The comparative studies on micromorphological features in diagnostic horizons of Stagnic Anthrosols, Ustic Ferrosols and Ustic Vertosols in southwestern China were conducted to underpin the rationale for Chinese Soil ...The comparative studies on micromorphological features in diagnostic horizons of Stagnic Anthrosols, Ustic Ferrosols and Ustic Vertosols in southwestern China were conducted to underpin the rationale for Chinese Soil Taxonomy. The following findings were explored: (1) Stagnic Anthrosols had the specific micromorphological features, e.g., the humic formation in anthrostagnic epipedon, the platy structures in plow subhorizon, the secondary formation of ferromanganese and the weakly optical-orientation clay domains in hydragric horizon, etc.: (2) The groundmasses of ferric horizon in Ustic Ferrosols appeared in hue of 2.5YR or redder, and had pellicular grain structure; (3) Ustic Vertosols had a crust horizon (Acr), and crack structure dominated in Acr and angular blocky structure in disturbed horizon; (4) Because of the distinct differences in micromorphological features among these three soils, the specific micromorphological features might be employed as diagnostic horizons to differentiate soils while the quantifiable micromorphological features might potentially be selected as diagnostic indices for Chinese soil taxonomic classification.展开更多
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
基金supported by the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant No. KZCX2-YW-409)
文摘The comparative studies on micromorphological features in diagnostic horizons of Stagnic Anthrosols, Ustic Ferrosols and Ustic Vertosols in southwestern China were conducted to underpin the rationale for Chinese Soil Taxonomy. The following findings were explored: (1) Stagnic Anthrosols had the specific micromorphological features, e.g., the humic formation in anthrostagnic epipedon, the platy structures in plow subhorizon, the secondary formation of ferromanganese and the weakly optical-orientation clay domains in hydragric horizon, etc.: (2) The groundmasses of ferric horizon in Ustic Ferrosols appeared in hue of 2.5YR or redder, and had pellicular grain structure; (3) Ustic Vertosols had a crust horizon (Acr), and crack structure dominated in Acr and angular blocky structure in disturbed horizon; (4) Because of the distinct differences in micromorphological features among these three soils, the specific micromorphological features might be employed as diagnostic horizons to differentiate soils while the quantifiable micromorphological features might potentially be selected as diagnostic indices for Chinese soil taxonomic classification.
基金Supported by the National Natural Science Foundation of China(61374137,61490701,61174119)the State Key Laboratory of Integrated Automation of Process Industry Technology and Research Center of National Metallurgical Automation Fundamental Research Funds(2013ZCX02-03)
文摘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.
