Online prediction of dense medium suspension density based on phase space reconstruction

In the dense medium separation process of the coal preparation plant, the fluctuation of raw coal quality and the lag of heavy media separation density adjustment often lead to the instability of clean coal quality. To solve this problem, phase space reconstruction with online data was carried out to derive a mathematical formula of working dense medium density with raw coal ash and clean coal ash produced. Then the data was trained by two ways, namely Least Square Support Vector Machine (LS-SVM) and LS-SVM based on time series. Simulation results show that the latter way can achieve a more accurate prediction of heavy medium suspension density.