Statistical monitoring of dynamic processes based on dynamic independent component analysis

Most multivariate statistical monitoring methods based on principal component analysis (PCA) assume implicitly that the observations at one time are statistically independent of observations at past time and the latent variables follow a Gaussian distribution. However, in real chemical and biological processes, these assumptions are invalid because of their dynamic and nonlinear characteristics. Therefore, monitoring charts based on conventional PCA tend to show many false alarms and bad detectability. In this paper, a new statistical process monitoring method using dynamic independent component analysis (DICA) is proposed to overcome these disadvantages. ICA is a recently developed technique for revealing hidden factors that underlies sets of measurements followed on a non-Gaussian distribution. Its goal is to decompose a set of multivariate data into a base of statistically independent components without a loss of information. The proposed DICA monitoring method is applying ICA to the augmenting matrix with time-lagged variables. DICA can show more powerful monitoring performance in the case of a dynamic process since it can extract source signals which are independent of the auto- and cross-correlation of variables. It is applied to fault detection in both a simple multivariate dynamic process and the Tennessee Eastman process. The simulation results clearly show that the method effectively detects faults in a multivariate dynamic process.     

Multivariate Analysis of Multiple Datasets: a Practical Guide for Chemical Ecology

Chemical ecology has strong links with metabolomics, the large-scale study of all metabolites detectable in a biological sample. Consequently, chemical ecologists are often challenged by the statistical analyses of such large datasets. This holds especially true when the purpose is to integrate multiple datasets to obtain a holistic view and a better understanding of a biological system under study. The present article provides a comprehensive resource to analyze such complex datasets using multivariate methods. It starts from the necessary pre-treatment of data including data transformations and distance calculations, to the application of both gold standard and novel multivariate methods for the integration of different omics data. We illustrate the process of analysis along with detailed results interpretations for six issues representative of the different types of biological questions encountered by chemical ecologists. We provide the necessary knowledge and tools with reproducible R codes and chemical-ecological datasets to practice and teach multivariate methods.     

Statistical process monitoring based on dissimilarity of process data

Multivariate statistical process control (MSPC) has been widely used for monitoring chemical processes with highly correlated variables. In this work, a novel statistical process monitoring method is proposed based on the idea that a change of operating condition can be detected by monitoring a distribution of process data, which reflects the corresponding operating conditions. To quantitatively evaluate the difference between two data sets, a dissimilarity index is introduced. The monitoring performance of the proposed method, referred to as DISSIM, and that of the conventional MSPC method are compared with their applications to simulated data collected from a simple 2 × 2 process and the Tennessee Eastman process. The results clearly show that the monitoring performance of DISSIM, especially dynamic DISSIM, is considerably better than that of the conventional MSPC method when a time-window size is appropriately selected.     

基于核主角的故障检测方法

基于主元分析（PCA）的统计检测方法已经被广泛应用于各种化工过程的故障检测和识别.移动主元分析（moving principal component analysis,简称MPCA）算法基于PCA,根据主元子空间的变化来判断故障是否发生.然而,基于主元分析的统计检测方法是线性方法,无法有效应用于非线性系统.因此,提出一种适合于非线性系统的故障检测方法——基于核主角(kernel principal angle,简称KPA)的故障检测方法,其基本思想与MPCA相似,主要内容包括构建特征子空间和核主角测量两部分.TE过程故障检测仿真实验证明,基于核主角的故障检测方法优于传统的多元统计检测方法（cMSPC）和MPCA.     

在线自适应批次过程监视的双滑动窗口MPCA方法

Online monitoring of chemical process performance is extremely important to ensure the safety of a chemical plant and consistently high quality of products. Multivariate statistical process control has found wide applications in process performance analysis, monitoring and fault diagnosis using existing rich historical database. In this paper, we propose a simple and straight forward multivariate statistical modeling based on a moving window MPCA （multiway principal component analysis） model along the time and batch axis for adaptive monitoring the progress of batch processes in real-time. It is an extension to minimum window MPCA and traditional MPCA. The moving window MPCA along the batch axis can copy seamlessly with variable run length and does not need to estimate any deviations of the ongoing batch from the average trajectories. It replaces an invariant fixed-model monitoring approach with adaptive updating model data structure within batch-to-batch, which overcomes the changing operation condition and slows time-varying behaviors of industrial processes. The software based on moving window MPCA has been successfully applied to the industrial polymerization reactor of polyvinyl chloride （PVC） process in the Jinxi Chemical Company of China since 1999.     

