Conventional principal component analysis(PCA) can obtain low-dimensional representations of original data space, but the selection of principal components(PCs) based on variance is subjective, which may lead to infor...Conventional principal component analysis(PCA) can obtain low-dimensional representations of original data space, but the selection of principal components(PCs) based on variance is subjective, which may lead to information loss and poor monitoring performance. To address dimension reduction and information preservation simultaneously, this paper proposes a novel PC selection scheme named full variable expression. On the basis of the proposed relevance of variables with each principal component, key principal components can be determined.All the key principal components serve as a low-dimensional representation of the entire original variables, preserving the information of original data space without information loss. A squared Mahalanobis distance, which is introduced as the monitoring statistic, is calculated directly in the key principal component space for fault detection. To test the modeling and monitoring performance of the proposed method, a numerical example and the Tennessee Eastman benchmark are used.展开更多
Originated from the pore space segmentation modification of a reported metal-organic framework （MOF） （NOTT-125）, a new porous MOF ZnX was obtained and characterized by single-crystal X-ray diffraction, elemental a...Originated from the pore space segmentation modification of a reported metal-organic framework （MOF） （NOTT-125）, a new porous MOF ZnX was obtained and characterized by single-crystal X-ray diffraction, elemental analysis, X-ray powder diffraction and TGA. The ZnX exhibits remarkable selective CO2 adsorption property compared with that of the NOTT-125, which should be attributed to the enhanced gas-framework interactions induced by the fragmented pore space in ZnX.展开更多
基金Supported by the National Natural Science Foundation of China(No.61374140)Shanghai Pujiang Program(Project No.12PJ1402200)
文摘Conventional principal component analysis(PCA) can obtain low-dimensional representations of original data space, but the selection of principal components(PCs) based on variance is subjective, which may lead to information loss and poor monitoring performance. To address dimension reduction and information preservation simultaneously, this paper proposes a novel PC selection scheme named full variable expression. On the basis of the proposed relevance of variables with each principal component, key principal components can be determined.All the key principal components serve as a low-dimensional representation of the entire original variables, preserving the information of original data space without information loss. A squared Mahalanobis distance, which is introduced as the monitoring statistic, is calculated directly in the key principal component space for fault detection. To test the modeling and monitoring performance of the proposed method, a numerical example and the Tennessee Eastman benchmark are used.
基金supported by the National Natural Science Foundation of China(21531005,21421001,and 21290171)Ministry of Education Innovation Team of China(IRT13022)
文摘Originated from the pore space segmentation modification of a reported metal-organic framework （MOF） （NOTT-125）, a new porous MOF ZnX was obtained and characterized by single-crystal X-ray diffraction, elemental analysis, X-ray powder diffraction and TGA. The ZnX exhibits remarkable selective CO2 adsorption property compared with that of the NOTT-125, which should be attributed to the enhanced gas-framework interactions induced by the fragmented pore space in ZnX.
