PLS-based EWMA fault detection strategy for process monitoring

Fault detection (FD) and diagnosis in industrial processes is essential to ensure process safety and maintain product quality. Partial least squares (PLS) has been used successfully in process monitoring because it can effectively deal with highly correlated process variables. However, the conventional PLS-based detection metrics, such as the Hotelling's T² and the Q statistics are ill suited to detect small faults because they only use information from the most recent observations. Other univariate statistical monitoring methods, such as the exponentially weighted moving average (EWMA) control scheme, has shown better abilities to detect small faults. However, EWMA can only be used to monitor single variables. Therefore, the main objective of this paper is to combine the advantages of the univariate EWMA and PLS methods to enhance their performances and widen their applicability in practice. The performance of the proposed PLS-based EWMA FD method was compared with that of the conventional PLS FD method through two simulated examples, one using synthetic data and the other using simulated distillation column data. The simulation results clearly show the effectiveness of the proposed method over the conventional PLS, especially in the presence of faults with small magnitudes.     

井下光谱多参数分析系统开发

本系统基于气体浓度光学分析方法理论朗伯-比尔(Lambert-Beer)定律、光谱气体检测技术开发,实现了对煤矿火灾与瓦斯灾害超前预警、灾害产生的有毒有害气体实时监测和煤矿环境气体爆炸危险性辨识,对于煤矿灾害防治、救灾过程中杜绝次生灾害,保障煤矿工人及救护队员的生命安全,促进煤矿安全生产具有重要意义。     

SELECTING TREND TESTS FOR WATER QUALITY SERIES WITH SERIAL CORRELATION AND MISSING VALUES1

ABSTRACT: The use of nonparametric tests for monotonic trend has flourished in recent years to support routine water quality data analyses. The validity of an assumption of independent, identically distributed error terms is an important concern in selecting the appropriate nonparametric test, as is the presence of missing values. Decision rules are needed for choosing between alternative tests and for deciding whether and how to pre-process data before trend testing. Several data pre-processing procedures in conjunction with the Mann-Kendall tau and the Seasonal Kendall test (with and without serial correlation correction) are evaluated using synthetic time series with generated serial correlation and missing data. A composite test (pre-testing for serial correlation followed by one of two trend tests) is evaluated and was found to perform satisfactorily.     

Decision making under uncertainty in case of soil remediation

Decisions on soil remediation are one of the most difficult management issues of municipal and state agencies. The assessment of contamination is uncertain, the costs of remediation are high, and the impacts on the environment are multiple. This paper presents a general, transparent, and consistent method for decision making among the remediation alternatives. Soil washing, phytoremediation, and no remediation are exemplarily considered. Multi-criteria utility functions including (a) the cost of remediation (b) the impact on human health and agricultural productivity, and (c) the economic gain after remediation are constructed using probability density functions representing contamination for all site coordinates. Herewith, the probability of different types of (i) correct decisions such as a hit or a true rejection and (ii) erroneous decisions such as a false alarm or miss are examined. The decision theoretic model is applied to a case study on heavy metal contaminated soil. This case study reveals the non-linear structure of multi-criteria-decision making. The case study shows that the geostatistical uncertainties of the log-normal distributed soil contamination must be taken into account: When uncertainties are not considered and the utilities are assessed according to the estimated value for a spatial unit, only few (N=26) spatial units result where the utility score of the alternative soil washing are higher than the utility score to the no remediation alternative. However, when taking into account geostatistical uncertainties of the log-normal soil distribution this number is about ten times greater (N=237). Furthermore, the use of 'maximizing expected utility' as decision rule is critical in that it may lead to a high probability of misses.     

全球液化石油气运输网络贸易社区特征及其演化分析

液化石油气在全球清洁能源消耗市场中扮演着极为重要的角色,其通过船舶在不同港口之间进行运输,而港口之间通过局部密集的运输关系,形成了联系极为紧密的贸易社区。采用复杂网络社区探测方法,基于2013—2017年全球液化石油气船舶轨迹大数据构建运输网络,并对其贸易社区特征及其演化趋势开展分析。结果表明：（1）液化石油气（Liquefied Petroleum Gas,LPG）贸易社区内的港口之间的联系更加紧密,不同社区内的枢纽港口联系也日益紧密;（2）各个贸易社区的规模呈现出增长趋势,且同一社区内的港口在地理空间上变得更为集聚;（3）亚太地区、中东、西北欧和地中海地区形成的社区在全球LPG贸易中一直保持着重要地位,而随着时间推移,美洲社区已逐渐从一个相对孤立的社区发展成为与其他社区存在紧密联系的社区。     

电化学微传感器检测水中痕量铜离子

为改善微电极在阳极溶出伏安法检测重金属离子过程中低电流响应和低电催化能力的缺点,提出了一种在碳纤维微电极表面合成还原氧化石墨烯/纳米金材料制得还原氧化石墨烯纳米金修饰碳纤维微电极（rGO/AuNPs CFMEs）的方法.通过SEM表征,所制备的rGO/AuNPs CFMEs具有比表面积高、吸附能力强和催化活性好的特点,因此改性微电极适合作为方波阳极溶出伏安法（SWASV）测定水中铜离子（Cu²⁺）的工作电极.在构建微传感器测试水中痕量铜离子系统后,对pH值、电导率、富集时间和富集电位等检测条件进行了优化.在pH值为4,电导率为36.1S/m,富集时间为360s,富集电位为-1.2V的最佳条件下,铜的线性范围和检出限分别0~1.0μmol/L和2.4nmol/L.此外,微传感器的可重复性、长期稳定性以及选择性也得到了验证.     

“天舟一号”货运飞船在轨污染探测数据分析

目的分析"天舟一号"(TZ-1)货运飞船的空间污染探测器的在轨探测数据。方法 TZ-1货运飞船上的污染探测器采用石英晶体微量天平(QCM),利用污染物沉积在QCM探测器传感晶片表面引起的振荡频率变化情况来分析航天器在轨空间污染环境。根据其探测原理,通过分析下传的原始数据,得到航天器在轨的污染累积量结果。结果航天器在轨飞行过程中,污染累积已达到会导致污染敏感器件光学性能明显退化的程度,并且污染源温度是影响航天器污染累积量的一个重要因素,可以将其作为航天器在轨污染控制的重要措施。结论该研究结果可以为航天器污染设计、控制提供参考,为提高航天器可靠性设计提供数据支持。     

地表水样品自然沉降时间对总磷测定结果的影响分析

地表水中总磷含量高低是评定水质优劣的一项重要指标,分析数据表明丰水期中总磷主要来源于泥沙中吸附的有机磷和无机磷,而溶解态磷含量较少;水样中总磷含量随着自然沉降时间的延长而逐渐降低.因此样品采集回来后样品的自然沉降时间是影响地表水中总磷测定结果的一个主要原因之一.