發(fā)布時(shí)間：2018-07-08 20:03

  本文選題：礦井瓦斯 + RBF神經(jīng)網(wǎng)絡(luò)��； 參考：《西安科技大學(xué)》2013年碩士論文

【摘要】：瓦斯災(zāi)害作為我國(guó)煤礦的主要災(zāi)害之一，長(zhǎng)期困擾著煤礦的安全生產(chǎn)，本文通過對(duì)瓦斯預(yù)測(cè)方法、預(yù)警技術(shù)調(diào)研分析的基礎(chǔ)上，以反應(yīng)瓦斯涌出特征的礦井瓦斯實(shí)時(shí)監(jiān)測(cè)數(shù)據(jù)、防突監(jiān)測(cè)數(shù)據(jù)為研究對(duì)象，通過將瓦斯涌出顯現(xiàn)出的瓦斯?jié)舛茸兓卣髋c瓦斯實(shí)時(shí)監(jiān)測(cè)數(shù)據(jù)與防突監(jiān)測(cè)數(shù)據(jù)之間的關(guān)聯(lián)特征，用神經(jīng)網(wǎng)絡(luò)方法來描述，進(jìn)行瓦斯?jié)舛阮A(yù)測(cè)預(yù)警與煤與瓦斯突出危險(xiǎn)性預(yù)測(cè)預(yù)警的研究，主要研究工作如下： 首先介紹了徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)（RBFNN）方法的基本理論及其預(yù)測(cè)方法，在此基礎(chǔ)上，，分析了其應(yīng)用于煤礦實(shí)際監(jiān)測(cè)數(shù)據(jù)分析的可行性，以及應(yīng)用于礦井瓦斯預(yù)測(cè)的基本原理。 其次，針對(duì)礦井實(shí)際監(jiān)測(cè)數(shù)據(jù)的特征，使用插值法對(duì)實(shí)測(cè)數(shù)據(jù)進(jìn)行預(yù)處理，建立基于綜采工作面瓦斯實(shí)時(shí)監(jiān)測(cè)數(shù)據(jù)處理的綜采工作面瓦斯?jié)舛阮A(yù)測(cè)預(yù)警模型，實(shí)現(xiàn)了回采工作面瓦斯?jié)舛鹊膶?shí)時(shí)預(yù)測(cè)預(yù)警。 再次，針對(duì)檢/監(jiān)測(cè)數(shù)據(jù)，提取瓦斯實(shí)時(shí)監(jiān)測(cè)數(shù)據(jù)的統(tǒng)計(jì)特征參數(shù)，建立了掘進(jìn)工作面煤與瓦斯突出危險(xiǎn)性預(yù)測(cè)預(yù)警模型，基于檢/監(jiān)測(cè)數(shù)據(jù)融合分析，實(shí)現(xiàn)煤與瓦斯突出危險(xiǎn)性預(yù)測(cè)預(yù)警。 最后，將預(yù)測(cè)預(yù)警模型應(yīng)用于實(shí)例礦井進(jìn)行現(xiàn)場(chǎng)分析驗(yàn)證，分析結(jié)果表明：預(yù)測(cè)的誤差較小，預(yù)測(cè)結(jié)果較準(zhǔn)確，從而保證了預(yù)警分析的可靠性。 本文研究的以瓦斯檢/監(jiān)測(cè)數(shù)據(jù)處理為手段的瓦斯預(yù)測(cè)預(yù)警技術(shù)，針對(duì)現(xiàn)場(chǎng)實(shí)測(cè)數(shù)據(jù)的應(yīng)用分析，表現(xiàn)出了良好的適用性，可作為擴(kuò)展煤礦安全監(jiān)測(cè)監(jiān)控系統(tǒng)功能的有效手段，具有一定的實(shí)際應(yīng)用價(jià)值。
[Abstract]:Gas disaster, as one of the main disasters in coal mine in China, has been puzzling the safety production of coal mine for a long time. Based on the investigation and analysis of gas prediction method and early warning technology, the real-time monitoring data of gas emission in coal mine are used to reflect the characteristics of gas emission. The outburst prevention monitoring data is used as the research object. The relationship between the gas concentration variation characteristics and the real-time gas monitoring data and the anti-outburst monitoring data is described by the neural network method. The main research work is as follows: firstly, the basic theory and prediction method of radial basis function neural network (RBFNN) are introduced. The feasibility of its application in coal mine monitoring data analysis and the basic principle of mine gas prediction are analyzed. Secondly, according to the characteristics of actual mine monitoring data, the interpolation method is used to preprocess the measured data, and a prediction and warning model of gas concentration in fully mechanized coal mining face based on real-time monitoring data processing of fully mechanized mining face is established. The real-time prediction and early warning of gas concentration in mining face are realized. Thirdly, aiming at the inspection / monitoring data, the statistical characteristic parameters of the real-time gas monitoring data are extracted, and the prediction and early warning model of coal and gas outburst risk in the tunneling face is established, which is based on the fusion analysis of the inspection / monitoring data. Coal and gas outburst risk prediction and early warning. Finally, the prediction and early warning model is applied to the field analysis and verification of an example mine. The analysis results show that the prediction error is small and the prediction result is more accurate, thus ensuring the reliability of the early warning analysis. The gas prediction and early warning technology based on gas inspection / monitoring data processing is studied in this paper. According to the application and analysis of field measured data, it shows good applicability and can be used as an effective means to expand the function of coal mine safety monitoring and monitoring system. It has certain practical application value.
【學(xué)位授予單位】：西安科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：TD712;TP183

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