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Cost-Conscious Analytics and Decision Support for Industrial Batch Processes
Mälardalen University, School of Business, Society and Engineering. Kanthal AB, Sweden.ORCID iD: 0000-0002-2455-3203
2024 (English)Licentiate thesis, comprehensive summary (Other academic)
Place, publisher, year, edition, pages
Eskilstuna: Mälardalens universitet , 2024.
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 362
National Category
Energy Systems
Research subject
Energy- and Environmental Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-67178ISBN: 978-91-7485-655-2 (print)OAI: oai:DiVA.org:mdh-67178DiVA, id: diva2:1865612
Presentation
2024-10-03, Gamma, Mälardalens universitet, Västerås, 09:15 (English)
Opponent
Supervisors
Available from: 2024-06-05 Created: 2024-06-05 Last updated: 2024-06-05Bibliographically approved
List of papers
1. Consolidating industrial batch process data for machine learning
Open this publication in new window or tab >>Consolidating industrial batch process data for machine learning
2022 (English)In: / [ed] Esko Juuso, Bernt Lie, Erik Dahlquist and Jari Ruuska, 2022, p. 76-83Conference paper, Published paper (Refereed)
Abstract [en]

The paradigm change of Industry 4.0 brings attention to data-driven modeling and the incentive to apply machine learning methods in the process industry. Further, capitalizing on a great deal of data available is an adverse task. For batch processes, the dataset is in a threeway format (Batch × Sensor × Time). Depending on the process and the goal of the analysis, it might be necessary to aggregate batches together. For this reason, a campaign unfolding structure is applied. By grouping the batches under new labels relevant to the analytical goal, campaigns are created. These labels can be created from periodical occurrences, such as refurbishing the refractory lining in the case of the case study. In order to utilize the three-way batch format, it is necessary to align the batches. In order to address this, the feature-oriented approach Statistical Pattern Analysis (SPA) is applied. SPA derives statistics, e.g., mean, skewness and kurtosis from the time series, consequently aligning the batches. The SPA and the campaign approach create a dataset consisting of select statistics instead of an irregular three-way array. Functional data analysis (FDA) is used to smooth and extract first- and second-order derivative information from the sensors in which functional behavior can be observed before creating features. Principal Component Analysis (PCA) is used to examine the final dataset. Further, industrial processes are notoriously nonlinear, and even more so batch processes. Therefore, kernel-based principal component analysis (KPCA) is used to review the final dataset. The KPCA can accommodate different underlying characteristics by modifying the kernel function used. 

Series
Linköping Electronic Conference Proceedings ; 185
Keywords
Batch Process Analysis (BDA), Batch preprocessing, Functional Data Analysis (FDA), Statistical Pattern Analysis (SPA), Kernel Principal Component Analysis (KPCA)
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-61115 (URN)10.3384/ecp21185 (DOI)978-91-7929-219-5 (ISBN)
Conference
The First SIMS EUROSIM Conference on Modelling and Simulation, SIMS EUROSIM 2021, and 62nd International Conference of Scandinavian Simulation Society, SIMS 2021, September 21-23, Virtual Conference, Finland
Available from: 2022-12-06 Created: 2022-12-06 Last updated: 2024-06-05Bibliographically approved
2. Cost-Sensitive Decision Support for Industrial Batch Processes
Open this publication in new window or tab >>Cost-Sensitive Decision Support for Industrial Batch Processes
2023 (English)In: Sensors, E-ISSN 1424-8220, Vol. 23, no 23, article id 9464Article in journal (Refereed) Published
Abstract [en]

In this work, cost-sensitive decision support was developed. Using Batch Data Analytics (BDA) methods of the batch data structure and feature accommodation, the batch process property and sensor data can be accommodated. The batch data structure organises the batch processes' data, and the feature accommodation approach derives statistics from the time series, consequently aligning the time series with the other features. Three machine learning classifiers were implemented for comparison: Logistic Regression (LR), Random Forest Classifier (RFC), and Support Vector Machine (SVM). It is possible to filter out the low-probability predictions by leveraging the classifiers' probability estimations. Consequently, the decision support has a trade-off between accuracy and coverage. Cost-sensitive learning was used to implement a cost matrix, which further aggregates the accuracy-coverage trade into cost metrics. Also, two scenarios were implemented for accommodating out-of-coverage batches. The batch is discarded in one scenario, and the other is processed. The Random Forest classifier was shown to outperform the other classifiers and, compared to the baseline scenario, had a relative cost of 26%. This synergy of methods provides cost-aware decision support for analysing the intricate workings of a multiprocess batch data system.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
Batch Data Analytics (BDA), feature-oriented, cost-sensitive learning, decision support, machine learning
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-65129 (URN)10.3390/s23239464 (DOI)001115971100001 ()38067837 (PubMedID)2-s2.0-85179140167 (Scopus ID)
Available from: 2023-12-20 Created: 2023-12-20 Last updated: 2024-06-05Bibliographically approved

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123 3 of 3
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