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In model‐based optimization in the presence of model‐plant mismatch, the set of model parameter estimates which satisfy an identification objective may not result in an accurate prediction of the gradients of the cost‐function and constraints. To ensure convergence to the optimum, the predicted gradients can be forced to match the measured gradients by adapting the model parameters. Since updating all available parameters is impractical due to estimability problems and overfitting, there is a motivation for adapting a subset of parameters for updating the predicted outputs and gradients. This article presents an approach to select a subset of parameters based on the sensitivities of the model outputs and of the cost function and constraint gradients. Furthermore, robustness to uncertainties in initial batch conditions is introduced using a robust formulation based on polynomial chaos expansions. The improvements in convergence to the process optimum and robustness are illustrated using a fed‐batch bioprocess. © 2017 American Institute of Chemical Engineers AIChE J, 63: 2660–2670, 2017

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Model errors in model-based optimization procedures can result in suboptimal policies. In that regard, structural mismatch is of particular concern since it results in inaccurate model predictions even when a large amount of data is available for model calibration. The method of simultaneous identification and optimization, proposed in our previous work, addresses the structural model mismatch by adapting the model parameters and matching the predicted to measured gradients thus ensuring progressive convergence to the actual process optimum. In the former implementation of this approach, the gradients have been corrected only at the most recent operating point. To achieve better prediction accuracy of the updated model and to obtain a faster convergence to the optimum, we therefore propose to use cost measurements from previous batch experiments in combination with additional optimally designed new experiments. The advantages of the presented approach versus previous versions of the algorithm are illustrated using two simulated run-to-run optimization case studies.