首页|A robust batch-to-batch optimization framework for pharmaceutical applications
A robust batch-to-batch optimization framework for pharmaceutical applications
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NETL
NSTL
Elsevier
The study proposes a robust algorithm for batch-to-batch optimization in the presence of model-mismatch. Robustness is achieved by the implementation of the following features: ⅰ - the gradient correction step is modified to consider the gradients of the cost function and constraints at both final and intermediate points, ⅱ - Economic Model Predictive Control is applied to mitigate the impact of unmeasured disturbances on the optimum, and ⅲ - an optimal design of experiments is performed to expedite convergence. Significant improvements of the proposed algorithm in convergence to the process optimum and robustness to noise, unmeasured disturbances, and model error are demonstrated using a fed-batch fermentation for penicillin production.
Model-based optimizationBatch-to-batch optimizationModel-plant mismatchModel gradient correctionDesign of experimentsEconomic model predictive control
Ali Ghodba、Anne Richelle、Chris McCready、Luis Ricardez-Sandoval、Hector Budman
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Chemical Engineering Department, University of Waterloo, Waterloo, N2L 3G1, ON, Canada
NETL
NSTL
Elsevier
