Soft sensor for apparent degree of calcination based on ANN

Abstract

Soft sensor technique is used to solve the problem of measuring the on-line apparent degree of calcination in New Suspension Preheater Dry Process (NSP) Kiln based on BP neural network in this paper. According to the actual working conditions of the calcinations, a soft sensor model with six Dimensional input vector and one Dimensional output vector is established by using Back-Propagation (BP) neural network. The Reliability and prediction accuracy of the model are verified and compared based on actual data. The results of the experiment show that the prediction accuracy of this soft sensor model can reach 96%. So the on-line measurement of the apparent degree of calcination in NSP Kiln can be realized by this soft sensor model.