Abstract
This article considers the real-time optimization under uncertainty of a compact reconfigurable system for on-demand continuous-flow pharmaceutical manufacturing. Self-optimizing control is employed, which optimizes operation in the presence of uncertainty by controlling a carefully chosen combination of measurements to a constant setpoint. The method is applied to a simulated plant based on the physical process. The closed-loop simulations indicate that this simple policy is able to maintain the process operation close to optimality despite disturbances, sensor noise, and parametric model uncertainty.
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