Probabilistic monitoring of chemical processes using adaptively weighted factor analysis and its application

As a probabilistic statistical method, factor analysis (FA) has recently been introduced into process monitoring for the probabilistic interpretation and performance enhancement of noisy processes. Generally, FA methods employ the first several factors that are regarded as the dominant motivation of the process for process monitoring; however, fault information has no definite mapping relationship to a certain factor, and useful information might be suppressed by useless factors or submerged under retained factors, leading to poor monitoring performance. Weighted FA (WFA) for process monitoring is proposed to solve the problem of useful information being submerged and to improve the monitoring performance of the GT² statistic. The main idea of WFA is firstly building a conventional FA model and then using the change rate of the GT² statistics (RGT²) to evaluate the importance of each factor. The important factors tend to have larger RGT² values, and the larger weighting values are then adaptively assigned to these factors to highlight useful fault information. Case studies on both a numerical process and the Tennessee Eastman process demonstrate the effectiveness of the WFA method. Monitoring results indicate that the performance of the GT² statistic is improved significantly compared with the conventional FA method.     

Predicting the behavior of a pipe network using the “critical Z-score” as its performance indicator

The paper deals with pipe reliability assessment in two networks using the Discriminant Analysis and classification (DAC) method. The pipes of each network are divided in two groups based on whether they failed at least once (failures group) or not (successes group). Several scenarios resulting from combining pipe characteristics (such as length, diameter, wall thickness, operating pressure, grade, and product (fluid in the pipe), lifetime) are being analyzed. A sensitivity analysis of the data available takes place to check the stability of the results. The criterion of the “critical Z-score” is finally used as an indicator predicting the pipe's future state (fail or not). The goals for each network are to develop a model that can correctly classify network pipes to successes or failures; define the pipe characteristics to be “blamed” for the pipes' behavior; and predict whether a pipe will fail or not. Studying the results of the DAC method application at the case study networks, a SWOT analysis is attempted in order to find out whether and under which presuppositions DAC can be successfully applied to water pipe networks.     

Improved multi-scale kernel principal component analysis and its application for fault detection

In this paper the multiscale kernel principal component analysis (MSKPCA) based on sliding median filter (SFM) is proposed for fault detection in nonlinear system with outliers. The MSKPCA based on SFM (SFM-MSKPCA) algorithm is first proposed and applied to process monitoring. The advantages of SFM-MSKPCA are: (1) the dynamical multiscale monitoring method is proposed which combining the Kronecker production, the wavelet decomposition technique, the sliding median filter technique and KPCA. The Kronecker production is first used to build the dynamical model; (2) there are more disturbances and noises in dynamical processes compared to static processes. The sliding median filter technique is used to remove the disturbances and noises; (3) SFM-MSKPCA gives nonlinear dynamic interpretation compared to MSPCA; (4) by decomposing the original data into multiple scales, SFM-MSKPCA analyze the dynamical data at different scales, reconstruct scales contained important information by IDWT, eliminate the effects of the noises in the original data compared to kernel principal component analysis (KPCA). To demonstrate the feasibility of the SFM-MSKPCA method, its process monitoring abilities are tested by simulation examples, and compared with the monitoring abilities of the KPCA and MSPCA method on the quantitative basis. The fault detection results and the comparison show the superiority of SFM-MSKPCA in fault detection.     

Nonlinear multiscale modelling for fault detection and identification

In order to detect abnormal events at different scales, a number of multiscale multivariate statistical process control (MSPC) approaches which combine a multivariate linear projection model with multiresolution analysis have been suggested. In this paper, a new nonlinear multiscale-MSPC method is proposed to address multivariate process performance monitoring and in particular fault diagnostics in nonlinear processes. A kernel principal component analysis (KPCA) model, which not only captures nonlinear relationships between variables but also reduces the dimensionality of the data, is built with the reconstructed data obtained by performing wavelet transform and inverse wavelet transform sequentially on measured data. A guideline is given for both off-line and on-line implementations of the approach. Two monitoring statistics used in multiscale KPCA-based process monitoring are used for fault detection. Furthermore, variable contributions to monitoring statistics are also derived by calculating the derivative of the monitoring statistics with respect to the variables. An intensive simulation study on a continuous stirred tank reactor process and a comparison of the proposed approach with several existing methods in terms of false alarm rate, missed alarm rate and detection delay, demonstrate that the proposed method for detecting and identifying faults outperforms current approaches.     

