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.     

First-trimester fetal sex determination in maternal serum using real-time PCR

An Erratum has been published for this article in Prenatal Diagnosis 22(13) 2002, 1241. Fetal sex prediction can be achieved using PCR targeted at the SRY gene by analysing cell-free fetal DNA in maternal serum. Unfortunately, the results reported to date show a lack of sensitivity, especially during the first trimester of pregnancy. Therefore, determination of fetal sex by maternal serum analysis could not replace karyotype analysis following chorionic villus sampling. A new highly sensitive real-time PCR was developped to detect an SRY gene sequence in maternal serum. Analysis was performed on 121 pregnant women during the first trimester of pregnancy (mean gestational age: 11.8 weeks). Among them, 51 had at least one previous male-bearing pregnancy. Results were compared with fetal sex. SRY PCR analysis of maternal serum was in complete concordance with fetal sex. Among the 121 pregnant women, 61 were bearing a male fetus and 60 a female fetus. No false-negative results were observed. Furthermore, no false-positive results occurred, even though 27 women carrying a female fetus during the current pregnancy had at least one previous male-bearing pregnancy. This study demonstrates that a reliable, non-invasive sex determination can be achieved by PCR analysis of maternal serum during the first trimester of pregnancy. This non-invasive approach for fetal sex prediction should have great implications in the management of pregnant women who are carriers of an X-linked genetic disorder. Prenatal diagnosis might thus be performed for male fetuses only, avoiding invasive procedures and the risk of the loss of female fetuses. Copyright © 2001 John Wiley & Sons, Ltd.     

基于LOADEST和数字滤波的水源地基流氮素输出定量方法应用实例

地表直接径流和基流均是流域非点源氮/磷养分输出的重要水文途径.科学认识和定量模拟基流氮/磷养分输出对于准确解析水源地水体非点源污染来源至关重要.基于Load Estimator模型和数字滤波算法,建立了定量水源地基流氮素输出的方法体系.以浙江省珊溪水源地的玉泉溪流域为例,利用玉泉溪2010-01—2013-12期间逐月总氮(TN)水质监测数据和逐日流量数据,展示了该方法的计算过程.结果表明,本文建立的水源地基流氮素输出定量方法结果合理,模拟精度高,决定系数和纳什系数分别为0.83和0.80;玉泉溪流域2010—2013年TN负荷量为141.21～274.68 t·a~(-1),平均208.63 t·a~(-1),年基流TN负荷量为84.39～168.68 t·a~(-1),平均127.69 t·a~(-1);基流对玉泉溪年均TN负荷量贡献率高达60%以上,流域基流养分输出对地表水体的污染应引起足够重视.     

基于改进遗传算法的河流水污染源反演方法

针对河流水污染应急响应过程中污染源排放历史迟知、未知的问题,结合多种群遗传算法和自适应遗传算法,利用一维河流水质模型和水质监测数据,研究建立了基于改进遗传算法的河流水污染定量源反演方法,实现了对河流污染源排放历史的识别与重构.将该方法应用于美国特拉基河流的3个不同流量下的示踪剂实验中,对示踪剂排放历史进行定量源反演分析.结果表明：IGA算法对高、中、低不同流量下的3次示踪剂实验均可以很好的重构和识别示踪剂排放历史,对于实际河流水污染源反演分析的误差均在可接受范围内.IGA算法在河流水污染源反演分析中具有一定的可靠性和稳定性,可为河流水污染精准溯源与治理提供科学的技术支撑.     

pH值对亚硝酸盐氧化菌动力学及功能基因的影响

为探究pH值对亚硝酸盐氧化菌（NOB）活性动力学影响,本试验采用序批式活性污泥（SBR）反应器,以富含NOB的活性污泥为对象,基于Monod模型考察不同pH值对NOB活性动力学的影响并进行统计学分析.结果表明,Monod方程可较好地反映不同pH值条件下基质底物浓度对NOB比亚硝态氮氧化速率（SNiOR）的影响,且pH=7.0时动力学参数Ks为（6.167mg/L）,r_max为[1.134g/（g·d）],此时NOB活性最好.利用钟形经验模型进行非线性回归拟合,最大比降解速率（r_max）随pH值的增大呈钟形变化,本试验NOB的最佳pH值为（6.9±0.1）,其中r_max维持在r_opt一半以上的pH值范围（ω）为（3.26±0.4）.以亚硝酸盐氧化还原酶类基因（nxrA、nxrB）为引物,基于荧光定量PCR技术分析结果显示,在不同pH值条件下nxrA基因和nxrB基因拷贝数的变化趋势均与动力学参数（Ks、r_max）的规律一致,且nxrA和nxrB基因在系统的降解过程中起协同作用.     

