A hybrid soft sensing approach of a cement mill using principal component analysis and artificial neural networks

Soft sensors play an important role in predicting the values of unmeasured process variables from knowledge of easily measured process variables. Online estimation of particle size is vital for efficient control of a grinding circuit. Due to high energy consumption in cement grinding processes and unavailability of reliable hardware sensors for continuous monitoring, soft sensors have tremendous scope of application in cement mills. Modern cement plants are increasingly using vertical roller mills for clinker grinding. While there have been some works reported in the literature about modelling of ball mills, very few research work is available on vertical roller mill modelling. In the present work a PCA based neural network model of a cement mill is developed based on the actual plant data for estimation of cement fineness. Real time data for all process variables relevant to cement grinding process were collected from a cement plant having a clinker grinding capacity of 235 TPH. The collected raw industrial data were pre processed for outlier removal and missing value imputation. Principal component analysis of the input data was performed to transform the original variables to a less number of un correlated principal components. The selected principal component scores were divided to a training set and a validation set using Kennard-Stone subset selection algorithm. The training set was used to develop a back propagation neural network model which was subsequently tested with the validation set. Simulations results show satisfactory prediction capabilities of the developed model over that of linear regression and principal component regression models.