Optimization of ion-exchange displacement separations. I. Validation of an iterative scheme and its use as a methods development tool.

Displacement chromatography has been demonstrated to be a powerful, high-resolution preparative tool. The performance of displacement systems can be affected by a variety of factors such as the feed load, flow-rate, initial salt concentration and the displacer partition ratio. Thus, the optimization of displacement separations is a uniquely challenging problem. In this manuscript, an iterative optimization scheme has been presented whereby one can identify the optimum operating conditions for displacement separations at a given level of loading on a given resin material. The solid film linear driving force model has been employed in concert with the Steric Mass Action formalism of ion-exchange chromatography to describe the chromatographic behavior in these systems. Simple pulse techniques have been employed to estimate the transport parameters. The iterative scheme has been validated using a rigorous Feasible Sequential Quadratic Programming algorithm. Finally, the utility of the iterative optimization scheme as a methods development tool for displacement separations has been demonstrated for a difficult separation. The results indicate that the use of the optimization scheme leads to significantly better performance than standard rules of thumb.

Issues in the design of a multirate model-based controller for a nonlinear drug infusion system.

Multivariable controller design for the regulation of mean arterial pressure (MAP) and cardiac output (CO) in congestive heart failure patients is restricted by the limited frequency of CO sampling. Performance criteria for the controller specify maximum allowable transient settling times for both variables, and the design should account for the inherent multirate nature of the process in order to satisfy these criteria. We present a multirate model predictive control (MPC) design for MAP and CO regulation by combined infusion of sodium nitroprusside and dopamine, based on a comprehensive nonlinear model of the system. The multirate MPC algorithm is based on nonlinear quadratic dynamic matrix control. To reduce computation time, we introduce a selective linearization technique that linearizes the model on the basis of trends in the plant-model mismatch. The problem is complicated by restrictions on initial dopamine infusion, prescribed to avoid extremely slow responses. We present a novel rule-based override (RBO) to the MPC controller that uses a set of heuristics to initialize dopamine. The performance of the MPC/RBO controller is illustrated using simulation results.

Control of a nonsquare drug infusion system: A simulation study.

A model predictive control strategy was developed and tested on a nonlinear canine circulatory model for the regulation of hemodynamic variables under critical care conditions. Different patient conditions such as congestive heart failure, post-operative hypertension, and sepsis shock were studied in closed-loop simulations. The model predictive controller, which uses a different linear model depending on the patient condition, allowed constraints to be explicitly enforced. The controller was initially tuned on the basis of a linear plant model, then tested on the nonlinear physiological model; the simulations demonstrated the ability to handle constraints, such as drug dosage specifications, commonly desired by critical care physicians.

Multiple-model adaptive predictive control of mean arterial pressure and cardiac output.

A multiple-model adaptive predictive controller has been designed to simultaneously regulate mean arterial pressure and cardiac output in congestive heart failure subjects by adjusting the infusion rates of nitroprusside and dopamine. The algorithm is based on the multiple-model adaptive controller and utilizes model predictive controllers to provide reliable control in each model subspace. A total of 36 linear small-signal models were needed to span the entire space of anticipated responses. To reduce computation time, only the six models with the highest probabilities were used in the control calculations. The controller was evaluated on laboratory animals that were either surgically or pharmacologically altered to exhibit symptoms of congestive heart failure. During trials, the controller performance was robust with respect to excessive switching between models and nonconvergence to a single dominant model. A comparison is also made with a previous multiple-drug controller design.

Simultaneous regulation of hemodynamic and anesthetic states: a simulation study.

A model predictive control strategy to simultaneously regulate hemodynamic and anesthetic variables in critical care patients is presented. A nonlinear canine circulatory model, which has been used to study the effect of inotropic and vasoactive drugs on hemodynamic variables, has been extended to include propofol pharmacokinetics and pharmacodynamics. Propofol blood concentration is used as a measure for depth of anesthesia. The simulation model is used to design and test the control strategy. The optimization-based model predictive control strategy assures that constraints imposed on the drug infusion rates are met. The physician always remains "in the loop" and serves as the "primary controller" by making propofol blood concentration setpoint changes based on observations about anesthetic depth. Results are shown for three simulated cases: (i) congestive heart failure, (ii) postcoronary artery bypass, and (iii) acute changes in hemodynamic variables.