Industrial-scale penicillin simulation
Industrial-scale penicillin simulation (IndPenSim) is a first principles mathematical model of a 100,000 Litre fermentation of an industrial strain of Penicillium chrysogenum validated using historical data collected from an industrial-scale penicillin fermentation process. Adapting a previously published structured model to describe penicillin fermentation and extending the model to include the main environmental effects of dissolved oxygen, viscosity, temperature, pH, and dissolved carbon dioxide. In addition the effects of nitrogen and phenylacetic acid concentrations on the biomass and penicillin production rates were also included. The manipulated variables recorded during each batch were used as inputs to the simulator and the predicted outputs were then compared with the on-line and off-line measurements recorded in the real process. Subsequently the simulation was extended to include the addition of a simulated Raman spectroscopy device with the purpose of this addition for the developing, evaluating and implementation of advanced and innovative control solutions applicable to biotechnology facilities.
As part of the Bioindustry4.0 project, the 100 batches of simulated data from IndPenSim have been used as a test dataset for the development of a number of tools and services given their scope and variability (e.g., faults, automated control, etc.).
References
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Goldrick, S., Ştefan, A., Lovett, D., Montague, G., and Lennox, B., 2015. The development of an industrial-scale fed-batch fermentation simulation, Journal of Biotechnology, Volume 193, Pages 70-82, DOI: 10.1016/j.jbiotec.2014.10.029, https://doi.org/10.1016/j.jbiotec.2014.10.029
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Goldrick, S., Duran-Villalobos, C. A., Jankauskas, K., Lovett, D., Farid, S. S., and Lennox, B., 2019. Modern day monitoring and control challenges outlined on an industrial-scale benchmark fermentation process, Computers and Chemical Engineering Journal Volume 130, 106471, DOI: 10.1016/j.compchemeng.2019.05.037, https://doi.org/10.1016/j.compchemeng.2019.05.037
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Programme: Bioindustry 4.0
SEEK ID: https://ibisbahub.eu/projects/83
Public web page: http://www.industrialpenicillinsimulation.com/
Organisms: Penicillium chrysogenum
IBISBA PALs: No PALs for this Project
Project start date: 1st Jan 2023
Project end date: 31st Dec 2026
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Projects: Industrial-scale penicillin simulation
Institutions: Universität Koblenz
Projects: Industrial-scale penicillin simulation, Bioindustry4.0 Work Package 7: Tools for high-quality datasets, requisite for data-driven methods and services, Hybrid Physics-Informed Neural Networks for Efficient Computational Modeling: A Workflow Framework Applied to Schwanniomyces occidentalis, STAMM: Soft sensor moniToring and mAintenance framework for Machine learning Models
Institutions: Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, INRAE
https://orcid.org/0000-0003-4717-3040
Expertise: machine learning, IA, Digital Twins
Projects: Uncurated Protocols Library, Muconic Acid Production, An inventory of the Aspergillus niger secretome by combining in silico predictions with shotgun proteomics data, Template library, Coculture, IBISBA 1.0 deliverables, PREP-IBISBA deliverables, Industrial-scale penicillin simulation, Sandbox, Bioindustry4.0 Work Package 7: Tools for high-quality datasets, requisite for data-driven methods and services, IBISBA-DIALS WP3, STAMM: Soft sensor moniToring and mAintenance framework for Machine learning Models, Bioindustry4.0
Institutions: Wageningen University & Research, UNLOCK
https://orcid.org/0000-0001-8172-8981
Projects: Industrial-scale penicillin simulation, Sandbox, Bioindustry4.0 Work Package 7: Tools for high-quality datasets, requisite for data-driven methods and services, IBISBA-DIALS WP3, BIOREM, Hybrid Physics-Informed Neural Networks for Efficient Computational Modeling: A Workflow Framework Applied to Schwanniomyces occidentalis, IBISBA-ERIC, Pseudomonas putida use case, STAMM: Soft sensor moniToring and mAintenance framework for Machine learning Models, Bioindustry4.0, Data Fabric Usage Demos
Institutions: Wageningen University & Research, INRAE
https://orcid.org/0000-0002-5873-9815
Expertise: Data Management, Metadata, Mathematical Modelling, Software Engineering, machine learning
Tools: Matlab, Python, Metadata, machine learning, Mass spectrometry, Microscopy
Postdoctoral Researcher Laboratory of Systems and Synthetic Biology at Wageningen University & Research (WUR) working on Digital Twins, Data Fabrics, and research data management.
