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
Data-driven process monitoring is an effective approach to assure safe operation of modern manufacturing and energy systems,such as thermal power plants being studied in this work.Industrial processes are inherently d...Data-driven process monitoring is an effective approach to assure safe operation of modern manufacturing and energy systems,such as thermal power plants being studied in this work.Industrial processes are inherently dynamic and need to be monitored using dynamic algorithms.Mainstream dynamic algorithms rely on concatenating current measurement with past data.This work proposes a new,alternative dynamic process monitoring algorithm,using dot product feature analysis(DPFA).DPFA computes the dot product of consecutive samples,thus naturally capturing the process dynamics through temporal correlation.At the same time,DPFA's online computational complexity is lower than not just existing dynamic algorithms,but also classical static algorithms(e.g.,principal component analysis and slow feature analysis).The detectability of the new algorithm is analyzed for three types of faults typically seen in process systems:sensor bias,process fault and gain change fault.Through experiments with a numerical example and real data from a thermal power plant,the DPFA algorithm is shown to be superior to the state-of-the-art methods,in terms of better monitoring performance(fault detection rate and false alarm rate)and lower computational complexity.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
为探究不同毛火方式对工夫红茶品质的影响,明确新型电磁内热式滚筒-热风耦合干燥设备的毛火效果,该研究以一芽一二叶初展嫩度的"福鼎大白"品种为原料进行工夫红茶加工,设定电磁滚筒-热风耦合(Rotary pot-Hot air coupling Fir...为探究不同毛火方式对工夫红茶品质的影响,明确新型电磁内热式滚筒-热风耦合干燥设备的毛火效果,该研究以一芽一二叶初展嫩度的"福鼎大白"品种为原料进行工夫红茶加工,设定电磁滚筒-热风耦合(Rotary pot-Hot air coupling First-Drying with electromagnetic heat,RHFD)、链板热风(Chain plate Hot air First-Drying,CHFD)、箱式热风(Box Hot air First-Drying,BHFD)、滚筒式滚炒(Rotary pot First-Drying,RFD)等4种毛火方式,比较所制茶样的茶多酚、儿茶素、茶色素、可溶性糖、咖啡碱、氨基酸等29个非挥发性指标,114个气相色谱-质谱技术(GasChromatography-Mass Spectrometry,GC-MS)检测的挥发性香气指标,10个外形和汤色色泽客观评价指标,同时进行了毛火方式的热效率、生产效率、生产成本等性能指标的分析比较,通过偏最小二乘判别统计(PartialLeastSquaresDiscriminationAnalysis,PLS-DA)分析毛火方式对优质工夫红茶品质的影响,并获得标志性差异化合物。结果表明:电磁内热式滚筒-热风耦合毛火处理下茶多酚和儿茶素总量显著最低(P<0.05),简单儿茶素含量较高,茶红素和可溶性糖含量、茶黄素综合指标TDE和茶色素综合指标10TFRB最高(P<0.05),毛火方式对茶黄素总量影响不显著(P>0.05);挥发性化合物总量以RHFD方式最高,RFD方式次之,CHFD方式最低;RHFD毛火方式芳香类、萜烯类等化合物含量最高。电磁内热式滚筒-热风耦合毛火升温快、温度分布均匀且稳定性好,热效率和生产效率高(分别为50.0%、220 kg/h),生产成本较低(仅0.32元/kg),预热时间仅14min;所制红茶在汤色透亮度、香气甜久度、滋味甜醇度等方面均得到提升,感官总分最高(P<0.05),达88.1。PLS-DA分析从挥发性和非挥发性角度均可将工夫红茶4种毛火方式显著区分,并分别获得了43种和18种差异化合物,结合差异性分析获得标志性差异化合物,2,4,6-三(1,1-二甲基乙基)-4-甲基环己-2,5-二烯-1-酮、香叶醇、3-辛酮、水杨酸甲酯、茶黄素、茶褐素、可溶性糖、表儿茶素等,可作为区分工夫红茶毛火方式,以及定向加工甜香、甜醇、高亮等优质工夫红茶的指标物质。该研究为红茶加工基础和品质提升提供技术参考和理论指导。