浓硫酸管道泄漏监测系统的设计与应用

介绍了长距离输送浓硫酸管道泄漏监测系统所采用的＂负压波＋输差分析＂测漏技术原理,有效地实现了较大泄漏的在线监测报警和定位,报警点定位误差小于或等于500 m,报警反应时间不超过120 s。测漏系统设计与应用是成功的,但不能实时发现渗漏情况,还需进一步改进。     

水质模型预测配水管网水质面临的问题与对策

受配水管网系统运行工况的复杂程度、建模方法的经验性及模型初始参数的不准确性等因素的影响,水质模型的预测结果具有一定的不确定性。通过采取控制模型初始参数、延时模拟校核或多工况校验、进行模型脆弱性评价等措施可以建立水质预测风险"防火墙",为帮助供水企业更广泛地应用模型预测水质提供了理论依据。     

Fault detection, identification and diagnosis using CUSUM based PCA

In this paper, a cumulative sum based statistical monitoring scheme is used to monitor a particular set of the Tennessee Eastman Process (TEP) faults that could not be properly detected or diagnosed with other fault detection and diagnosis methodologies previously reported.T² and Q statistics based on the cumulative sums of all available measurements were successful in observing these three faults. For the purpose of fault isolation, contribution plots were found to be inadequate when similar variable responses are associated with different faults. Fault historical data is then used in combination with the proposed CUSUM based PCA model to unambiguously characterize the different fault signatures. The proposed CUSUM based PCA was successful in detecting, identifying and diagnosing both individual as well as simultaneous occurrences of these faults.     

Application of multivariate latent variable modeling to pilot-scale spray drying monitoring and fault detection: Monitoring with fundamental knowledge

This work describes the use of multivariate latent variable modeling (LVM) to enhance fundamental understanding of the operational space, the scale differences and the common-cause variability present in the operation of a pharmaceutical spray-dryer. LVM provided a real-time process monitoring and fault detection tool for continuous quality assurance. A latent variable model was built and tested using commercially available software in a pilot-scale facility at Bend Research Pharmaceutical Process Development Inc. (BRPPD) in Bend, OR. The key learning from the exercise at the pilot-scale helped identify and understand the normal variability of the commercial scale equipment.A key advantage of the LVM approach is that the variability that drives the process is easily understood in a fundamental way by interpreting the model parameters in light of fundamental engineering knowledge (e.g., transport phenomena, thermodynamics). The understanding of the common-cause variability enables the better understanding of the differences across scales for this unit. In monitoring the process, the faults are not only detected in a statistical way, but also understood in a fundamental way by using the model to track down the driving forces that were involved in detecting such fault (e.g., an abnormal behavior of the gas momentum across the unit).     

Constrained optimization of combustion in a simulated coal-fired boiler using artificial neural network model and information analysis

Combustion in a boiler is too complex to be analytically described with mathematical models. To meet the needs of operation optimization, on-site experiments guided by the statistical optimization methods are often necessary to achieve the optimum operating conditions. This study proposes a new constrained optimization procedure using artificial neural networks as models for target processes. Information analysis based on random search, fuzzy c-mean clustering, and minimization of information free energy is performed iteratively in the procedure to suggest the location of future experiments, which can greatly reduce the number of experiments needed. The effectiveness of the proposed procedure in searching optima is demonstrated by three case studies: (1) a bench-mark problem, namely minimization of the modified Himmelblau function under a circle constraint; (2) both minimization of NO_x and CO emissions and maximization of thermal efficiency for a simulated combustion process of a boiler; (3) maximization of thermal efficiency within NO_x and CO emission limits for the same combustion process. The simulated combustion process is based on a commercial software package CHEMKIN, where 78 chemical species and 467 chemical reactions related to the combustion mechanism are incorporated and a plug-flow model and a load-correlated temperature distribution for the combustion tunnel of a boiler are used.     

