受老空水威胁工作面巷道防治水实践

针对高庄煤矿3_上1104回风巷为沿空掘巷,巷道掘进期间受3_上1102老空水威胁,掘进开采存在严重的水患安全问题,采用超前打设疏水孔的方案来治理老空水威胁,对巷道采用调向增大护巷煤柱厚度等措施实现巷道安全快速掘进,探放水期间无淹巷情况发生,探放水后巷道顶板和上帮无淋水现象发生。     

渗流作用下新型装配重力式挡土墙有限元分析

为研究渗流作用下新型装配重力式挡土墙应力变化及墙体排水通道对墙后土体固结的影响,采用ABAQUS进行有限元分析,有限元分析结果与模型试验结果相吻合,证明该模型的合理性。在此基础上研究渗流作用下墙体应力变化趋势及墙体排水通道对墙后土体固结效果的影响。研究结果表明:横向排水通道对墙后土体固结效果的影响优于纵向排水通道对土体固结效果的影响,墙体底部横向排水通道的影响作用最显著;渗流作用下墙体应力由上往下呈先增大后减小的趋势,墙体下部所受最大水平剪应力最大。     

采场动压分布特征及高效瓦斯抽放技术研究

河南地区大部分煤矿所采煤层均属于三软煤层，煤层透气性差，瓦斯治理困难。现阶段河南区域煤与瓦斯突出矿井主要采用穿层钻孔及开采保护层作为区域防突措施，而低瓦斯矿井或临突矿井（低瓦斯矿井与煤与瓦斯突出矿井相邻）存在高瓦斯区时，仅仅用风排瓦斯很难达到治理效果，近些年煤矿发生瓦斯事故多数为低瓦斯矿井，所以低瓦斯矿井和临突矿井存在高瓦斯区域的工作面回采期间瓦斯治理尤为重要。根据采场“竖三带”和“横三区”理论，利用钻孔抽放效果考察法和工作突出预测指标法研究了采场围岩动压分布特征。在此基础上，通过优化动压区抽放钻孔设计，提高了瓦斯抽放效果。瓦斯抽放量提高了1.8倍，有效降低了工作面生产期间的风排瓦斯量，提高了生产效率，保证了矿井安全生产。     

煤层顶板裂隙高位定向长钻孔安全高效抽采技术的研究与应用

放顶煤开采期间，上覆岩层受到矿压的影响，形成冒落带、裂隙带、弯曲下沉带，工作面采空区遗煤和围岩涌出的大量瓦斯飘浮在上方裂隙带区域，造成瓦斯聚集并向外涌出，形成了安全隐患，因此必须将该区域瓦斯抽出来；以往治理采空区瓦斯主要采用顶板裂隙高位钻场、顶板高抽巷等措施，但是这2种方法施工成本较高，且施工周期长，对生产接替影响较大。煤层顶板裂隙高位定向长钻孔安全高效抽采采用大功率钻机+定向钻进技术，在裂隙带施工控制整个回采范围的长钻孔，减少采空区和邻近层瓦斯向工作面空间的流动，真正实现了“以孔代巷”，既节省了成本，又缩短了工期，还提高了采空区瓦斯抽采的连续性、稳定性，减少了采空区瓦斯向外涌出，提升了瓦斯抽采效果，促进了煤矿安全高效发展。     

矿井钻孔测井分析仪在云冈矿上覆采空区积水探放中的应用

通过分析云冈矿12号层410盘区81010回采工作面上覆采空区情况,发现上覆3号层、7号层采空区中均存在积水,为消除该水患对回采工作的影响,须先行探放。文章结合81010回采工作面探放水实践,介绍了YCJ90/360矿用钻孔测井分析仪对钻孔轨迹的指导意义,以及测井技术对成孔准确性和可靠性的保障作用,证明其对采空区探放水钻孔施工具有必不可少的保障作用。     

Soft sensing of water depth in combined sewers using LSTM neural networks with missing observations

Information and communication technologies combined with in-situ sensors are increasingly being used in the management of urban drainage systems. The large amount of data collected in these systems can be used to train a data-driven soft sensor, which can supplement the physical sensor. Artificial Neural Networks have long been used for time series forecasting given their ability to recognize patterns in the data. Long Short-Term Memory (LSTM) neural networks are equipped with memory gates to help them learn time dependencies in a data series and have been proven to outperform other type of networks in predicting water levels in urban drainage systems. When used for soft sensing, neural networks typically receive antecedent observations as input, as these are good predictors of the current value. However, the antecedent observations may be missing due to transmission errors or deemed anomalous due to errors that are not easily explained. This study quantifies and compares the predictive accuracy of LSTM networks in scenarios of limited or missing antecedent observations. We applied these scenarios to an 11-month observation series from a combined sewer overflow chamber in Copenhagen, Denmark. We observed that i) LSTM predictions generally displayed large variability across training runs, which may be reduced by improving the selection of hyperparameters (non-trainable parameters); ii) when the most recent observations were known, adding information on the past did not improve the prediction accuracy; iii) when gaps were introduced in the antecedent water depth observations, LSTM networks were capable of compensating for the missing information with the other available input features (time of the day and rainfall intensity); iv) LSTM networks trained without antecedent water depth observations yielded larger prediction errors, but still comparable with other scenarios and captured both dry and wet weather behaviors. Therefore, we concluded that LSTM neural network may be trained to act as soft sensors in urban drainage systems even when observations from the physical sensors are missing.     

综放面本煤层预抽瓦斯的治理技术

为了极大限度地提高本煤层预抽效果，以马兰煤矿为研究对象，针对目前马兰煤矿的瓦斯赋存情况和现阶段所采取的抽放措施，从抽放钻孔的孔径、孔深、布置方式、封孔质量和增加煤层透气性等方面进行探索。实践表明，以上方法的运用，能够有效地提高本煤层预抽抽放效果，保证工作面安全生产。