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| 1 | Fault diagnosis for distillation process based on CNN–DAE显示文摘Distillation is the most widely used operation for liquid mixture separation in the chemical industry. It is of great importance to detect and diagnose faults in distillation process. Due to the strong feedback and coupling of processes in a distillation column, it is difficult to use deep auto-encoders(DAEs) alone to achieve good results in detecting and diagnosing faults, in terms of accuracy and efficiency. This paper proposes a hybrid fault-diagnosis model based on convolutional neural networks(CNNs) and DAEs, by integrating the powerful capability of CNN in feature extraction and of DAE in classification. A case study was carried out with the distillation process of depropanization. It is shown that the proposed hybrid model is of good performance compared to other models, in terms of the accuracy of fault detection in such a process. Also, with the increase of structural layers of the CNN–DAE model, the diagnostic accuracy will be improved, with an optimal accuracy of 92.2%. | Chuankun Li Dongfeng Zhao Shanjun Mu Weihua Zhang Ning Shi Lening Li | 2019 | Chinese Journal of Chemical Engineering2019，27，3： | 12 |
| 2 | Identification of abnormal conditions in high-dimensional chemical process based on feature selection and deep learning显示文摘Identification of abnormal conditions is essential in the chemical process.With the rapid development of artificial intelligence technology,deep learning has attracted a lot of attention as a promising fault identification method in chemical process recently.In the high-dimensional data identification using deep neural networks,problems such as insufficient data and missing data,measurement noise,redundant variables,and high coupling of data are often encountered.To tackle these problems,a feature based deep belief networks(DBN)method is proposed in this paper.First,a generative adversarial network(GAN)is used to reconstruct the random and non-random missing data of chemical process.Second,the feature variables are selected by Spearman’s rank correlation coefficient(SRCC)from high-dimensional data to eliminate the noise and redundant variables and,as a consequence,compress data dimension of chemical process.Finally,the feature filtered data is deeply abstracted,learned and tuned by DBN for multi-case fault identification.The application in the Tennessee Eastman(TE)process demonstrates the fast convergence and high accuracy of this proposal in identifying abnormal conditions for chemical process,compared with the traditional fault identification algorithms. | Wende Tian Zijian Liu Lening Li Shifa Zhang Chuankun Li | 2020 | Chinese Journal of Chemical Engineering2020，28，7： | 3 |
| 3 | Modified method for estimating the organic carbon density of discontinuous soils in peak-karst regions in southwest China显示文摘 | Hua Zheng Yirong Su Xunyang He Lening Hu Jinshui Wu Daoyou Huang Lei Li Cixian Zhao | 2012 | Environmental Earth Sciences2012，，6： | 1 |
| 4 | 显示文摘 | Li Y L Li J Wang N L Flavonoids and a New Polyacety- lene from Bidens parviflora Willd | 2008 | Molecules2008，13，： | 1 |
| 5 | Supplemental Feeding of Laying Hens with Wood Vinegar to Decrease the Ratio of n-6 to n-3 Fatty Acids in Eggs显示文摘A balanced ratio of fatty acids n-6 to n-3 in chicken eggs is important for health and to help prevent and manage obesity and other diseases.Traditionally,fish oil or flax seed has been utilized as feed additives to decrease the ratio of n-6 to n-3(n-6:n-3)fatty acids in eggs.The hull of spina date seed(HSDS)is a common agricultural waste product in China,from which wood vinegar(HSDSWV)may be derived.This study evaluated HSDSWV as a sup-plement in hen feeds to improve the quality of eggs and decrease the ratio of fatty acids n-6:n-3.HSDSWV was obtained via carbonization,and refined.Six concentrations(nil to 0.5%)of HSDSWV were prepared and fed to 6 hen groups,respectively,for 50 d.The fatty acids of the hen’s egg yolks were analyzed by gas chromatography/electron ionization-mass spectrometry(GC/EI-MS)in the selected ion monitoring(SIM)mode.The 0.2%HSDSWV resulted in the best egg yolk quality,with a lower percentage of linoleic acid(C18:2n6)and higher percentages of cis-5,8,11,14,17-eicosapentaenoic acid(C20:5n3)and cis-4,7,10,13,16,19-docosahexaenoic acid(C22:6n3),and thus a lower n-6:n-3 ratio compared with the other HSDSWV concentrations.In addition,the eggs contained higher levels of yolk fat and egg yolk than the controls did.In conclusion,to modify the fatty acid composition of hens’eggs and obtain a balanced ratio of n-6:n-3,0.2%HSDSWV may be considered suitable as a dietary supplement in hens’feed. | ZHAO Nan XIN Hua LI Zhanchao WANG Ziming ZHANG Lening | 2019 | Chemical Research in Chinese Universities2019，35，6： | 0 |
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