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Prediction method of coal gas permeability based on lvq-cpso-bp algorithm

A technology of LVQ-CPSO-BP and CPSO-BP, which is applied in the field of coal gas permeability prediction based on LVQ-CPSO-BP algorithm

Inactive Publication Date: 2019-03-19
XINJIANG UNIVERSITY
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Problems solved by technology

However, the single prediction model algorithm based on BP neural network can only answer specific questions. Therefore, the research on the hybrid prediction model based on the combination algorithm has been developed rapidly. The intelligent optimization algorithm combined with the neural network analyzes the change characteristics of the coal gas permeability under different conditions, and establishes a double combination algorithm combining the optimization algorithm and the neural network, which improves the prediction accuracy. However, a higher precision coal gas permeability can be obtained Rate forecasting requires more combinations and improvements in algorithms

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  • Prediction method of coal gas permeability based on lvq-cpso-bp algorithm
  • Prediction method of coal gas permeability based on lvq-cpso-bp algorithm
  • Prediction method of coal gas permeability based on lvq-cpso-bp algorithm

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experiment example

[0066] Such as figure 1 As shown,

[0067] The coal gas permeability prediction method based on LVQ-CPSO-BP algorithm consists of the following steps:

[0068] Selection of main influencing factors and determination of delamination critical value and stress inflection point

[0069] The gas permeability of coal is closely related to the temporal and spatial characteristics of geology. With the change of coal mining process, it presents the characteristics of time-varying, non-linear, and ambiguity, showing the characteristics of dynamic changes. The influencing factors are mainly embodied in the aspects of geological structure, geological coal seam structure, coal seam mining depth, coal quality characteristics, etc. in the macroscopic view, and mainly in the effective stress, temperature, gas pressure, compressive strength, pore structure, etc., in the microscopic view. The above factors are ignored. The relationship with penetration rate cannot truly reflect the internal connectio...

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Abstract

A learning vector quantization (LVQ)-chaos particle swarm optimization (CPSO)-back propagation(BP) coal-body gas permeability predicting method is provided based on an algorithm of LVQ neural network classifying, the CPSO and BP neural network predicting. A critical value is determined, and the buried depth of a coal seam is divided into two layers. Based on the inflection point relation existing between effective stress and gas permeability, an inflection point value is determined, and the effective stress is divided into two sections. Four microcosmic sample parameters are classified and identified through the LVQ according to the inflection point characteristic, a BP neural network is adopted to study and train, and the predicting result is output, and a weight value and a threshold of the BP neural network are optimized through the CPSO. Finally, the predicting result of the built LVQ-CPSO-BP algorithm is verified, and the predicting results of a BP algorithm, a GA-BP algorithm and a PSO-BP algorithm are compared and analyzed.

Description

Technical field [0001] The invention relates to the field of coal gas permeability prediction, in particular to a coal gas permeability prediction method based on LVQ-CPSO-BP algorithm. Background technique [0002] Coal mine gas disaster is one of the main disasters in the process of coal mining. Due to the influence of geological conditions, mine gas flows in the coal seam in an unstable state. Since gas permeability is closely related to the earth's temporal and spatial characteristics such as ground stress, temperature and gas pressure, the gas permeability presents time-varying, non-linear, and fuzzy characteristics. How to accurately predict the dynamic change of coal gas permeability , It is of great significance to prevent the occurrence of coal mine gas disasters in mining. [0003] Researchers at home and abroad have conducted research on the change of coal gas permeability. Lu Runsheng and others have studied the difference of coal gas permeability under different struc...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): E21F7/00G06F17/50G06N3/08
CPCE21F7/00G06F30/20G06N3/084
Inventor 谢丽蓉路朋王晋瑞高磊牛永朝王忠强
Owner XINJIANG UNIVERSITY