This presentation describes an integrated approach for end-to-end process optimization from mathematical modeling through to the control that explicitly addresses uncertainty throughout. The approach, which is based on polynomial chaos theory [1, 2], is demonstrated through its full implementation to a continuous pharmaceutical manufacturing process in which drying is used to produce a thin film that enables precisely controllable drug delivery [3]. A multi-scale model is constructed that consists of a two-dimensional differential algebraic equation (DAE) model for the polymer-solvent in the film [4, 5], a three-dimensional computational fluid dynamics (CFD) model for considering air flow dynamics, and a data-driven model for foil/support temperature dynamics. The multi-scale model is expanded to a stochastic model using non-intrusive polynomial chaos expansion (PCE). Since the multiscale model is too computationally costly to be used for parameter estimation, a PCE-based surrogate model [6,7] is constructed prior to estimation. The experiments are designed by D-optimal design method using the Fisher information matrix, and parameter estimation is carried out by using maximum-likelihood method with selected optimal parameter subset by Sobol indices [8]. The surrogate model is employed in a stochastic model predictive control (SMPC) algorithm for startup through quasi-steady operations. All of the algorithms are implemented on an experimental continuous thin film dryer process located at the Massachusetts Institute of Technology [3]. In the SMPC, the PCE method is combined with the input-output-based dynamic matrix control (DMC) to generate a quadratic program with feasible online computational time. The startup control was handled by extending the SMPC algorithm in [9] to apply to time-varying systems. Experimental results showed that the proposed method achieved the startup control, which aims to heat and cast simultaneously as well as reducing off-spec product. Furthermore, improved robustness with respect to the parametric uncertainty compared to the conventional DMC is demonstrated in a rigorous, non-conservative, and highly accurate uncertainty analysis applied to the full-order multiscale simulation model involving coupled nonlinear partial differential algebraic equations. Overall, this study proposes and demonstrates an approach to the self-consistent and systematic design of a control system for nonlinear distributed parameter systems in which the information is seamlessly passed from on task to the next, the effects of uncertainty are explicitly addressed in each task, no unrealistic assumptions are used at any stage of the analysis or synthesis, and the approach is computationally appropriate.
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