Monitoring method for gasification process instability using BEE-RBFNN pattern recognition. (26th April 2021)

• Record Type: Journal Article
• Authors: Zhang, Jinchun; Zhang, Zichuan; Hou, Jinxiu
• Is Part Of: International journal of hydrogen energy. Volume 46:Number 29(2021)
• Page Start: 16202
• Page End: 16216
• Publication Date: 2021-04-26
• Subjects: Gasification process -- Instability monitoring -- Pattern recognition -- Radial basis neural network -- Bees algorithm; Hydrogen as fuel -- Periodicals; Hydrogène (Combustible) -- Périodiques; Hydrogen as fuel; Periodicals; 665.81
• DOI: 10.1016/j.ijhydene.2021.02.047
• Languages: English
• ISSNs: 0360-3199

Abstract

Abstract: To identify large fluctuations of parameters in the process of gasification, a control chart pattern recognition method based on optimized radial basis function neural network (RBFNN) is proposed in this paper to conduct pattern recognition of the parameters of gasification process. The proposed method is described in four aspects, feature description, feature extraction, classifier and algorithm learning and training. The initial data is described from shape features and statistical features to reduce the dimension of the data. The optimal characteristic set is selected by the association rule mining algorithm to reduce the complexity of the model. The performance of neural network is affected by the learning method. With RBFNN as a classifier, a learning method based on Bees algorithm is therefore put forward and then training recognition is compared between the proposed method and the traditional method. The results show that the proposed method has higher recognition rate and simpler structure than the traditional method, and it is very effective in monitoring and identifying abnormal fluctuations of gasification process parameters. Highlights: A control chart pattern recognition method based on BEE-RBFNN was proposed. The parameter instability pattern of gasification process was conducted using the proposed method. The proposed method can accurately identify the abnormal pattern of parameter.