2种灰色模型在矿区地表沉陷预计中的适用性

目前矿区地表单点沉陷动态预计方法主要基于传统的水准测量数据,监测方法单一,成本高,观测点易破坏,不能保证地表形变信息的实时性,且采用灰色模型进行地表沉陷预计时只针对单一模型的应用,没有结合模型自身特点分析其适用性。以袁店二矿7221工作面为试验区域,采用合成孔径雷达差分干涉测量技术监测矿区地表沉陷量,分别建立了描述沉陷量与时间关系的GM(1,1)与灰色Verhulst模型进行地表沉陷量预计,实现了矿区地表沉陷监测与动态预计一体化。通过比较、分析GM(1,1)与灰色Verhulst模型对地表沉陷量的拟合及预计结果,得出了2种灰色模型在矿区地表沉陷预计中的适用性:在矿区开采沉陷开始至活跃前期,若地表单点沉陷量曲线呈近似单峰型,则宜采用GM(1,1)进行短期预计;当矿区地表沉陷进入衰退阶段,单点沉陷量曲线呈平底饱和状态,则宜采用灰色Verhulst模型进行中长期预计。     

基于灰色关联度的微孔端面机械密封开启力共性影响参数分析

目前微孔端面机械密封采用的几何参数繁多,缺乏明确物理意义。为找出跨孔型的共性几何参数,基于Fluent多相流空化模型,针对矩形孔、菱形孔、椭圆孔端面机械密封,建立密封间隙流体的三维数值计算模型,计算得到144条开启力数据;在分析开启力和几何尺寸之间的关系、额外开启力产生机制的基础上,提出等效长度和等效宽度2个具有物理意义的几何参数;采用灰色关联度分析法,分析和验证等效长度、等效宽度、方向角、面积比对开启力的影响,发现4个因素对3种微孔密封的间隙流体开启力都有影响,但等效长度与等效宽度的影响较方向角、面积比大。     

Emergency decision support modeling for COVID-19 based on spherical fuzzy information

Significant emergency measures should be taken until an emergency event occurs. It is understood that the emergency is characterized by limited time and information, harmfulness and uncertainty, and decision-makers are always critically bound by uncertainty and risk. This paper introduces many novel approaches to addressing the emergency situation of COVID-19 under spherical fuzzy environment. Fundamentally, the paper includes six main sections to achieve appropriate and accurate measures to address the situation of emergency decision-making. As the spherical fuzzy set (FS) is a generalized framework of fuzzy structure to handle more uncertainty and ambiguity in decision-making problems (DMPs). First, we discuss basic algebraic operational laws (AOLs) under spherical FS. In addition, elaborate on the deficiency of existing AOLs and present three cases to address the validity of the proposed novel AOLs under spherical fuzzy settings. Second, we present a list of Einstein aggregation operators (AgOp) based on the Einstein norm to aggregate uncertain information in DMPs. Thirdly, we are introducing two techniques to demonstrate the unknown weight of the criteria. Fourthly, we develop extended TOPSIS and Gray relational analysis approaches based on AgOp with unknown weight information of the criteria. In fifth, we design three algorithms to address the uncertainty and ambiguity information in emergency DMPs. Finally, the numerical case study of the novel carnivorous (COVID-19) situation is provided as an application for emergency decision-making based on the proposed three algorithms. Results explore the effectiveness of our proposed methodologies and provide accurate emergency measures to address the global uncertainty of COVID-19.     

灰色关联分析法在评价珠一坳陷盖层质量中的应用

随着盖层研究的深入，其评价方法有了不少新转变，从物性封闭的测井评价方法、超压封闭的测井评价方法、烃浓度封闭的测井评价方法、岩石力学评价方法、微观角度分析法等，到现在BP神经网络法、灰色关联分析法、灰色规划聚类法等。基于灰色关联分析法配合测井资料定量综合分析研究区珠一坳陷盖层的质量，选取了盖层累计厚度、盖地比、总孔隙度、有效孔隙度、渗透率、含砂量、突破压力7个参数进行评价，得到研究区盖层质量等级:珠江组下段以及文昌组、恩平组为Ⅰ类盖层；珠海组和珠江组上段为Ⅱ类盖层；韩江组为Ⅲ类盖层，其分析结果与实际勘探结果相一致。     

准噶尔盆地南缘四棵树地区中浅层砾岩层识别刻画

准噶尔盆地南缘中、下组合构造普遍位于中浅层砾岩层之下,砾岩层具有横向展布范围广且不规则、纵横向速度变化大等特点,严重制约中、下组合目标的精细落实。因此,搞清砾岩层结构及分布特征,对下伏构造形态和高点位置的准确落实尤为重要。结合微测井、钻井、测井资料以及地震剖面对准噶尔盆地南缘四棵树地区中浅层砾岩层结构特征进行分析,对低速和高速砾岩层的顶底界面进行识别刻画,并分区、分段建立了砾岩层时深关系曲线,用于指导深度域下组合构造成图和叠前深度偏移中浅层速度模型的建立。实例应用证明,南缘四棵树地区中浅层砾岩层识别刻画对准确落实构造目标,提高地震剖面成像品质至关重要,同样也可以在准噶尔盆地南缘其他类似地区进行推广应用。     