Simple sensor for the determination of phenol and its derivatives in water based on enzyme tyrosinase

Design of a new sensor, based on enzyme tyrosinase, suitable for phenol determination in water was described. The influence of measuring parameters such as pH of the sample and enzyme concentration on analytical performance of the sensor was evaluated. Calibration curves were constructed for catechol, 4-methylcatechol, 4-tertbutylcatechol, phenol, 4-chlorophenol, 2-tertbutylphenol and cresols (p and m isomers of cresol were treated separately) and sensitivity as well as LOD and LOQ were estimated for these phenols. Additionally, the influence of sample matrix components on the electrode response was studied according to the Plackett-Burman experimental design. The following potential interferents which are usually met in waters were taken into account in the examination: Mg²⁺, Ca²⁺, HCO₃⁻, SO₄²⁻, Cl⁻ as well as Cu²⁺. It was found that among tested ions only Cu²⁺ directly affected the electrode response. Determination of phenol index in tap water samples together with recovery studies was also performed. Obtained results suggest that developed sensor can be successfully used for the determination of phenols in concentration range covering its environmental levels.     

Leak detection in gas pipeline networks using an efficient state estimator. Part II. Experimental and field evaluation

In Part-I of this two part paper, a method is proposed for on-line leak detection and identification in gas pipeline networks using flow and pressure measurements. Simulations on two illustrative networks were used to demonstrate the applicability of the proposed method. In this paper, the performance of the proposed leak detection and identification methodology was evaluated using experiments with compressed air on a laboratory scale network. The on-line applicability of the proposed methodology was demonstrated through field level leak detection tests carried out on a 204.7 km long pipeline in India, supplying natural gas to a power plant. The laboratory and field tests demonstrated that the proposed methodology can be used for quick on-line detection of leaks, and locating the leaks reasonably accurately.     

Decision model for coagulant dosage using genetic programming and multivariate statistical analysis for coagulation/flocculation at water treatment process

In this research, genetic programming and multivariate statistical analysis techniques have been applied for decision support on the coagulant dosage and the mixing ratio as two kinds of coagulants have been injected at the same time in the coagulating sedimentation process of water treatment. The coagulant dosage has typically been determined through the Jar-test, which requires a long experiment time in a field-water treatment plant. It is difficult to efficiently determine the coagulant dosage since water quality changes with time. As there are no human experts who have sufficient knowledge and experience in the field, coagulants may be injected with an improper mixing ratio, which causes poor performance in the coagulating sedimentation process. In this study, a model for the approximation of coagulant dosage has been developed using genetic programming (GP). The performance of this model was evaluated through validation. A guideline on the optimal mixing ratio between PACS (Poly Aluminum Chloride Silicate) and PAC (Poly Aluminum Chloride) has been provided through statistical analysis.     

企业配电网的断相分析及处理

分析了首创轮胎配电网的故障类型及出现的次数,找出容易造成严重经济损失的断相故障进行理论分析,结合具体实际案例制定出且实可行的现场规程,指导企业安全可靠供电。     

Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM

In this paper, some drawbacks of original kernel independent component analysis (KICA) and support vector machine (SVM) algorithms are analyzed for the purpose of multivariate statistical process monitoring (MSPM). When the measured variables follow non-Gaussian distribution, KICA provides more meaningful knowledge by extracting higher-order statistics compared with PCA and kernel principal component analysis (KPCA). However, in real industrial processes, process variables are complex and are not absolutely Gaussian or non-Gaussian distributed. Any single technique is not sufficient to extract the hidden information. Hence, both KICA (non-Gaussion part) and KPCA (Gaussion part) are used for fault detection in this paper, which combine the advantages of KPCA and KICA to develop a nonlinear dynamic approach to detect fault online compared to other nonlinear approaches. Because SVM is available for classifying faults, it is used to diagnose fault in this paper.For above mentioned kernel methods, the calculation of eigenvectors and support vectors will be time consuming when the sample number becomes large. Hence, some dissimilar data are analyzed in the input and feature space.The proposed approach is applied to the fault detection and diagnosis in the Tennessee Eastman process. Application of the proposed approach indicates that proposed method effectively captures the nonlinear dynamics in the process variables.     

