Model reduction of aerobic bioprocess models for efficient simulation

Duan, Zhaoyang; Wilms, Terrance; Neubauer, Peter; Kravaris, Costas; Bournazou, Mariano Nicolás Cruz

doi:10.1016/j.ces.2020.115512

Cited by 10 publications

(8 citation statements)

References 40 publications

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“…Henry constant. Considering that DOT present the fast dynamics (Duan et al, 2020), we use the reduced form expressed in the algebraic equation as DOT = DOT * − q O XH k la (A.12) Specific oxygen uptake rate q O is obtained by q O = (q S,en + q m )Y OS + q Ac Y OA (A.13) where Y OS and Y OA (g/g) are the yield coefficients of the oxygen over biomass and acetate, respectively.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Model predictive control and moving horizon estimation for adaptive optimal bolus feeding in high-throughput cultivation of \textit{E. coli}

Kim¹,

Krausch²,

Aizpuru³

et al. 2022

Preprint

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We discuss the application of a nonlinear model predictive control (MPC) and a moving horizon estimation (MHE) to achieve an optimal operation of E. coli fed-batch cultivations with intermittent bolus feeding. 24 parallel experiments were considered in a high-throughput microbioreactor platform at 10 mL scale. The robotic island in question can run up to 48 fed-batch processes in parallel with an automated liquid handling and online and atline analytics. The implementation of the model-based monitoring and control framework reveals that there are mainly three challenges which need to be addressed; First, the inputs are given in an instantaneous pulsed form by bolus injections, second, online and atline measurement frequencies are severely imbalanced, and third, optimization for the distinctive multiple reactors can

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Section: Discussionmentioning

confidence: 99%

“…(A.13)); H (mol/(m 3 • P a)) denotes the Henry constant. Considering that DOT has fast dynamics (Duan et al, 2020), we use the reduced form expressed in the algebraic equation as…”

Section: Macro-kinetic E Coli Growth Modelmentioning

confidence: 99%

Model predictive control and moving horizon estimation for adaptive optimal bolus feeding in high-throughput cultivation of \textit{E. coli}

Kim¹,

Krausch²,

Aizpuru³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The ODE system exhibits dynamics in very different timescales, especially regarding biomass growth and DOT, leading to a very stiff system. Since the dynamics of DOT are usually very fast compared to the other states, they can be expressed in a reduced form as an algebraic equation and thereby reduce the stiffness of the system significantly building a differential‐algebraic system of equations (Duan et al, 2020). Since the actual DOT (

{DO}{T}_{a}

) can be only measured with a first order delay, the measured DOT (

{DO}{T}_{m}

) is also considered as a state variable, taking the response time of the sensor into account.…”

Section: Methodsmentioning

confidence: 99%

High‐throughput screening of optimal process conditions using model predictive control

Krausch

Kim

Barz

et al. 2022

Biotech & Bioengineering

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Modern biotechnological laboratories are equipped with advanced parallel mini‐bioreactor facilities that can perform sophisticated cultivation strategies (e.g., fed‐batch or continuous) and generate significant amounts of measurement data. These systems require not only optimal experimental designs that find the best conditions in very large design spaces, but also algorithms that manage to operate a large number of different cultivations in parallel within a well‐defined and tightly constrained operating regime. Existing advanced process control algorithms have to be tailored to tackle the specific issues of such facilities such as: a very complex biological system, constant changes in the metabolic activity and phenotypes, shifts of pH and/or temperature, and metabolic switches, to name a few. In this study we implement a model predictive control (MPC) framework to demonstrate: (1) the challenges in terms of mathematical model structure, state, and parameter estimation, and optimization under highly nonlinear and stiff dynamics in biological systems, (2) the adaptations required to enable the application of MPC in high throughput bioprocess development, and (3) the added value of MPC implementations when operating parallel mini‐bioreactors aiming to maximize the biomass concentration while coping with hard constrains on the dissolved oxygen tension profile.

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“…In future work, sophistication of the mathematical methods that are utilized for the tasks of data pre-processing, data analysis and model generation would improve the insights gained with the program. Some possibilities are PLS models for prediction, or the integration of existing mechanistic models [45][46][47] for the use of semi-parametric hybrid models [48,49]. In addition, alternative methods to the Euclidean distance that account for the correlation nature of the variable should be explored to ensure broader applicability of the classification of "unwanted" reactor behavior based on the scores.…”

Section: Mva For Monitoring Parallel Cultivationsmentioning

confidence: 99%

Monitoring Parallel Robotic Cultivations with Online Multivariate Analysis

et al. 2020

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In conditional microbial screening, a limited number of candidate strains are tested at different conditions searching for the optimal operation strategy in production (e.g., temperature and pH shifts, media composition as well as feeding and induction strategies). To achieve this, cultivation volumes of >10 mL and advanced control schemes are required to allow appropriate sampling and analyses. Operations become even more complex when the analytical methods are integrated into the robot facility. Among other multivariate data analysis methods, principal component analysis (PCA) techniques have especially gained popularity in high throughput screening. However, an important issue specific to high throughput bioprocess development is the lack of so-called golden batches that could be used as a basis for multivariate analysis. In this study, we establish and present a program to monitor dynamic parallel cultivations in a high throughput facility. PCA was used for process monitoring and automated fault detection of 24 parallel running experiments using recombinant E. coli cells expressing three different fluorescence proteins as the model organism. This approach allowed for capturing events like stirrer failures and blockage of the aeration system and provided a good signal to noise ratio. The developed application can be easily integrated in existing data- and device-infrastructures, allowing automated and remote monitoring of parallel bioreactor systems.

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Model reduction of aerobic bioprocess models for efficient simulation

Cited by 10 publications

References 40 publications

Model predictive control and moving horizon estimation for adaptive optimal bolus feeding in high-throughput cultivation of \textit{E. coli}

Model predictive control and moving horizon estimation for adaptive optimal bolus feeding in high-throughput cultivation of \textit{E. coli}

High‐throughput screening of optimal process conditions using model predictive control

Monitoring Parallel Robotic Cultivations with Online Multivariate Analysis

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