The method of simultaneous identification and optimization aims at satisfying the conditions of optimality while providing accurate predictions of the process outputs. The model parameters are updated in a run-to-run procedure as to account for changes in operating points and to correct for errors in the predicted gradients of the cost-function and constraints. To make this parameter updating step more robust, we propose a parameter identification objective that includes a ratio of the sum of squared errors to a parametric gradient sensitivity function. This results in an identified set of parameters which provide larger sensitivities for the subsequent gradient correction step thus leading to faster convergence to the optimum. Moreover, worst-case uncertainty bounds on the model outputs are utilized to enforce an adequate model fitting. This is especially valuable when identifying dynamic metabolic models with many parameters. The resulting improvements are illustrated using two simulated cell culture processes. (C) 2018 Elsevier Ltd. All rights reserved.