Abstract
In the traditional optimization mathod, the process control parameters for fully mechanized mining face are determined by experts or technicians based on their own experience, which is lack of scientific basis, and need long production adjustment cycle. It is cause large loss, and not conducive to improving mine production efficiency. In order to solve this problem, the study proposes a process control parameter optimization method based on a mixed strategy of artificial neural network and genetic algorithm and uses a cross-entropy cost function to optimize an artificial neural network, which improves the learning speed and fitting accuracy of the neural network. Using the historical production data of a fully mechanized coal mining face, taking the pulling speed of the shearer, hydraulic support moving speed, chain speed of scraper conveyor, chain speed of stage loader, emulsion pump outlet pressure, and spray pump outlet pressure as the optimization objects and taking the value range of each process control parameter as a constraint condition to establish a mixed strategy optimization model of process control parameters for a fully mechanized mining face, each process control parameter is optimized with the output of coal per minute as the optimization goal. The results show that the method has high accuracy and short optimization process time and can effectively improve the production efficiency of the working face.
Highlights
Dataset 1 Description. is dataset is the historical production data of the key process control parameters of each production equipment collected by the sensors at the production site of the fully mechanized mining face, which are the pulling speed of shearer (PSS), hydraulic support moving speed (HSMS), chain speed of scraper conveyor (CSSC), chain speed of stage loader (CSSL), emulsion pump 1 outlet pressure (EPOP1), emulsion pump 2 outlet pressure (EPOP2), spray pump 1 outlet pressure (SPOP1), and spray pump 2 outlet pressure (SPOP2), and the comprehensive production index is the output of coal per minute (OCPM)
We use the deviation standardization method to standardize the data; the standardized data of the PSS, HSMS, CSSC, CSSL, EPOP1, EPOP2, SPOP1, and SPOP2 were used as the input of the artificial neural network; and the OCPM is used as the output of the artificial neural network. en, establish the improved neural networkbased process control parameter coupling relationship model
Dataset 2 made according to historical data is randomly divided into two parts according to the ratio of 8 : 2. 80% of the data is used as the training set and 20% of the data is used as the test set to train the process control parameter coupling relationship model
Summary
Introduction to the Production System ofFully Mechanized Mining Face e coal mining process of a fully mechanized coal mining face is shown in Figure 1, which includes five major processes: coal mining, coal loading, coal transportation, support, and goaf treatment. e coal cut by the shearer passes through the scraper conveyor transported to the bottom of the working surface, after passing through the stage loader and the crusher, it is transported to the outbound system by the flat lane belt conveyor. e scraper conveyor and hydraulic support need to be matched with the shearer to maximize the production capacity of the shearer. e conveying capacity of the scraper conveyor should be greater than the production capacity of the shearer, and the moving speed of the hydraulic support should be greater than the working speed of the shearer.e fully mechanized mining face production system is a complex series system, which can be divided into three subsystems, namely, the equipment subsystem, the environment subsystem, and the coal mining process subsystem. Erefore, the production system of fully mechanized mining face urgently needs a new intelligent optimization method of process control parameters to improve production efficiency.
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