Autor:

Liu, Shida ; Ji, Honghai ; Hou, Zhongsheng ; Zuo, Jiashuo ; Fan, Lingling

Współtwórca:

Korbicz, Józef (1951- ) - red. ; Uciński, Dariusz - red.

Tytuł:

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

Tytuł publikacji grupowej:

AMCS, volume 31 (2021)

Temat i słowa kluczowe:

data-driven modelling ; UGI gasification process ; relevance vector machine ; modified lazy learning

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.

Wydawca:

Zielona Góra: Uniwersytet Zielonogórski

Data wydania:

2021

Typ zasobu:

artykuł

DOI:

10.34768/amcs-2021-0022

Strony:

321-335

Źródło:

AMCS, volume 31, number 2 (2021) ; kliknij tutaj, żeby przejść

Jezyk:

eng

Licencja CC BY 4.0:

kliknij tutaj, żeby przejść

Prawa do dysponowania publikacją:

Biblioteka Uniwersytetu Zielonogórskiego