Abstract Data-driven tools, such as principal component analysis （PCA） and independent component analysis （ICA） have been applied to different benchmarks as process monitoring methods. The difference between the t...Abstract Data-driven tools, such as principal component analysis （PCA） and independent component analysis （ICA） have been applied to different benchmarks as process monitoring methods. The difference between the two methods is that the components of PCA are still dependent while ICA has no orthogonality constraint and its latentvariables are independent. Process monitoring with PCA often supposes that process data or principal components is Gaussian distribution. However, this kind of constraint cannot be satisfied by several practical processes. To ex-tend the use of PCA, a nonparametric method is added to PCA to overcome the difficulty, and kernel density estimation （KDE） is rather a good choice. Though ICA is based on non-Gaussian distribution intormation, .KDE can help in the close monitoring of the data. Methods, such as PCA, ICA, PCA.with .KDE（KPCA）, and ICA with KDE,（KICA）, are demonstrated and. compared by applying them to a practical industnal Spheripol craft polypropylene catalyzer reactor instead of a laboratory emulator.展开更多
Multivariate statistical process monitoring and control (MSPM&C) methods for chemical process monitoring with statistical projection techniques such as principal component analysis (PCA) and partial least squares ...Multivariate statistical process monitoring and control (MSPM&C) methods for chemical process monitoring with statistical projection techniques such as principal component analysis (PCA) and partial least squares (PLS) are surveyed in this paper. The four-step procedure of performing MSPM&C for chemical process, modeling of processes, detecting abnormal events or faults, identifying the variable(s) responsible for the faults and diagnosing the source cause for the abnormal behavior, is analyzed. Several main research directions of MSPM&C reported in the literature are discussed, such as multi-way principal component analysis (MPCA) for batch process, statistical monitoring and control for nonlinear process, dynamic PCA and dynamic PLS, and on-line quality control by inferential models. Industrial applications of MSPM&C to several typical chemical processes, such as chemical reactor, distillation column, polymerization process, petroleum refinery units, are summarized. Finally, some concluding remarks and future considerations are made.展开更多
A new method using discriminant analysis and control charts is proposed for monitoring multivariate process operations more reliably.Fisher discriminant analysis （FDA） is used to derive a feature discriminant direct...A new method using discriminant analysis and control charts is proposed for monitoring multivariate process operations more reliably.Fisher discriminant analysis （FDA） is used to derive a feature discriminant direction （FDD） between each normal and fault operations,and each FDD thus decided constructs the feature space of each fault operation.Individuals control charts （XmR charts） are used to monitor multivariate processes using the process data projected onto feature spaces.Upper control limit （UCL） and lower control limit （LCL） on each feature space from normal process operation are calculated for XmR charts,and are used to distinguish fault from normal.A variation trend on an XmR chart reveals the type of relevant fault operation.Applications to Tennessee Eastman simulation processes show that this proposed method can result in better monitoring performance than principal component analysis （PCA）-based methods and can better identify step type faults on XmR charts.展开更多
AIM To identify the effects and mechanism of action of Polygonatum kingianum(P. kingianum) on dyslipidemia in rats using an integrated untargeted metabolomic method.METHODS A rat model of dyslipidemia was induced with...AIM To identify the effects and mechanism of action of Polygonatum kingianum(P. kingianum) on dyslipidemia in rats using an integrated untargeted metabolomic method.METHODS A rat model of dyslipidemia was induced with a high-fat diet(HFD) and rats were given P. kingianum [4 g/(kg·d)] intragastrically for 14 wk. Changes in serum and hepatic lipid parameters were evaluated. Metabolites in serum, urine and liver samples were profiled using ultra-highperformance liquid chromatography/mass spectrometry followed by multivariate statistical analysis to identify potential biomarkers and metabolic pathways.RESULTS P. kingianum significantly inhibited the HFD-induced increase in total cholesterol and triglyceride in the liver and serum. P. kingianum also significantly regulated metabolites in the analyzed samples toward normal status. Nineteen, twenty-four and thirty-eight potential biomarkers were identified in serum, urine and liver samples, respectively. These biomarkers involved biosynthesis of phenylalanine, tyrosine, tryptophan, valine, leucine and isoleucine, along with metabolism of tryptophan, tyrosine, phenylalanine, starch, sucrose, glycerophospholipid, arachidonic acid, linoleic acid, nicotinate, nicotinamide and sphingolipid.CONCLUSION P. kingianum alleviates HFD-induced dyslipidemia by regulating many endogenous metabolites in serum, urine and liver samples. Collectively, our findings suggest that P. kingianum may be a promising lipid regulator to treat dyslipidemia and associated diseases.展开更多
基金Supported by the National Natural Science Foundation of China （No.60574047） and the Doctorate Foundation of the State Education Ministry of China （No.20050335018）.
