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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.

The production system of the fully mechanized mining face is the most important part of the coal mining system. The production capacity of the working face directly determines the production capacity of the mine. At present, the level of automation and intelligence of fully mechanized mining faces in our country is low, and there is a widespread problem of inadequate connection between working procedures [

Due to the late start of our country’s research on the automation and intelligence of fully mechanized mining faces. At present, the research is mainly about the optimization of the performance of a single device or the optimization of the reasonable cooperation between two devices at the equipment level, such as the cutting mode recognition of the shearer [

At present, the intelligent optimization method of process control parameters represented by the genetic algorithm [

The coal mining process of a fully mechanized coal mining face is shown in Figure

Coal mining process flow chart of fully mechanized mining face.

The 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. Each process in the production process generally contains multiple process control parameters. The fluctuation of any process control parameter will have an impact on other subsequent production processes and affect the comprehensive production index of the mine. The coupling relationship between the processes and process control parameters of the fully mechanized face production system is shown in Figure

Coupling relationship diagram of the production system in fully mechanized mining face.

In the actual production process, the process control parameter optimization process of each production equipment is shown in Figure

Flow chart for optimization of working face process control parameters.

This 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).

In this paper, the Pearson correlation analysis method is used to analyse the historical production data and preliminary analysis of the relationship between the parameters. Figure

Pearson correlation analysis matrix diagram.

We propose a process control parameter optimization method based on a mixed strategy of artificial neural network (ANN) and genetic algorithm (GA). The method includes two core modules, namely, ANN-based process control parameter coupling relationship modelling and GA-based process control parameters optimization, and the process is shown in Figure

Mixed optimization process of process.

The specific calculation steps for constructing the process control parameter model using the traditional neural network are as follows:

Step 1: initialize the weights

Step 2: randomly select input samples

Step 3: calculate the weighted input value

The activation function

Step 4: calculate the mean square error between the network output value

Step 5: use stochastic gradient to update the weights and bias of each layer, layer by layer.

Step 6: repeat steps 3 to 5 until the error meets the set conditions.

The traditional neural network uses the mean square error function when measuring the network output value

Sigmoid function image. Control parameters for coal mining face.

In order to solve this problem, this study uses a cross-entropy cost function to replace the traditional mean square error cost function to optimize the neural network to improve the learning speed of the neural network and its form is as equation (

Neural network learning situation with mean square error cost function.

Neural network learning situation with cross-entropy cost function.

After obtaining the process control parameter coupling relationship model with a better fitting effect, directly use the difference between the output value of the established neural network coupling relationship model and the target value of the optimization target as the fitness function of the genetic algorithm to optimize each process control parameter; the specific steps are as follows:

Step 1: initialize the population. The population evolution generation is set to

Step 2: coding. In this study, the binary coding method is used, and each chromosome is encoded with a binary number. The representation of each individual in the population is

Step 3: calculate the fitness vector. The codes of all individuals in the population are sequentially input into the trained process control parameter coupling relationship model, and the difference between the output value and the target value is used as the fitness vector. The mathematical model is shown in the following equation:

In the previous equation,

Step 4: Pareto sorting.

Step 5: according to the selection operator, select the corresponding individuals from the individuals sorted by Pareto to reproduce their offspring.

Step 6: according to the crossover operator, the genes between the individuals selected in the fifth step are crossed to produce the next generation of individuals.

Step 7: randomly mutate the next generation of individuals according to the mutation operator.

Step 8: repeat steps 3 to 7 until the result reaches the set evolution generation.

Step 9: denormalize the output results, and the result is the optimized process parameter solution set.

The data in this dataset is selected from dataset 1 after data preprocessing steps to remove outliers and used to train and test the model in the paper. In this study, 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. Then, establish the improved neural network-based 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.

In this study, the historical production data of a fully mechanized coal mining face was used to train the process control parameter coupling relationship model based on artificial neural networks, and the mean square error was used as the model evaluation method to evaluate the prediction results of the model. The mean square error [

The optimized neural network error loss.

In order to further verify the reliability and superiority of the process control parameter coupling relationship model based on the improved ANN. This study uses the same data set with the standardized data of the PSS, HSMS, CSSC, CSSL, EPOP, and SPOP as the input of the model and the OCPM as the output of the model. Establish a support vector regression- (SVR-) based [

Comparison chart of forecast results.

