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.