The influence of mining factors on seismic activity during longwall mining of a coal seam

In this article an attempt to determine the influence of mining factors on the seismic activity during the longwall mining of the upper layer of coal seam no. 405/2 in one of the Polish hard coal mines in the Upper Silesian Coal Basin was conducted. Two longwall panels were mined in analogous geological conditions and based on the same mining system and technology. However, there was significant difference with regards to the mining factors, which was reflected in the observed seismic activity. Some tools used in mining seismology were applied to illustrate the aforementioned influence of mining factors, e.g. the frequency-energy distribution, the frequency-magnitude distribution, the 2 D distribution of released seismic energy, the relationship between released seismic energy and the volume of mined coal, the Benioff strain release, and the Gutenberg-Richter(GR) b coefficient distribution(b is the proportion between high and low energy tremors). Concerning the Benioff strain release, a new solution, based on the slope of a fitted line in a moving time window, is proposed.     

煤炭资源型城市转型模式研究：以徐州贾汪区为例

煤炭资源型城市为我国经济发展提供了重要的资源和能源支持,研究资源型城市转型的经验模式对调整区域经济结构、确保社会稳定和改善生态环境具有重要的实践意义。本文采用文献综述法和实证分析法,研究我国东部煤炭资源枯竭型城市转型所面临的共性难题,并以徐州贾汪区转型探索经历为例,总结城市转型的"徐州贾汪区模式",主要包括放大正向外部效应、长期坚持矿地融合、大力建设矿区社会生态系统恢复力三条路径。研究结果表明,煤炭城市转型发展的共性问题相互联系、相互影响,是一个系统性难题,必须引入系统性思维。我国东部矿区普遍人口密集,农业发达、沉陷积水是最主要的共性特征,煤炭开采产生的社会问题、经济问题、生态问题、环境问题基本相同,转型发展模式值得互鉴。     

对“垃圾衍生燃料”RDF之探析

当前,我国水泥工业在可燃废弃物应用技术方面都还处于一家一户、自制自用、效率极低的初级阶段。发达国家的替代燃料:“垃圾衍生燃料”RDF、“固体回收燃料”SRF、“次煤”Subcoal和“纸塑垃圾衍生燃料”RPF制成的原材料都是可燃废弃物,只是处理工艺技术不同或者由垃圾中分拣出的可燃废弃物不同,制成颗粒状衍生燃料的品质不同,这些都可以替代部分甚或替代全部化石燃料在水泥窑炉中应用。我国大力发展“替代燃料”产业,有助于水泥工业消纳更多的“可燃废弃物”,为改善环境尤其是城镇环境和面貌,为我国的节能减排和绿色高质量发展发挥更大的作用。     

肇庆市固体废物产业发展方向探索

本文以肇庆市固体废物产业发展为实例,阐明全市固体废物产业发展现状以及发展方向,并提出了相应的策略。     

乌兰矿采矿现状及技术应对方案

本文分析了乌兰矿投产前期采矿现状及存在的主要问题,针对该矿所处蒙古国经济落后、投资风险大的现实状况,为避免生产中断、规避投资风险,早日回收前期投资考虑,采取了双斜坡道开拓、全尾胶结充填、高端壁空场嗣后充填采矿、多中段组合式连续开采等系列技术应对方案。大大降低了一次性投资规模及投资风险,前期投资得以快速回笼的同时,矿山产能也充分释放,确保了矿山的持续稳定,取得了较好的经济和社会效益。为海外地下近地表矿体开采矿山规避投资风险提供了很好的技术方案借鉴。     

陕北沙漠区浅埋厚煤层开采潜水位及生态响应

煤炭开采造成上覆岩层破裂、移动，破坏了矿区含（隔）水层结构，改变了地表水及地下水循环演化状态，对浅表层生态环境造成严重影响，引发一系列环境地质问题。为深入研究沙漠地区厚煤层开采给潜水位及生态环境造成的影响，以毛乌素沙漠金鸡滩矿为例，对水文地质条件进行了研究，采用数值模拟的方法，模拟了沙漠区厚煤层开采潜水位的变化，并通过采动潜水位监测数据验证了预测结果。调查分析了研究区及周边地表生态环境现状，预测采后潜水位基本满足植物生长需求，对生态环境影响不大。     

Reduction of chlorophenols and sludge management from paper industry wastewater using electrocoagulation process

ABSTRACT

In this study, electrocoagulation (EC) was used to determine the optimum conditions on the basis of maximum chemical oxygen demand (COD) and color removal. At the optimum conditions chlorophenols (CPs), biological oxygen demand and total organic carbon (TOC) were determined. The biodegradability of wastewater was increased significantly with 63% COD, 98% color, 61% TOC and overall 65.51% reductions in CPs. Further, the electro-coagulated sludge was characterized by using different analytical techniques to assist the physicochemical and elemental phases, to find-out better management option, reusability for plant growth and safe disposal. Additionally, aluminum content (70.62%) was successfully recovered from sludge.     

突发疫情对染料行业影响及对策

一场新型冠状病毒肺炎疫情的爆发给染料行业的发展带来了影响,既有负面消极,也有积极影响。疫情将促进染料行业绿色发展,推动染料行业走向信息化、智能化时代,走向高质量发展,指出只要坚持绿色、创新和智能化,就能战胜疫情,推动中国染料行业稳健发展。     

Industry 4.0: a supply chain innovation perspective

The Fourth Industrial Revolution – also known as Industry 4.0 (i4.0) – comprises the digitalisation of the industrial sector. This paper uses the theoretical lens of supply chain innovation (SCI) to investigate the implications of i4.0 on supply chain management. For these purposes, the method of structured content analysis is applied to more than 200 use cases of i4.0-enabled SCI introduced by both established and startup companies. i4.0-enabled SCI manifests along three dimensions: process, technology, and business architecture. The key findings of this study can be summarised as follows: first, i4.0-enabled SCI extends the initial focus on productivity improvements in SC processes towards scalability and flexibility. Second, extant i4.0 solutions rely mostly on analytics and smart things while omitting smart people technology and the human-centric approach associated with the i4.0 paradigm. Third, established companies adopt i4.0 merely to sustain their existing business architectures while startup companies radically change their operating models, relying heavily on data analytics and the platform economy. Consequently, established companies pursue a problem-driven, engineering-based approach to SCI while startup companies follow an ‘asset-light’, business-driven approach. Lastly, there are two distinct approaches to digitalising operational SC processes: platform-based crowdsourcing of standard processes and on-demand provision of customised services.     

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.