NMPC-Based Workflow for Simultaneous Process and Model Development Applied to a Fed-Batch Process for Recombinant C. glutamicum

Abstract

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.