云岗矿极近距离煤层安全开采采掘技术研究

为防止311盘区11-2^#层工作面掘进及采煤过程中造成近距离煤层的漏顶事故发生,通过优化工作面布置方式、掘进期间支护形式及参数,制定工作面开采期间的回采工艺及顶板管理措施,在生产实践过程中总结了一套安全可靠的极近距离煤层采掘技术,对近距离煤层开采有一定的指导意义。     

采区煤仓穿越交叉巷道延伸改造设计及工程实践

为解决采区煤仓容量小、运煤路线复杂等问题，提出了穿越煤仓上口交叉巷道向上延伸煤仓的方法，并通过理论分析和计算，确定了在不破坏原有煤仓的基础上利用“锚杆+锚索+喷射混凝土”和混凝土支护相结合的方式对煤仓进行永久支护的设计方案，并利用反井钻机快速施工缩小工期。城峰公司二采区采区煤仓使用该方法进行改造，结果表明此方法能在短时间内通过改造增加煤仓的容量，缩小运煤路线|SCCT软件的配筋计算能够获得钢筋混凝土穿过交叉巷道影响区域的合理参数|可为类似条件的井下煤仓改造施工提供借鉴。     

宏阳煤矿薄煤层沿空留巷实践应用

为了缓解矿井采掘接替紧张局面,宏阳煤矿采用沿空留巷技术,对12102运输顺槽采取顶板超前采面打设锚索梁、顶板超前预裂爆破,留巷段采用“钢梁+单体液压支柱”的方式进行巷道支护。该技术现场应用后,减少了巷道掘进工程量,减少了煤炭损失,增加了经济效益,降低了职工劳动强度,有利于采掘接替。     

回采巷道过断层施工技术

受断层影响,巷道过断层期间掘进速度缓慢,影响采掘接替和矿井生产效率。为了更好地促进矿井生产,以23上04轨顺掘进为研究对象,采用FLAC 3D软件模拟巷道过断层时巷道围岩变形量和塑性区范围,并将围岩划分为正常围岩、过渡带和破碎带3部分。根据模拟结果可知,围岩变形量和塑性区范围都在过渡带开始增大,在破碎带达到最大值。巷道采用后退卧底法过断层,并对断层破碎带及时采用锚网索、注浆、U型钢棚进行支护,通过联合支护相互配合,可以共同提高巷道支护强度,增强巷道顶帮抗变形能力。     

Decision support system of unplanned dilution and ore-loss in underground stoping operations using a neuro-fuzzy system

Unplanned dilution and ore-loss are the most critical challenges in underground stoping operations. These problems are the main cause behind a mine closure and directly influencing the productivity of the underground stope mining and the profitability of the entire operation. Despite being aware of the significance of unplanned dilution and ore-loss, prediction of these phenomena is still unexplained as they occur through complex mechanisms and causative factors. Current management practices primarily rely on similar stope reconciliation data and the intuition of expert mining engineers. In this study, an innovative unplanned dilution and ore-loss (uneven break: UB) management system is established using a neuro-fuzzy system. The aim of the proposed decision support system is to overcome the UB phenomenon in underground stope blasting which provides quantitative prediction of unplanned dilution and ore-loss with practical recommendations simultaneously. To achieve the method proposed, an uneven break (UB) prediction system was developed by an artificial neural network (ANN) considering 1076 datasets covering 10 major UB causative factors collected from three underground stoping mines in Western Australia. In succession, the UB consultation system was established via a fuzzy expert system (FES) in reference to surveyed results of fifteen underground-mining experts. The UB prediction and consultation system were combined as one concurrent neuro-fuzzy system that is named the ‘uneven break optimiser’. Because the current UB prediction systems in investigated mines were highly unsatisfactory with correlation coefficient (R) of 0.088 and limited to only unplanned dilution, the performance of the proposed UB prediction system (R of 0.719) is a remarkable achievement. The uneven break optimiser can be directly employed to improve underground stoping production, and this tool will be beneficial not only for underground stope planning and design but also for production management.     

A novel approach to adaptive relational association rule mining

The paper focuses on the adaptive relational association rule mining problem. Relational association rules represent a particular type of association rules which describe frequent relations that occur between the features characterizing the instances within a data set. We aim at re-mining an object set, previously mined, when the feature set characterizing the objects increases. An adaptive relational association rule method, based on the discovery of interesting relational association rules, is proposed. This method, called ARARM (Adaptive Relational Association Rule Mining) adapts the set of rules that was established by mining the data before the feature set changed, preserving the completeness. We aim to reach the result more efficiently than running the mining algorithm again from scratch on the feature-extended object set. Experiments testing the method's performance on several case studies are also reported. The obtained results highlight the efficiency of the ARARM method and confirm the potential of our proposal.     

Mining urban data (part A)

Modern cities are flooded with data. New information sources like public transport and wearable devices provide opportunities for novel applications that will improve citizens׳ quality of life. From a data science perspective, data emerging from smart cities give rise to a lot of challenges that constitute a new inter-disciplinary field of research. This article introduces the first part of a special issue on the topic ‘Mining Urban Data’ published in the journal Information Systems.     

Model repair — aligning process models to reality

Process mining techniques relate observed behavior (i.e., event logs) to modeled behavior (e.g., a BPMN model or a Petri net). Process models can be discovered from event logs and conformance checking techniques can be used to detect and diagnose differences between observed and modeled behavior. Existing process mining techniques can only uncover these differences, but the actual repair of the model is left to the user and is not supported. In this paper we investigate the problem of repairing a process model w.r.t. a log such that the resulting model can replay the log (i.e., conforms to it) and is as similar as possible to the original model. To solve the problem, we use an existing conformance checker that aligns the runs of the given process model to the traces in the log. Based on this information, we decompose the log into several sublogs of non-fitting subtraces. For each sublog, either a loop is discovered that can replay the sublog or a subprocess is derived that is then added to the original model at the appropriate location. The approach is implemented in the process mining toolkit ProM and has been validated on logs and models from several Dutch municipalities.     

A method to infer the need to update situations in business process adaptation

Contextual knowledge is an essential resource for adapting business processes in order to keep them aligned with its goals. A context-based adaptation environment should learn from the dynamism of the context as well as the decisions made, and continuously identify new unforeseen situations. Data mining is a possibility to maintain the analysis of the processes updated. This paper presents a method that infers the need to learn new situations that influence a business process execution. The method is based on the results of the Apriori algorithm application. Case studies were conducted to evaluate the proposal. We observed evidences of context changes over time and the potential to learn with this dynamics through the method proposed.     

综采长壁工作面推进速度对优势瓦斯通道的诱导与控制作用

基于采动裂隙椭抛带理论，确定了采动优势瓦斯通道带的上、下边界，构建了采动优势瓦斯通道带的时空形态理论模型。以淮北祁南矿34下采区为原型，开展了上覆岩采动瓦斯通道扩展数值仿真研究，发现优势瓦斯通道在上覆岩的空间位置随推进速度的增加而降低，发育高度、宽度和范围随推进速度的增加而减小等特征。以此为依据，对综采工作面推进速度加快后的高位钻孔参数进行了相应调整，并在祁南矿34下2工作面开展了卸压瓦斯抽采人工导流试验，现场试验结果验证了高位钻孔参数依据推进速度而进行的优化是合理的，从而也验证了本文提出的推进速度对优势瓦斯通道的诱导与控制规律是正确的。