Identification and control of the raw material blending process in cement industry

Abstract

This paper deals with the identification and advanced control of the raw material blending process in cement industry. The process is multivariable and coupled one, because the feeder tanks do not contain homogeneous raw materials chemically. The time delays in the system are also considerable. The disturbances coming from the variations in the chemical compositions of the raw materials from long-term average compositions cause the changes of the system parameters. Therefore, for providing the target values of the oxide compositions of the raw meal determining the high quality of cement, the stochastic multivariable dynamic models are developed and model predictive controllers are designed to calculate the optimal feed ratios of the raw materials despite disturbances. This study consists of two parts; in the identification part, three different linear multivariable stochastic ARX models are proposed. The identification results show that these MISO and MIMO models are good models. In the control part, model predictive control strategy is applied. At the end of the simulation study, the output values reach the specified set points quickly. Also the significant decrease in the variance of controlled outputs is obtained.

A New Chemical Method to Predict Grindability Index (HGI) for Limestones

“…In the study of Mendis et al [21], chemical composition, moisture content and particle size are considered as the effective factors of raw material in terms of grindability and authors have claimed the fact that grindability is mainly affected by the chemical composition. According to Kural and Ozsoy [22], chemical and physical factors of raw material are the main reasons in terms of low grindability. Mendis et al [21] have addressed the percentages of SiO2, Al2O3, Fe2O3, CaO, MgO, Cl, SO3, Na2O, and K2O in limestones in order to assess relationships between grindability and chemical composition.…”

Modelling and simulation of raw material blending process in cement raw mix milling installations

“…Thus, maintaining the stability of the raw meal quality in superior levels is of high importance concluding that advanced raw mill control delivers improved economic performance in cement industry. A series of realizations comprises adaptive, model predictive and self-tuning controllers [13][14][15] and rule-based artificial intelligence techniques. Because of the complexity and significance of the control process, various automated systems are available for sampling and analyzing the raw mix as well as for adjustment of the mill weight feeders according to the raw meal chemical modules in RM outlet or inlet.…”

“…Several control strategies were developed during the last years using a variety of techniques. A series of realizations comprises adaptive, model predictive and self-tuning controllers [13][14][15] and rule-based artificial intelligence techniques. [16] Traditional PID controllers and other techniques approaching the PID logic were also implemented.…”

Mathematical modeling of a cement raw-material blending process using a neural network

“…The identification and control of the cement raw-material blending system in a cement factory were examined and in the identification part of the studies, three different linear multivariable stochastic time-series models (ARX) in which the inputs are the feed ratios of the raw-material components (low grade and iron ore) and the outputs are the iron oxide and/or the lime module of the raw meal, were constructed. 7 K. Kizilaslan et al 8 modeled the rawmaterial blending process in the cement industry using intelligent techniques and the results are compared with classic system-identification methods. A fuzzy controller is proposed to improve the real-time performance in the blending process.…”