【作者】 刘晓明；

【导师】 王新；

【作者基本信息】 中国矿业大学 ， 计算机应用技术， 2021， 硕士

【摘要】 煤与瓦斯突出是我国煤矿开采过程中面临的巨大威胁之一。研究显示,构造煤的存在会导致煤与瓦斯突出问题的出现,瓦斯突出的易发区多数是构造煤的发育区域。因此,如果能够通过预测构造煤厚度来辨别瓦斯突出区域,也就可以为防治煤与瓦斯突出问题提供理论依据。考虑到当前对构造煤厚度多使用单目标方法进行预测,没有利用不同类型构造煤内部的联系,本文构建了回归随机森林模型对多种类型的构造煤厚度进行同步预测。首先,通过主成分分析方法对原始的煤层样本数据进行降维,取得贡献较大的相互独立的主成分代表原属性;其次,考虑到回归随机森林的表现不如其在分类预测中的表现,引入二次训练过程,赋予个体模型和特征属性权重,在提升个体模型预测效果的同时,提高集成的效果,并通过鲸鱼优化算法对回归随机森林模型参数进行优选;同时,考虑到鲸鱼优化算法全局勘探能力较弱以及搜索能力资源的浪费问题,通过改变鲸鱼优化算法勘探和开采能力的比例,提高了算法的全局勘探能力,并且改变搜索代理在勘探和螺旋狩猎上的搜寻方式,扩大参数范围,以提高鲸鱼优化算法的搜寻猎物能力,获得更好的预测效果。本文将模型应用于芦岭煤矿的实际煤层属性数据中,对三种类型的构造煤进行同步预测,得到三种构造煤的厚度。通过对比该模型与单目标预测模型及多目标支持向量回归模型,得出该模型具有较优的预测性能。因此,本文提出的预测模型具有较好的预测效果,可以应用于其他瓦斯突出易发区的多种类型构造煤的同步预测。该论文有图25幅,表11个,参考文献93篇。更多还原

【Abstract】 Coal and gas outburst is one of the great threats in the process of coal mining in China.The research shows that the existence of tectonic deformed coal will promote the occurrence of coal and gas outburst,and the tectonic deformed coal development area is often the prone area of gas outburst.Therefore,if the gas outburst area can be identified by predicting the thickness of tectonic deformed coal,it can provide a theoretical basis for the prevention and control of coal and gas outburst.Considering that the single objective method is often used to predict the thickness of tectonic deformed coal at present,and the internal relationship of different types of tectonic deformed coal is not used,this paper constructs a regression random forest model to synchronously predict the thickness of various tectonic deformed coal.Firstly,the original coal seam attribute data is dimensionally reduced by principal component analysis,and independent principal components are obtained to represent the original attributes.secondly,considering that the performance of the regression random forest is not as good as its performance in classification prediction,the secondary training process is introduced to give the individual model and feature attribute weights,so as to improve the prediction effect of the individual model and improve the integration efficiency.At the same time,considering the weak global exploration capability of the whale optimization algorithm and the waste of search capability resources,the overall exploration capability of the algorithm is improved by changing the ratio of the exploration and mining capabilities of the whale optimization algorithm.And change the search method of the search agent in exploration and spiral hunting and expand the parameter range to improve the hunting ability of the whale optimization algorithm and obtain better prediction results.In this paper,the model is applied to the actual coal seam attribute data of Luling coal mine,three types of tectonic deformed coal are predicted synchronously,and the thickness of three types of tectonic deformed coal is predicted.By comparing the model with single objective prediction model and multi-target support vector regression model,it is concluded that the model has better prediction performance.Therefore,the prediction model proposed in this paper has good prediction effect,and can be applied to the synchronous prediction of multiple tectonic deformed coal in other gas outburst prone areas.更多还原