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Bioengineering
Special Issues
The Role of Digital Twins in Bioprocessing
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The Role of Digital Twins in Bioprocessing

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biochemical Engineering".

Deadline for manuscript submissions: closed (15 January 2024) | Viewed by 5739

Special Issue Editors

Dr. Ulrich Kruhne

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Guest Editor
Department of Chemical and Biochemical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark
Interests: microfluidics; computational fluid dynamics applied acrosse scales; bioprocess engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Carina L. Gargalo

E-Mail Website
Guest Editor
Process and Systems Engineering Center (PROSYS), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Building 228A, 2800 Kongens Lyngby, Denmark
Interests: bioprocess engineering; digital twins; modelling and optimization; digitalization and sustainability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The process industry, especially the bio-manufacturing industry, is currently participating in the latest industrial revolution, commonly known as Industry 4.0. For a successful outcome in this process, the physical-to digital-to-physical information loop should be carefully developed. One way to accomplish this is with the help of the implementation of digital twins (DTs), which are basically virtual copies of the processes under study. However, even though the topic is very popular and broadly discussed, a thorough understanding of the needs and challenges of the bio-manufacturing industry is necessary when dealing with this digitalized paradigm.

This Special Issue titled “The Role of Digital Twins in Bioprocessing” welcomes original research papers and comprehensive reviews covering the advancements and applications of digital twins in the bioprocessing context. The topics of interest for this Special Issue include, but are not limited to, the following.

  • Data characteristics and collection strategies, as well as new methods and tools for data processing.
  • Modelling approaches and their potential for use in DTs. This includes mechanistic, hybrid and data-based models, including any kind of application (analysis, control, software sensors, etc.).
  • Visions, show cases and successful implementations regarding the use of DTs in the biomanufacturing industry aiming at bringing DTs a step closer to their full potential and realization (e.g., lab- and pilot-scale implementations).
  • Simulation and experimental studies focusing on the validation and applicability of DTs in increasing the efficiency of traditional processes.
  • New benchmark systems, datasets and literature overviews for DTs used in bioprocesses.
  • Digital twins, artificial intelligence and virtual reality in the bioprocessing industry applied to production and education.

Dr. Ulrich Kruhne
Dr. Carina L. Gargalo
Guest Editors

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. Bioengineering 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 2700 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

  • digital twins
  • biomanufacturing
  • data processing
  • artificial intelligence
  • virtual reality
  • Education 4.0

Published Papers (3 papers)

