Analysis and Prediction of Influencing Parameters on the Coal Classification Performance of a Novel Three Products Hydrocyclone Screen (TPHS) Based on Grey System Theory

Abstract

A novel hydrocyclone including a cylindrical screen embedded in a conventional hydrocyclone (CH), named three products hydrocyclone screen (TPHS), has been successfully designed. In TPHS, the combination of centrifugal classification and screening was employed to separate particles. In this paper, Grey theory, as an effective means to the laws of both complex and uncertainty system’s behavior with small samples, was used to investigate the operational (feed concentration and feed pressure) and structural (aperture size, spigot diameter, and vortex finder diameter) parameters on performance evaluation Hancock classification efficiency (HE), imperfection (I), and cut size (d50c). The experiments of coal sample (0–1 mm) show that TPHS with coarser particles in underflow exhibited the absent “fish-hook”. The closeness calculated using the Grey System algorithm indicates that the performance of TPHS was closely related to the operation and structure parameters. Further, the order of grey incidence degree between different parameters and HE (or I or d50c) is the spigot diameter and aperture size with the highest value, the feed pressure and vortex finder diameter with the middle value, and the feed concentration with the lowest value. The prediction using the GM(1, N) algorithm implies that the dynamic prediction model for the performance evaluation can be created depending on the operation, structure and previous performance value. The mean relative errors between the predicted and actual HE, I, and d50 were 2.84%, 5.83%, and 3.57%, respectively, which exhibit the accurate prediction.

Analysis of the Characteristics of Fatal Accidents in the Construction Industry in China Based on Statistical Data

“…The original data on deaths in the construction industry are X (0) = (772, 738, 624, 653, 648, 554, 735, 807, 840, 904). The grey coefficients a = −0.0424 and b = 559.9831 can be calculated based on grey theory [ 27 ]. Therefore, the white equation for the GM(1,1) model is shown below: where t indicates time.…”

“…In addition, the initial condition of the first value of the first-order accumulating generation operator (1-AGO) data was chosen as only the first value of the original data in the GM(1,1) model [ 27 ]. Future research should focus on the influence of the initial condition on the prediction results.…”

“…First, the appropriate prediction method should be determined. There are many prediction methods, such as the grey model [ 27 ], neural networks [ 31 ], and linear and nonlinear equations [ 32 ]. Of these prediction methods, the GM(1,1) model has many advantages, such as the requirement of a small amount of data, a simple modeling process, and ease of learning and use.…”

“…Of these prediction methods, the GM(1,1) model has many advantages, such as the requirement of a small amount of data, a simple modeling process, and ease of learning and use. The GM(1,1) model has been adopted to solve various prediction problems in production and life [ 27 ]. In this study, the GM(1,1) model was adopted to predict deaths in construction accidents in 2020.…”

“…In this study, we adopted clustering analysis [ 26 ] to rank the severity of the fatal construction accidents in different provinces. In addition, we used the GM(1,1) model [ 27 ] used to predict possible deaths from construction accidents in 2020 ( Figure 1 ).…”