In this study, the value 113 of OCPM was taken as an optimization target to analyse the process parameters optimized by the process control parameter optimization model. Table

Historical data.

PSS m/min | HSMS number/min | CSSC r/min | CSSL r/min | EPOP1 0.1 MPa | EPOP2 0.1 MPa | SPOP1 0.1 MPa | SPOP2 0.1 MPa |
---|---|---|---|---|---|---|---|

11.56 | 3.34 | 1099.92 | 1448.91 | 59.64 | 61.59 | 293.36 | 297.04 |

11.56 | 3.34 | 1099.69 | 1449.08 | 54.06 | 56.46 | 292.46 | 296.60 |

11.56 | 3.34 | 1099.34 | 1449.77 | 59.67 | 61.87 | 291.88 | 296.61 |

11.56 | 3.34 | 1099.92 | 1449.78 | 55.96 | 57.74 | 292.46 | 299.18 |

11.56 | 3.34 | 1099.54 | 1450.00 | 55.26 | 57.43 | 293.82 | 297.13 |

11.56 | 3.34 | 1099.58 | 1449.84 | 54.62 | 56.63 | 292.93 | 297.89 |

11.56 | 3.34 | 1099.52 | 1449.89 | 52.49 | 54.15 | 294.15 | 300.57 |

Optimized data.

PSS m/min | HSMS number/min | CSSC r/min | CSSL r/min | EPOP1 0.1 MPa | EPOP2 0.1 MPa | SPOP1 0.1 MPa | SPOP2 0.1 MPa |
---|---|---|---|---|---|---|---|

11.57 | 3.20 | 1099.89 | 1449.43 | 56.92 | 54.61 | 297.58 | 297.08 |

11.57 | 3.28 | 1099.79 | 1449.63 | 56.57 | 54.61 | 294.40 | 297.08 |

11.57 | 3.42 | 1099.80 | 1449.62 | 56.57 | 54.37 | 294.40 | 297.08 |

After verification in the production site of a fully mechanized mining face, the intelligent optimization method proposed in this paper reduces the time of manual decision-making, strengthens the cooperation between various procedures, and greatly improves the production efficiency of the mining face. Before using this method, the process control parameters of the mining face production site are set and adjusted manually based on experience. As shown in Figure

Trajectory diagram of shearer before using intelligent optimization method.

Trajectory diagram of shearer after using intelligent optimization method.

This paper uses the cross-entropy cost function to optimize the traditional neural network, which speeds up the learning speed of the model and reduces the number of iterations of the model. Compared with the SVR model, the improved neural network model has a higher fitting accuracy for the coupling relationship of the process control parameters, which is more suitable for establishing the nonlinear relationship between process control parameters and production goals.

This paper proposes a process control parameter optimization method based on a mixed strategy of ANN and GA, taking the neural network-based process control parameter coupling relationship model as the fitness function of genetic algorithm, which fully combines the nonlinear modelling capabilities of artificial neural networks and the global optimization capabilities of genetic algorithms. This method can quickly optimize the process control parameters of the fully mechanized mining face without artificial interference, which solves the problem of low efficiency of traditional optimization methods. And it was verified on the production site, which shortened the cutting time of each cut by 8 minutes and improved the production efficiency of the working face.

The method proposed in this study can only optimize the initial value of each process control parameter of the fully mechanized mining face. When the working environment changes, the process control parameters will also fluctuate. Therefore, future research should also add a dynamic adjustment model of process control parameters to dynamically adjust each process control parameter in response to changing working environments.

The data used to support the findings of this study are shown in Supplementary Materials.

The authors declare that they have no conflicts of interest.

The work was supported by National Key Research and Development Program (no. 2018YFC0808301), National Key Research and Development Program (no. 2017YFC0804307), and State Key Laboratory for GeoMechanics and Deep Underground Engineering (no. SKLGDUEK1923).

The three supplementary data files provided in this paper are obtained by our laboratory. Data 1 is dataset 1 used in the paper; it contains all 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. Data 2 is dataset 2 used in the paper which is filtered from dataset 1 to train and test the model in the paper. Data 3 is the operating trajectory data of the shearer.