Multi step prediction of dense medium clean coal ash content based on time series alignment and TCNformer

Abstract

Due to the different positions of various sensors during the dense medium separation process, there is a time lag between the main process parameters of dense medium separation and ash content, which affects the results of clean coal ash content. The grey prediction method based on regression models lacks the utilization of time series information and cannot capture the dynamic features of the dense medium production process over time. The time series based ash prediction method fails to fully consider the time dependence relationship between the main process parameters of ash content and dense medium separation. In order to solve the above problems, a multi step prediction method for dense medium clean coal ash content based on time series alignment and TCNformer is proposed. The method quantifies the lag step between the main process parameters of ash content and dense medium separation through lag correlation analysis. The method moves the main process parameters of dense medium separation in the time dimension accordingly, aligning the time series of the main process parameters of ash content and dense medium separation, and eliminating the time lag between the main process parameters of ash content and dense medium separation. On the basis of the Transformer model, a time convolutional network (TCN) is introduced to extract features, and the unidirectional encoder is extended to a bidirectional encoder to construct the TCNformer model for multi-step prediction of clean coal ash content. The sequence of process variables corresponding to the grey data at future moments obtained from the time series alignment is used as an input to the decoder to improve the model prediction precision. The experimental results show that the average absolute error of this method is 0.157 9%, the root mean square error is 0.215 2%, and the average Pearson correlation coefficient is 0.505 1, which can effectively improve the precision of predicting clean coal ash content.

Keywords

• dense medium separation
• prediction of clean coal ash content
• lag correlation
• time series
• tcnformer
• bidirectional encoder