Data driven soft sensor of a cement mill using generalized regression neural network

Soft sensors play an important role in predicting the values of unmeasured process variables from knowledge of easily measured process variables. Most of the present day soft sensors for complex chemical processes are designed from actual industrial data because of the various difficulties associated with developing first principle models such as poor process understanding, impossible or difficult to determine model parameters and mathematical complexity of the models. No hardware sensors for online estimation of particle size is available for solid grinding units. In the present work a generalized regression neural network based model of a cement mill is developed based on the actual plant data for estimation of cement fineness. The collected raw industrial data is pre processed to get rid of the outliers and missing values followed by model development. Optimal selection of smoothing parameter for the regression model has been done by investigating the model performance at different spread values. Simulations results show satisfactory prediction capabilities of the developed model over that of linear regression model and quadratic response surface model.