Identification of Nonlinear Predictor and Simulator Models of a Cement Rotary Kiln by Locally Linear Neuro-Fuzzy Technique

Abstract

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, the physical exertion and bilateral movement of air and materials, together with chemical reactions take place. Thus, this system has immensely complex and nonlinear dynamic equations. These equations have not worked out yet. Only in exceptional case; 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 presented nonlinear predictor and simulator models for a real cement rotary kiln by using nonlinear 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 fifteen minute prediction horizon for a cement rotary kiln is 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 presented the pros and cons of these models. The data collected from White Saveh Cement Company is used for modeling.

Genetically optimized extreme learning machine

“…Outside the kiln these clinkers are cooled and grounded to produce cement [20]. The control of the temperature inside the kiln is crucial: insufficiently high maximum temperatures in the kiln result in incompletely reacted products and poorquality cement, while excessive maximum temperatures waste energy and propitiate the formation of NO x pollutant compounds that have several environmental impacts [19].…”

“…If V is ill-conditioned, problem (19) should be preferred over problem (18) because the solution is numerically more stable [1]. Furthermore, using the Tikhonov's regularization, the robustness of the least squares solution against noise is improved.…”

On-line sequential extreme learning machine based on recursive partial least squares

“…Outside the kiln these clinkers are cooled and grounded to produce cement [16]. The control of the temperature inside the kiln is crucial: insufficiently high maximum temperatures in the kiln result in incompletely reacted products and poor-quality cement, while excessive maximum temperatures waste energy and propitiate the formation of NO x pollutant compounds that have several negative environmental impacts [17].…”