基于LWPT-DTW的间歇过程不等长时段数据同步化

间歇过程不等长时段数据直接影响数据驱动的多元统计分析时段建模精度,导致间歇过程的监控性能降低。针对间歇过程不等长时段数据问题,提出一种基于提升小波包变换（LWPT）和动态时间规整（DTW）算法的间歇过程不等长时段数据同步化方法。该方法引入LWPT对间歇过程不等长时段数据轨迹进行高低频的多级分解,充分提取数据轨迹的所有时频域信息;采用DTW算法对不同频段的系数矩阵进行同步化,并利用提升小波包逆变换对同步化后的系数矩阵进行合成,降低吉布斯现象对数据轨迹合成的影响,获得等长的时段轨迹,实现了间歇过程不等长时段数据同步化。青霉素发酵过程仿真实验表明,所提出的方法运算速度快、稳定,不等长时段数据的同步化结果具有较高的准确性,为间歇过程时段建模提供了可靠的过程数据。     

Convolutional neural nets in chemical engineering: Foundations,computations, and applications

In this article, we review the mathematical foundations of convolutional neural nets (CNNs) with the goals of: (i) highlighting connections with techniques from statistics, signal processing, linear algebra, differential equations, and optimization, (ii) demystifying underlying computations, and (iii) identifying new types of applications. CNNs are powerful machine learning models that highlight features from grid data to make predictions (regression and classification). The grid data object can be represented as vectors (in 1D), matrices (in 2D), or tensors (in 3D or higher dimensions) and can incorporate multiple channels (thus providing high flexibility in the input data representation). CNNs highlight features from the grid data by performing convolution operations with different types of operators. The operators highlight different types of features (e.g., patterns, gradients, geometrical features) and are learned by using optimization techniques. In other words, CNNs seek to identify optimal operators that best map the input data to the output data. A common misconception is that CNNs are only capable of processing image or video data but their application scope is much wider; specifically, datasets encountered in diverse applications can be expressed as grid data. Here, we show how to apply CNNs to new types of applications such as optimal control, flow cytometry, multivariate process monitoring, and molecular simulations.     

氯胺及其与二氧化氯联用对原水管道溶解性有机物降解的影响

采用环状反应器模拟原水输水管道, 考察氯胺(NH₂Cl)及其与二氧化氯联用(NH₂Cl/ClO₂)对溶解性有机物(DOM)、溶解性有机碳(DOC)和UV₂₅₄降解以及有机物荧光特性的影响。结果表明:NH₂Cl或NH₂Cl/ClO₂对DOC、UV₂₅₄和DOM的降解均产生较大的影响。相比NH₂Cl, NH₂Cl/ClO₂的影响更大, 但是有机物降解的恢复速度没有明显差异, DOC、UV₂₅₄和DOM的降解分别在停止投加氧化剂的第5 d、4 d和1 d恢复。投加氧化剂后, 芳香族有机物以向易生物降解的极性有机物的转化为主, 溶解性有机物的可生物降解能力增加。NH₂Cl和NH₂Cl/ClO₂作用后, 类蛋白质物质以及紫外区类腐殖质类物质明显减少。NH₂Cl/ClO₂更易破坏有机物中的芳香族化合物的结构, 而NH₂Cl氧化使得有机物分子结构中羰基、羟基、羧基和胺基等官能团增加, 从而有机物可生物降解能力较强。NH₂Cl和NH₂Cl/ClO₂作用后, 有机物降解作用可恢复至比未加氧化剂更高的水平, 基于卤代副产物生成的考虑, 相比NH₂Cl, NH₂Cl/ClO₂更适合用于原水输水管道的氧化。     

A new multivariate statistical process monitoring method using principal component analysis

Principal component analysis (PCA) has been used successfully as a multivariate statistical process control (MSPC) tool for detecting faults in processes with highly correlated variables. In the present work, a novel statistical process monitoring method is proposed for further improvement of monitoring performance. It is termed ‘moving principal component analysis’ (MPCA) because PCA is applied on-line by moving the time-window. In MPCA, changes in the direction of each principal component or changes in the subspace spanned by several principal components are monitored. In other words, changes in the correlation structure of process variables, instead of changes in the scores of predefined principal components, are monitored by using MPCA. The monitoring performance of the proposed method and that of the conventional MSPC method are compared with application to simulated data obtained from a simple 2×2 process and the Tennessee Eastman process. The results clearly show that the monitoring performance of MPCA is considerably better than that of the conventional MSPC method and that dynamic monitoring is superior to static monitoring.     

