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Borehole imaging, one of the main methods of rock formation detection, is widely used in mining, tunnels, petroleum, and other fields of engineering. However, the method of manually identifying the characteristic of rock formation based on borehole imaging is greatly affected by subjective factors. For example, it should be noted that when the rock layer color is similar, the position of the rock interface may be different. In this paper, a method of quantitatively describing the characteristics of the rock formation is proposed, and the automatic recognition of the rock interface is realized. Firstly, the calculation method of rock dip angle and position based on borehole image with 360° wall surface development is proposed. Secondly, the three-dimensional distribution characteristics of the gray values of the rock layer are used to quantitatively describe the characteristics of the rock layer. Finally, an automatic recognition method of rock formation interface based on change-point detection algorithm is proposed. In addition, the recognition effect of the rock formation interface is analyzed by experiments and applied in the field. Research shows that, compared with method two, method one can determine the rock inclination. But its anti-interference ability is poor. Method two can better determine the position of the rock interface. The method is of great significance to the intelligent development of the borehole imaging system and promotes safe, efficient, and sustainable mining in coal mines [ABSTRACT FROM AUTHOR]

Sedimentary rocks provide information on previous environments on the surface of the Earth. As a result, they are the principal narrators of the former climate, life, and important events on the surface of the Earth. The complexity and cost of direct destructive laboratory tests adversely affect the data scarcity problem, making the development of intelligent indirect methods an integral step in attempts to address the problem faced by rock engineering projects. This study established an artificial neural network (ANN) approach to predict the uniaxial compressive strength (UCS) in MPa of sedimentary rocks using different input parameters; i.e., dry density (ρd) in g/cm3, Brazilian tensile strength (BTS) in MPa, and wet density (ρwet) in g/cm3. The developed ANN models, M1, M2, and M3, were divided as follows: the overall dataset, 70% training dataset and 30% testing dataset, and 60% training dataset and 40% testing dataset, respectively. In addition, multiple linear regression (MLR) was performed for comparison to the proposed ANN models to verify the accuracy of the predicted values. The performance indices were also calculated by estimating the established models. The predictive performance of the M2 ANN model in terms of the coefficient of determination (R2), root mean squared error (RMSE), variance accounts for (VAF), and a20-index was 0.831, 0.27672, 0.92, and 0.80, respectively, in the testing dataset, revealing ideal results, thus it was proposed as the best-fit prediction model for UCS of sedimentary rocks at the Thar coalfield, Pakistan, among the models developed in this study. Moreover, by performing a sensitivity analysis, it was determined that BTS was the most influential parameter in predicting UCS. [ABSTRACT FROM AUTHOR]

Hardgrove Grindability Index (HGI) is a determining factor of energy consumption during grinding. The current study is aimed at investigating the influence of the blending behavior on HGIs of two dissimilar coal samples. In this study, one local coal sample is taken from Lakhra coal deposits and second sample is extracted from Indonesian coal reserves for empirical experiment and analysis. The total number of nine blends with different blending ratios based on the Net Calorific Value (NCV) was made. In addition, the samples were further tested for proximate and ultimate analysis, Gross Calorific Values and HGI respectively. The ASTM D409-D409M standard Hardgrove grindability tester was used for the determination of HGI of cited two coal samples. The main purpose of this study is to determine the blends that specify the optimum results those are appropriate for power sectors and cement industries. According to the above study, the blend no 2 and 3 demonstrate the optimum results for cement factories with approximately HGI 46 and 49 with sulphur content 1.01 and 1.17, whereas blend no 4 and 5 are appropriate for power sectors with approximately HGI 52 and 54 with sulphur content 1.37 and 1.58. Furthermore, the value of the blends for HGI and moisture content shows the inverse relation, as the moisture content increases HGI value decreases. Similarly, research results also determine that there is also inverse relationship between the blend value of volatile matter and total carbon with HGI. Additionally, the GCV also exhibits inverse relationship while ash and fixed carbon reveal direct relationship with the value of blends for HGI. In the same manner, sulfur also exhibits direct relation with HGI value. [ABSTRACT FROM AUTHOR]