Qualitative path estimation: A fast and reliable algorithm for qualitative trend analysis

Fault detection and identification is challenged by a lack of detailed understanding of process dynamics under anomalous circumstances as well as a lack of historical data concerning rare events in a typical process. Qualitative trend analysis (QTA) techniques provide a way out by focusing on a coarse‐grained representation of time series data. Such qualitative representations are valid in a larger set of operating conditions and thus provide a robust way to handle the detection and identification of rare events. Unfortunately, available methods fail when faced with moderate noise levels or result in rather large computational efforts. For this reason, this article provides a novel method for QTA. This leads to dramatic improvements in computational efficiency compared to the previously established shape constrained splines method while the accuracy remains high. © 2015 American Institute of Chemical Engineers AIChE J, 61: 1535–1546, 2015     

A test of sphericity for high-dimensional data and its application for detection of divergently spiked noise

Abstract

In this article, we consider a test of the sphericity for high-dimensional covariance matrices. We produce a test statistic by using the extended cross-data-matrix (ECDM) methodology. We show that the ECDM test statistic is based on an unbiased estimator of a sphericity measure. In addition, the ECDM test statistic enjoys consistency properties and the asymptotic normality in high-dimensional settings. We propose a new test procedure based on the ECDM test statistic and evaluate its asymptotic size and power theoretically and numerically. We give a two-stage sampling scheme so that the test procedure can ensure a prespecified level both for the size and power. We apply the test procedure to detect divergently spiked noise in high-dimensional statistical analysis. We analyze gene expression data by the proposed test procedure.     

Process‐based graphical approach for simultaneous targeting and design of water network

A new process‐based graphical approach (PGA) is presented for the simultaneous targeting and design of water network. The PGA is extended from the limiting water profile which was developed for flow rate targeting for a water network. Via PGA procedure, apart from locating the minimum freshwater and wastewater flow rate targets, the water network that corresponds to the minimum flow rate targets is also synthesized simultaneously. The proposed approach handles both fixed load (including operations with water loss and/or gain) and fixed flow rate problems equally well. In addition, the approach can be used to synthesize direct reuse/recycle, regeneration reuse/recycling, and total water network. Furthermore, the proposed approach is applicable for water network with multiple freshwater sources. Three literature examples are presented to illustrate the proposed approach. © 2011 American Institute of Chemical Engineers AIChE J, 2011     

Statistical analysis of the concentrations of trace elements in a wide diversity of coals and its implications for understanding elemental modes of occurrence

Seventeen trace elements in 24 coals from worldwide deposits of differing ranks and sulfur contents were determined with the use of inductively coupled plasma optical emission spectrometry (ICP-OES), inductively coupled plasma mass spectrometry (ICP-MS), and flow injection (FI) ICP-MS. By examining multiple correlations between each trace element and three major elements, calcium, aluminum, and iron, we have found that thirteen trace elements (Li, Be, V, Cr, Mn, Ni, Cu, Zn, Ga, As, Se, Sr, and Ba) in the coals show significant correspondence. Elements correlating with aluminum are lithium, beryllium, vanadium, chromium, copper, gallium, and selenium; of these elements, vanadium, chromium, and copper also have a relationship with iron. Manganese, strontium and barium are correlated with calcium, while nickel, zinc, and arsenic are correlated with iron. In the geochemical and mineralogical senses, the significant correlation of a trace element with calcium reflects its common association with carbonate minerals for medium- to high-rank coals, while that with aluminum is implicative of the common association with aluminosilicate minerals and that with iron is characteristic of the association with sulfide minerals for high-sulfur coals, and with iron-bearing carbonate and clay minerals for low-sulfur coals. It is observed that most trace elements have more than one common association(s) in the 24 coals.