Generalized Dice's coefficient‐based multi‐block principal component analysis with Bayesian inference for plant‐wide process monitoring

Plant‐wide process monitoring is challenging because of the complex relationships among numerous variables in modern industrial processes. The multi‐block process monitoring method is an efficient approach applied to plant‐wide processes. However, dividing the original space into subspaces remains an open issue. The loading matrix generated by principal component analysis (PCA) describes the correlation between original variables and extracted components and reveals the internal relations within the plant‐wide process. Thus, a multi‐block PCA method that constructs principal component (PC) sub‐blocks according to the generalized Dice coefficient of the loading matrix is proposed. The PCs corresponding to similar loading vectors are divided within the same sub‐block. Thus, the PCs in the same sub‐block share similar variational behavior for certain faults. This behavior improves the sensitivity of process monitoring in the sub‐block. A monitoring statistic T² corresponding to each sub‐block is produced and is integrated into the final probability index based on Bayesian inference. A corresponding contribution plot is also developed to identify the root cause. The superiority of the proposed method is demonstrated by two case studies: a numerical example and the Tennessee Eastman benchmark. Comparisons with other PCA‐based methods are also provided. Copyright © 2014 John Wiley & Sons, Ltd.     

Semi‐supervised kernel partial least squares fault detection and identification approach with application to HGPWLTP

In this paper, fault detection and identification methods based on semi‐supervised Laplacian regularization kernel partial least squares (LRKPLS) are proposed. In Laplacian regularization learning framework, unlabeled and labeled samples are used to improve estimate of data manifold so that one can establish a more robust data model. We show that LRKPLS can avoid the over‐fitting problem which may be caused by sample insufficient and outliers present. Moreover, the proposed LRKPLS approach has no special restriction on data distribution, in other words, it can be used in the case of nonlinear or non‐Gaussian data. On the basis of LRKPLS, corresponding fault detection and identification methods are proposed. Those methods are used to monitor a numerical example and Hot Galvanizing Pickling Waste Liquor Treatment Process (HGPWLTP), and the cases study show effeteness of the proposed approaches. Copyright © 2016 John Wiley & Sons, Ltd.     

Characteristic component profiling and identification of different Uncaria species based on high‐performance liquid chromatography–photodiode array detection tandem ion trap and time of flight mass spectrometry coupled with rDNA ITS sequence

Uncaria is a multi‐source herb and its species identification has become a bottleneck in quality control. To study the identification method of different Uncaria species herbs through HPLC–MS coupled with rDNA Internal Transcribed Spacer (rDNA ITS) sequence, both plant morphological traits and molecular identification were used to determine the species of every collected Uncaria herb. The genetic analysis of different Uncaria species was performed using their rDNA ITS sequence as a molecular marker. Meanwhile, the phylogenetic relationships of 22 samples from six Uncaria species were divided and classified clearly. By optimizing the chromatographic conditions, a practical HPLC method to differentiate various varieties of Uncaria herbs was set up based on a set of characteristic components across each species. A high‐performance liquid chromatography–photodiode array detector tandem ion trap and time of flight mass spectrometry technique combined with reference substances was utilized to derive 21 characteristic compounds containing six groups of six Uncaria species in China. Thus, this study provides a feasible method to solve the current problem of confusion in Uncaria species, and makes a significant step forward in the appropriate clinical use, in‐depth research and further utilization of different Uncaria species.