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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