Modeling the Glucose Concentration for the Recombinant E.coli Bioprocesses

This article presents the sophisticated-to-date carbon mass balance for fed-batch E.coli bioprocesses. The model originates from the distribution of carbon mass from glucose in biomass, off-gas, and hypothetical solutes. The suggested model complements Pirt's equation as a particular case scenario. The approach uses the linear relationship between biomass carbon content per carbon grams in glucose and average cell population age. The carbon balance brings two potential practical benefits. First, it has the potential to assess the type of cell metabolism pathway and to have a soft sensor for the concentration of dissolved products such as acetates. The measure of glucose concentration suggests another finding, assuring the reliance on off-gas information only. The paper introduces an average carbon content ratio in biomass and off-gas, with numerical values of 0.5 in growth-limiting experiments and 0.27 in nonlimiting ones, which may serve as a decision-making criterion for metabolic pathway detection in the future

Adaptive Control of Biomass Specific Growth Rate in Fed-Batch Biotechnological Processes. A Comparative Study

This article presents a comparative study on the development and application of two distinct adaptive control algorithms for biomass specific growth rate control in fed-batch biotechnological processes. A typical fed-batch process using Escherichia coli for recombinant protein production was selected for this research. Numerical simulation results show that both developed controllers, an adaptive PI controller based on the gain scheduling technique and a model-free adaptive controller based on the artificial neural network, delivered a comparable control performance and are suitable for application when using the substrate limitation approach and substrate feeding rate manipulation. The controller performance was tested within the realistic ranges of the feedback signal sampling intervals and measurement noise intensities. Considering the efforts for controller design and tuning, including development of the adaptation/learning algorithms, the model-free adaptive control algorithm proves to be more attractive for industrial applications, especially when only limited knowledge of the process and its mathematical model is available. The investigated model-free adaptive controller also tended to deliver better control quality under low specific growth rate conditions that prevail during the recombinant protein production phase. In the investigated simulation runs, the average tracking error did not exceed 0.01 (1/h). The temporary overshoots caused by the maximal disturbances stayed within the range of 0.025–0.11 (1/h). Application of the algorithm can be further extended to specific growth rate control in other bacterial and mammalian cell cultivations that run under substrate limitation conditions