Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.1
3.5
Journals
Processes
Special Issues
Fermentation Optimization and Modeling
share announcement

Fermentation Optimization and Modeling

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Biological Processes and Systems".

Deadline for manuscript submissions: closed (15 July 2020) | Viewed by 12766

Special Issue Editor

Prof. Dr. Rudibert King

E-Mail Website
Guest Editor
Chair of Measurement and Control, Faculty III Process Sciences, Technische Universität Berlin, 10623 Berlin, Germany
Interests: automatic modeling; closed-loop control; supervision; biotechnology; flow and combustion control

Special Issue Information

Dear Colleagues,

Unlike other areas of application where the word ‘optimization’ is often misused to just describe an improvement of a process without clearly specifying the underlying criterion, for fermentation optimization, maximizing the product or cell yield, or minimizing substrate expenditure and fermentation time offer some obvious choices for optimization in the strict sense. The majority of fermentations are still run in a discontinuous fashion, e.g., as batch or fed-batch processes, where the fed-batch variant offers significantly more handles to perform optimization. These discontinuous processes, however, result in time-varying variables, such as the concentrations of biomass, substrates, and products. Moreover, the intracellular composition of the cells or flux patterns through the metabolism may change when limitations by substrates occur that are often desired, e.g., to initiate a secondary metabolism. To account for this dynamic behavior on a rational basis in the context of optimization, mathematical models to describe growth and production have to be formulated. While this has been known for decades and many simulation studies have shown the high potential of model-based approaches, not that many true success stories for real processes have been described. For this reason, it is the intent of this Special Issue to collect new results which show—based on a mathematical modeling of a real, experimental system—the application of a model-based optimization for the real process. Both experiments in lab-scale and industrial applications are welcome.

Prof. Dr. Rudibert King
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Modeling of fermentations
  • Model-based optimization
  • Experimental verification of models
  • Real-time application

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

25 pages, 3743 KiB  
Article
NMPC-Based Workflow for Simultaneous Process and Model Development Applied to a Fed-Batch Process for Recombinant C. glutamicum
by Philipp Levermann, Fabian Freiberger, Uma Katha, Henning Zaun, Johannes Möller, Volker C. Hass, Karl Michael Schoop, Jürgen Kuballa and Ralf Pörtner
Processes 2020, 8(10), 1313; https://doi.org/10.3390/pr8101313 - 19 Oct 2020
Viewed by 2459
Abstract
For the fast and improved development of bioprocesses, new strategies are required where both strain and process development are performed in parallel. Here, a workflow based on a Nonlinear Model Predictive Control (NMPC) algorithm is described for the model-assisted development of biotechnological processes. [...] Read more.
For the fast and improved development of bioprocesses, new strategies are required where both strain and process development are performed in parallel. Here, a workflow based on a Nonlinear Model Predictive Control (NMPC) algorithm is described for the model-assisted development of biotechnological processes. By using the NMPC algorithm, the process is designed with respect to a target function (product yield, biomass concentration) with a drastically decreased number of experiments. A workflow for the usage of the NMPC algorithm as a process development tool is outlined. The NMPC algorithm is capable of improving various process states, such as product yield and biomass concentration. It uses on-line and at-line data and controls and optimizes the process by model-based process extrapolation. In this study, the algorithm is applied to a Corynebacterium glutamicum process. In conclusion, the potency of the NMPC algorithm as a powerful tool for process development is demonstrated. In particular, the benefits of the system regarding the characterization and optimization of a fed-batch process are outlined. With the NMPC algorithm, process development can be run simultaneously to strain development, resulting in a shortened time to market for novel products. Full article
(This article belongs to the Special Issue Fermentation Optimization and Modeling)
►▼ Show Figures

Figure 1

27 pages, 1135 KiB  
Article
Model-Based Process Optimization for the Production of Macrolactin D by Paenibacillus polymyxa
by Dominik Krämer, Terrance Wilms and Rudibert King
Processes 2020, 8(7), 752; https://doi.org/10.3390/pr8070752 - 28 Jun 2020
Cited by 6 | Viewed by 2842
Abstract
In this study, we show the successful application of different model-based approaches for the maximizing of macrolactin D production by Paenibacillus polymyxa. After four initial cultivations, a family of nonlinear dynamic biological models was determined automatically and ranked by their respective Akaike [...] Read more.
In this study, we show the successful application of different model-based approaches for the maximizing of macrolactin D production by Paenibacillus polymyxa. After four initial cultivations, a family of nonlinear dynamic biological models was determined automatically and ranked by their respective Akaike Information Criterion (AIC). The best models were then used in a multi-model setup for robust product maximization. The experimental validation shows the highest product yield attained compared with the identification runs so far. In subsequent fermentations, the online measurements of CO2 concentration, base consumption, and near-infrared spectroscopy (NIR) were used for model improvement. After model extension using expert knowledge, a single superior model could be identified. Model-based state estimation with a sigma-point Kalman filter (SPKF) was based on online measurement data, and this improved model enabled nonlinear real-time product maximization. The optimization increased the macrolactin D production even further by 28% compared with the initial robust multi-model offline optimization. Full article
(This article belongs to the Special Issue Fermentation Optimization and Modeling)
►▼ Show Figures

Figure 1

16 pages, 2629 KiB  
Article
Monitoring Parallel Robotic Cultivations with Online Multivariate Analysis
by Sebastian Hans, Christian Ulmer, Harini Narayanan, Trygve Brautaset, Niels Krausch, Peter Neubauer, Irmgard Schäffl, Michael Sokolov and Mariano Nicolas Cruz Bournazou
Processes 2020, 8(5), 582; https://doi.org/10.3390/pr8050582 - 14 May 2020
Cited by 10 | Viewed by 3499
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Fermentation Optimization and Modeling)
►▼ Show Figures

Figure 1

17 pages, 2540 KiB  
Article
Experimental Validation of a Cascade Control Strategy for Continuously Perfused Animal Cell Cultures
by Thomas Abbate, Mihaela Sbarciog, Laurent Dewasme and Alain Vande Wouwer
Processes 2020, 8(4), 413; https://doi.org/10.3390/pr8040413 - 1 Apr 2020
Cited by 4 | Viewed by 3358
Abstract
This paper is dedicated to the experimental validation of a cascade control strategy for simultaneously regulating the glucose and biomass levels in continuously perfused HEK-293 cell cultures. The inner loop consists of a partial feedback linearization, which requires the estimation of the biomass [...] Read more.
This paper is dedicated to the experimental validation of a cascade control strategy for simultaneously regulating the glucose and biomass levels in continuously perfused HEK-293 cell cultures. The inner loop consists of a partial feedback linearization, which requires the estimation of the biomass specific growth rate and glucose uptake rate. This latter task is achieved by sliding mode observers, which do not require a priori process knowledge in the form of a process model. The linearized process is then regulated by the outer loop, including two classical PI controllers with autotuning. The four manipulated variables are a feed flow rate with low glucose concentration, another feed flow with a higher glucose content, a bleed flow, and a perfusion stream. The experimental results demonstrate the ability of the control strategy to reach and regulate the prescribed setpoints. The main advantage of the strategy is that it can be applied in a plug and play manner and shows satisfactory robustness. To the best of our knowledge, this is the first time that such a multivariable control strategy, together with sliding mode observers, is applied at the lab scale to an industrial process in the pharmaceutical sector. Full article
(This article belongs to the Special Issue Fermentation Optimization and Modeling)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop