Soft-Sensor Modeling for Separation Performance of Dense-Medium Cyclone by Field Data

Four measurable parameters, that is, density of dense-medium suspension (d), inlet pressure of dense-medium suspension (p), content of magnetic substance (c), and coal feed rate (r) were adopted to build a soft-sensor model for calculating the two performance index of a dense-medium cyclone in a Taixi plant. Uniform design was adopted to reduce the number of experiments. The models of actual separation density (delta(p)) and probable error (E-p) obtained by genetic algorithm and regression were proved to be basically right by the 12 training records and another test result. The accuracy of the delta(p) model was 0.7% for the training set and 0.63% for the test data while that of the E-p model was 9.41% and 13.54%, respectively. The behavior of the models were in accordance with field experiences, which showed that p had the most significant effect on E-p and c affected delta(p) most prominently in daily operation.