Abstract

In order to solve the problem that the temperature field in cement rotary kiln can not be monitored in real time,this paper proposes a novel soft-sensing method for temperature field in rotary kiln based on mechanism-and-data-driven.This paper firstly uses the CFD (Computational Fluid Dynamics) technology to establish a finite element model of the rotary kiln to simulate the combustion process in the kiln,and calculates the temperature field in the kiln under the different conditions of temperature of secondary wind,feed coal amount,and velocity of swirl and axis wind.Then, the BPNN (BP Neural Network)is trained using the data set obtained from the numerical simulation of the finite element model, thereby obtaining a soft-sensing model of the temperature field in the kiln.The method can predict the temperature distribution in the kiln in real time by several measurable variables in the field,and the visualization of temperature distribution in rotary kiln is given.

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