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.     

基于不同废污泥源的短程反硝化快速启动及稳定性

为探究不同废污泥源快速启动短程反硝化和实现稳定NO_2~--N积累的可行性,在3个完全相同的SBR反应器(S1、S2和S3)分别接种:实验室城市污水反硝化除磷系统排泥、城市污水厂剩余污泥及河涌底泥,比较其短程反硝化启动快慢和NO_2~--N积累特性,考察系统短程反硝化活性和NO_3~--N→NO_2~--N转化性能,并从微生物学角度分析反应器功能菌群特征.结果表明,在乙酸钠为唯一碳源、高碱度和适宜COD/NO_3~--N比进水条件下,3个SBR短程反硝化反应器在短时间内均能够成功启动,系统平均NO_3~--N→NO_2~--N转化率为S1 S2 S3(75. 92% 73. 36% 69. 90%).同时发现持续低温条件下S1和S2呈现不同程度的短程反硝化性能恶化趋势,但S3能够稳定维持良好NO_2~--N积累性能.微生物高通量测序表明,变形菌门和拟杆菌门居PD系统主导地位,3个短程反硝化反应器NO_2~--N积累关键功能菌属Thauera属丰度差异明显:S3 S1 S2(25. 09% 4. 71% 3. 60%),表明S3具备稳定高效的NO_2~--N积累性能,同时高丰度Thauera属可能是维持低温短程反硝化活性的重要原因.     

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.     

方头柱塞冷挤镦工艺及模具设计

分析了硬铝合金方头柱塞件的冷挤镦工艺 ,设计了方头柱塞冷挤镦件图 ,计算及选用了毛坯形状及尺寸 ,验算了许可变形程度 ,选择了冷成形用的设备 ,拟制了冷挤镦工艺前的毛坯处理并设计了方头柱塞冷挤镦模具结构。     

Insights into the mechanism of the deterioration of mainstream partial nitritation/anammox under low residual ammonium

Residual ammonium is a critical parameter affecting the stability of mainstream partial nitritation/anammox (PN/A), but the underlying mechanism remains unclear. In this study, mainstream PN/A was established and operated with progressively decreasing residual ammonium. PN/A deteriorated as the residual ammonium decreased to below 5 mg/L, and this was paralleled by a significant loss in anammox activity in situ and an increasing nitrite oxidation rate. Further analysis revealed that the low-ammonium condition directly decreased anammox activity in situ via two distinct mechanisms. First, anammox bacteria were located in the inner layer of the granular sludge, and thus were disadvantageous when competing for ammonium with ammonium-oxidizing bacteria (AOB) in the outer layer. Second, the complete ammonia oxidizer (comammox) was enriched at low residual ammonium concentrations because of its high ammonium affinity. Both AOB and comammox presented kinetic advantages over anammox bacteria. At high residual ammonium concentrations, nitrite-oxidizing bacteria (NOB) were effectively suppressed, even when their maximum activity was high due to competition for nitrite with anammox bacteria. At low residual ammonium concentrations, the decrease in anammox activity in situ led to an increase in nitrite availability for nitrite oxidation, facilitating the activation of NOB despite the dissolved oxygen limitation (0.15–0.35 mg/L) for NOB persisting throughout the operation. Therefore, the deterioration of mainstream PN/A at low residual ammonium was primarily triggered by a decline in anammox activity in situ. This study provides novel insights into the optimized design of mainstream PN/As in engineering applications.     

Application of Bayesian regularized BP neural network model for analysis of aquatic ecological data-A case study of chlorophyll-a prediction in Nanzui water area of Dongting Lake

Bayesian regularized BP neural network（BRBPNN） technique was applied in the chlorophyll-α prediction of Nanzui water area in Dongting Lake. Through BP network interpolation method, the input and output samples of the network were obtained. After the selection of input variables using stepwise/multiple linear regression method in SPSS i1.0 software, the BRBPNN model was established between chlorophyll-α and environmental parameters, biological parameters. The achieved optimal network structure was 3-11-1 with the correlation coefficients and the mean square errors for the training set and the test set as 0.999 and 0.000？8426, 0.981 and 0.0216 respectively. The sum of square weights between each input neuron and the hidden layer of optimal BRBPNN models of different structures indicated that the effect of individual input parameter on chlorophyll- α declined in the order of alga amount 〉 secchi disc depth（SD） 〉 electrical conductivity （EC）. Additionally, it also demonstrated that the contributions of these three factors were the maximal for the change of chlorophyll-α concentration, total phosphorus（TP） and total nitrogen（TN） were the minimal. All the results showed that BRBPNN model was capable of automated regularization parameter selection and thus it may ensure the excellent generation ability and robustness. Thus, this study laid the foundation for the application of BRBPNN model in the analysis of aquatic ecological data（chlorophyll-α prediction） and the explanation about the effective eutrophication treatment measures for Nanzui water area in Dongting Lake.     

后张梁管道摩阻损失测试技术与数据处理

针对常规测试中存在的问题 ,改进了管道摩阻损失的测试方法 ,消除了喇叭口等因素影响 ,使得管道摩阻测试更加合理和准确。介绍了后张法预应力管道摩阻损失的计算 ,结合秦沈客运专线双线整孔简支箱梁的管道摩阻损失测试 ,利用最小二乘原理对测试数据进行处理 ,得到了摩阻系数 μ和k的计算公式 ,所得参数为施工提供了依据。最后讨论了摩阻系数对张拉控制应力的影响 ,提出现场测试的一般性原则和要求。     

室内有毒易爆气体的检测

随着城市生活水平的提高,室内装修装饰日益普及.各种新材料的引入,使得室内的各种有害气体总量日益增加.长期生活在这样的环境下,会对人体健康产生极为不利的影响.居民生活用煤气,在给人们生活带来便利的同时,作为一种可燃性、有毒气体,也具有极强的危害性.因此,研制一种能检测多种有害气体的实用仪器,具有很大的应用价值.本仪器为便携式多功能仪器,采用可充电电池供电,可以检测ppb的挥发性气体,显示气体浓度值,也可检测煤气,并具有声光报警功能.     

Using multivariate regression modeling for sampling and predicting chemical characteristics of mixed waste in old landfills

Municipal solid waste landfills pose a threat on environment and human health, especially old landfills which lack facilities for collection and treatment of landfill gas and leachate. Consequently, missing information about emission flows prevent site-specific environmental risk assessments. To overcome this gap, the combination of waste sampling and analysis with statistical modeling is one option for estimating present and future emission potentials. Optimizing the tradeoff between investigation costs and reliable results requires knowledge about both: the number of samples to be taken and variables to be analyzed.This article aims to identify the optimized number of waste samples and variables in order to predict a larger set of variables. Therefore, we introduce a multivariate linear regression model and tested the applicability by usage of two case studies. Landfill A was used to set up and calibrate the model based on 50 waste samples and twelve variables. The calibrated model was applied to Landfill B including 36 waste samples and twelve variables with four predictor variables.The case study results are twofold: first, the reliable and accurate prediction of the twelve variables can be achieved with the knowledge of four predictor variables (Loi, EC, pH and Cl). For the second Landfill B, only ten full measurements would be needed for a reliable prediction of most response variables. The four predictor variables would exhibit comparably low analytical costs in comparison to the full set of measurements. This cost reduction could be used to increase the number of samples yielding an improved understanding of the spatial waste heterogeneity in landfills.Concluding, the future application of the developed model potentially improves the reliability of predicted emission potentials. The model could become a standard screening tool for old landfills if its applicability and reliability would be tested in additional case studies.