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Research

18 pages, 1130 KiB  
Article
Regression Metamodel-Based Digital Twin for an Industrial Dynamic Crossflow Filtration Process
by Matthias Heusel, Gunnar Grim, Joel Rauhut and Matthias Franzreb
Bioengineering 2024, 11(3), 212; https://doi.org/10.3390/bioengineering11030212 - 23 Feb 2024
Viewed by 962
Abstract
Dynamic crossflow filtration (DCF) is the state-of-the-art technology for solid–liquid separation from viscous and sensitive feed streams in the food and biopharma industry. Up to now, the potential of industrial processes is often not fully exploited, because fixed recipes are usually applied to [...] Read more.
Dynamic crossflow filtration (DCF) is the state-of-the-art technology for solid–liquid separation from viscous and sensitive feed streams in the food and biopharma industry. Up to now, the potential of industrial processes is often not fully exploited, because fixed recipes are usually applied to run the processes. In order to take the varying properties of biological feed materials into account, we aim to develop a digital twin of an industrial brownfield DCF plant, allowing to optimize setpoint decisions in almost real time. The core of the digital twin is a mechanistic–empirical process model combining fundamental filtration laws with process expert knowledge. The effect of variation in the selected process and model parameters on plant productivity has been assessed using a model-based design-of-experiments approach, and a regression metamodel has been trained with the data. A cyclic program that bidirectionally communicates with the DCF asset serves as frame of the digital twin. It monitors the process dynamics membrane torque and transmembrane pressure and feeds back the optimum permeate flow rate setpoint to the physical asset in almost real-time during process runs. We considered a total of 24 industrial production batches from the filtration of grape juice from the years 2022 and 2023 in the study. After implementation of the digital twin on site, the campaign mean productivity increased by 15% over the course of the year 2023. The presented digital twin framework is a simple example how an industrial established process can be controlled by a hybrid model-based algorithm. With a digital process dynamics model at hand, the presented metamodel optimization approach can be easily transferred to other (bio)chemical processes. Full article
(This article belongs to the Special Issue The Role of Digital Twins in Bioprocessing)
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17 pages, 4061 KiB  
Article
An Automatic Method for Generation of CFD-Based 3D Compartment Models: Towards Real-Time Mixing Simulations
by Johan Le Nepvou De Carfort, Tiago Pinto and Ulrich Krühne
Bioengineering 2024, 11(2), 169; https://doi.org/10.3390/bioengineering11020169 - 09 Feb 2024
Viewed by 1187
Abstract
This article aims to develop a method to automatically generate CFD-based compartment models. This effort to simplify mixing models aims at capturing the interactions between material transport and chemical/biochemical conversions in large-scale reactors. The proposed method converts the CFD results into a system [...] Read more.
This article aims to develop a method to automatically generate CFD-based compartment models. This effort to simplify mixing models aims at capturing the interactions between material transport and chemical/biochemical conversions in large-scale reactors. The proposed method converts the CFD results into a system of mass balance equations for each defined component. The compartmentalization method is applied to two bioreactor geometries and was able to replicate tracer mixing profiles observed in CFD simulations. The generated compartment models were successfully coupled with, a simple Monod-type biokinetic model describing microbial growth, substrate consumption and product formation. The coupled model was used to simulate a four-hour fermentation in a 190 L reactor and a 10 m3 reactor. Resolving the substrate gradients had a clear impact on the biokinetics, increasing with the scale of the reactor. Moreover, the coupled model could simulate the fermentation faster than real-time. Having a real-time-solvable model is essential for implementations in digital twins and other real-time applications using the models as predictive tools. Full article
(This article belongs to the Special Issue The Role of Digital Twins in Bioprocessing)
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Graphical abstract

15 pages, 2528 KiB  
Article
Architectural and Technological Improvements to Integrated Bioprocess Models towards Real-Time Applications
by Christopher Taylor, Barbara Pretzner, Thomas Zahel and Christoph Herwig
Bioengineering 2022, 9(10), 534; https://doi.org/10.3390/bioengineering9100534 - 09 Oct 2022
Cited by 3 | Viewed by 2837
Abstract
Integrated or holistic process models may serve as the engine of a digital asset in a multistep-process digital twin. Concatenated individual-unit operation models are effective at propagating errors over an entire process, but are nonetheless limited in certain aspects of recent applications that [...] Read more.
Integrated or holistic process models may serve as the engine of a digital asset in a multistep-process digital twin. Concatenated individual-unit operation models are effective at propagating errors over an entire process, but are nonetheless limited in certain aspects of recent applications that prevent their deployment as a plausible digital asset, particularly regarding bioprocess development requirements. Sequential critical quality attribute tests along the process chain that form output–input (i.e., pool-to-load) relationships, are impacted by nonaligned design spaces at different scales and by simulation distribution challenges. Limited development experiments also inhibit the exploration of the overall design space, particularly regarding the propagation of extreme noncontrolled parameter values. In this contribution, bioprocess requirements are used as the framework to improve integrated process models by introducing a simplified data model for multiunit operation processes, increasing statistical robustness, adding a new simulation flow for scale-dependent variables, and describing a novel algorithm for extrapolation in a data-driven environment. Lastly, architectural and procedural requirements for a deployed digital twin are described, and a real-time workflow is proposed, thus providing a final framework for a digital asset in bioprocessing along the full product life cycle. Full article
(This article belongs to the Special Issue The Role of Digital Twins in Bioprocessing)
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