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Experimental Design in Simultaneous Identification and Optimization of Batch Processes under Model-Plant Mismatch

机译：模型-工厂不匹配的批处理过程同时识别和优化的实验设计

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Model-plant mismatch commonly arises from simplifications and assumptions during the development of first-principles models. Hence, when employing such models in iterative optimization schemes, structural mismatch may lead to inaccurate prediction of the necessary conditions of optimality. This results in convergence to a predicted optimum which does not coincide with the actual process optimum. The method ofsimultaneous identification and optimizationaims to correct for errors in the predicted gradients of the cost and constraints by adapting the model parameters. In a former implementation of this approach, the gradients have been corrected only locally at the current operating point. To achieve a better prediction of the cost function over a wider range of input conditions, we propose to consider cost measurements from previous batch experiments combined with an optimal experimental design of future experiments. Using this approach, it is possible to achieve a better prediction, especially around the optimum, and to make the gradient correction step less susceptible to uncertainty in local gradient measurements. The improvements are illustrated using a simulated run-to-run optimization study of a cell-culture process.
机译：在第一性原理模型的开发过程中，模型工厂的不匹配通常是由简化和假设引起的。因此，当在迭代优化方案中采用此类模型时，结构失配可能导致对最优性必要条件的不准确预测。这导致收敛到与实际过程最佳值不一致的预测最佳值。同时识别和优化的方法是通过调整模型参数来校正成本和约束的预测梯度中的误差。在该方法的先前实现中，仅在当前操作点处局部校正了梯度。为了在更宽的输入条件范围内更好地预测成本函数，我们建议考虑先前批次实验的成本测量以及未来实验的最佳实验设计。使用这种方法，可以实现更好的预测，尤其是在最佳值附近，并使梯度校正步骤更不易受到局部梯度测量不确定性的影响。使用细胞培养过程的模拟运行优化研究说明了这些改进。

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