Implementation of real-time incremental learning for ensemble hybrid model prediction in pilot scale bubble column aeration
Digital twins are frequently discussed in a bio-manufacturing context, but actual realisations of digital twins are rare. To use digital twin instances, significant investments in digital infrastructure and high-fidelity mathematical models are required. This work presents a real-time implementation...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Digital Chemical Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772508124000747 |
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| _version_ | 1850100953661833216 |
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| author | Peter Jul-Rasmussen Mads Stevnsborg Xiaodong Liang Jakob Kjøbsted Huusom |
| author_facet | Peter Jul-Rasmussen Mads Stevnsborg Xiaodong Liang Jakob Kjøbsted Huusom |
| author_sort | Peter Jul-Rasmussen |
| collection | DOAJ |
| description | Digital twins are frequently discussed in a bio-manufacturing context, but actual realisations of digital twins are rare. To use digital twin instances, significant investments in digital infrastructure and high-fidelity mathematical models are required. This work presents a real-time implementation of an ensemble hybrid model with incremental learning for predicting dissolved oxygen concentration in a pilot-scale bubble column. A bootstrap-aggregated hybrid modelling framework is applied for constructing an ensemble of 1000 hybrid models using different partitions of the training/validation data, providing a measure of the parameter distributions and prediction uncertainty. Each model in the ensemble hybrid model has the same model structure relying on first-principles material balances and an Artificial Neural Network for prediction of the liquid phase volumetric mass transfer coefficient. Incremental learning is applied, efficiently enabling the model to adapt to new data acquired during runtime. The software implementation follows recent ISO issues using a modular structure allowing for flexible allocation of server resources and an intuitive User-Interface is developed for controlling the application. From a real-time prediction study, the models using incremental learning are found to have superior performance both at normal operating conditions, when interpolating, and when extrapolating compared to using only the pre-trained model. |
| format | Article |
| id | doaj-art-0a10a448323340e99c82144f0dbe82d8 |
| institution | DOAJ |
| issn | 2772-5081 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Digital Chemical Engineering |
| spelling | doaj-art-0a10a448323340e99c82144f0dbe82d82025-08-20T02:40:10ZengElsevierDigital Chemical Engineering2772-50812025-03-011410021210.1016/j.dche.2024.100212Implementation of real-time incremental learning for ensemble hybrid model prediction in pilot scale bubble column aerationPeter Jul-Rasmussen0Mads Stevnsborg1Xiaodong Liang2Jakob Kjøbsted Huusom3Process and Systems Engineering Center (PROSYS), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Kongens Lyngby, 2800, DenmarkProcess and Systems Engineering Center (PROSYS), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Kongens Lyngby, 2800, DenmarkCenter for Energy Resources Engineering (CERE), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Kongens Lyngby, 2800, DenmarkProcess and Systems Engineering Center (PROSYS), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Kongens Lyngby, 2800, Denmark; Corresponding author.Digital twins are frequently discussed in a bio-manufacturing context, but actual realisations of digital twins are rare. To use digital twin instances, significant investments in digital infrastructure and high-fidelity mathematical models are required. This work presents a real-time implementation of an ensemble hybrid model with incremental learning for predicting dissolved oxygen concentration in a pilot-scale bubble column. A bootstrap-aggregated hybrid modelling framework is applied for constructing an ensemble of 1000 hybrid models using different partitions of the training/validation data, providing a measure of the parameter distributions and prediction uncertainty. Each model in the ensemble hybrid model has the same model structure relying on first-principles material balances and an Artificial Neural Network for prediction of the liquid phase volumetric mass transfer coefficient. Incremental learning is applied, efficiently enabling the model to adapt to new data acquired during runtime. The software implementation follows recent ISO issues using a modular structure allowing for flexible allocation of server resources and an intuitive User-Interface is developed for controlling the application. From a real-time prediction study, the models using incremental learning are found to have superior performance both at normal operating conditions, when interpolating, and when extrapolating compared to using only the pre-trained model.http://www.sciencedirect.com/science/article/pii/S2772508124000747Digital twinHybrid semi-parametric modellingUncertainty quantificationIncremental learningReal-time predictions |
| spellingShingle | Peter Jul-Rasmussen Mads Stevnsborg Xiaodong Liang Jakob Kjøbsted Huusom Implementation of real-time incremental learning for ensemble hybrid model prediction in pilot scale bubble column aeration Digital Chemical Engineering Digital twin Hybrid semi-parametric modelling Uncertainty quantification Incremental learning Real-time predictions |
| title | Implementation of real-time incremental learning for ensemble hybrid model prediction in pilot scale bubble column aeration |
| title_full | Implementation of real-time incremental learning for ensemble hybrid model prediction in pilot scale bubble column aeration |
| title_fullStr | Implementation of real-time incremental learning for ensemble hybrid model prediction in pilot scale bubble column aeration |
| title_full_unstemmed | Implementation of real-time incremental learning for ensemble hybrid model prediction in pilot scale bubble column aeration |
| title_short | Implementation of real-time incremental learning for ensemble hybrid model prediction in pilot scale bubble column aeration |
| title_sort | implementation of real time incremental learning for ensemble hybrid model prediction in pilot scale bubble column aeration |
| topic | Digital twin Hybrid semi-parametric modelling Uncertainty quantification Incremental learning Real-time predictions |
| url | http://www.sciencedirect.com/science/article/pii/S2772508124000747 |
| work_keys_str_mv | AT peterjulrasmussen implementationofrealtimeincrementallearningforensemblehybridmodelpredictioninpilotscalebubblecolumnaeration AT madsstevnsborg implementationofrealtimeincrementallearningforensemblehybridmodelpredictioninpilotscalebubblecolumnaeration AT xiaodongliang implementationofrealtimeincrementallearningforensemblehybridmodelpredictioninpilotscalebubblecolumnaeration AT jakobkjøbstedhuusom implementationofrealtimeincrementallearningforensemblehybridmodelpredictioninpilotscalebubblecolumnaeration |