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• W2800782822 abstract "Model predictive control (MPC) is a technology for the plant-wide control of integrated manufacturing plants that can explicitly handle multiple performance objectives, multivariable interactions, physical constraints, and disturbances. In addition to these capabilities, the MPC algorithm described in this chapter also explicitly accounts for the effects of model uncertainties, which are always present in pharmaceutical manufacturing plants. The uncertainties are described by probability distribution functions whose effects on the closed-loop dynamics are described by polynomial chaos expansions. Low online computational cost is achieved by employing an input-output formulation of the stochastic MPC problem. The stochastic MPC algorithm is shown to be effective for a first-principles simulation of end-to-end continuous pharmaceutical manufacturing plant described by nearly 8000 coupled differential and algebraic equations and a realistic description of model uncertainties. Stochastic MPC effectively regulated the critical quality attributes of the product, with a factor of 25 lower variations in the dosage of active pharmaceutical ingredient compared with standard industrial MPC technology. In addition, the computational cost of the stochastic MPC algorithm was less than 1 s at each time step so that the algorithm can easily be implemented in real time." @default.
• W2800782822 created "2018-05-17" @default.
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• W2800782822 date "2018-01-01" @default.
• W2800782822 modified "2023-09-27" @default.
• W2800782822 title "Fast stochastic model predictive control of end-to-end continuous pharmaceutical manufacturing 1 1Financial support from Novartis is acknowledged." @default.
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• W2800782822 doi "https://doi.org/10.1016/b978-0-444-63963-9.00014-2" @default.
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