Online monitoring of cement clinker quality using multivariate statistics and Takagi-Sugeno fuzzy-inference technique

作者：Pani Ajaya Kumar^*; Mohanta Hare Krishna

来源：Control Engineering Practice, 2016, 57: 1-17.

DOI：10.1016/j.conengprac.2016.08.011

• Soft sensor
• Cement clinker
• Rotary kiln
• Neural network
• Fuzzy inference
• Multivariate outlier detection

This article addresses the issue of outlier detection in industrial data using robust multivariate techniques and soft sensing of clinker quality in cement industries. Feed-forward artificial neural network (back propagation, radial basis function and regression neural network) and fuzzy inference (Mamdani and Takagi-Sugeno (T-S)) based soft sensor models are developed for simultaneous prediction of eight clinker quality parameters (free lime, lime saturation factor, silica modulus, alumina modulus, alite, belite, aluminite and ferrite). Required input-output data for cement clinkerization process were obtained from a cement plant with a production capacity of 10000 t of clinker per day. In the initial data preprocessing activity, various distance based robust multivariate outlier detection techniques were applied and their performances were compared. The developed soft-sensors were investigated for their performance by computing various statistical model performance parameters. Results indicate that the accuracy and computation time of the T-S fuzzy inference model is quite acceptable for online monitoring of clinker quality.

• 出版日期2016-12