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 in part by the National Science Fund for Distinguished Young Scholars of China(62225303)the National Natural Science Fundation of China(62303039,62433004)+2 种基金the China Postdoctoral Science Foundation(BX20230034,2023M730190)the Fundamental Research Funds for the Central Universities(buctrc202201,QNTD2023-01)the High Performance Computing Platform,College of Information Science and Technology,Beijing University of Chemical Technology
文摘Data-driven process monitoring is an effective approach to assure safe operation of modern manufacturing and energy systems,such as thermal power plants being studied in this work.Industrial processes are inherently dynamic and need to be monitored using dynamic algorithms.Mainstream dynamic algorithms rely on concatenating current measurement with past data.This work proposes a new,alternative dynamic process monitoring algorithm,using dot product feature analysis(DPFA).DPFA computes the dot product of consecutive samples,thus naturally capturing the process dynamics through temporal correlation.At the same time,DPFA's online computational complexity is lower than not just existing dynamic algorithms,but also classical static algorithms(e.g.,principal component analysis and slow feature analysis).The detectability of the new algorithm is analyzed for three types of faults typically seen in process systems:sensor bias,process fault and gain change fault.Through experiments with a numerical example and real data from a thermal power plant,the DPFA algorithm is shown to be superior to the state-of-the-art methods,in terms of better monitoring performance(fault detection rate and false alarm rate)and lower computational complexity.
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.
文摘为探究不同毛火方式对工夫红茶品质的影响,明确新型电磁内热式滚筒-热风耦合干燥设备的毛火效果,该研究以一芽一二叶初展嫩度的"福鼎大白"品种为原料进行工夫红茶加工,设定电磁滚筒-热风耦合(Rotary pot-Hot air coupling First-Drying with electromagnetic heat,RHFD)、链板热风(Chain plate Hot air First-Drying,CHFD)、箱式热风(Box Hot air First-Drying,BHFD)、滚筒式滚炒(Rotary pot First-Drying,RFD)等4种毛火方式,比较所制茶样的茶多酚、儿茶素、茶色素、可溶性糖、咖啡碱、氨基酸等29个非挥发性指标,114个气相色谱-质谱技术(GasChromatography-Mass Spectrometry,GC-MS)检测的挥发性香气指标,10个外形和汤色色泽客观评价指标,同时进行了毛火方式的热效率、生产效率、生产成本等性能指标的分析比较,通过偏最小二乘判别统计(PartialLeastSquaresDiscriminationAnalysis,PLS-DA)分析毛火方式对优质工夫红茶品质的影响,并获得标志性差异化合物。结果表明:电磁内热式滚筒-热风耦合毛火处理下茶多酚和儿茶素总量显著最低(P<0.05),简单儿茶素含量较高,茶红素和可溶性糖含量、茶黄素综合指标TDE和茶色素综合指标10TFRB最高(P<0.05),毛火方式对茶黄素总量影响不显著(P>0.05);挥发性化合物总量以RHFD方式最高,RFD方式次之,CHFD方式最低;RHFD毛火方式芳香类、萜烯类等化合物含量最高。电磁内热式滚筒-热风耦合毛火升温快、温度分布均匀且稳定性好,热效率和生产效率高(分别为50.0%、220 kg/h),生产成本较低(仅0.32元/kg),预热时间仅14min;所制红茶在汤色透亮度、香气甜久度、滋味甜醇度等方面均得到提升,感官总分最高(P<0.05),达88.1。PLS-DA分析从挥发性和非挥发性角度均可将工夫红茶4种毛火方式显著区分,并分别获得了43种和18种差异化合物,结合差异性分析获得标志性差异化合物,2,4,6-三(1,1-二甲基乙基)-4-甲基环己-2,5-二烯-1-酮、香叶醇、3-辛酮、水杨酸甲酯、茶黄素、茶褐素、可溶性糖、表儿茶素等,可作为区分工夫红茶毛火方式,以及定向加工甜香、甜醇、高亮等优质工夫红茶的指标物质。该研究为红茶加工基础和品质提升提供技术参考和理论指导。
