Liu Xiaoyan, Jin Jiao, Wu Weining, Herz Fabian
College of Electrical and Information Engineering, Hunan University, 410082 Changsha, China; Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing, 410082 Changsha, China.
College of Electrical and Information Engineering, Hunan University, 410082 Changsha, China.
ISA Trans. 2020 Apr;99:479-487. doi: 10.1016/j.isatra.2019.09.003. Epub 2019 Sep 3.
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.
