In chemical process, a large number of measured and manipulated variables are highly correlated. Principal component analysis(PCA) is widely applied as a dimension reduction technique for capturing strong correlation ...In chemical process, a large number of measured and manipulated variables are highly correlated. Principal component analysis(PCA) is widely applied as a dimension reduction technique for capturing strong correlation underlying in the process measurements. However, it is difficult for PCA based fault detection results to be interpreted physically and to provide support for isolation. Some approaches incorporating process knowledge are developed, but the information is always shortage and deficient in practice. Therefore, this work proposes an adaptive partitioning PCA algorithm entirely based on operation data. The process feature space is partitioned into several sub-feature spaces. Constructed sub-block models can not only reflect the local behavior of process change, namely to grasp the intrinsic local information underlying the process changes, but also improve the fault detection and isolation through the combination of local fault detection results and reduction of smearing effect.The method is demonstrated in TE process, and the results show that the new method is much better in fault detection and isolation compared to conventional PCA method.展开更多
为探讨毕赤酵母对酸粥风味品质的影响,该研究将酵母菌与乳酸菌进行复配发酵制备酸粥样品,同时使用乳酸菌单一菌株发酵作为对照。采用电子鼻结合气相色谱-质谱联用技术对酸粥挥发性风味物质进行分析,同时利用多元统计学方法对酸粥的风味...为探讨毕赤酵母对酸粥风味品质的影响,该研究将酵母菌与乳酸菌进行复配发酵制备酸粥样品,同时使用乳酸菌单一菌株发酵作为对照。采用电子鼻结合气相色谱-质谱联用技术对酸粥挥发性风味物质进行分析,同时利用多元统计学方法对酸粥的风味品质进行评价。通过主成分及多元方差分析发现:酵母菌组及复配组酸粥与乳酸菌组酸粥风味品质存在极显著差异(P<0.001)。通过气相色谱-质谱联用(gas chromatography and mass spectrometry,GCMS)共检测出162种挥发性成分,主要为酯类、烷烃类、醇类、醛类、酮类、酸类和胺类等化合物。进一步利用配对t检验分析发现,造成酸粥风味品质存在差异的主要挥发性化合物为3-甲基-1-丁醇、己醛、乙酸乙酯和乙醇(P<0.01)。展开更多
For complex chemical processes,process optimization is usually performed on causal models from first principle models.When the mechanism models cannot be obtained easily,restricted model built by process data is used ...For complex chemical processes,process optimization is usually performed on causal models from first principle models.When the mechanism models cannot be obtained easily,restricted model built by process data is used for dynamic process optimization.A new strategy is proposed for complex process optimization,in which latent variables are used as decision variables and statistics is used to describe constraints.As the constraint condition will be more complex by projecting the original variable to latent space,Hotelling T^2 statistics is introduced for constraint formulation in latent space.In this way,the constraint is simplified when the optimization is solved in low-dimensional space of latent variable.The validity of the methodology is illustrated in pH-level optimal control process and practical polypropylene grade transition process.展开更多
基金Support by the National Natural Science Foundation of China(61174114)the Research Fund for the Doctoral Program of Higher Education in China(20120101130016)Zhejiang Provincial Science and Technology Planning Projects of China(2014C31019)
文摘In chemical process, a large number of measured and manipulated variables are highly correlated. Principal component analysis(PCA) is widely applied as a dimension reduction technique for capturing strong correlation underlying in the process measurements. However, it is difficult for PCA based fault detection results to be interpreted physically and to provide support for isolation. Some approaches incorporating process knowledge are developed, but the information is always shortage and deficient in practice. Therefore, this work proposes an adaptive partitioning PCA algorithm entirely based on operation data. The process feature space is partitioned into several sub-feature spaces. Constructed sub-block models can not only reflect the local behavior of process change, namely to grasp the intrinsic local information underlying the process changes, but also improve the fault detection and isolation through the combination of local fault detection results and reduction of smearing effect.The method is demonstrated in TE process, and the results show that the new method is much better in fault detection and isolation compared to conventional PCA method.
文摘为探讨毕赤酵母对酸粥风味品质的影响,该研究将酵母菌与乳酸菌进行复配发酵制备酸粥样品,同时使用乳酸菌单一菌株发酵作为对照。采用电子鼻结合气相色谱-质谱联用技术对酸粥挥发性风味物质进行分析,同时利用多元统计学方法对酸粥的风味品质进行评价。通过主成分及多元方差分析发现:酵母菌组及复配组酸粥与乳酸菌组酸粥风味品质存在极显著差异(P<0.001)。通过气相色谱-质谱联用(gas chromatography and mass spectrometry,GCMS)共检测出162种挥发性成分,主要为酯类、烷烃类、醇类、醛类、酮类、酸类和胺类等化合物。进一步利用配对t检验分析发现,造成酸粥风味品质存在差异的主要挥发性化合物为3-甲基-1-丁醇、己醛、乙酸乙酯和乙醇(P<0.01)。
基金Supported by the National Natural Science Foundation of China(61174114)the Research Fund for the Doctoral Program of Higher Education in China(20120101130016)+1 种基金the Natural Science Foundation of Zhejiang Province(LQ15F030006)the Educational Commission Research Program of Zhejiang Province(Y201431412)
文摘For complex chemical processes,process optimization is usually performed on causal models from first principle models.When the mechanism models cannot be obtained easily,restricted model built by process data is used for dynamic process optimization.A new strategy is proposed for complex process optimization,in which latent variables are used as decision variables and statistics is used to describe constraints.As the constraint condition will be more complex by projecting the original variable to latent space,Hotelling T^2 statistics is introduced for constraint formulation in latent space.In this way,the constraint is simplified when the optimization is solved in low-dimensional space of latent variable.The validity of the methodology is illustrated in pH-level optimal control process and practical polypropylene grade transition process.
