European legal framework related to underground mining and tunnelling concerning commission directive(EU) 2017/164, 31 January establishing a fourth list of indicative occupational exposure limit values

Directive(EU) 2017/164 establishes a fourth list of indicative occupational exposure limit values(IOELVs)to protect workers from risks of exposure to hazardous chemicals. It states that in underground mining and tunnelling, Member States may benefit from a transitional period regarding IOELVs for nitrogen monoxide, nitrogen dioxide, and carbon monoxide, during which the existing established IOELVs may be applied. The European Advisory Committee on Health and Safety at Work questions the technical feasibility of the proposed IOELVs in underground mining(CO, NO and NO_2) and tunnelling(NO and NO_2).Challenges arise concerning the availability of measurement methodologies for compliance with proposed IOELVs(NO_2) in underground mining and tunnelling environments.     

An Innovative Non-Pillar Coal-Mining Technology with Automatically Formed Entry: A Case Study

A non-pillar coal-mining technology with an automatically formed entry is proposed, which reduces the waste of coal resources and the underground entry drivage workload. Three key techniques in this technology cooperate to achieve automatic formation and retaining of the gob-side entry, and to realize non-pillar mining. Constant-resistance large deformation (CRLD) support ensures the stability of the entry roof; directional presplitting blasting (DPB) separates the entry roof and the gob roof; and a blocking-gangue support system (BGSS) integrates the caved rock material as an effective entry rib. An industrial test was conducted to verify the engineering effects of these key techniques. The field application results showed that the retained entry was under the pressure-relief zone due to the broken-expansion nature of the caved rock mass within the DPB height. After going through a provisional dynamic pressure-bearing zone, the retained entry entered the stability zone. The final stable entry meets the requirements of safety and production. The research results demonstrate the good engineering applicability of this technology. By taking the framework of the technology design principles into consideration and adjusting the measures according to different site conditions, it is expected that the proposed non-pillar coal-mining technology can be popularized on a large scale.     

Flexible Distribution-Based Regression Models for Count Data: Application to Medical Diagnosis

Abstract

Data mining techniques have been successfully utilized in different applications of significant fields, including medical research. With the wealth of data available within the health-care systems, there is a lack of practical analysis tools to discover hidden relationships and trends in data. The complexity of medical data that is unfavorable for most models is a considerable challenge in prediction. The ability of a model to perform accurately and efficiently in disease diagnosis is extremely significant. Thus, the model must be selected to fit the data better, such that the learning from previous data is most efficient, and the diagnosis of the disease is highly accurate. This work is motivated by the limited number of regression analysis tools for multivariate counts in the literature. We propose two regression models for count data based on flexible distributions, namely, the multinomial Beta-Liouville and multinomial scaled Dirichlet, and evaluated the proposed models in the problem of disease diagnosis. The performance is evaluated based on the accuracy of the prediction which depends on the nature and complexity of the dataset. Our results show the efficiency of the two proposed regression models where the prediction performance of both models is competitive to other previously used regression models for count data and to the best results in the literature.     

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。     

Synthesis of photoluminescent SiC-SiOx nanowires using coal tar pitch as carbon source

SiC-SiOx nanowires (NWs) with core-shell and chain-bead structures were synthesized via a catalyst-free chemical vapor deposition (CVD) route without Ar gas, using silicon and coal tar pitch powders as raw materials. The SiC-SiOx NWs with sharp tips formed by solid-vapor between Si (s) and CO (g) or C (s) and SiO (g), liquid-vapor between Si (l) and CO (g), and vapor-vapor between SiO (g) and CO (g) growth process along the [111] direction. The NWs were several millimeters in length, and the average diameters of the chains and beads were 40–90 nm and 325 nm, respectively. The obtained NWs had good blue-green photoluminescence property owing to the stacking faults and amorphous SiOx. This novel CVD route is simple, low cost and suitable for large-scale production.     

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.