Bioprocess Modeling and Control

Caramihai, Mihai; Severin, Irina

doi:10.5772/55362

Cited by 16 publications

(10 citation statements)

References 35 publications

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“…[5] Further, due to operational considerations, it is preferable to decrease the pH rather than increase it, since it can be decreased by sparging CO 2 , but increasing pH would require the addition of a base that could potentially disturb the cell environment negatively. In essence, since the production of a specific product such as a protein by these cells is heavily affected by the environment in the reactor, such as the pH and glucose levels [3,8,9] and with such a diversity of variables affecting the system dynamics with many of these having contradictory effects in different ranges, the modelling and control problem is a challenging one.…”

Section: Introductionmentioning

confidence: 99%

Process‐aware data‐driven modelling and model predictive control of bioreactor for the production of monoclonal antibodies

Sarna

Patel

Corbett³

et al. 2022

Can J Chem Eng

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This manuscript addresses the problem of controlling a bioreactor to maximize the production of a desired product while respecting the constraints imposed by the nature of the bio-process. The approach is demonstrated by first building a data-driven model and then formulating a model predictive controller (MPC) with the results illustrated by implementing a detailed monoclonal antibody production model (the test bed) created by Sartorius Inc. In particular, a recently developed data-driven modelling approach using an adaptation of subspace identification techniques is utilized that enables the incorporation of known physical relationships in the data-driven model development (constrained subspace model identification), making the data-driven model process aware. The resultant controller implementation demonstrates a significant improvement in production compared to the existing proportional integral (PI) controller strategy used in the monoclonal antibody production. Simulation results also demonstrate the superiority of the process-aware or constrained subspace MPC compared to traditional subspace MPC. Finally, the robustness of the controller design is illustrated via the implementation of a model developed using data from a test bed with a different set of parameters, thus showing the ability of the controller design to maintain good performance in the event of changes such as a different cell line or feed characteristics.

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Section: Introductionmentioning

confidence: 99%

Process‐aware data‐driven modelling and model predictive control of bioreactor for the production of monoclonal antibodies

Sarna

Patel

Corbett³

et al. 2022

Can J Chem Eng

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“…Substrate limitation, the substrate limitation and inhibition, and the substrate limitation and biomass inhibition occur during the fermentation process. The substrate limitation is included in the Monod and Tessier laws, substrate limitation and inhibition are considered in the Haldane law, and substrate limitation and biomass inhibition are defined in Contois law [29].…”

Section: Non-linear Mathematical Model Of a Fermentation Process In A Batch Bioreactormentioning

confidence: 99%

Model Reference Adaptive Control for Milk Fermentation in Batch Bioreactors

2020

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This paper presents the advanced control theory’s original utilisation to realise a system that controls the fermentation process in batch bioreactors. Proper fermentation control is essential for quality fermentation products and the economical operation of bioreactors. Batch bioreactors are very popular due to their simple construction. However, this simplicity presents limitations in implementing control systems that would ensure a controlled fermentation process. Batch bioreactors do not allow the inflow/outflow of substances during operation. Therefore, we have developed a control system based on a stirrer drive instead of material flow. The newly developed control system ensures tracking of the fermentation product time course to the reference trajectory by changing the stirrer’s speed. Firstly, the paper presents the derivation of the enhanced mathematical model suitable for developing a control system. A linearisation and eigenvalue analysis of this model were made. Due to the time-consuming determination of the fermentation model and the variation of the controlled plant during operation, the use of adaptive control is advantageous. Secondly, a comparison of different adaptive approaches was made. The model reference adaptive control was selected on this basis. The control theory is presented, and the control realisation described. Experimental results obtained with the laboratory batch bioreactor confirm the advantages of the proposed adaptive approach compared to the conventional PI-control.

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“…In Equation (1), X is dry biomass concentration (g/L), t is duration time since inoculation (h) and stoichiometric coefficients α and β represent cell metabolism of oxygen consumption, where α describes the cell's oxygen consumption yield for biomass growth (α ≡ Y o 2 /X , [ g(O 2 )/g(X)]) and β describes the cell's oxygen consumption for maintenance (β ≡ m O 2 /X , [g(O 2 )/g(X)/h]) [16][17][18]. Çalik [19], studying the effects of pH on benzaldehyde lyase production by Escherichia coli, and Kocabaş [20], studying l-tryptophan production, clarified that oxygen consumption consisted of three parts: Cell growth, maintenance, and byproduct formation.…”

Section: Basis Of Biomass and Product Model Fittingmentioning

confidence: 99%

Identification of Functional Bioprocess Model for Recombinant E. Coli Cultivation Process

Urniezius

Survyla

2019

Entropy

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The purpose of this study is to introduce an improved Luedeking–Piret model that represents a structurally simple biomass concentration approach. The developed routine provides acceptable accuracy when fitting experimental data that incorporate the target protein concentration of Escherichia coli culture BL21 (DE3) pET28a in fed-batch processes. This paper presents system identification, biomass, and product parameter fitting routines, starting from their roots of origin to the entropy-related development, characterized by robustness and simplicity. A single tuning coefficient allows for the selection of an optimization criterion that serves equally well for higher and lower biomass concentrations. The idea of the paper is to demonstrate that the use of fundamental knowledge can make the general model more common for technological use compared to a sophisticated artificial neural network. Experimental validation of the proposed model involved data analysis of six cultivation experiments compared to 19 experiments used for model fitting and parameter estimation.

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Bioprocess Modeling and Control

Cited by 16 publications

References 35 publications

Process‐aware data‐driven modelling and model predictive control of bioreactor for the production of monoclonal antibodies

Process‐aware data‐driven modelling and model predictive control of bioreactor for the production of monoclonal antibodies

Model Reference Adaptive Control for Milk Fermentation in Batch Bioreactors

Identification of Functional Bioprocess Model for Recombinant E. Coli Cultivation Process

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