Abstract
Hydraulic support is the primary equipment used for surrounding rock control at fully mechanized mining faces. The load, location, and attitude of the hydraulic support are important sets of basis data to predict roof disasters. This paper summarized and analyzed the status of coal mine safety accidents and the primary influencing factors of roof disasters. This work also proposed monitoring characteristic parameters of roof disasters based on support posture-load changes, such as the support location and support posture. The data feature decomposition method of the additive model was used with the monitoring load data of the hydraulic support in the Yanghuopan coal mine to effectively extract the trend, cycle period, and residuals, which provided the period weighting characteristics of the longwall face. The autoregressive, long-short term memory, and support vector regression algorithms were used to model and analyze the monitoring data to realize single-point predictions. The seasonal autoregressive integrated moving average (SARIMA) and autoregressive integrated moving average (ARIMA) models were adopted to predict the support cycle load of the hydraulic support. The SARIMA model is shown to be better than the ARIMA model for load predictions in one support cycle, but the prediction effect of these two algorithms over a fracture cycle is poor. Therefore, we proposed a hydraulic support load prediction method based on multiple data cutting and a hydraulic support load template library. The constructed technical framework of the roof disaster intelligent prediction platform is based on this method to perform predictions and early warnings of roof disasters based on the load and posture monitoring information from the hydraulic support.
Highlights
Numerous studies have considered the problem of surrounding rock control and roof disaster prevention
The column pressure monitoring data of the No.54 hydraulic support in the middle of the 30112 longwall face of Yanghuopan coal mine in Shaanxi Province are used to analyze the support load based on the data modeling method, which provides the basis for roof disaster predictions
The autoregressive integrated moving average (ARIMA) model requires that the data must be stationary, which means the mean and variance must not change over time
Summary
Longwall mining is the most common type of coal mining technology throughout China. The excavation breaks the original stress balance in the surrounding rock, which gives the overlying strata periodic fracture instability. Wang et al (2016, 2017) studied the process of roof stratum breakdown instability and dynamic evolutions of the hydraulic support load They introduced the “stiffness-strength-stability coupling model” between the hydraulic support and surrounding rock, which provides an approach to dynamically analyze and predict hydraulic support loads in longwall mining faces. Trueman et al (2008, 2009, 2010) developed the Longwall Visual Analysis (LVA) system to extract the time-weighted working resistance of the hydraulic support, initial support force, opening times of the safety valve, and other parameters They studied the influence of the buried depth, longwall face width, and other factors on the interactions between the hydraulic support and surrounding rock. The technical architecture of the roof disaster intelligent prediction platform was built, and the data modeling and roof disaster prevention technologies were integrated as new methods for roof disaster predictions
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