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Paper details
Number 2 - June 2021
Volume 31 - 2021
Data-driven online modelling for a UGI gasification process using modified lazy learning with a relevance vector machine
Shida Liu, Honghai Ji, Zhongsheng Hou, Jiashuo Zuo, Lingling Fan
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.
Keywords
data-driven modelling, UGI gasification process, relevance vector machine, modified lazy learning