Identification of non-linear predictor and simulator models of a cement rotary kiln by locally linear neuro-fuzzy technique

摘要

One of the most important parts of a cement factory is the cement rotary kiln which plays a key role in quality and quantity of produced cement. In this part of the process, the physical exertion and bilateral movement of air and materials, together with chemical reactions take place. Thus, this system has immensely complex and non-linear dynamic equations. These equations have not completely extracted yet. Even in special cases, however, a large number of the involved parameters were crossed out and an approximation model was presented instead. This issue caused many problems for designing a cement rotary kiln controller. In this paper, we present non-linear predictor and simulator models for a real cement rotary kiln by using non-linear identification technique on the locally linear neuro-fuzzy (LLNF) model. For the first time, a simulator model as well as a predictor one with a precise 15-minute horizon prediction for a cement rotary kiln are presented. These models are trained by LOLIMOT algorithm which is an incremental tree-structure algorithm. At the end, the characteristics of these models are expressed. Furthermore, we present the pros and cons of these models. The data collected from White Saveh Cement Company is used for modelling.