Data-driven online modelling for a UGI gasification process using modified lazy learning with a relevance vector machine - Liu, Shida; Ji, Honghai; Hou, Zhongsheng; Zuo, Jiashuo; Fan, Lingling (Zielona Góra: Uniwersytet Zielonogórski)
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.
Keywords: data-driven modelling; UGI gasification process; relevance vector machine; modified lazy learning