Brett Metcalfe adminproject_administratorprogramme_administrator

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

Institutions: WUR - Wageningen University & Research, INRAE

https://orcid.org/0000-0002-5873-9815
David Camilo Corrales project_administratorprogramme_administrator

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

Institutions: Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, INRAE

https://orcid.org/0000-0003-4717-3040
ANTONIS KOKOSSIS programme_administrator

Projects: Uncurated Protocols Library, DBTL-P

Institutions: NTUA - National Technical University of Athens

Gopal Peddinti project_administratorprogramme_administrator

Projects: Uncurated Protocols Library, Promoters 4 Pichia

Institutions: VTT - Teknologian tutkimuskeskus VTT Oy

https://orcid.org/ 0000-0002-8767-968X
Jean-Loup Faulon programme_administrator

Projects: Uncurated Protocols Library, IBISBA 1.0 deliverables, PREP-IBISBA deliverables

Institutions: Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement

https://orcid.org/0000-0003-4274-2953
Hybrid Physics-Informed Neural Networks for Efficient Computational Modeling: A Workflow Framework Applied to Schwanniomyces occidentalis

Hybrid Physics-Informed Neural Networks for Efficient Computational Modeling: A Workflow Framework Applied to Schwanniomyces occidentalis

Programme: Bioindustry 4.0

Public web page: Not specified

Start date: 1st Sep 2025

End date: 31st Dec 2028

BIOREM

Project: Systems Biology, Artificial Intelligence and Advanced BiOtechnology Approaches to Improve Soil BioREMediation (BIOREM)

Harnessing microbes to clean up pollution

Hazardous compounds, including persistent organic pollutants, increasingly endanger ecosystems and human health. While natural recovery processes do exist, leveraging biological agents such as plants and microorganisms could provide cost-effective and sustainable clean-up solutions. Supported by the Marie Skłodowska-Curie ...

Programme: Horizon Europe – the Framework Programme for Research and Innovation (2021 to 2027)

Public web page: https://cordis.europa.eu/project/id/101183128

Start date: 1st Jan 2025

End date: 31st Dec 2028

Soft sensors based on interpretable learners for industrial-scale fed-batch fermentation: Learning from simulations

Abstract (Expand)

Monitoring bioprocesses is a challenging task where most of the variables of interest can only be measured offline. Soft sensors have emerged as a solution to provide online estimations. This work … compares interpretable learners such as CART, M5, CUBIST, and Random Forest as soft sensors for industrial-scale fed-batch fermentation of penicillin production. A structured model of industrial-scale penicillin fermentation is implemented to generate the dataset and train the interpretable learners. Variables such as substrate feed rate, agitation, temperature, pH, dissolved oxygen, vessel volume, CO2, and O2 percent in off-gas are considered as independent (predictors). The CUBIST model has achieved the best results with values of 0.908, 9.916, 3.149, and 1.920 for the coefficient of determination, Mean Squared Error, Root Mean Squared Error, and Mean Absolute Error, respectively. These results demonstrate the feasibility of developing soft sensors based on interpretable models to predict penicillin concentration at an industrial scale.

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

Towards a machine learning operations (MLOps) soft sensor for real-time predictions in industrial-scale fed-batch fermentation

Abstract (Expand)

Real-time predictions in fermentation processes are crucial because they enable continuous monitoring and control of bioprocessing. However, the availability of online measurements is limited by the … availability and feasibility of sensing technology. Soft sensors - or software sensors that convert available measurements into measurements of interest (product yield, quality, etc.) - have the potential to improve efficiency and product quality. Machine learning (ML) based soft sensors have gained increased popularity over the years since they can incorporate variables that are measured in real-time, and exploit the intricate patterns embedded in such voluminous datasets. However, ML-based soft sensor requires more than just a classical ML learner with an unseen test set to evaluate the quality prediction of the model. When a ML model is deployed in production, its performance can deteriorate rapidly leading to an unanticipated decline in the quality of the output and predictions. Here a proof concept of Machine Learning Operations (MLOps) to automate the end-to-end soft sensor lifecycle in industrial scale fed-batch fermentation, from development and deployment to maintenance and monitoring is proposed. Using the industrial-scale penicillin fermentation (IndPenSim) dataset that includes 100 fermentation batches, to build a soft sensor based on Long Short Term Memory (LSTM) for penicillin concentration prediction. The batches containing deviations in the processes (91–100) were used to assess concept drift of the LSTM soft sensor. The evaluation of concept drift is evidenced by the soft sensor performance falling below the set threshold based on the Population Stability Index (PSI), which automatically triggers an alert to run the retraining pipeline.

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

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