predicting the coal production rate of shearer device based on the gas properties and coal strength in tabas no. 1 parvade coal mine

To increase production in coal mining panels along with the use of other equipment, the use of coal machines (shearers) is very beneficial. predicting the shearer rate and determining the effective parameters in it plays an essential role in estimating costs. full knowledge of the strength and properties of coal gas and evaluation of the performance of shearer loader devices causes an increase in the speed of the loader and coal-rock production. therefore, to achieve high production efficiency in the extraction of coal seams, it is necessary to predict the shearer rate and determine the effective parameters in it. in this paper, the shear rate prediction in relation to the strength and gas bitumen properties of coal is investigated with the help of statistical analysis . for this purpose, 1260 types of coal cutting were done by coal machine (shearer) in e3 tabas extraction panel no. 1 of parvadeh coal mine. in the first stage, after harvesting and recording the shearer rate of each cut, information about degassing was done at three points along the entire length of the panel. these three points include the percentage of methane gases emitted in sensor number 88 and the input sensor (tg) and the sensor installed on the armored face conveyor (afc). then, using the strength properties such as coal hardness and methane degassing system, the shearer rate prediction was investigated. using statistical studies, shearer rate prediction was performed with three models of linear and nonlinear multivariate regression (exponential and logarithmic). to develop the predicted models, 70% of the data (882 data) were used as educational data and 30% of the data (378 data) as test data. among the three regression models performed, the results show that the linear multivariate regression model has a more accurate prediction than the other two methods. therefore, using the linear multivariate regression model, the amount of shearer rate in the coal mine number one of parvadeh tabas can be predicted with good accuracy.

Predicting the coal production rate of shearer device based on the gas properties and coal Strength in Tabas No. 1 Parvade coal mine

To increase production in coal mining panels along with the use of other equipment, the use of coal machines (shearers) is very beneficial. Predicting the shearer rate and determining the effective parameters in it plays an essential role in estimating costs. Full knowledge of the Strength and properties of coal gas and evaluation of the performance of shearer loader devices causes an increase in the speed of the loader and coalrock production. Therefore, to achieve high production efficiency in the extraction of coal seams, it is necessary to predict the shearer rate and determine the effective parameters in it. In this paper, the shear rate prediction in relation to the Strength and gas bitumen properties of coal is investigated with the help of statistical analysis.For this purpose, 1260 types of coal cutting were done by coal machine (shearer) in E3 Tabas extraction panel No. 1 of Parvadeh coal mine. In the first stage, after harvesting and recording the shearer rate of each cut, information about degassing was done at three points along the entire length of the panel. These three points include the percentage of methane gases emitted in sensor number 88 and the input sensor (TG) and the sensor installed on the armored face conveyor (AFC). Then, using the strength properties such as coal hardness and methane degassing system, the shearer rate prediction was investigated. Using statistical studies, Shearer rate prediction was performed with three models of linear and nonlinear multivariate regression (exponential and logarithmic). To develop the predicted models, 70% of the data (882 data) were used as educational data and 30% of the data (378 data) as test data. Among the three regression models performed, the results show that the linear multivariate regression model has a more accurate prediction than the other two methods. Therefore, using the linear multivariate regression model, the amount of shearer rate in the coal mine number one of parvadeh Tabas can be predicted with good accuracy.