文摘Abstract Data-driven tools, such as principal component analysis （PCA） and independent component analysis （ICA） have been applied to different benchmarks as process monitoring methods. The difference between the two methods is that the components of PCA are still dependent while ICA has no orthogonality constraint and its latentvariables are independent. Process monitoring with PCA often supposes that process data or principal components is Gaussian distribution. However, this kind of constraint cannot be satisfied by several practical processes. To ex-tend the use of PCA, a nonparametric method is added to PCA to overcome the difficulty, and kernel density estimation （KDE） is rather a good choice. Though ICA is based on non-Gaussian distribution intormation, .KDE can help in the close monitoring of the data. Methods, such as PCA, ICA, PCA.with .KDE（KPCA）, and ICA with KDE,（KICA）, are demonstrated and. compared by applying them to a practical industnal Spheripol craft polypropylene catalyzer reactor instead of a laboratory emulator.
基金Supported by the National High-Tech Development Program of China(No.863-511-920-011,2001AA411230).
文摘Multivariate statistical process monitoring and control (MSPM&C) methods for chemical process monitoring with statistical projection techniques such as principal component analysis (PCA) and partial least squares (PLS) are surveyed in this paper. The four-step procedure of performing MSPM&C for chemical process, modeling of processes, detecting abnormal events or faults, identifying the variable(s) responsible for the faults and diagnosing the source cause for the abnormal behavior, is analyzed. Several main research directions of MSPM&C reported in the literature are discussed, such as multi-way principal component analysis (MPCA) for batch process, statistical monitoring and control for nonlinear process, dynamic PCA and dynamic PLS, and on-line quality control by inferential models. Industrial applications of MSPM&C to several typical chemical processes, such as chemical reactor, distillation column, polymerization process, petroleum refinery units, are summarized. Finally, some concluding remarks and future considerations are made.
基金Sponsored by the Scientific Research Foundation for Returned Overseas Chinese Scholars of the Ministry of Education of China
文摘A new method using discriminant analysis and control charts is proposed for monitoring multivariate process operations more reliably.Fisher discriminant analysis （FDA） is used to derive a feature discriminant direction （FDD） between each normal and fault operations,and each FDD thus decided constructs the feature space of each fault operation.Individuals control charts （XmR charts） are used to monitor multivariate processes using the process data projected onto feature spaces.Upper control limit （UCL） and lower control limit （LCL） on each feature space from normal process operation are calculated for XmR charts,and are used to distinguish fault from normal.A variation trend on an XmR chart reveals the type of relevant fault operation.Applications to Tennessee Eastman simulation processes show that this proposed method can result in better monitoring performance than principal component analysis （PCA）-based methods and can better identify step type faults on XmR charts.
基金Supported by the National Natural Science Foundation of China,No.81660596 and No.81760733the Application and Basis Research Project of Yunnan,China,No.2016FD050 and No.2017FF117-013the Fund for Young and Middle-aged Academic and Technological Leaders of Yunnan,No.2015HB053
文摘AIM To identify the effects and mechanism of action of Polygonatum kingianum(P. kingianum) on dyslipidemia in rats using an integrated untargeted metabolomic method.METHODS A rat model of dyslipidemia was induced with a high-fat diet(HFD) and rats were given P. kingianum [4 g/(kg·d)] intragastrically for 14 wk. Changes in serum and hepatic lipid parameters were evaluated. Metabolites in serum, urine and liver samples were profiled using ultra-highperformance liquid chromatography/mass spectrometry followed by multivariate statistical analysis to identify potential biomarkers and metabolic pathways.RESULTS P. kingianum significantly inhibited the HFD-induced increase in total cholesterol and triglyceride in the liver and serum. P. kingianum also significantly regulated metabolites in the analyzed samples toward normal status. Nineteen, twenty-four and thirty-eight potential biomarkers were identified in serum, urine and liver samples, respectively. These biomarkers involved biosynthesis of phenylalanine, tyrosine, tryptophan, valine, leucine and isoleucine, along with metabolism of tryptophan, tyrosine, phenylalanine, starch, sucrose, glycerophospholipid, arachidonic acid, linoleic acid, nicotinate, nicotinamide and sphingolipid.CONCLUSION P. kingianum alleviates HFD-induced dyslipidemia by regulating many endogenous metabolites in serum, urine and liver samples. Collectively, our findings suggest that P. kingianum may be a promising lipid regulator to treat dyslipidemia and associated diseases.
