Levermann, PhilippPhilippLevermannFreiberger, FabianFabianFreibergerKatha, UmaUmaKathaZaun, HenningHenningZaunMöller, JohannesJohannesMöllerHass, Volker C.Volker C.HassSchoop, Karl MichaelKarl MichaelSchoopKuballa, JürgenJürgenKuballaPörtner, RalfRalfPörtner2020-10-292020-10-292020-10-19Processes 8 (10): 1313 (2020-10-19)http://hdl.handle.net/11420/7717For 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 <i>Corynebacterium glutamicum</i> 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.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.en2227-9717202010Multidisciplinary Digital Publishing Institutehttps://creativecommons.org/licenses/by/4.0/NMPC algorithmC. glutamicummodel-based process developmentdigitalizationprocess optimizationprocess modelingBiowissenschaften, BiologieJournal Article2020-10-2610.15480/882.303110.3390/pr810131310.15480/882.3031Other