A novel support vector machine ensemble model for estimation of free lime content in cement clinkers. (April 2020)

• Record Type: Journal Article
• Authors: Liu, Xiaoyan; Jin, Jiao; Wu, Weining; Herz, Fabian
• Is Part Of: ISA transactions. Volume 99(2020)
• Page Start: 479
• Page End: 487
• Publication Date: 2020-04
• Subjects: Free lime content -- Soft sensor -- Support vector machine -- Fuzzy c-means clustering -- Ensemble model; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2019.09.003
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: Free lime (f-CaO) content is a crucial quality parameter for cement clinkers in rotary cement kiln. Due to lack of hardware sensors, f-CaO content in cement clinker is mostly obtained by offline laboratory measurement, making timely control rather difficult and even impossible. In this work, a soft sensor approach named as support vector machine ensemble (ESVM) model is proposed to estimate f-CaO content. The process data employed to train and test the model were collected from a cement plant in China, covering a time span of about 30 days. The raw data were preprocessed by filters and time-series matching. The processed data were then clustered by fuzzy c-means clustering algorithm to capture process features at different operating conditions. For each individual cluster, a base SVM regressor was trained to estimate f-CaO content. Finally, an ensemble model consisting of four base SVM regressors was established to estimate f-CaO content at multifarious process conditions. The effectiveness of the proposed ESVM model was investigated by comparing it with manual measurements and other models available in literature. The results demonstrate that the proposed ESVM model achieves improvements in model accuracy as well as generalization capability. The proposed ESVM model has a broad application space in cement production process for automatic monitoring of f-CaO content. Highlights: A novel SVM ensemble model for cement clinker quality estimation is developed. Process features at various operating conditions are captured by the model. The effect of time lag between inputs and output is considered in the model. The model is compared with soft sensor models available in literature. The prediction accuracy and generalization ability of the model are improved.