An intelligent EGWO-SCA-CS algorithm for PSS parameter tuning under system uncertainties

This paper proposes a novel hybrid technique called enhanced grey wolf optimization-sine cosine algorithm-cuckoo search (EGWO-SCA-CS) algorithm to improve the electrical power system stability. The proposed method comprises of a popular grey wolf optimization (GWO) in an enhanced and hybrid form. It embraces the well-balanced exploration and exploitation using the cuckoo search (CS) algorithm and enhanced search capability through the sine cosine algorithm (SCA) to elude the stuck to the local optima. The proposed technique is validated with the 23 benchmark functions and compared with state-of-the-art methods. The benchmark functions consist of unimodal, multimodal function from which the best suitability of the proposed technique can be identified. The robustness analysis also presented with the proposed method through boxplot, and a detailed statistical analysis is performed for a set of 30 individual runs. From the inferences gathered from the benchmark functions, the proposed technique is applied to the stability problem of a power system, which is heavily stressed with the nonlinear variation of the load and thereby operating conditions. The dynamics of power system components have been considered for the mathematical model of a multimachine system, and multiobjective function has been framed in tuning the optimal controller parameters. The effectiveness of the proposed algorithm has been assessed by considering two case studies, namely, (i) the optimal controller parameter tuning, and (ii) the coordination of oscillation damping devices in the power system stability enhancement. In the first case study, the power system stabilizer (PSS) is considered as a controller, and a self-clearing three-phase fault is considered as the system uncertainty. In contrast, static synchronous compensator (STATCOM) and PSS are considered as controllers to be coordinated, and perturbation in the system states as uncertainty in the second case study.     

云南镇源难处理金精矿工艺矿物学研究

利用X射线衍射、X射线荧光光谱（XRF）、X射线能谱分析（EDX）和矿物解离分析（MLA）等检测手段对云南镇源难处理金精矿的化学组成、矿物组成、硫化物特征以及金的分布情况进行了详细的工艺矿物学研究。结果显示：该金矿以硫化矿、碳酸盐和硅酸盐类矿物为主，含有3.18%的有机碳和2.37%的无机碳。通过金的诊断浸出发现96.16%的金被硫化物包裹，少量金以单体形式存在。通过对主要载金矿物黄铁矿、辉锑矿和毒砂的粒度、包裹及裸露情况进行分析，得出大部分硫化矿以解离单体的形式存在，少部分与其他矿物共生。根据MLA测试及金的诊断浸出，认为大部分金被硫化矿完全包裹，处理该矿石时应先进行硫化物包裹层的氧化处理，再进行金矿的浸出。     

基于DCCA-NSEn的系统耦合网络建模与评估

针对流程工业生产系统监测点多,各监测点间具有相关性的特点,提出了一种基于去趋势交叉分析-网络结构熵(detrended cross-correlation analysis-net structure entropy,简称DCCA-NSEn)的复杂机电系统多变量耦合网络建模与状态评估的方法。该方法利用DCCA算法计算多变量两两之间的相关性,构建反映多变量耦合关系的加权网络模型,对监测序列进行滑移求解,得到系统耦合关系网络动态演化模型。利用NSEn方法分析耦合关系网络的结构熵,根据熵随时间的变化趋势对复杂机电系统的服役演化状态进行评估。笔者选取某压缩机组的真实生产数据进行DCCA-NSEn方法验证,然后用耦合去趋势波动分析(coupling detrended fluctuation analysis,简称CDFA)方法对同一组生产数据进行多变量分析,对两方法的分析结果进行对比。结果表明:与DCCA方法相比,本方法具有多变量同时监测评估的优势;与同样是多变量分析的CDFA方法相比,本方法具有评估效果稳定,对系统的异常状态检测效果更明显的优势。     

基于灰色关联度与BP神经网络的清河水库总氮浓度预测模型

为了实现清河水库总氮浓度的预测,建立了基于灰色关联分析的BP神经网络水质预测模型(GRABP),即采用灰色关联度的方法,选取总磷、挥发酚、化学需氧量、pH值、氨氮五项水质指标作为BP神经网络总氮预测模型的输入变量,根据五项最优影响因子与总氮浓度的对应关系,对模型进行了训练,并将训练好的模型应用于2016年8~12月的总氮浓度预测中。结果表明,GRA-BP网络模型较BP网络具有较高的预测精度,预测的相对误差均在5%以内,可为清河水库的水质管理提供科学的指导。