Soft-sensing for calcining zone temperature in rotary kiln based on model migration

The calcining zone temperature in a rotary kiln is a very important process parameter to control the kiln, but the temperature is so difficult to be measured directly and no sufficient measured data are available to soft-sensing. For the purpose of obtaining the accurate soft-sensing model in case the data are less with the temperature similarity between kiln head and calcining zone taken into consideration, the process modeling based on process similarity (PMBPS) is introduced, i. e., the rotary kiln head temperature model is developed with lots of accurately measured values involved by T-S fuzzy neural network which is trained by the chaotic hybrid learning algorithm, then the PMBPS algorithm is applied to the kiln head temperature model to correct its deviation as a model migration algorithm so as to obtain the soft-sensing model of calcining zone temperature. Simulation results verified the effectiveness of the modeling method of soft-sensing.