Cloud point prediction model for polyvinyl alcohol production plants considering process dynamics
In polyvinyl alcohol (PVA) production plants, the cloud point is controlled as one of the product properties. The cloud point is the temperature at which the water solubility rapidly decreases because hydrogen bonds between PVA and water are broken. If the cloud point falls below a controlled value,...
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| Language: | English |
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Elsevier
2024-12-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024017274 |
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| author | Ayami Ohkuma Yoshihito Yamauchi Nobuhito Yamada Satoshi Ooyama Hiromasa Kaneko |
| author_facet | Ayami Ohkuma Yoshihito Yamauchi Nobuhito Yamada Satoshi Ooyama Hiromasa Kaneko |
| author_sort | Ayami Ohkuma |
| collection | DOAJ |
| description | In polyvinyl alcohol (PVA) production plants, the cloud point is controlled as one of the product properties. The cloud point is the temperature at which the water solubility rapidly decreases because hydrogen bonds between PVA and water are broken. If the cloud point falls below a controlled value, the performance of the PVA as a product deteriorates, and thus it is necessary to monitor the cloud point in real time and ensure that it does not deviate from the controlled value. However, the cloud point cannot be analysed frequently in practical operations. In this study, a soft sensor that continuously predicts the cloud point from process variables that can be measured in real time was constructed using machine learning. Furthermore, the prediction accuracy of the model was improved by (1) increasing the number of samples using a data set of similar quality to the cloud point, considering that the number of samples near the control value of the cloud point is small, and (2) selecting the optimum process variable and time-delay range, considering the time delay of the process variable to accommodate the dynamic behaviour of the plant. Analysis was conducted using data measured from an actual PVA manufacturing plant. When comparing the conventional method with the proposed method, the coefficient of determination, used as an indicator to evaluate model performance, improved from 0.288 to 0.729. Furthermore, the root mean squared error decreased from 1.52 to 0.937. These results demonstrate that the proposed method contributes to improved prediction accuracy. |
| format | Article |
| id | doaj-art-db00a9c36a3a4c49a6162443684303d0 |
| institution | OA Journals |
| issn | 2590-1230 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-db00a9c36a3a4c49a6162443684303d02025-08-20T02:34:40ZengElsevierResults in Engineering2590-12302024-12-012410347510.1016/j.rineng.2024.103475Cloud point prediction model for polyvinyl alcohol production plants considering process dynamicsAyami Ohkuma0Yoshihito Yamauchi1Nobuhito Yamada2Satoshi Ooyama3Hiromasa Kaneko4Department of Applied Chemistry, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa 214-8571, JapanMitsubishi Chemical Corporation, 3-10, Ushiodori, Kurashiki-shi, Okayama 712-8054, JapanMitsubishi Chemical Corporation, 3-10, Ushiodori, Kurashiki-shi, Okayama 712-8054, JapanMitsubishi Chemical Corporation, 3-10, Ushiodori, Kurashiki-shi, Okayama 712-8054, JapanDepartment of Applied Chemistry, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa 214-8571, Japan; Corresponding author.In polyvinyl alcohol (PVA) production plants, the cloud point is controlled as one of the product properties. The cloud point is the temperature at which the water solubility rapidly decreases because hydrogen bonds between PVA and water are broken. If the cloud point falls below a controlled value, the performance of the PVA as a product deteriorates, and thus it is necessary to monitor the cloud point in real time and ensure that it does not deviate from the controlled value. However, the cloud point cannot be analysed frequently in practical operations. In this study, a soft sensor that continuously predicts the cloud point from process variables that can be measured in real time was constructed using machine learning. Furthermore, the prediction accuracy of the model was improved by (1) increasing the number of samples using a data set of similar quality to the cloud point, considering that the number of samples near the control value of the cloud point is small, and (2) selecting the optimum process variable and time-delay range, considering the time delay of the process variable to accommodate the dynamic behaviour of the plant. Analysis was conducted using data measured from an actual PVA manufacturing plant. When comparing the conventional method with the proposed method, the coefficient of determination, used as an indicator to evaluate model performance, improved from 0.288 to 0.729. Furthermore, the root mean squared error decreased from 1.52 to 0.937. These results demonstrate that the proposed method contributes to improved prediction accuracy.http://www.sciencedirect.com/science/article/pii/S2590123024017274Machine learningSoft sensorTime delayProcess controlVariable selection |
| spellingShingle | Ayami Ohkuma Yoshihito Yamauchi Nobuhito Yamada Satoshi Ooyama Hiromasa Kaneko Cloud point prediction model for polyvinyl alcohol production plants considering process dynamics Results in Engineering Machine learning Soft sensor Time delay Process control Variable selection |
| title | Cloud point prediction model for polyvinyl alcohol production plants considering process dynamics |
| title_full | Cloud point prediction model for polyvinyl alcohol production plants considering process dynamics |
| title_fullStr | Cloud point prediction model for polyvinyl alcohol production plants considering process dynamics |
| title_full_unstemmed | Cloud point prediction model for polyvinyl alcohol production plants considering process dynamics |
| title_short | Cloud point prediction model for polyvinyl alcohol production plants considering process dynamics |
| title_sort | cloud point prediction model for polyvinyl alcohol production plants considering process dynamics |
| topic | Machine learning Soft sensor Time delay Process control Variable selection |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024017274 |
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