A compartment model for risk-based monitoring of lactic acid bacteria cultivations

Spann, R. N.; Gernaey, Krist V.; Sin, Gürkan

doi:10.1016/j.bej.2019.107293

Cited by 16 publications

(13 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Keep in mind that this solution is not “grid‐independent,” it is acquired by balancing out errors. Still, if the predicted magnitude of the gradient and subsequent metabolic calculations are not impacted by the low

{N}_{{\rm{c}}}

, this forms a pragmatic, computationally manageable approach to model heterogeneity similar to the coarse manual compartmentalization used by Spann, Gernaey et al (2019). We further explore the impact of

{N}_{{\rm{c}}}

in Section 3.3.…”

Section: Resultsmentioning

confidence: 99%

“…Originally they were based on experimental data (Oosterhuis & Kossen, 1984;Vrábel et al, 1999Vrábel et al, , 2001, now CFD is typically used as a basis (Bezzo et al, 2003(Bezzo et al, , 2004Delafosse et al, 2014). Combined with blackbox kinetics (Nadal-Rey et al, 2021, 2020Spann, Gernaey, et al, 2019;Spann, Glibstrup, et al, 2019;Tajsoleiman et al, 2019), large-scale gradients are estimated in seconds. However, blackbox kinetics assume instantaneous equilibrium between intra-and extracellular conditions, which is questionable.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Stochastic parcel tracking in an Euler–Lagrange compartment model for fast simulation of fermentation processes

Haringa

Tang

Noorman

2022

Biotech & Bioengineering

View full text Add to dashboard Cite

The compartment model (CM) is a well‐known approach for computationally affordable, spatially resolved hydrodynamic modeling of unit operations. Recent implementations use flow profiles based on Computational Fluid Dynamics (CFD) simulations, and several authors included microbial kinetics to simulate gradients in bioreactors. However, these studies relied on black‐box kinetics that do not account for intracellular changes and cell population dynamics in response to heterogeneous environments. In this paper, we report the implementation of a Lagrangian reaction model, where the microbial phase is tracked as a set of biomass‐parcels, each linked with an intracellular composition vector and a structured reaction model describing their intracellular response to extracellular variations. A stochastic parcel tracking approach is adopted, in contrast to the resolved trajectories used in CFD implementations. A penicillin production process is used as a case study. We show good performance of the model compared with full CFD simulations, both regarding the extracellular gradients and intracellular pool response, using the mixing time as a matching criterion and taking into account that the mixing time is sensitive to the number of compartments. The sensitivity of the model output towards some of the inputs is explored. The coarsest representative CM requires a few minutes to solve 80 h of flow time, compared with approximately 2 weeks for a full Euler–Lagrange CFD simulation of the same case. This alleviates one of the major bottlenecks for the application of such CFD simulations towards the analysis and optimization of industrial fermentation processes.

show abstract

{N}_{{\rm{c}}}

{N}_{{\rm{c}}}

in Section 3.3.…”

Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Stochastic parcel tracking in an Euler–Lagrange compartment model for fast simulation of fermentation processes

Haringa

Tang

Noorman

2022

Biotech & Bioengineering

View full text Add to dashboard Cite

show abstract

“…Soft sensors have become an important tool within the QbD/PAT framework, as reviewed by Mandenius and Gustavsson (2015) , Randek and Mandenius (2018) , and Rathore et al (2021) . One reason is that they are often the only means of determining critical process parameters (CPP) or critical quality attributes (CQA) online at all ( Capito et al, 2015 ; Melcher et al, 2015 ; Sauer et al, 2019 ; Spann et al, 2019 ; Walch et al, 2019 ; Pais et al, 2020 ; Wasalathanthri et al, 2020a ). Making these quantities measurable by means of soft sensors, in turn, allows CPPs or CQAs to be closed-loop controlled ( Birle et al, 2015 ; Matthews et al, 2016 ; Voss et al, 2017 ; Brunner et al, 2020 ; Gomis-Fons et al, 2020 ).…”

Section: Soft Sensors: the Status Quomentioning

confidence: 99%

Challenges in the Development of Soft Sensors for Bioprocesses: A Critical Review

Brunner

Siegl

Geier

et al. 2021

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

Among the greatest challenges in soft sensor development for bioprocesses are variable process lengths, multiple process phases, and erroneous model inputs due to sensor faults. This review article describes these three challenges and critically discusses the corresponding solution approaches from a data scientist’s perspective. This main part of the article is preceded by an overview of the status quo in the development and application of soft sensors. The scope of this article is mainly the upstream part of bioprocesses, although the solution approaches are in most cases also applicable to the downstream part. Variable process lengths are accounted for by data synchronization techniques such as indicator variables, curve registration, and dynamic time warping. Multiple process phases are partitioned by trajectory or correlation-based phase detection, enabling phase-adaptive modeling. Sensor faults are detected by symptom signals, pattern recognition, or by changing contributions of the corresponding sensor to a process model. According to the current state of the literature, tolerance to sensor faults remains the greatest challenge in soft sensor development, especially in the presence of variable process lengths and multiple process phases.

show abstract

“…Zoning of compartment models is often performed based on changes in flow pattern, e.g., placing a compartment interface at the impeller in the case of RDTs, which is known to physically compartmentalize and create a barrier for the axial flow [14,27,28]. However, this approach does not account for the situation where the flows inside these compartments are weak and the assumption of perfect mixing fails.…”

Section: Comparison Of Automatic Zoningmentioning

confidence: 99%

Automated Compartment Model Development Based on Data from Flow-Following Sensor Devices

et al. 2021

Self Cite

View full text Add to dashboard Cite

Due to the heterogeneous nature of large-scale fermentation processes they cannot be modelled as ideally mixed reactors, and therefore flow models are necessary to accurately represent the processes. Computational fluid dynamics (CFD) is used more and more to derive flow fields for the modelling of bioprocesses, but the computational demands associated with simulation of multiphase systems with biokinetics still limits their wide applicability. Hence, a demand for simpler flow models persists. In this study, an approach to develop data-based flow models in the form of compartment models is presented, which utilizes axial-flow rates obtained from flow-following sensor devices in combination with a proposed procedure for automatic zoning of volume. The approach requires little experimental effort and eliminates the necessity for computational determination of inter-compartmental flow rates and manual zoning. The concept has been demonstrated in a 580 L stirred vessel, of which models have been developed for two types of impellers with varying agitation intensities. The sensor device measurements were corroborated by CFD simulations, and the performance of the developed compartment models was evaluated by comparing predicted mixing times with experimentally determined mixing times. The data-based compartment models predicted the mixing times for all examined conditions with relative errors in the range of 3–27%. The deviations were ascribed to limitations in the flow-following behavior of the sensor devices, whose sizes were relatively large compared to the examined system. The approach provides a versatile and automated flow modelling platform which can be applied to large-scale bioreactors.

show abstract

A compartment model for risk-based monitoring of lactic acid bacteria cultivations

Cited by 16 publications

References 37 publications

Stochastic parcel tracking in an Euler–Lagrange compartment model for fast simulation of fermentation processes

Stochastic parcel tracking in an Euler–Lagrange compartment model for fast simulation of fermentation processes

Challenges in the Development of Soft Sensors for Bioprocesses: A Critical Review

Automated Compartment Model Development Based on Data from Flow-Following Sensor Devices

Contact Info

Product

Resources

About