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

Abstract

Raw-material blending is an important process affecting cement quality. The aim of this process is to mix a variety of materials such as limestone, shale, sandstone and iron to produce cement raw meal for the kiln. One of the fundamental problems in cement manufacture is ensuring the appropriate chemical composition of the cement raw meal. A raw meal with a good fineness and well-controlled chemical composition by a control system can improve the cement quality. The first step in designing a control system for the process is obtaining an appropriate mathematical model. In this study, Linear and Nonlinear Neural Network models were investigated for the raw-material blending process in the cement industry and their results were compared with the experimental data. The results showed that the nonlinear model has a higher predictive accuracy. Keywords: mathematical modeling, cement, raw material blending, neural network Me{anje sestavin je pomemben postopek, ki vpliva na kvaliteto cementa. Naloga tega postopka je zme{ati razli~ne materiale, kot so: apnenec, {krilavec, pe{~enjak,`elezo in drugi; da se dobi surovino za cement za rotacijsko pe~. Ena od osnovnih te`av pri izdelavi cementa je zagotoviti primerno kemijsko sestavo surovine za cement. Kontrolni sistem za surovino z dobro zrnatostjo in dobro kontrolirano kemijsko sestavo, lahko izbolj{a kvaliteto cementa. Prvi korak pri postavitvi kontrole procesa je postavitev primernega matemati~nega modela procesa. V {tudiji sta bila preiskovana linearni in nelinearni model nevronske mre`e za postopek me{anja v cementni industriji in rezultati so bili primerjani z eksperimentalnimi podatki. Rezultati so pokazali, da ima nelinearni model ve~jo to~nost napovedovanja.