基于平稳性能不确定信息盲源信号提取的过程监控方法

针对工业过程中的信息不一定平稳,提出了一种基于平稳性能不确定信息盲源信号提取的过程监控方法,并利用该方法提取过程盲源信号,采用k-近邻法进行分类,从而实现对过程性能的监控.通过对简单AR(1)过程和双效蒸发过程的仿真研究表明,这种方法是可行的.为了与基于传统独立成分分析(ICA)和多元统计过程控制(MSPC)的过程监控方法相比较,还作了相应的对比研究.结果表明,该方法比基于传统ICA的过程监控方法具有更少的误报率和漏报率,而比基于MSPC的过程监控方法具有更少的误报率,从而说明了该方法不仅是可行的,而且是有效的.     

Human Primary Dermal Fibroblasts Interacting with 3-Dimensional Matrices for Surgical Application Show Specific Growth and Gene Expression Programs

Several types of 3-dimensional (3D) biological matrices are employed for clinical and surgical applications, but few indications are available to guide surgeons in the choice among these materials. Here we compare the in vitro growth of human primary fibroblasts on different biological matrices commonly used for clinical and surgical applications and the activation of specific molecular pathways over 30 days of growth. Morphological analyses by Scanning Electron Microscopy and proliferation curves showed that fibroblasts have different ability to attach and proliferate on the different biological matrices. They activated similar gene expression programs, reducing the expression of collagen genes and myofibroblast differentiation markers compared to fibroblasts grown in 2D. However, differences among 3D matrices were observed in the expression of specific metalloproteinases and interleukin-6. Indeed, cell proliferation and expression of matrix degrading enzymes occur in the initial steps of interaction between fibroblast and the investigated meshes, whereas collagen and interleukin-6 expression appear to start later. The data reported here highlight features of fibroblasts grown on different 3D biological matrices and warrant further studies to understand how these findings may be used to help the clinicians choose the correct material for specific applications.     

Engineering proteolytically-degradable artificial extracellular matrices

Hydrogels are widely used as provisional matrices for tissue engineering and regenerative medicine, showing also great promise as platforms for 3D cell culture. Different bio-functionalization strategies have been proposed to enhance the biological performance of hydrogels, particularly when they lack intrinsic bioactivity. In this context, the design of artificial materials that mimic structural and functional features of the natural extracellular matrix (ECM) has been pursued. This review presents an overview on bioengineering approaches of integrating protease-sensitive motifs into hydrogels, for the creation of cell-responsive biomimetic scaffolding materials that degrade in response to their proteolytic microenvironment. The successful incorporation of protease-sensitive motifs in several synthetic and natural polymers, which has been achieved using various chemical routes, is described. In each case, the selected peptide sequences and their target proteases are highlighted, along with the main achievements of the study. A critical analysis of current limitations and recent advances is also provided, along with suggestions for further improvements.     

基于共同趋势模型的非平稳过程在线监控

对于非平稳过程监控,传统的基于数据平稳假设的多元统计过程控制方法是不适用的。针对上述问题,提出了一种基于共同趋势模型的非平稳过程监控方法。共同趋势模型从存在协整关系的非平稳多元变量中辨识出共同因子,将各非平稳过程变量分解成非平稳的共同趋势成分与平稳成分之和的形式。不同于现有的基于协整模型的非平稳过程监控方法,共同趋势模型能够获取各非平稳变量中的平稳成分,消除非平稳共同因子的影响并体现变量间全部的动态均衡关系。将对非平稳过程的监控变为应用共同趋势模型,分解得到各非平稳过程变量中的平稳成分,然后应用传统的多元统计方法,估计平稳成分的统计量及相应的控制限进行监测。石油蒸馏过程监控的实例研究结果表明,所提出的方法比基于协整新息变量的方法具有更可靠的监控效果。     

The integration of spectroscopic and process data for enhanced process performance monitoring

Batch process performance monitoring has been achieved primarily using process measurements with the extracted information being associated with the physical parameters of the process. With increasing attention now being paid to the application of on‐line real‐time process analytics through spectrometry, together with the FDA Process Analytical Technologies (PAT) initiative, the use of spectroscopic information for enhanced monitoring of reactions is gaining impetus. The harmonious integration of process data and spectroscopic data then becomes a major challenge. By integrating the process and spectroscopic measurements for multivariate statistical data modelling and analysis, it is conjectured that improved process understanding and fault diagnosis can be achieved. An investigation into combining process and spectral data using multiblock and multiresolution analysis is proposed and the results from the analysis of experimental data from two industrial application studies are presented to demonstrate the improvements achievable in terms of process performance monitoring and fault diagnosis.     

