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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Veröffentlicht in:	Control engineering practice Jg. 149; S. 105986
Hauptverfasser:	Wang, Xiuli, Sun, Zhifei, He, Defeng, Wu, Shaomin, Zhao, Lianna
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.08.2024
Schlagworte:	Extended fast relevance vector regression algorithm Incremental learning method k-means clustering method Pollutant emission prediction Extended fast relevance vector regression algorithm Pollutant emission prediction Incremental learning method k-means clustering method
ISSN:	0967-0661, 1873-6939
Online-Zugang:	Volltext
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Zusammenfassung:	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.
ISSN:	0967-0661 1873-6939
DOI:	10.1016/j.conengprac.2024.105986