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.