A novel model variable selection method based on energy variation and its application to predictive modeling for achromic power

Achmoric power, one of the most important quality indices in a Lithopone calcination process, cannot be measured online economically, which is a similar problem many rotary kiln processes encounter. This paper talks about its predictive model by applying data modeling technique and focuses on the model variable selection. According to the Lithopone calcination mechanism, several schemes of model variable selection are discussed, and the one based on energy variation which mixes the information of calcinating temperature and calcinating duration together is chosen at last. To calculate the energy absorbed by the material, ldquounit energyrdquo is introduced, which offers a convenient expression in the form of relative value. Using this novel model variable selection method, the model structure is simplified, which has an advantage in model efficiency. A predictive model for achmoric power is obtained in the next step by least square support vector machines method, and the simulation result shows its promising performance.