Ensemble Just-In-Time Learning-Based Soft Sensor for Mooney Viscosity Prediction in an Industrial Rubber Mixing Process
The lack of online sensors for Mooney viscosity measurement has posed significant challenges for enabling efficient monitoring, control, and optimization of industrial rubber mixing process. To obtain real-time and accurate estimations of Mooney viscosity, a novel soft sensor method, referred to as...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Polymer Technology |
| Online Access: | http://dx.doi.org/10.1155/2020/6575326 |
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| _version_ | 1832560672449232896 |
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| author | Huaiping Jin Jiangang Li Meng Wang Bin Qian Biao Yang Zheng Li Lixian Shi |
| author_facet | Huaiping Jin Jiangang Li Meng Wang Bin Qian Biao Yang Zheng Li Lixian Shi |
| author_sort | Huaiping Jin |
| collection | DOAJ |
| description | The lack of online sensors for Mooney viscosity measurement has posed significant challenges for enabling efficient monitoring, control, and optimization of industrial rubber mixing process. To obtain real-time and accurate estimations of Mooney viscosity, a novel soft sensor method, referred to as multimodal perturbation- (MP-) based ensemble just-in-time learning Gaussian process regression (MP-EJITGPR), is proposed by exploiting ensemble JIT learning. This method employs perturbations on similarity measure and input variables for generating the diversity of JIT learners. Furthermore, a set of accurate and diverse JIT learners are built through an evolutionary multiobjective optimization by balancing the accuracy and diversity objectives explicitly. Moreover, all base JIT learners are combined adaptively using a finite mixture mechanism. The proposed method is applied to an industrial rubber mixing process for Mooney viscosity prediction, and the experimental results demonstrate its effectiveness and superiority over traditional soft sensor methods. |
| format | Article |
| id | doaj-art-6f01dafdb3a44f75ad12d28c5d1f35ef |
| institution | Kabale University |
| issn | 0730-6679 1098-2329 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Polymer Technology |
| spelling | doaj-art-6f01dafdb3a44f75ad12d28c5d1f35ef2025-02-03T01:27:02ZengWileyAdvances in Polymer Technology0730-66791098-23292020-01-01202010.1155/2020/65753266575326Ensemble Just-In-Time Learning-Based Soft Sensor for Mooney Viscosity Prediction in an Industrial Rubber Mixing ProcessHuaiping Jin0Jiangang Li1Meng Wang2Bin Qian3Biao Yang4Zheng Li5Lixian Shi6Department of Automation, Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, ChinaDepartment of Automation, Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, ChinaDepartment of Automation, Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, ChinaDepartment of Automation, Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, ChinaDepartment of Automation, Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, ChinaDepartment of Automation, Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, ChinaDepartment of Automation, Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, ChinaThe lack of online sensors for Mooney viscosity measurement has posed significant challenges for enabling efficient monitoring, control, and optimization of industrial rubber mixing process. To obtain real-time and accurate estimations of Mooney viscosity, a novel soft sensor method, referred to as multimodal perturbation- (MP-) based ensemble just-in-time learning Gaussian process regression (MP-EJITGPR), is proposed by exploiting ensemble JIT learning. This method employs perturbations on similarity measure and input variables for generating the diversity of JIT learners. Furthermore, a set of accurate and diverse JIT learners are built through an evolutionary multiobjective optimization by balancing the accuracy and diversity objectives explicitly. Moreover, all base JIT learners are combined adaptively using a finite mixture mechanism. The proposed method is applied to an industrial rubber mixing process for Mooney viscosity prediction, and the experimental results demonstrate its effectiveness and superiority over traditional soft sensor methods.http://dx.doi.org/10.1155/2020/6575326 |
| spellingShingle | Huaiping Jin Jiangang Li Meng Wang Bin Qian Biao Yang Zheng Li Lixian Shi Ensemble Just-In-Time Learning-Based Soft Sensor for Mooney Viscosity Prediction in an Industrial Rubber Mixing Process Advances in Polymer Technology |
| title | Ensemble Just-In-Time Learning-Based Soft Sensor for Mooney Viscosity Prediction in an Industrial Rubber Mixing Process |
| title_full | Ensemble Just-In-Time Learning-Based Soft Sensor for Mooney Viscosity Prediction in an Industrial Rubber Mixing Process |
| title_fullStr | Ensemble Just-In-Time Learning-Based Soft Sensor for Mooney Viscosity Prediction in an Industrial Rubber Mixing Process |
| title_full_unstemmed | Ensemble Just-In-Time Learning-Based Soft Sensor for Mooney Viscosity Prediction in an Industrial Rubber Mixing Process |
| title_short | Ensemble Just-In-Time Learning-Based Soft Sensor for Mooney Viscosity Prediction in an Industrial Rubber Mixing Process |
| title_sort | ensemble just in time learning based soft sensor for mooney viscosity prediction in an industrial rubber mixing process |
| url | http://dx.doi.org/10.1155/2020/6575326 |
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