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Real-time online monitoring of bioprocesses
Industrial biotechnology (IB) has the potential to create new markets while protecting the environment. With this in mind, the EU-funded BIOINDUSTRY 4.0 project will pave the way for IB to become a major manufacturing technology. Supporting the digitalisation of IB, the project will create new services delivered by European research infrastructure. These will address several challenges, focusing on the acceleration of bioprocess development pipelines. ...
Projects: Industrial-scale penicillin simulation, Scorpion antivenom nanobody production in Escherichia coli (Recombinant bispecific VHH), Production of the monoclonal antibody Trastuzumab in Komagataella phaffii (Pichia pastoris) for breast cancer treatment, Bioindustry4.0 Work Package 7: Tools for high-quality datasets, requisite for data-driven methods and services, Hybrid Physics-Informed Neural Networks for Efficient Computational Modeling: A Workflow Framework Applied to Schwanniomyces occidentalis, Saccharomyces cerevisiae, Pseudomonas putida use case, STAMM: Soft sensor moniToring and mAintenance framework for Machine learning Models, Pichia pastoris fermentation, Bioindustry4.0, Bioindustry4.0 Work Package 2: Dissemination, Communication Exploitation and capacity building, Data Fabric Usage Demos
Web page: https://www.bioindustry4.eu
ROR ID: https://ror.org/003vg9w96
Department: Not specified
Country:
France
City: Paris
Web page: https://www.inrae.fr/
ROR ID: Not specified
Department: Not specified
Country:
Germany
City: Koblenz
Web page: https://www.uni-koblenz.de/de
ROR ID: https://ror.org/04qw24q55
Department: Not specified
Country:
Netherlands
City: Wageningen
Web page: https://www.wur.nl/
Abstract (Expand)
Authors: Brett Metcalfe, Juan Camilo Acosta-Pavas, Carlos Eduardo Robles-Rodriguez, George K. Georgakilas, Theodore Dalamagas, Cesar Arturo Aceves-Lara, Fayza Daboussi, Jasper J Koehorst, David Camilo Corrales
Date Published: 1st Mar 2025
Publication Type: Journal
DOI: 10.1016/j.compchemeng.2024.108991
Citation: Computers & Chemical Engineering 194:108991
Abstract (Expand)
Authors: Juan Camilo Acosta-Pavas, Carlos Eduardo Robles-Rodriguez, David Griol, Fayza Daboussi, Cesar Arturo Aceves-Lara, David Camilo Corrales
Date Published: 1st Aug 2024
Publication Type: Journal
DOI: 10.1016/j.compchemeng.2024.108736
Citation: Computers & Chemical Engineering 187:108736
Abstract (Expand)
Authors: Stephen Goldrick, Carlos A. Duran-Villalobos, Karolis Jankauskas, David Lovett, Suzanne S. Farid, Barry Lennox
Date Published: 1st Nov 2019
Publication Type: Journal
DOI: 10.1016/j.compchemeng.2019.05.037
Citation: Computers & Chemical Engineering 130:106471.
Abstract (Expand)
Authors: Stephen Goldrick, Andrei Ştefan, David Lovett, Gary Montague, Barry Lennox
Date Published: 2015
Publication Type: Journal
DOI: 10.1016/j.jbiotec.2014.10.029
Citation: Journal of Biotechnology 193:70-82.
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