Investigation of Copper Recovery from a New Copper Deposit (Nussir) in Northern Norway

ABSTRACT

Norway has seen an upsurge of interest in exploiting its mineral deposits during the last decade. One of the major areas of interest is a huge copper deposit, operated by Nussir ASA located in the Repparfjord tectonic window in the Caledonides of west Finnmark. Nussir ASA is evaluating the Nussir and Ulveryggen mineral resources that contain copper sulfides with a small amount of gold and silver bearing minerals. The performance of flotation operations is generally evaluated on the basis of degree of liberation of minerals or the surface interactions between the collector and mineral. Firstly, size-by-size mineralogical characterization of the flotation feed and cumulative final product using Zeiss automated mineralogy techniques (Mineralogic) revealed that mineral liberation heavily influenced the flotation behavior of the ore. Furthermore, in order to assess the role of pH, collector concentration and flotation time, bench scale flotation experiments were performed. The metallurgical results were maximum between pH 6 and 8 and at collector concentration 6 × 10^?5 M. A correlation is established using the zeta potential and Hallimond flotation tests to assess the role of operating parameters in flotation. The distinctive role of process mineralogy and flotation chemical influence for copper mineral flotation of Nussir ore is presented in this article.     

Computational personality recognition from Facebook text: psycholinguistic features,words and facets

ABSTRACT

Advances in the Natural Language Processing (NLP) and machine learning fields have led to the development of automated methods for the recognition of personality traits from text available from social media and similar sources. Systems of this kind exploit the close relation between lexical knowledge and personality models – such as the well-known Big Five model – to provide information about the author of an input text in a non-intrusive fashion, and at a low cost. Although now a well-established research topic in the field, the computational recognition of personality traits from text still leaves a number of research questions worth further exploration. In particular, this paper attempts to shed light on three main issues: (i) whether we may develop psycholinguistics-motivated models of personality recognition when such knowledge sources are not available for the target language under consideration; (ii) whether the use of psycholinguistic knowledge may be still superior to contemporary word vector representations; and (iii) whether we may infer certain personality facets from a corpus that does not explicitly convey this information. In this paper these issues are dealt with in a series of individual experiments of personality recognition from Facebook text, whose initial results should aid the future development of more robust systems of this kind.     

基于双重金字塔网络的视频目标分割方法

针对复杂视频场景中难以分割特定目标的问题，提出一种基于双重金字塔网络（DPN）的视频目标分割方法。首先，通过调制网络的单向传递让分割模型适应特定目标的外观。具体而言，从给定目标的视觉和空间信息中学习一种调制器，并通过调制器调节分割网络的中间层以适应特定目标的外观变化。然后，通过基于不同区域的上下文聚合的方法，在分割网络的最后一层中聚合全局上下文信息。最后，通过横向连接的自左而右结构，在所有尺度中构建高阶语义特征图。所提出的视频目标分割方法是一个可以端到端训练的分割网络。大量实验结果表明，所提方法在DAVIS2016数据集上的性能与较先进的使用在线微调的方法相比，可达到相竞争的结果，且在DAVIS2017数据集上性能较优。     

Extended vertical lists for temporal pattern mining from multivariate time series

In this paper, the problem of mining complex temporal patterns in the context of multivariate time series is considered. A new method called the Fast Temporal Pattern Mining with Extended Vertical Lists is introduced. The method is based on an extension of the level‐wise property, which requires a more complex pattern to start at positions within a record where all of the subpatterns of the pattern start. The approach is built around a novel data structure called the Extended Vertical List that tracks positions of the first state of the pattern inside records and links them to appropriate positions of a specific subpattern of the pattern called the prefix. Extensive computational results indicate that the new method performs significantly faster than the previous version of the algorithm for Temporal Pattern Mining; however, the increase in speed comes at the expense of increased memory usage.     

胶东三山岛西岭金矿区NW向断裂带的确定：综合物探证据

三山岛西岭金矿区位于渤海之滨，与北部海域金矿区相互连接，形成金资源量近千吨的巨型金矿床，矿床为地下与海底开采。区域内断裂发育，分布有NE向和NW向断裂，其中NE向断裂为控矿构造，而NW向断裂多具有不同程度的导水性，对矿床安全开采影响重大。基于矿区地质条件与地球物理特征，采用高精度磁测法和音频大地电磁（AMT）法在矿区东部进行了勘查试验，根据显示的磁性异常与电性异常，在平面上解译了NW向断裂(F-1、F-2)与NE向断裂（F-3、F-4），在L1剖面上解译了4条断裂，在L2剖面上解译了3条断裂。综合判断解译信息，NW向F-1断裂在平面与剖面上都反映良好。同时，与北部海域NW向构造研究资料进行对比分析，结果显示西岭矿区F-1推断断层与北部海域B-F4断层特征一致，吻合较好，推断西岭矿区NW向F-2断裂被F-3断裂错断。研究结果说明综合物探方法在该区域可行有效，为下一步钻孔验证NW向断裂存在，判断断裂富水及导水性质，以及未来矿床开发利用提供了地球物理依据。     

