Control of biotechnological processes is currently recipe-based with insufficient ability to handle possible uncertainties, which results in suboptimal production processes. To address this problem, model-based optimization and control approaches can be implemented to derive optimal control strategies. However, for reliable performance of model-based control, it is crucial to use flexible and adaptive control strategies which address biological variability while compensating for uncertainties. In this work, we present an approach for adaptive control of a bioprocess based on dynamic flux balance models. A previously developed bilevel approach for bioprocess optimization is implemented inside a model predictive control (MPC) routine. To account for model uncertainties, a moving horizon estimation algorithm is combined with the MPC in order to estimate uncertain parameters of the underlying model online for different metabolic modes. We apply this method to maximize the productivity of a target metabolite under microaerobic conditions by adapting the degree of oxygen-limitation online.

目前，对生物技术过程的控制是基于配方的，不足以处理可能的不确定性，这导致生产过程欠佳。为了解决该问题，可以实施基于模型的优化和控制方法以得出最佳控制策略。但是，对于基于模型的控制的可靠性能，至关重要的是要使用灵活，自适应的控制策略来解决生物学差异，同时补偿不确定性。在这项工作中，我们提出了一种基于动态通量平衡模型的生物过程自适应控制方法。在模型预测控制（MPC）例程中实现了先前开发的用于生物过程优化的双层方法。为了说明模型的不确定性，移动视野估计算法与MPC组合在一起，以便针对不同的代谢模式在线估计基础模型的不确定参数。通过应用在线限制氧的程度，我们应用此方法来最大化微需氧条件下目标代谢物的生产率。