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<p>A modified lazy learning algorithm combined with a relevance vector machine (MLL-RVM) is presented to address a data-driven modelling problem for a gasification process inside a united gas improvement (UGI) gasifier. During the UGI gasification process, the measured online temperature of the produced crude gas is a crucial aspect. However, the gasification process complexities, especially severe changes in the temperature versus infrequent manipulation of the gasifier and the unknown noise in collected data, pose difficulties in dynamics process descriptions via conventional first principles. In the MLL-RVM, a novel weighted neighbour selection method is adopted based on the proposed dynamic cost functions. Moreover, the RVM is utilized in the implementation and design of the proposed online local modelling owing to its short test time and sparseness. Furthermore, the leave-one-out cross-validation technique is used for local model validation, by which the modelling performance is further improved. The MLL-RVM is applied to a series of real data collected from a pragmatic UGI gasifier, and its effectiveness is verified.</p>
</abstract>

A modified lazy learning algorithm combined with a relevance vector machine (MLL-RVM) is presented to address a data-driven modelling problem for a gasification process inside a united gas improvement (UGI) gasifier. During the UGI gasification process, the measured online temperature of the produced crude gas is a crucial aspect. However, the gasification process complexities, especially severe changes in the temperature versus infrequent manipulation of the gasifier and the unknown noise in collected data, pose difficulties in dynamics process descriptions via conventional first principles. In the MLL-RVM, a novel weighted neighbour selection method is adopted based on the proposed dynamic cost functions. Moreover, the RVM is utilized in the implementation and design of the proposed online local modelling owing to its short test time and sparseness. Furthermore, the leave-one-out cross-validation technique is used for local model validation, by which the modelling performance is further improved. The MLL-RVM is applied to a series of real data collected from a pragmatic UGI gasifier, and its effectiveness is verified.

Data–driven online modelling for a UGI gasification process using modified lazy learning with a relevance vector machine

School of Electronic Engineering Yanshan University

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A modified lazy learning algorithm combined with a relevance vector machine (MLL-RVM) is presented to address a data-driven modelling problem for a...

Data–driven online modelling for a UGI gasification process using modified lazy learning with a relevance vector machine

Publié en ligne: 08 juil. 2021

Pages: 321 - 335

Reçu: 24 avr. 2020

Accepté: 09 févr. 2021

DOI: https://doi.org/10.34768/amcs-2021-0022

Mots clés
data-driven modelling, UGI gasification process, relevance vector machine, modified lazy learning

© 2021 Shida Liu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Data–driven online modelling for a UGI gasification process using modified lazy learning with a relevance vector machine

Publié en ligne: 08 juil. 2021

Pages: 321 - 335

Reçu: 24 avr. 2020

Accepté: 09 févr. 2021

DOI: https://doi.org/10.34768/amcs-2021-0022

Mots clésdata-driven modelling, UGI gasification process, relevance vector machine, modified lazy learning

© 2021 Shida Liu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
data-driven modelling, UGI gasification process, relevance vector machine, modified lazy learning