Making sense of parameter estimation and model simulation in bioprocesses

Sadino-Riquelme, M. Constanza; Rivas, José; Jeison, David; Hayes, Robert E.; Donoso‐Bravo, Andrés

doi:10.1002/bit.27294

Cited by 22 publications

(10 citation statements)

References 36 publications

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“…As a result, although the kinetic model can well fit the overall trend of the process, its parameters suffer from large standard deviations (e.g.,

β = 0.236 \pm 31.0

K_{C} = 63.72 \pm 27.1

) causing the model to have high uncertainties when simulating long‐term bioprocess dynamics. This issue has also been highlighted by other researchers in their recent work (Sadino‐Riquelme et al, 2020). Figures 3a, 3c, and 3e show the uncertainty propagation result for the kinetic model when simulating the single sugar scenario.…”

Section: Resultsmentioning

confidence: 52%

Kinetic and hybrid modeling for yeast astaxanthin production under uncertainty

Vega-Ramon

Xian-feng

Savage

et al. 2021

Biotech & Bioengineering

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Astaxanthin is a high-value compound commercially synthesized through Xanthophyllomyces dendrorhous fermentation. Using mixed sugars decomposed from biowastes for yeast fermentation provides a promising option to improve process sustainability. However, little effort has been made to investigate the effects of multiple sugars on X. dendrorhous biomass growth and astaxanthin production. Furthermore, the construction of a high-fidelity model is challenging due to the system's variability, also known as batch-to-batch variation. Two innovations are proposed in this study to address these challenges. First, a kinetic model was developed to compare process kinetics between the single sugar (glucose) based and the mixed sugar (glucose and sucrose) based fermentation methods. Then, the kinetic model parameters were modeled themselves as Gaussian processes, a probabilistic machine learning technique, to improve the accuracy and robustness of model predictions. We conclude that although the presence of sucrose does not affect the biomass growth kinetics, it introduces a competitive inhibitory mechanism that enhances astaxanthin accumulation by inducing adverse environmental conditions such as osmotic gradients. Moreover, the hybrid model was able to greatly reduce model simulation error and was particularly robust to uncertainty propagation. This study suggests the advantage of mixed sugar-based fermentation and provides a novel approach for bioprocess dynamic modeling.

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“…As a result, although the kinetic model can well fit the overall trend of the process, its parameters suffer from large standard deviations (e.g.,

β = 0.236 \pm 31.0

K_{C} = 63.72 \pm 27.1

Section: Resultsmentioning

confidence: 52%

Kinetic and hybrid modeling for yeast astaxanthin production under uncertainty

Vega-Ramon

Xian-feng

Savage

et al. 2021

Biotech & Bioengineering

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“…Azotobacter vinelandii (ATCC9046) strain was used. Experiments were performed in triplicate as reported in Sadino-Riquelme et al [22]. During the fermentation, the temperature was maintained at 30 • C, the impeller speed was 400 rpm, and the aeration rate was 4.0 L/min (1 vvm).…”

Section: Bacterial Alginate Batch Productionmentioning

confidence: 99%

Investigating a Stirred Bioreactor: Impact of Evolving Fermentation Broth Pseudoplastic Rheology on Mixing Mechanisms

et al. 2022

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The culture medium in many fermentations is a non-Newtonian fluid. In bacterial alginate batch production, the broth becomes more pseudoplastic as the alginate concentration increases, which impairs the mixing process. This work characterizes the effect of the interaction between changing broth rheology and impeller mixing on a bioreactor fluid dynamics. Experimentally, a fermentation with evolving broth pseudoplastic rheology is reproduced. Three fermentation stages are mimicked using appropriate solutions of water and xanthan gum. Impeller torque measurements are reported. The weakening of the impellers’ interaction over the fermentation process is identified. To overcome the experimental limitations, CFD is applied to study the evolution of the fermentation fluid flow patterns, velocity field, dead zones, and vortical structures. Precessional vortex macro-instabilities are identified as being responsible for the unstable flow patterns identified at the earlier stages of the fermentation. A stable parallel flow pattern accounts for the weakest impellers’ interaction at the final stage. Overall, this work contributes with a complete workflow to adapt CFD models for characterization and aided design of stirred tanks with changing broth pseudoplastic rheology as well as an evolving flow regime.

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“…(A) Photo of the tank with the dual impeller, baffles, probes, and sparger; (B) diagram of the tank dimensions; (C) view of the computational fluids dynamics (CFD) domain GeomA; (D) view of the CFD domain GeomB. Figure adapted from Sadino‐Riquelme et al [ 28 ]…”

Section: Methodsmentioning

confidence: 99%

Computational modelling of mixing tanks for bioprocesses: Developing a comprehensive workflow

et al. 2021

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This paper reports a computational modelling study of a mixing tank for bioprocess applications. A dual impeller mixer containing several probes is used with a mixing speed of 400 rpm. The single-phase transient model is validated against experimental measurements using torque and velocity profiles as the validation variables. A complete workflow is illustrated that addresses all the relevant steps in the modelling of mixing. Many assumptions that are commonly made are explored and their importance is demonstrated. The necessity of including the probes in the computational domain is illustrated. The grid and time-step size are examined, and the relationship between the two is explained. Two turbulence models for RANS equation closure are compared, and the efficacy of the appropriate solution methodology for each is described.The importance of using the correct near-wall treatment is shown. Overall, this paper presents a standardized framework for the modelling of mixing tanks with turbulent flow.

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Making sense of parameter estimation and model simulation in bioprocesses

Cited by 22 publications

References 36 publications

Kinetic and hybrid modeling for yeast astaxanthin production under uncertainty

Kinetic and hybrid modeling for yeast astaxanthin production under uncertainty

Investigating a Stirred Bioreactor: Impact of Evolving Fermentation Broth Pseudoplastic Rheology on Mixing Mechanisms

Computational modelling of mixing tanks for bioprocesses: Developing a comprehensive workflow

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