發(fā)布時間：2018-08-20 12:16

【摘要】：瓦斯災害是煤礦最嚴重的災害形式，往往造成大量的人員傷亡和重大的經(jīng)濟損失。回采工作面巷道是主要的瓦斯涌出區(qū)域，容易發(fā)生瓦斯積聚，煤礦瓦斯事故絕大多數(shù)發(fā)生在回采工作面區(qū)域。當前，對工作面的瓦斯監(jiān)測主要采用監(jiān)測站點超限報警的方式，采集的數(shù)據(jù)為孤立的點數(shù)據(jù)，監(jiān)測范圍有限，不能有效地描述整個工作面的瓦斯分布和安全狀況。如何通過合理部署瓦斯傳感器節(jié)點并利用節(jié)點間的內(nèi)在聯(lián)系，實現(xiàn)對瓦斯涌出及積聚區(qū)域做出分析判斷，對未來瓦斯場的運移態(tài)勢進行預測預警具有重要現(xiàn)實意義。針對瓦斯數(shù)據(jù)的預測主要以時間序列方法為主，對瓦斯分布場的構(gòu)建主要以空間信息學作為主要手段，而瓦斯的運移和分布規(guī)律與時間、空間密切相關，避開兩者關系孤立地處理問題將造成重要先驗知識的丟失。因此本文從充分利用時間和空間內(nèi)在關系的角度出發(fā)，采用研制的無線瓦斯傳感器陣列作為研究工具，通過時空建模的方式對瓦斯?jié)舛阮A測、瓦斯分布場構(gòu)建、瓦斯傳感器布點優(yōu)化三個漸進問題進行理論和試驗研究，，實現(xiàn)對瓦斯分布場的實時重構(gòu)、對瓦斯場運移態(tài)勢的預測分析、對瓦斯異常行為的時空反演和對瓦斯傳感器陣列的優(yōu)化布置。 論文完成的主要工作有： （1）對煤礦通風系統(tǒng)進行分析，研究工作面巷道風流特性，根據(jù)對風流性質(zhì)的數(shù)學模型的分析和工作面巷道實際參數(shù)確定巷道中的風流狀態(tài)。然后對工作面瓦斯的主要運移形式進行分析，在菲克擴散定律和守恒方程的基礎上，推導出瓦斯在巷道湍流風流作用下的運移擴散方程，理論分析表明瓦斯的運移和擴散與時間和空間變量有關，證明采用時空建模的方式監(jiān)測瓦斯的運移行為是合理的，為工作面瓦斯?jié)舛鹊臅r空預測和瓦斯分布場時空重構(gòu)提供理論依據(jù)。利用數(shù)值分析的方法對工作面瓦斯時空規(guī)律進行分析，研制瓦斯傳感器陣列設備，對瓦斯在工作面的分布進行試驗測試，測試結(jié)果驗證了理論分析的正確性。 （2）選取ELM作為基本學習模型并對其進行時空擴展，增加空間位置信息作為先驗知識，提出一種新的時空極限學習機模型STELM。以空間關聯(lián)性作為輸入權重，鄰接站點的時間面板數(shù)據(jù)作為時空神經(jīng)元的輸入，簡化了時空建模的復雜性，使算法僅需要兩個輸入?yún)?shù)：空間延遲算子邊界值和時間延遲算子邊界值。在理論仿真數(shù)據(jù)以及現(xiàn)場監(jiān)測數(shù)據(jù)中的應用表明，較僅依托于時間維度信息的預測方法在泛化能力上有較大的提升。在此基礎上，結(jié)合選擇性集成學習思想，提出了一種基于L1正則化的STELM選擇性集成學習方法SERSTELM，通過L1正則化稀疏加權組合多個STELM學習機從而避開了定義和度量多樣性的問題，可以直接獲得選擇性稀疏解，該方法在STELM的基礎上進一步提升了預測精度和泛化性能。 （3）提出基于時空克里金模型的瓦斯分布場重構(gòu)技術�？臻g信息統(tǒng)計方法采用區(qū)域化變量為基礎，以變異函數(shù)為基本工具，對研究具有隨機性和結(jié)構(gòu)相關性的數(shù)據(jù)可以實現(xiàn)最佳無偏估計，借鑒空間克里金方法，對其進行時間擴展，采用積和模型通過擬合空間半變異函數(shù)和時間半變異函數(shù)構(gòu)建時空半變異函數(shù)，獲得時間和空間之間的內(nèi)在關系，利用交叉驗證的方式對算法進行分析比較，各項評價指標均表明，時空克里金模型能夠獲得更好的瓦斯場重構(gòu)效果。同時，結(jié)合時空克里金方法和STELM能夠?qū)崿F(xiàn)對未來瓦斯運移態(tài)勢預測，為研究減少和預防瓦斯災害事故提供了新的研究手段和思路。 （4）提出了兼顧已知監(jiān)測點瓦斯?jié)舛阮A測和未知監(jiān)測點瓦斯場插值效果的瓦斯傳感器布置方式。監(jiān)測系統(tǒng)的性能在很大程度上取決于傳感器的布置數(shù)量和位置，相同數(shù)目的傳感器，不同的布置方式將產(chǎn)生不同的監(jiān)測效果。在瓦斯傳感器數(shù)量一定的情況下，為了使本文提出的時空建模方法能夠在實際現(xiàn)場獲得更好的效果，采用多目標粒子群算法進行建模并對其進行改進，引入增量比率占優(yōu)作為適應度優(yōu)勝策略，增加精英保留和被動更新機制，使其適用于煤礦傳感器布點場景，試驗結(jié)果表明其布局方式隨著節(jié)點數(shù)量的減少而呈現(xiàn)明顯規(guī)律性，尋優(yōu)結(jié)果可以在兩種算法中進行有效折衷。
[Abstract]:Gas disaster is the most serious form of coal mine disasters, often resulting in a large number of casualties and major economic losses. Mining face roadway is the main gas emission area, easy to occur gas accumulation, the vast majority of coal mine gas accidents occur in the mining face area. At present, gas monitoring in the working face mainly uses monitoring stations. The method of point-out alarm can not effectively describe the gas distribution and safety condition of the whole working face because the data collected are isolated point data and the monitoring range is limited. It is of great practical significance to predict and warn the migration situation of gas field.The prediction of gas data is mainly based on time series method,and the construction of gas distribution field is mainly based on spatial informatics.The migration and distribution law of gas is closely related to time and space,so it is necessary to deal with the problem in isolation from the relationship between them. Therefore, this paper makes full use of the inherent relationship between time and space, uses the wireless gas sensor array as a research tool, and carries out theoretical and Experimental Research on three progressive problems of gas concentration prediction, gas distribution field construction and gas sensor distribution optimization through space-time modeling. The research realizes the real-time reconstruction of gas distribution field, the prediction and analysis of gas migration situation, the space-time inversion of abnormal gas behavior and the optimization of gas sensor array.
The main tasks of the thesis are as follows:
