COMPARISON OF NEURAL NETWORK AND SUPPORT VECTOR REGRESSION MODELING OF HEAD TEMPERATURE OF DRY ROTARY CEMENT PLANT KILN, Abdolhossein Khosrozade, Nasir Mehranbod
Head temperature of rotary cement plant kiln is one of the most important variables by which clinker quality can be determined. It is very hard to develop a first principal model for prediction of kiln head temperature due to the presence of nonlinearity, time lag, and hysteresis in kiln operation. ten process variables were identified that affect kiln head temperature significant enough to be included in model development. Eighty percent of randomly selected plant data is used for training, optimization and testing and the balance of 20% is used for model validation. Support Vector Regression (SVR) and Artificial Neural Networks (ANN) are utilized to develop models for eight different sets of feature variables dictated by PCA and SVM to predict kiln head temperature. The parameters of these two models are optimized by Genetic Algorithm (GA) method and model predictions are compared. SVR-based model predictions with a minimum and maximum average absolute relative error of 0.742% and 1.413% outperformed ANN-based models.