|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Simultaneous desulfurization and denitrification of flue gas by pre-ozonation combined with ammonia absorption显示文摘A process of simultaneous desulfurization and denitrification of flue gas was conducted in this study.The flue gas containing 200 mg·m^-3NO,1000-4000 mg·m^-3SO(2,)3%-9%O(2)and 10%-20%CO(2)was first oxidized b(y)O3 and then absorbed by ammonia in a bubbling reactor.Increasing the ammonia concentration or the SO2 content in flue gas can promote the absorption of NOx and extend the effective absorption time.On the contrary,both increasing the absorbent temperature or the O(2)content shorten the effective absorption time of NO((x.))The change of solution pH had substantial influence on NOx absorption.In the presence of CO(2)the NOx removal efficiency reached 89.2%when the absorbent temperature was raised to 60℃and the effective absorption time can be maintained for 8 h,which attribute to the buffering effect in the absorbent.Besides,both the addition of Na(2)S2 O3 and urea can promote the NOx removal efficiency when the absorbent temperature is 25℃and the addition of Na(2)S2 O3 had achieved better results.The advantage of adding Na(2)S2 O3 became less evident at higher absorbent temperature and coexistence of CO(2.)In all experiments,SO(2)removal efficiency was always above 99%,and it was basically not affected by the above factors. | Baowei Wang Shumei Yao Yeping Peng | 2020 | Chinese Journal of Chemical Engineering2020，28，9： | 5 |
| 2 | Intelligent Identification of Wear Mechanism via On-line Ferrograph Images显示文摘Condition based maintenance(CBM) issues a new challenge of real-time monitoring for machine health maintenance. Wear state monitoring becomes the bottle-neck of CBM due to the lack of on-line information acquiring means. The wear mechanism judgment with characteristic wear debris has been widely adopted in off-line wear analysis; however, on-line wear mechanism characterization remains a big problem. In this paper, the wear mechanism identification via on-line ferrograph images is studied. To obtain isolated wear debris in an on-line ferrograph image, the deposition mechanism of wear debris in on-line ferrograph sensor is studied. The study result shows wear debris chain is the main morphology due to local magnetic field around the deposited wear debris. Accordingly, an improved sampling route for on-line wear debris deposition is designed with focus on the self-adjustment deposition time. As a result, isolated wear debris can be obtained in an on-line image, which facilitates the feature extraction of characteristic wear debris. By referring to the knowledge of analytical ferrograph, four dimensionless morphological features, including equivalent dimension, length-width ratio, shape factor, and contour fractal dimension of characteristic wear debris are extracted for distinguishing four typical wear mechanisms including normal, cutting, fatigue, and severe sliding wear. Furthermore, a feed-forward neural network is adopted to construct an automatic wear mechanism identification model. By training with the samples from analytical ferrograph, the model might identify some typical characteristic wear debris in an on-line ferrograph image. This paper performs a meaningful exploratory for on-line wear mechanism analysis, and the obtained results will provide a feasible way for on-line wear state monitoring. | WU Tonghai PENG Yeping SHENG Chenxing WU Jiaoyi | 2014 | Chinese Journal of Mechanical Engineering2014，27，2： | 4 |
| 3 | Watershed-based morphological separation of wear debris chains for on-line ferrograph analysis显示文摘 | WU Hongkun WU Tonghai PENG Yeping | 2014 | Tribology Letters2014，53，2： | 1 |
| 4 | Dimensional Description of On-line Wear Debris Images for Wear Characterization显示文摘As one of the most wear monitoring indicator, dimensional feature of individual particles has been studied mostly focusing on off-line analytical ferrograph. Recent development in on-line wear monitoring with wear debris images shows that merely wear debris concentration has been extracted from on-line ferrograph images. It remains a bottleneck of obtaining the dimension of on-line particles due to the low resolution, high contamination and particle's chain pattern of an on-line image sample. In this work, statistical dimension of wear debris in on-line ferrograph images is investigated. A two-step procedure is proposed as follows. First, an on-line ferrograph image is decomposed into four component images with different frequencies. By doing this, the size of each component image is reduced by one fourth, which will increase the efficiency of subsequent processing. The low-frequency image is used for extracting the area of wear debris, and the high-frequency image is adopted for extracting contour. Second, a statistical equivalent circle dimension is constructed by equaling the overall wear debris in the image into equivalent circles referring to the extracted total area and premeter of overall wear debris. The equivalent circle dimension, reflecting the statistical dimension of larger wear debris in an on-line image, is verified by manual measurement. Consequently, two preliminary applications are carried out in gasoline engine bench tests of durability and running-in. Evidently, the equivalent circle dimension, together with the previously developed concentration index, index of particle coverage area(IPCA), show good performances in characterizing engine wear conditions. The proposed dimensional indicator provides a new statistical feature of on-line wear particles for on-line wear monitoring. The new dimensional feature conveys profound information about wear severity. | WU Tonghai PENG Yeping DU Ying WANG Junqun | 2014 | Chinese Journal of Mechanical Engineering2014，27，6： | 1 |
| 5 | Full-life dynamic identification of wear state based on on-line wear debris image features显示文摘 | Wu Tonghai Peng Yeping Wu Hong Kun | 2014 | Mechanical Systems and Signal Processing2014，42，： | 1 |
您所在的机构暂未开通此部分的功能，请联系管理员