基于支持向量机的矿井风温预测

发布时间：2018-06-10 06:17

本文选题：矿井 + 热害　；参考：《西安科技大学》2013年硕士论文

【摘要】：随着矿井开采深度的增加和采掘机械化程度的不断提高，高温热害对矿井的安全生产和井下作业人员的身心健康造成了严重的威胁，导致矿井事故率上升、作业人员劳动率下降，煤矿的正常生产受到影响。因此，矿井降温就变得越来越重要，而准确预测矿井风温是合理设计通风空调系统的首要任务和重要基础。根据矿井风流热湿交换原理，研究矿井各热源对矿井风温的影响因素，，在此基础上，通过理论分析及合理简化，确定影响井筒风温、巷道风温及工作面风温的主要影响因素，为风温预测模型输入因子的确定提供了重要依据。由于支持向量机具有良好的非线性系统的能力，可以有效的捕捉矿井风流温度非线性变化规律和特性，并且支持向量机适用于小样本数据的预测，解决矿井的预测样本有限的问题。因此，应用支持向量机的方法，建立并优化了基于支持向量机的矿井风温预测模型。以矿井淋水井筒为例，将支持向量机应用于矿井淋水井筒风温预测，通过理论分析和交叉试验的方法，确定支持向量机类型、核函数及有关参数的选择，预测结果表明，该支持向量机预测模型具有预测精度高、拟合效果好及计算速度快等优点。利用C语言编程，开发完成了基于支持向量机的矿井风温预测软件，用于矿井井筒、巷道、采掘工作面的风温预测，该软件同时具备建模和预测的功能，通过对软件功能的界定表明，该软件是具有可视化程度高、运算速度快、人机界面友好、实际操作方便的矿井风温预测软件。
[Abstract]:With the increase of mining depth and the increasing degree of mechanization of mining, the high temperature heat damage poses a serious threat to the safety of mine production and the physical and mental health of underground workers, which leads to the increase of mine accident rate. The normal production of coal mines has been affected by the decline of the labor rate of the workers. Therefore, mine cooling becomes more and more important, and accurate prediction of mine air temperature is the most important task and important foundation for rational design of ventilation and air conditioning system. This paper studies the influence factors of mine heat sources on mine air temperature. On the basis of this, through theoretical analysis and reasonable simplification, the main influencing factors of shaft air temperature, roadway air temperature and face air temperature are determined. It provides an important basis for determining the input factor of the wind temperature prediction model. Because the support vector machine has a good ability of nonlinear system, it can effectively capture the nonlinear variation law and characteristics of mine air temperature. Support vector machine (SVM) is suitable for prediction of small sample data to solve the problem of limited prediction samples in mines. Therefore, the prediction model of mine air temperature based on support vector machine is established and optimized by using support vector machine (SVM). The support vector machine (SVM) is applied to predict the air temperature of the well bore with mine flooding as an example. The selection of the type of support vector machine, kernel function and related parameters are determined by theoretical analysis and cross test. The prediction results show that the support vector machine can be used to predict the air temperature of the shaft. The prediction model of support vector machine has the advantages of high prediction precision, good fitting effect and fast calculation speed. By using C language programming, the prediction software of mine air temperature based on support vector machine is developed, which is used in mine shaft and roadway. The software has the function of modeling and forecasting simultaneously. The definition of the function of the software shows that the software has high visualization, fast operation speed and friendly man-machine interface. Practical operation of mine air temperature prediction software.
【学位授予单位】：西安科技大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：TD727.2;TP181

【参考文献】