红树林土壤解磷菌的分离鉴定及解磷特性

从罗源湾红树林根际土壤中分离解磷菌,筛选出洋葱伯克霍尔德菌（Burkholderia cepacia, NR 113645.1）和短小芽孢杆菌（Bacillus pumilus, NR 043242.1）,研究它们的解磷特性和动态解磷过程. HPLC结果表明, B. cepacia菌液上清中含葡萄糖酸、丙酮酸、乳酸、乙酸、丁二酸, 这些有机酸的产生导致磷酸盐溶解. B.pumilus菌液上清中仅检测到少量的葡萄糖酸, 因此该菌溶磷效果不佳. 在细菌动态溶磷过程中发现, 经B. cepacia菌处理的磷酸钙, 其X射线衍射（XRD）特征峰强度随时间的增长降低, 显微镜图像显示磷酸钙颗粒随时间增长变小, 甚至消失. 而经B.pumilus菌处理的结果表明, 在试验期间内磷酸钙峰强度和颗粒大小没有明显变化. 细菌产生有机酸是溶解磷酸盐的重要前提, 这是解决板结土壤中难溶性磷源转化为生物可利用磷源问题的关键.     

典型西南工业城市春冬季PM2.5来源与潜在源区分析——以柳州市为例

为了揭示柳州城区春冬季PM_2.5的来源及其潜在源区分布和贡献,利用2018年24h自动监测数据和气象数据对柳州市大气污染物浓度变化特征进行了分析,并且使用后向轨迹模型（HYSPLIT）对春冬季柳州市PM_2.5逐日72h气流后向轨迹和前向轨迹进行聚类分析,同时结合潜在源贡献因子分析法（WPSCF）和轨迹浓度权重法（WCWT）对其潜在源区和浓度贡献进行了分析.结果显示,（1）在研究期内,不利的主导风向和工业区布局导致研究区PM_2.5在春冬季污染较严重,且工业源和交通源是其主要本地来源;（2）春冬季PM_2.5高值主要来源于西北和东南方向,其中,西北向PM_2.5主要来源于本地排放,且浓度在空间上呈现西高东低的趋势;（3）春季后向轨迹PM_2.5浓度整体大于冬季,春冬季中对柳州市PM_2.5影响最大轨迹均来自东部的短距离输送,而来自西北的气流轨迹输对PM_2.5贡献最低.春冬季柳州市大气PM_2.5通过气流传输对贵州地区大气环境有较大影响;（4）春季,柳州市PM_2.5的主要潜在源区分布在广西东南部、广东中西部、南海沿岸海域、湖南中部、江西西北部、湖北东部及安徽西北部;冬季,主要分布在广西东南部、广东西南部和南海沿岸海域.     

中国化学品环境暴露评估领域的综合区划

大尺度空间（中国）化学品环境暴露多介质模型的建立需要先对空间做相应的区划.本文以ArcGIS软件为平台,通过有空间约束的聚类方法和水文分析方法相耦合,建立全国范围的综合分区.全国范围分为华南综合大区、华北综合大区、西北综合大区、华东综合大区、东北综合大区和西南综合大区6个综合大区.每个综合大区又分别包含了若干个综合小区,小区总数为38个.该研究为我国暴露评估的综合分区提供了一个方案,在多介质模型的建立中起着重要的作用.     

基于FY-4A卫星数据反演气溶胶光学厚度及分析应用

风云四号A星（FY-4A）是我国新一代静止气象卫星,多通道扫描成像辐射计（AGRI）是风云四号静止气象卫星的主要载荷之一.为探究FY4A数据用于气溶胶光学厚度（AOD）反演的可行性,基于FY-4A/AGRI数据,利用暗像元算法对2019年2月23~26日及10月27~30日京津冀地区AOD进行反演研究,并与AERONET地基观测AOD数据进行了对比分析.结果表明,基于FY-4A数据及构建响应函数库,通过暗像元方法能较好的反演出京津冀地区气溶胶空间分布;AOD主要的高值区体现在京津冀中南部地区并向周边郊区逐渐降低,在AOD值较大时此特征较为明显;将反演值与同期AERONET地基观测数据对比验证,相关系数达到0.869,均方根误差为0.221,表明AOD反演值与观测值吻合较好,FY-4A卫星数据反演AOD具有一定可行性.     

A Variance Estimator for Constrained Estimates of Change in Relative Categorical Frequencies

Consistent estimators of change and state becomes an issue when sample data come from a mix of permanent and temporary observation units. A joint maximum likelihood estimator of state and change creates estimates of state that depend on antecedent viz. posterior survey results and may differ from estimates of state derived from a single-date analysis of the sample data. A constrained estimator of change in relative categorical frequencies that eliminates this potential inconsistency is proposed and a model based estimator of their sampling variance is developed. The performance of the constrained estimator is quantified against six criteria and a joint maximum likelihood estimator in simulated sampling from 15 populations with three combinations of permanent and temporary samples, four to six categorical class attributes, and constant size between sampling dates. Bias of the constrained estimators was negligible but larger than for joint maximum likelihood estimators. Mean absolute deviations and variances of constrained estimators were generally at par with the joint estimators. Constrained estimators of root mean square errors and achieved coverage of nominal confidence intervals of constrained estimators were occasionally better. A generalized variance function for the constrained estimates of change is provided as a computational shortcut.