How Reliable is the Analysis of Complex Cuticular Hydrocarbon Profiles by Multivariate Statistical Methods?

Numerous recent studies have correlated cuticular hydrocarbon profiles with a wide range of behaviors, particularly in social insects. These findings are wholly or partly based on multivariate statistical methods such as discriminate analysis (DA) or principal component analysis (PCA). However, these methods often provide limited insight into the biological processes that generate the small differences usually detected. This may be a consequence of variability in the system due to inadequate sample sizes and the assumption that all compounds are independent. A fundamental problem is that these methods combine rather than separate the effects of signal components. By using cuticular hydrocarbon data from previous social insect studies, we showed that: (1) in 13 species of Formica ants and seven species of Vespa hornets, at least one group of hydrocarbons in each species was highly (r ² > 0.8) correlated, indicating that all compounds are not independent; (2) DA was better at group separation that PCA; (3) the relationships between colonies (chemical distance) were unstable and sensitive to variability in the system; and (4) minor compounds had a disproportionately large effect on the analysis. All these factors, along with sample size, need to be considered in the future analysis of complex chemical profiles.     

Nonparametric manifold learning approach for improved process monitoring

A novel nonparametric method based on manifold learning is proposed for industrial process monitoring. In conventional algorithms, to preserve the global and local structure information of data, heat kernels containing two auxiliary parameters are introduced to define the global and local weight matrices, respectively. However, it is difficult to identify and choose these two parameters empirically. The inadequate selection of parameters can lead to one-sided and inappropriate global and local feature extractions, resulting in an inadequate fault detection performance. To resolve the above problems, a nonparametric strategy is used in this study to generate two nonparametric weight matrices to replace the heat kernel-based weight matrices. Consequently, the proposed method requires no auxiliary parameters in defining the weight matrices, making it more practical. Moreover, it automatically determines a good trade-off between global and local feature extractions. A process monitoring model based on the proposed method was developed. The feasibility and effectiveness of the new nonparametric method are evaluated using a synthetic example and the Tennessee Eastman chemical process.     

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.     

基于主元子空间富信息重构的过程监测方法

作为一种经典的多元投影方法,主元分析（PCA）已在多变量统计过程监测领域得到了广泛应用。然而,传统的主元挑选方法往往选择方差较大的主元以表征建模样本中包含的较大信息量,但当过程信息发生变化时,方差较小的主元所表现出来的变异性可能更为明显,即包含的信息量更为丰富,也更有利于故障检出。为此,提出一种基于主元子空间富信息重构的过程监测方法（informative PCA,Info-PCA）。该方法通过计算过程数据在各主元方向上累积T²统计量的变化率,选择变化较为明显的主元以重构主元子空间。在此基础上,建立相应的统计监测模型。最后,通过实例验证该方法用于过程监测的可行性与有效性。     

基于小波变换去噪的多元统计投影分析及其在化工过程监控中的应用

引　言化工过程数据的重要特点之一是受噪声污染严重 ,这严重影响了过程信息处理和分析的效果 .例如 ,在运用多元统计过程控制 (MSPC)进行化工过程监控时 ,直接利用这些受到污染的测量信息对过程进行分析 ,必然会导致较大的误差 ,使结果置信度下降 .对于过程故障诊断来说 ,就会产生较高的误报率或漏报率 .为解决这一问题 ,人们运用小波变换对测量信号进行去噪 ,提高了信号的置信度并取得了较　.　好的应用效果[1] .然而 ,通常的小波变换去噪是针对过程测量信号直接进行分频去噪 ,并没有考虑信号间的相关关系 ,因而这一处理方法不能更有效…     

Multivariate statistical monitoring of process operating performance

Process computers routinely collect hundreds to thousands of pieces of data from a multitude of plant sensors every few seconds. This has caused a “data overload” and due to the lack of appropriate analyses very little is currently being done to utilize this wealth of information. Operating personnel typically use only a few variables to monitor the plant's performance. However, multivariate statistical methods such as PLS (Partial Least Squares or Projection to Latent Structures) and PCA (Principal Component Analysis) are capable of compressing the information down into low dimensional spaces which retain most of the information. Using this method of statistical data compression a multivariate monitoring procedure analogous to the univariate Shewart Chart has been developed to efficiently monitor the performance of large processes, and to rapidly detect and identify important process changes. This procedure is demonstrated using simulations of two processes, a fluidized bed reactor and an extractive distillation column.