Robust Optimizing Control of Fermentation Processes Based on a Set of Structurally Different Process Models

Abstract

The performance of most bioprocesses can be improved significantly by the application of model-based methods from advanced process control (APC). However, due to the complexity of the processes and the limited knowledge of them, plant–model mismatch is unavoidable. A variety of different modeling strategies (each with individual advantages and deficiencies) can be applied, but still, the confidence in a single process model is often low; therefore, the application of classical APC is difficult. In order to operate under possible plant–model mismatch, a robust closed-loop optimizing control strategy was developed in which the mismatch is counteracted by an adaptive model correction and the parallel usage and evaluation of structurally different models. Robust multistage nonlinear model predictive control is used for the online optimization of the process trajectories in order to maximize the performance. The adapted, structurally different models are used herein as weighted scenarios for the prediction of the process, which account for structural uncertainties. It is shown in simulation studies of a CHO cultivation process that the usage of multiple, adapted models as scenarios improves (1) the accuracy of the state estimation and (2) the overall process performance.

Development of a Dynamic Gray‐Box Model of a Fermentation Process for Spore Production

“…Another possible application of model is process monitoring. A state estimation routine can be applied to combine noisy measurements with a dynamic model to give an operator better feedback about the state of the process at any point in time [11].…”

“…In this work, a general recipe to develop a dynamic graybox model is proposed, building upon the work in [11]. The approach is illustrated by the application to the fermenta-tion process to produce spores of Bacillus subtilis.…”

Network Modeling and Control of Dynamic Disease Pathways, Review and Perspectives

“…De Oliveira et al [223] proposed a model predictive controller to maximize ethanol production of Saccharomyces cerevisiae with the model based on a consensus yeast metabolic network by manipulating the glucose feed and the dissolved oxygen level. Hebing et al [224] used a robust multistage nonlinear model predictive control in a reduced metabolic network for Chinese Hamster Ovary (CHO) cells to maximize the cell concentration in the beginning of the process and the product concentration in the later phase by adjusting the pH value of the cell and the feed rate of the main substrate. Murthy et al [225] developed an optimal controller based on iterative gradient descent algorithm using a simplified yeast metabolic network to improve the production of ethanol from corn in dry grind corn process under temperature and pH disturbances with lower enzyme usage and reduced cooling requirement.…”

Modeling the Effect of Temperature on Ethanol Fermentation Using Saccharomyces Cerevisiae CSI-1 by Genetic Programming

“…Modeling has been employed to elucidate cell growth in relation to temperature (Abunde et al, 2019;Nor-Khaizura et al, 2019;Pereira et al, 2020). However, the outcome of simulation and optimization tools heavily relies on the quality of the mathematical model (Carrillo-Ahumada et al, 2020;Castillo-Santos et al, 2017;Darvishi et al, 2020;Díaz &Tost, 2018;Goelzer et al, 2009;Hebing et al, 2020;Jorayev et al, 2022;Meng et al, 2021;Müller et al, 2020;Rodríguez-Mariano et al, 2015;Salmi et al, 2021;Torralba-Morales et al, 2020;Vignesh & Chandraraj, 2021;Wu et al, 2015). Typically, fermentations rely on ideal laboratory conditions (e.g., synthetic media, stirring devices, heating modes), and it is preferable to consider industrial processes (real fermentation media, steady state, etc.)…”