Heavy medium separation density identification based on heavy-tailed noise distribution

During the process of heavy-medium separation of coal, the identification of the separation density is likely to be contaminated by heavy-tailed noise. To address this issue, the ARX model and the student′s t distribution model are used to identify the separation density and the heavy-tailed noise involved in the separation density identification system. Then the identification procedure is turned formulized using the Expectation Maximum (EM) algorithm. The effectiveness of the separation density identification model developed based on the derived parameters is validated through simulation analysis. Analysis shows that compared with the traditional maximum likelihood estimation (MLE) method, the use of the EM algorithm with an indication of its higher robustness can effectively tackle problems regarding implicite variables and data loss; the bias norm (BN) and variance norm (VN) of the EM algorithm are all lower than those of the MLE method; the estimated model parameters derived using EM algorithm can be converged to approximately the true values after finite iteration operation, well demonstrating the effectiveness of the heavy-tailed noise identification method. The study made herein can help enhance to a certain degree the medium suspension density automatic detection accuracy in the process of heavy-medium separation of coal.