The performance of most bioprocesses can be improved significantly by the application of model-based methods from advanced process control (APC). However, due to the complexity of the processes and the limited knowledge of them, plant–model mismatch is unavoidable. A variety of different modeling strategies (each with individual advantages and deficiencies) can be applied, but still, the confidence in a single process model is often low; therefore, the application of classical APC is difficult. In order to operate under possible plant–model mismatch, a robust closed-loop optimizing control strategy was developed in which the mismatch is counteracted by an adaptive model correction and the parallel usage and evaluation of structurally different models. Robust multistage nonlinear model predictive control is used for the online optimization of the process trajectories in order to maximize the performance. The adapted, structurally different models are used herein as weighted scenarios for the prediction of the process, which account for structural uncertainties. It is shown in simulation studies of a CHO cultivation process that the usage of multiple, adapted models as scenarios improves (1) the accuracy of the state estimation and (2) the overall process performance.

中文翻译：

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基于一组结构不同的过程模型的发酵过程的鲁棒优化控制

通过应用来自高级过程控制（APC）的基于模型的方法，可以大大提高大多数生物过程的性能。但是，由于过程的复杂性以及对它们的了解有限，因此不可避免地会出现工厂模型不匹配的情况。可以应用多种不同的建模策略（每种都有各自的优势和不足），但是，单个流程模型的可信度通常很低。因此，经典APC的应用很困难。为了在可能的工厂模型不匹配下运行，开发了一种鲁棒的闭环优化控制策略，其中通过自适应模型校正以及结构上不同模型的并行使用和评估来抵消不匹配。鲁棒的多级非线性模型预测控制用于过程轨迹的在线优化，以使性能最大化。适应的，结构上不同的模型在本文中用作加权预测方案，以预测过程，这说明了结构上的不确定性。在CHO培养过程的模拟研究中表明，使用多个适应模型作为场景可以提高（1）状态估计的准确性和（2）整个过程的性能。