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化学检测中的综合比较法的应用初探 被引量:1
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作者 沈国权 《科技视界》 2017年第12期221-221,共1页
在进行化学实验过程中,要想确保化学实验能够顺利展开,那么就非常有必要做好相应的检测工作。在开展化学检测的过程中,检验的精准性与方法的选用有着紧密的联系。通常而言,综合比较法的运用不仅能够确保化学检测的结果更加精准无误,而... 在进行化学实验过程中,要想确保化学实验能够顺利展开,那么就非常有必要做好相应的检测工作。在开展化学检测的过程中,检验的精准性与方法的选用有着紧密的联系。通常而言,综合比较法的运用不仅能够确保化学检测的结果更加精准无误,而且可以促使化学实验能顺利进行下去。伴随着化学检测技术水平的持续提升,综合比较法的运用变得愈加频繁,相关研究人员也更加注重这种方法。文章将详尽探析综合比较法在化学检测中的运用,并结合实际情况来展开实际案例分析。 展开更多
关键词 化学检测过程 综合比较法 运用 案例分析
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Fault diagnosis of chemical processes based on partitioning PCA and variable reasoning strategy 被引量:4
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作者 Guozhu Wang Jianchang Liu +1 位作者 Yuan Li Cheng Zhang 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2016年第7期869-880,共12页
Fault detection and identification are challenging tasks in chemical processes, the aim of which is to decide out of control samples and find fault sensors timely and effectively. This paper develops a partitioning pr... Fault detection and identification are challenging tasks in chemical processes, the aim of which is to decide out of control samples and find fault sensors timely and effectively. This paper develops a partitioning principal component analysis(PPCA) method for process monitoring. A variable reasoning strategy is proposed and applied to recognize multiple fault variables. Compared with traditional process monitoring methods, the PPCA strategy not only reflects the local behavior of process variation in each model(each direction of principal components),but also improves the monitoring performance through the combination of local monitoring results. Then, a variable reasoning strategy is introduced to locate fault variables. Unlike the contribution plot, this method locates normal and fault variables effectively, and gives initiatory judgment for ambiguous variables. Finally, the effectiveness of the proposed process monitoring and fault variable identification schemes is verified through a numerical example and TE chemical process. 展开更多
关键词 Fault detectionFault identificationProcess monitoringPartitioning PCAVariable reasoning strategy
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An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes 被引量:5
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作者 杨雅伟 马玉鑫 +1 位作者 宋冰 侍洪波 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2015年第8期1357-1363,共7页
A novel approach named aligned mixture probabilistic principal component analysis(AMPPCA) is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations,the... A novel approach named aligned mixture probabilistic principal component analysis(AMPPCA) is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations,the AMPPCA algorithm first estimates a statistical description for each operating mode by applying mixture probabilistic principal component analysis(MPPCA). As a comparison, the combined MPPCA is employed where monitoring results are softly integrated according to posterior probabilities of the test sample in each local model. For exploiting the cross-mode correlations, which may be useful but are inadvertently neglected due to separately held monitoring approaches, a global monitoring model is constructed by aligning all local models together. In this way, both within-mode and cross-mode correlations are preserved in this integrated space. Finally, the utility and feasibility of AMPPCA are demonstrated through a non-isothermal continuous stirred tank reactor and the TE benchmark process. 展开更多
关键词 Multimode process monitoring Mixture probabilistic principal component analysis Model alignment Fault detection
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