horizon.

Algorithms↗
PubMed2003

Experimental studies on multiple-model predictive control for automated regulation of hemodynamic variables.

A model-based control methodology was developed for automated regulation of mean arterial pressure and cardiac output in critical care subjects using inotropic and vasoactive drugs. The control algorithm used a multiple-model adaptive approach in a model predictive control framework to account for variability and explicitly handle drug rate constraints. The controller was experimentally evaluated on canines that were pharmacologically altered to exhibit symptoms of hypertension and depressed cardiac output. The controller performed better as compared to experiments on manual regulation of the hemodynamic variables. After the model bank was determined, mean arterial pressure was held within +/- 5 mm Hg 88.9% of the time with a standard deviation of 3.9 mm Hg. The cardiac output was held within +/- 1 l/min 96.1% of the time with a standard deviation of 0.5 l/min. The manual runs maintain mean arterial pressure only 82.3% of the time with a standard deviation of 5 mm Hg, and cardiac output 92.2% of the time with a standard deviation of 0.6 l/min.

Algorithms↗
PubMedDate not supplied

A hybrid model framework for the optimization of preparative chromatographic processes.

An optimization framework based on the use of hybrid models is presented for preparative chromatographic processes. The first step in the hybrid model strategy involves the experimental determination of the parameters of the physical model, which consists of the full general rate model coupled with the kinetic form of the steric mass action isotherm. These parameters are then used to carry out a set of simulations with the physical model to obtain data on the functional relationship between various objective functions and decision variables. The resulting data is then used to estimate the parameters for neural-network-based empirical models. These empirical models are developed in order to enable the exploration of a wide variety of different design scenarios without any additional computational requirements. The resulting empirical models are then used with a sequential quadratic programming optimization algorithm to maximize the objective function, production rate times yield (in the presence of solubility and purity constraints), for binary and tertiary model protein systems. The use of hybrid empirical models to represent complex preparative chromatographic systems significantly reduces the computational time required for simulation and optimization. In addition, it allows both multivariable optimization and rapid exploration of different scenarios for optimal design.

Algorithms↗