高温深井通风降温技术的适用条件研究

随着矿山开采深度的不断增加，“三高一扰动”恶劣开采环境问题日益突出，给矿井通风降温工作带来极大困难。对于深井矿山，仅采用通风降温难以满足深部开采的需要，系统研究高温深井矿山通风降温技术的适用性问题，对矿山深部开采具有重要的理论意义和工程适用价值。首先从热力学角度，揭示风流在通风线路中的热交换规律；其次利用差分法原理计算深井筒风流温度，并依此推导出巷道和回采工作面风流温度变化趋势；然后结合采场安全生产允许温度进行反推，最终获取通风降温条件下的可采极限深度计算公式，并选取广西铜坑矿锌多金属矿体作为工程应用试验区进行论证分析。结果表明：基于风流的热交换模型可以推导出风流在井筒、巷道及工作面的温度计算公式，该公式与低温梯度、风流流经路径长度有关；假定工作面温度达到安全开采允许最高温度，可反推出该条件下矿山可采极限深度和巷道通风极限长度（仅采用通风降温措施时）；基于铜坑矿区锌多金属矿的实际条件，代入相关数据，验证计算所得极限开采深度符合实际，即所推导公式是可行的。     

煤矿岩巷快速掘进技术的探讨

随着煤矿开采强度的增大,采掘接替紧张问题已经成为限制煤炭产量进一步提升的障碍。要想缓解采掘接替紧张的情况,就要实现巷道的快速掘进。虽然煤巷的快速掘进问题已经解决,但是,岩巷的快速掘进是难点。简要阐述了目前常用的几种岩巷掘进技术,在此基础上分析了实现岩巷快速掘进的方法,以期为采矿掘进技术人员日后工作的顺利进行提供参考。     

边角煤短壁机械化充填开采技术研究

根据我国煤矿的地质条件,将传统房柱式采煤提升为安全高效的短壁机械化采煤方法,并与充填技术相结合,针对充填技术特点合理布置采区巷道,充分利用充填物料凝固后的支撑强度,控制地表下沉和岩层移动,对煤柱、不规则块段、地质构造较复杂的煤层以及"三下"压煤等边角煤开采等提供了切实可行的技术途径,提高了煤炭资源回收率和开采效率。     

采动影响区抽采钻井井身固化及抽采效果考察

为提高地面瓦斯抽采效果,对抽采期间钻井进行了窥视,发现当回采工作面推过地面钻井位置后,钻井井壁迅速破坏,无法充分发挥其应有的瓦斯抽采效应。通过对煤层顶板岩层受采动影响顶板活动过程的分析,优化地面抽采钻井井身结构,在原有的施工工艺上增加三开下筛管,增强钻孔的防护强度,抽采浓度提高了1.54.5倍,抽采纯量提高了1.55倍,工作面回风流和回风隅角瓦斯浓度显著下降,抽采效果明显提高。     

基于决策树的堆芯物理参数预测研究

反应堆堆芯核设计涉及大量方案的搜索与详细计算，缩短方案搜索时间有利于提高核设计效率。数据挖掘技术通过对大量数据进行学习与模式识别，可实现核设计方案物理参数的快速预测，更快地筛选出可行的备选堆芯方案。本文基于数据挖掘的决策树4种算法：C4.5、RepTree、Random Forest及Random Tree，在计算时以燃料富集度、含可燃毒物燃料棒数量及含量作为自变量，以寿期内k_eff不均匀系数偏差（KUCD）、径向功率不均匀系数偏差（RPNCD）、径向中子通量不均匀系数偏差（RFNCD）、堆芯寿期（CL）作为目标函数，构成目标函数符合度（CPF），利用大量已知核设计参数的组件及堆芯设计方案作为数据挖掘训练集，构建数据挖掘模型，并用于对未知核设计参数的组件方案集合（测试集）进行CPF快速预测。结果表明，4种算法利用训练集构建数据挖掘模型的时间在0.6 s以内，各算法的交叉验证精度均在0.7以上，其中C4.5算法对CPF预测精度最高；对测试集方案的核设计参数预测中，单个方案的预测时间均在0.9 s以内，而Random Forest算法对CPF等于4的预测效果最好。