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.

同时识别和优化的方法旨在满足最优条件，同时提供过程输出的准确预测。在运行过程中更新模型参数，以考虑操作点的变化并校正成本函数和约束条件的预测梯度中的误差。为了使此参数更新步骤更鲁棒，我们提出了一个参数识别目标，其中包括平方误差和与参数梯度灵敏度函数的比值。这导致识别出的一组参数，这些参数为随后的梯度校正步骤提供更大的灵敏度，从而导致更快地收敛到最优值。此外，利用模型输出的最坏情况不确定性边界来实施适当的模型拟合。当识别具有许多参数的动态代谢模型时，这特别有价值。使用两种模拟的细胞培养过程说明了所得的改进。