(1) Analyze the ventilation system of coal mine, study the airflow characteristics of the roadway, determine the airflow state in the roadway according to the analysis of the mathematical model of the airflow properties and the actual parameters of the roadway. Then analyze the main migration forms of the gas in the working face, deduce the gas on the basis of Fick diffusion law and conservation equation. The equation of gas migration and diffusion under the action of turbulent airflow in roadway shows that gas migration and diffusion are related to time and space variables, which proves that the method of space-time modeling is reasonable to monitor gas migration behavior, and provides theoretical basis for the space-time prediction of gas concentration and the space-time reconstruction of gas distribution field in working face. The method of analysis is to analyze the space-time law of gas in working face, develop gas sensor array equipment, and test the distribution of gas in working face. The test results verify the correctness of theoretical analysis.
(2) Selecting ELM as the basic learning model and extending it to space-time, adding spatial location information as a prior knowledge, a new spatio-temporal limit learning machine model STELM is proposed. Spatial correlation is used as the input weight, and temporal panel data of adjacent stations is used as the input of spatio-temporal neurons, which simplifies the complexity of spatio-temporal modeling and makes calculation easier. The method only needs two input parameters: the spatial delay operator boundary value and the time delay operator boundary value. The application in theoretical simulation data and field monitoring data shows that the generalization ability of the prediction method based on time dimension information is improved greatly. A selective ensemble learning method for STELM based on L1 regularization, SERSTELM, combines multiple STELM learning machines by L1 regularization and sparse weighting to avoid the problem of definition and metric diversity, and obtains the selective sparse solution directly. This method further improves the prediction accuracy and generalization performance based on STELM.
(3) The reconstruction technology of gas distribution field based on spatio-temporal Kriging model is proposed. Spatial information statistical method is based on regionalized variables and variogram is used as a basic tool to realize the best unbiased estimation of the data with randomness and structural correlation. Spatial Kriging method is used for reference to expand its time and product is adopted. The spatio-temporal semi-variogram is constructed by fitting the spatio-temporal semi-variogram and the spatio-temporal semi-variogram, and the intrinsic relationship between time and space is obtained. The algorithm is analyzed and compared by cross-validation. All the evaluation indexes show that the spatio-temporal Kriging model can obtain better gas field reconstruction effect. Spatio-temporal Kriging method and STELM can predict the future gas migration situation, and provide new research means and ideas for reducing and preventing gas disaster accidents.
(4) Presents a gas sensor layout method which takes into account the prediction of gas concentration at known monitoring points and the interpolation effect of gas field at unknown monitoring points. In order to make the spatio-temporal modeling method proposed in this paper achieve better results in the actual scene, the multi-objective particle swarm optimization (MPSO) algorithm is used to model and improve it. Incremental ratio dominance is introduced as the fitness strategy to increase the elite retention and passive update mechanism, which makes it suitable for coal mine sensor distribution. In the point scenario, the experimental results show that the placement pattern is obviously regular as the number of nodes decreases, and the optimization results can be effectively compromised between the two algorithms.
【學位授予單位】：中國礦業(yè)大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：TD712

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