Prediction model for coal-gas outburst using the genetic projection pursuit method

Liang, Yueqiang; Guo, Deyong; Huang, Zhanfeng; Jiang, Xihui

doi:10.1504/ijogct.2017.087042

Cited by 17 publications

(13 citation statements)

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“…e mechanism of gas outburst in the working face is complicated, which is the result of a combination of factors such as geostress, coal seam gas, and physical and mechanical properties of the coal body [5]. Based on the historical data of gas outburst accidents and consulting relevant references [2][3][4]24], this paper selects failure type of coal (X 1 ), initial velocity of gas emission (X 2 , mL/s), coal solidity coefficient (X 3 ), gas content (X 4 , m3/t), drill cuttings amount (X 5 , kg/ m), gas pressure (X 6 , MPa), depth of coal mining (X 7 , m), coal seam thickness (X 8 , m) as the influencing factors for the occurrence of outbursts. e amount of coal (rock) outburst is used as the basis for classification usually.…”

Section: Extraction Of Factors Influencing Gas Outburstmentioning

confidence: 99%

Gas Outburst Prediction Model Using Improved Entropy Weight Grey Correlation Analysis and IPSO-LSSVM

Liu

Yang

Wang

2020

Mathematical Problems in Engineering

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This paper investigates the problem of gas outburst prediction in the working face of coal mine. Firstly, based on a comprehensive analysis of influence factors of gas outburst, an improved entropy weight algorithm is introduced into a grey correlation analysis algorithm; thus, the reasonable weights and correlation order of the influencing factors are obtained to improve the objectivity of the evaluation. The main controlling factors obtained are used as the input of the prediction model. Secondly, by utilizing the improved particle swarm optimization (IPSO), the penalty factor and kernel parameter of least square support vector machine (LSSVM) are optimized to enhance the global search ability and avoid the occurrence of the local optimal solutions, and a new prediction model of gas outburst based on IPSO-LSSVM is established. At last, the prediction model is applied in the tunneling heading face 14141 of Jiuli Hill mine in Jiaozuo City, China. The case study demonstrates that the prediction accuracy of the proposed model is 92%, which is improved compared with that of the SVM model and GA-LSSVM model.

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Section: Extraction Of Factors Influencing Gas Outburstmentioning

confidence: 99%

Gas Outburst Prediction Model Using Improved Entropy Weight Grey Correlation Analysis and IPSO-LSSVM

Liu

Yang

Wang

2020

Mathematical Problems in Engineering

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“…(4) Solving the Optimum Projection Direction Equations ( 6) and ( 7) are utilized to compute the optimal projection vector. Generally, most scholars would use GA [13,15,16] and other algorithms for the solution. However, the GA has drawbacks, including the computing result having certain dependence on the initial population selection, its slow convergence speed, and its excessive parameter settings [18,19].…”

Section: Projection Pursuit Modelmentioning

confidence: 99%

“…To effectively analyse complex indexes in the research of gas outburst prediction, Liang [13] established a prediction model by using a PPM optimized by a genetic algorithm (GA). In this model, the one-dimensional projection values calculated by the GA were used to indicate the potential gas outburst risk, and the PPM was confirmed to be objective and effective.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Waterlogging Risk in the Deep Foundation Pit Projects Based on Projection Pursuit Model

Wang

2020

Advances in Civil Engineering

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As a result of global climate change and urbanization, waterlogging disasters have occurred frequently around the world, and deep foundation pit projects with lower terrain suffer even more. This study puts forward a method for the waterlogging risk assessment of deep foundation pit projects via the combination of a projection pursuit model, particle swarm optimization, and an interpolation algorithm. First, through a comprehensive analysis of the water circulation process in waterlogging and the characteristics of deep foundation pit projects, a risk index system with 11 indicators is identified and constructed. Then, a projection pursuit model optimized by particle swarm optimization is leveraged to determine the weights of the indicators and the best projection values of evaluation objects, and the mathematical function between the best projection values and the risk levels is constructed by an interpolation algorithm. Finally, three deep foundation pit projects of the Chengdu Metro Line 11 in China are selected as case studies. The results demonstrate that the frequency of storms, intensity of rainfall, preparation of emergency rescue plans, and proportion of older workers have the greatest impacts on waterlogging risk in deep foundation pits. The risk ranking of the case studies is found to be consistent with the actual situations, which proves the objectivity and effectiveness of the proposed method.

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“…temperature and coal dust) by multiple types of sensors, integrated the collected data on an expert system, and assigned the confidence to each prediction outcome. Liang et al [16] analyzed the general framework of multi-sensor data fusion, designed the structure of a gas outburst prewarning system based on multi-sensor data fusion, built up a data fusion model with a feature layer and a decision layer, and realized feedbacks through a multi-sensor management subsystem, achieving closed-loop control of the prediction system. He et al [17] proposed a systematic gas outburst prewarning model that integrates multi-sensor data, in the light of the hierarchical fusion principle.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The basic idea of fuzzy set theory is to make memberships in general sets more flexible, and thus extend the membership of an element for a set to any number in the interval [0, 1], rather than 0 or 1. Hence, the FCE is a suitable method to describe and process the uncertainty of sensor data [16].…”

Section: Fuzzy Comprehensive Evaluation (Fce) Of Multi-sensor Datamentioning

confidence: 99%

A Novel Fuzzy Fusion Algorithm of Multi-sensor Data and Its Application in Coalmine Gas Monitoring

Zhu¹,

Jiang²,

Zhao³

et al. 2019

I2M

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In coalmines, gas disaster is a complex uncertain process influenced by multiple factors. It is difficult to predict gas disaster in an accurate manner. The traditional methods for gas disaster prediction face several common problems, such as the inaccuracy of gas monitoring data, and the unreliable evaluation of gas safety. To solve the problems, this paper proposes a gas monitoring and prewarning method based on fuzzy fusion of multi-sensor data. To realize data fusion and decision-making, the field parameters like wind speed, gas content and temperature, which are monitored by multiple sensors in the coalmine, were allocated to the set of factors, while the decisions on gas state were allocated to the set of remarks. Then, the data collected by the sensors were fuzzified by the fuzzy set theory, creating a fuzzy membership matrix. The fuzzy Cauchy-Riemann equations were introduced to establish the membership function. Furthermore, the coalmine gas state was evaluated and determined in the fusion center under decision-making rules through compositional operation. Based on local decisions, a data fusion decision-making model for coalmine gas disaster was established to make the global decision. The proposed method was applied to analyze the temperature, gas content and wind speed of tunnel face monitored by multiple sensors at three different time points in a coalmine of Shanxi Province, China. The results show that the gas states at all time points were evaluated accurately, without any false or missed alarm, and the prediction based on multi-sensor data fusion was 34% more accurate than that based on single-sensor data. The research findings provide an effective way to monitor and prewarn the coalmine gas state.

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Prediction model for coal-gas outburst using the genetic projection pursuit method

Cited by 17 publications

References 0 publications

Gas Outburst Prediction Model Using Improved Entropy Weight Grey Correlation Analysis and IPSO-LSSVM

Gas Outburst Prediction Model Using Improved Entropy Weight Grey Correlation Analysis and IPSO-LSSVM

Assessment of Waterlogging Risk in the Deep Foundation Pit Projects Based on Projection Pursuit Model

A Novel Fuzzy Fusion Algorithm of Multi-sensor Data and Its Application in Coalmine Gas Monitoring

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