We present a constrained model-based optimization and predictive control framework to maximize the production efficiency of batch fermentations based on the core idea of manipulating adenosine triphosphate (ATP) wasting. In many bioprocesses, enforced ATP wasting —rerouting ATP use towards an energetically possibly suboptimal path— allows increasing the metabolic flux towards the product, thereby enhancing product yields and specific productivities. However, this often comes at the expense of lower biomass yields and reduced volumetric productivities. To maximize the overall efficiency, we formulate ATP wasting as a model-based optimal control problem. This allows for balancing trade-offs between different objectives such as product yield and volumetric productivity for batch fermentations. Unlike static metabolic control, one obtains a higher degree of flexibility, adaptability, and competitiveness. This can be advantageous towards achieving a sustainable and economically efficient biotechnology industry. To compensate for model-plant mismatch, disturbances, and uncertainties, we propose not only solving the optimal control problem once. Instead, we exploit the concept of moving horizon model predictive control combined with constraint-based dynamic modeling to capture the fermentation dynamics. The approach is underlined considering the industrially relevant bioproduction of lactate by Escherichia coli. We discuss practical challenges for the described control strategy and provide an outlook towards future developments.

中文翻译：

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通过基于模型的约束优化和三磷酸腺苷周转的预测控制最大化批量发酵效率

基于控制三磷酸腺苷 (ATP) 浪费的核心思想，我们提出了一个基于约束模型的优化和预测控制框架，以最大限度地提高批量发酵的生产效率。在许多生物过程中，强制 ATP 浪费（将 ATP 的使用重新安排到能量可能不是最佳的路径）允许增加流向产品的代谢通量，从而提高产品产量和比生产率。然而，这通常是以降低生物质产量和降低体积生产率为代价的。为了最大化整体效率，我们将 ATP 浪费制定为基于模型的最优控制问题。这允许平衡不同目标之间的权衡，例如批量发酵的产品产量和体积生产力。与静态代谢控制不同，一个人获得了更高程度的灵活性、适应性和竞争力。这有利于实现可持续和经济高效的生物技术产业。为了补偿模型-工厂不匹配、干扰和不确定性，我们建议不仅解决一次最优控制问题。相反，我们利用移动水平模型预测控制与基于约束的动态建模相结合的概念来捕捉发酵动态。考虑到乳酸的工业相关生物生产，该方法得到了强调 我们建议不仅解决一次最优控制问题。相反，我们利用移动水平模型预测控制与基于约束的动态建模相结合的概念来捕捉发酵动态。考虑到乳酸的工业相关生物生产，该方法得到了强调 我们建议不仅解决一次最优控制问题。相反，我们利用移动水平模型预测控制与基于约束的动态建模相结合的概念来捕捉发酵动态。考虑到乳酸的工业相关生物生产，该方法得到了强调大肠杆菌。我们讨论了所描述的控制策略的实际挑战，并提供了对未来发展的展望。