\textit{E. coli}高通量培养中自适应最优推注的模型预测控制和滚动时域估计
Model predictive control and moving horizon estimation for adaptive optimal bolus feeding in high-throughput cultivation of \textit{E. coli}
摘要：
我们讨论了非线性模型预测控制（MPC）和滚动时域估计（MHE）的应用，以实现最佳操作的\textit{E. coli}流加培养间歇推注。在10 mL规模的高通量微生物反应器平台中考虑了24个平行实验。所讨论的机器人岛可以并行运行多达48个补料分批过程，同时进行自动液体处理和在线分析。基于模型的监测和控制框架的实施表明，主要有三个挑战需要解决;第一，通过团注以瞬时脉冲形式给出输入，第二，在线和在线测量频率严重不平衡，第三，可以并行化或集成针对不同的多个反应器的优化。我们解决这些挑战，将脉冲控制系统的概念，制定多速率MHE与可识别性分析，并建议用于决定反应堆配置的标准。在这项研究中，我们提出的关键要素和背景理论的实施与\textit{in silico}模拟细菌补料分批培养。
英文摘要：
We discuss the application of a nonlinear model predictive control (MPC) and a moving horizon estimation (MHE) to achieve an optimal operation of \textit{E. coli} fed-batch cultivations with intermittent bolus feeding. 24 parallel experiments were considered in a high-throughput microbioreactor platform at a 10 mL scale. The robotic island in question can run up to 48 fed-batch processes in parallel with automated liquid handling and online and at-line analytics. The implementation of the model-based monitoring and control framework reveals that there are mainly three challenges that need to be addressed; First, the inputs are given in an instantaneous pulsed form by bolus injections, second, online and at-line measurement frequencies are severely imbalanced, and third, optimization for the distinctive multiple reactors can be either parallelized or integrated. We address these challenges by incorporating the concept of impulsive control systems, formulating multi-rate MHE with identifiability analysis, and suggesting criteria for deciding the reactor configuration. In this study, we present the key elements and background theory of the implementation with \textit{in silico} simulations for bacterial fed-batch cultivation.
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