For the fast and improved development of bioprocesses, new strategies are required where both strain and process development are performed in parallel. Here, a workflow based on a Nonlinear Model Predictive Control (NMPC) algorithm is described for the model-assisted development of biotechnological processes. By using the NMPC algorithm, the process is designed with respect to a target function (product yield, biomass concentration) with a drastically decreased number of experiments. A workflow for the usage of the NMPC algorithm as a process development tool is outlined. The NMPC algorithm is capable of improving various process states, such as product yield and biomass concentration. It uses on-line and at-line data and controls and optimizes the process by model-based process extrapolation. In this study, the algorithm is applied to a Corynebacterium glutamicum process. In conclusion, the potency of the NMPC algorithm as a powerful tool for process development is demonstrated. In particular, the benefits of the system regarding the characterization and optimization of a fed-batch process are outlined. With the NMPC algorithm, process development can be run simultaneously to strain development, resulting in a shortened time to market for novel products.

中文翻译：

---

基于NMPC的同时流程和模型开发的工作流应用于重组谷氨酸棒杆菌的分批补料流程

为了快速，更好地发展生物过程，需要同时进行应变和过程开发的新策略。在此，描述了一种基于非线性模型预测控制（NMPC）算法的工作流，用于生物技术过程的模型辅助开发。通过使用NMPC算法，针对目标功能（产品产量，生物质浓度）设计了该过程，而实验数量却大大减少了。概述了将NMPC算法用作过程开发工具的工作流程。NMPC算法能够改善各种过程状态，例如产品产量和生物质浓度。它使用在线和在线数据并通过基于模型的过程外推来控制和优化过程。在这项研究中，该算法适用于谷氨酸棒杆菌过程。总之，展示了NMPC算法作为过程开发的强大工具的潜力。特别地，概述了该系统在分批补料过程的表征和优化方面的优势。借助NMPC算法，可以同时进行工艺开发以进行应变开发，从而缩短了新产品的上市时间。