Surrounding Rock Stability Classification Method of Coal Roadway Based on In Situ Stress

In situ stress is one of the most important factors affecting surrounding rock stability classification of coal roadway. Most surrounding rock stability classification methods do not fully consider the influence of in situ stress. In this paper, the author applied a fuzzy clustering method to the classification of surrounding rock stability of coal roadway. Taking into account the complexity of the classification of surrounding rock, some factors such as the strength of surrounding rock, in situ stress, the main roof first weighting interval, the size of the chain pillar, and the immediate roof backfilled ratio are selected as the evaluation indexes. The weight coefficients of these evaluation indexes are determined by unary regression and multiple regression methods. Using fuzzy clustering and empirical evaluation method, the classification model of surrounding rock stability of coal roadway is proposed, which is applied to 37 coal roadways of Zibo Mining Group Ltd., China. The result is in good agreement with practical situation of surrounding rock, which proves that the fuzzy clustering method used to classify the surrounding rock in coal roadway is reasonable and effective. The present model has important guiding significance for reasonably determining the stability category of surrounding rock and supporting design of coal roadway.

Odour Impact Assessment in a Changing Climate

(1) Background: The impact of odour sources as stock farms on neighbouring residential areas might increase in the future because the relevant climatic parameters will be modified due to climate change. (2) Methodology: Separation distances are calculated for two Central European sites with considerable livestock activity influenced by different orographic and climatic conditions. Furthermore, two climate scenarios are considered, namely, the time period 1981–2010 (present climate) and the period 2036–2065 (future climate). Based on the provided climatic parameters, stability classes are derived as input for local-scale air pollution modelling. The separation distances are determined using the Lagrangian particle diffusion model LASAT. (3) Results: Main findings comprise the changes of stability classes between the present and the future climate and the resulting changes in the modelled odour impact. Model results based on different schemes for stability classification are compared. With respect to the selected climate scenarios and the variety of the stability schemes, a bandwidth of affected separation distances results. (4) Conclusions: The investigation reveals to what extent livestock husbandry will have to adapt to climate change, e.g., with impacts on today’s licensing processes.

Fuzzy rule based classification method of surrounding rock stability of coal roadway using artificial intelligence algorithm

As the stability of surrounding rock of coal roadway is affected by many factors, which makes the classification result hard to be consistent with the field practice. To solve the above problems, this paper proposes a method for the classification of stability of rock which is present in roadway of coal using the artificial intelligence algorithm. In this paper, the influencing factors of stability of rock which is present in roadway are analyzed, and seven influential factors are selected as classification indexes. To solve the problem of slow convergence speed and easy to fall into the local minimum of the back propagation artificial neural network (BP-ANN), an improved BP-ANN algorithm based on additional momentum and Levenberg-Marquardt optimization is proposed based on the analysis of the existing improved methods, which improves the convergence speed and avoids the local minimum effectively. Based on the learning model available, classification system based on fuzzy rule have been implemented and yielded better behavior in the situation of uncertain data sets. Finally, the stability classification model of surrounding rocks of coal roadway using BP-ANN was established in MATLAB environment, and the model was applied to 13 data samples of coal roadway for testing, with the identification rate of 92.3%. The experimental results verify that the method proposed based on fuzzy rule classification system in this paper has a high accuracy of type identification and is applicable to the stability classification of surrounding rock in the coal roadway.

Classification and Application of Roof Stability of Bolt Supporting Coal Roadway Based on BP Neural Network

In view of the frequent occurrence of roof accidents in coal roadways supported by bolts, the widespread application of bolt support technology in coal roadways has been restricted. Through on-site investigation, numerical analysis, and other research methods, 6 evaluation indicators were determined, and according to the relevant evaluation factors and four types of coal roadway roof stability, a neural network structure for roof stability prediction was constructed to realize the quantitative prediction of the roof stability of bolt-supported coal roadway. The method of adding momentum is used to improve the BP neural network algorithm. After passing the simulation test, it is applied to the field experiment of the roof stability classification. In order to facilitate on-site application, on the basis of the established BP neural network prediction model, a coal mine roof stability classification software recognition system was developed. Using the developed software system, the stability of coal roadway roof is classified into mine, coal seam, and region. According to the recognition result, the surfer software is used to draw the contour map of the stability of the roof of each coal mining roadway. The classification results are consistent with the actual situation on site.

The atmospheric turbulence characteristics in a diurnal cycle

During a whole-day period, profiles of mean wind speed, wind shear and turbulence level shows great variability due to continuously varying atmospheric stability. Clearly understanding the spatial and temporal behaviors of the atmospheric wind flow is of great importance for science purposes. Large-eddy simulation (LES) technique is employed here to reproduce the evolution of atmospheric flow during a diurnal cycle. With the obtained LES results, wind characteristics in terms of wind speed, wind shear, turbulence intensity and turbulent kinetic energy can be examined referring to the stability classification. Besides, wind profiles obtained using currently available engineering models are also included for comparison. Disparities between the model predictions and the LES results illustrate that the standard engineering models cannot well capture the wind characteristics driven by the varying atmospheric stability solely, and a further improvement in models is highly needed.