Incremental fast relevance vector regression model based multi-pollutant emission prediction of biomass cogeneration systems
Exact and trusty prediction of pollutant emissions is pivotal for optimal combustion control in biomass cogeneration systems, which possess multiple variables, high-volume data streams, and dynamic characteristics. Aiming at the multivariate dynamic systems, this paper extends a classical fast relev...
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| Published in: | Control engineering practice Vol. 149; p. 105986 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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01.08.2024
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| ISSN: | 0967-0661, 1873-6939 |
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| Abstract | Exact and trusty prediction of pollutant emissions is pivotal for optimal combustion control in biomass cogeneration systems, which possess multiple variables, high-volume data streams, and dynamic characteristics. Aiming at the multivariate dynamic systems, this paper extends a classical fast relevance vector regression (FRVR) algorithm into a multivariate form to accomplish synchronous multi-pollutant prediction. Meanwhile, a flexible and effective online training strategy is proposed to solve the problems of low accuracy of multi-step prediction and lack of dynamic updating capability. First, the given dataset is divided utilizing the k-means clustering method to enhance the clustering of similar features and expedite the prediction process. Then, the classical FRVR algorithm is extended into a multiple-output form, enabling the simultaneous prediction of multiple pollutant emissions. Moreover, the incremental learning method is introduced into the proposed multivariate FRVR model to improve its dynamic performance and online learning ability. Finally, the proposed method’s effectiveness is verified through a biomass cogeneration systems case. Experimental findings fully illustrate that the proposed method provides the lower RMSE and MAE while runtime decreases by 50% and R2 reaches 96%. The proposed method significantly outperforms others, showing excellent potential in the pollutant prediction field.
•An IMFRVR algorithm is proposed to predict multiple pollutant concentrations.•The k-means method cluster the data to extract useful information from the original data.•The MFRVR model is built by setting a Gaussian distribution to the FRVR weight matrix.•The incremental learning algorithm is employed to update prediction model dynamically. |
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| AbstractList | Exact and trusty prediction of pollutant emissions is pivotal for optimal combustion control in biomass cogeneration systems, which possess multiple variables, high-volume data streams, and dynamic characteristics. Aiming at the multivariate dynamic systems, this paper extends a classical fast relevance vector regression (FRVR) algorithm into a multivariate form to accomplish synchronous multi-pollutant prediction. Meanwhile, a flexible and effective online training strategy is proposed to solve the problems of low accuracy of multi-step prediction and lack of dynamic updating capability. First, the given dataset is divided utilizing the k-means clustering method to enhance the clustering of similar features and expedite the prediction process. Then, the classical FRVR algorithm is extended into a multiple-output form, enabling the simultaneous prediction of multiple pollutant emissions. Moreover, the incremental learning method is introduced into the proposed multivariate FRVR model to improve its dynamic performance and online learning ability. Finally, the proposed method’s effectiveness is verified through a biomass cogeneration systems case. Experimental findings fully illustrate that the proposed method provides the lower RMSE and MAE while runtime decreases by 50% and R2 reaches 96%. The proposed method significantly outperforms others, showing excellent potential in the pollutant prediction field.
•An IMFRVR algorithm is proposed to predict multiple pollutant concentrations.•The k-means method cluster the data to extract useful information from the original data.•The MFRVR model is built by setting a Gaussian distribution to the FRVR weight matrix.•The incremental learning algorithm is employed to update prediction model dynamically. |
| ArticleNumber | 105986 |
| Author | Zhao, Lianna He, Defeng Wang, Xiuli Sun, Zhifei Wu, Shaomin |
| Author_xml | – sequence: 1 givenname: Xiuli surname: Wang fullname: Wang, Xiuli organization: College of Information Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China – sequence: 2 givenname: Zhifei surname: Sun fullname: Sun, Zhifei organization: College of Information Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China – sequence: 3 givenname: Defeng surname: He fullname: He, Defeng email: hdfzj@zjut.edu.cn organization: College of Information Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China – sequence: 4 givenname: Shaomin surname: Wu fullname: Wu, Shaomin organization: Kent Business School, University of Kent, Canterbury, Kent CT2 7FS, United Kingdom – sequence: 5 givenname: Lianna surname: Zhao fullname: Zhao, Lianna organization: Information School, The University of Sheffield, Sheffield, South Yorkshire S10 2AH, United Kingdom |
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| CitedBy_id | crossref_primary_10_1016_j_apenergy_2024_124179 crossref_primary_10_1016_j_energy_2025_135049 crossref_primary_10_1016_j_energy_2025_138442 crossref_primary_10_1016_j_eswa_2025_127969 crossref_primary_10_1016_j_applthermaleng_2025_126709 crossref_primary_10_1002_qre_70075 |
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| Keywords | Extended fast relevance vector regression algorithm Pollutant emission prediction Incremental learning method k-means clustering method |
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| Title | Incremental fast relevance vector regression model based multi-pollutant emission prediction of biomass cogeneration systems |
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