Intelligent dynamic modeling for online estimation of burning zone temperature in cement rotary kiln

Abstract

Cement rotary kiln is a complex multivariable, large-disturbances and nonlinear system which is full of mass transfer, heat transfer, and physical and chemical reactions. The burning zone temperature (BZT) in cement rotary kiln is a very important production index and has a significant role on the quality of the clinker. However, the BZT is generally difficult to be measured online using the conventional instruments. Although the BZT can be detected by using the expensive infrared pyrometer which located at the kiln head hood, it generally loses veracity due to the complex dynamics of the cement rotary kiln. Obviously, such an inaccurate measurement may guide the operator to do some improper operations in practice. To attack such a practical engineering problem, an intelligence-based dynamic soft-sensor modeling approach is proposed to online estimate the BZT in cement rotary kiln in this paper. The proposed approach mainly includes two digital filters which are used to pre-process the original measurement data, and an intelligent CBR soft-sensor system which is adapted to online predict the BZT in time, according to the measured secondary variables. At last, industrial tests have been performed to demonstrate the good estimation performance of the proposed method for a real cement rotary kiln process.