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Experimental study on filtration performance of the moving bed granular filter with axial flow

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Particuology 2023年第1期72卷 17-28页

作者： Han Lv Yuxue Liu yingying dong Yiping Fan Chunxi Lu State Key Laboratory of Heavy Oil Processing Department of Chemical Engineering and EnvironmentChina University of PetroleumChangpingBeijing 102249China

The filtration performance of the moving bed granular filter with axial flow (MBGF-AF) is investigated through a large cold experiment. The effect of different operation parameters on the filtration performance (collection efficiency, pressure drop) of the axial-flow moving bed filter is investigated in combination with the dust deposition effect and the mechanism of trapping dust by the capturing particles. The results show that the collection efficiency of MBGF-AF is enhanced by decreasing the superficial gas velocity, increasing the inlet dust concentration properly, or decreasing the moving velocity of the capturing particles. A model covering the above operation parameters is established to calculate the collection efficiency of the moving bed granular filter. It is used in a wide range of operating parameters for the MBGFs.

关键词： Moving bed Granular filter Specific deposit Pressure drop Collection efficiency model

Thermal Stability of Fe_2O_3 Nanowires

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Journal of Materials Science & Technology 2008年第4期24卷 594-596页

作者： Xiongdian ZHANG Qin HAN Zhao dong yingying XU Han ZHANG State Key Laboratory for Mesoscopic Physics Department of Physics Peking University. Beijing 100871 China

The thermal stability of α-Fe203 and γ-Fe2O3 nanowires was studied by post annealing the samples at different temperatures. Before and after annealing, the samples were characterized by X-ray diffraction and scanning electron microscopy. The α-Fe2O3 nanowires are stable at the temperatures up to 600℃, and the crystalline structure becomes more perfect after annealing. This behavior supplies a way to improve the quality of the α-Fe2O3 nanowires. The γ-Fe2O3 nanowires become unstable when annealed at 350℃. Raman spectra of both nanowires have been measured, which also indicate that the γ-Fe203 nanowires are transformed into α-Fe2O3 under the strong laser beam.

关键词： Fe2O3 nanowire Thermal stability Structural transformation

维普期刊数据库

Development of Spectral Features for Monitoring Rice Bacterial Leaf Blight Disease Using Broad-Band Remote Sensing Systems

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Phyton-International Journal of Experimental Botany 2024年第4期93卷 745-762页

作者： Jingcheng Zhang Xingjian Zhou dong Shen Qimeng Yu Lin Yuan yingying dong College of Artificial Intelligence Hangzhou Dianzi UniversityHangzhou310018China School of Information Engineering Zhejiang University of Water Resources and Electric PowerHangzhou310018China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing100094China

As an important rice disease, rice bacterial leaf blight (RBLB, caused by the bacterium Xanthomonas oryzae ***), has become widespread in east China in recent years. Significant losses in rice yield occurred as a result ofthe disease’s epidemic, making it imperative to monitor RBLB at a large scale. With the development of remotesensing technology, the broad-band sensors equipped with red-edge channels over multiple spatial resolutionsoffer numerous available data for large-scale monitoring of rice diseases. However, RBLB is characterized by rapiddispersal under suitable conditions, making it difficult to track the disease at a regional scale with a single sensorin practice. Therefore, it is necessary to identify or construct features that are effective across different sensors formonitoring RBLB. To achieve this goal, the spectral response of RBLB was first analyzed based on the canopyhyperspectral data. Using the relative spectral response (RSR) functions of four representative satellite or UAVsensors (i.e., Sentinel-2, GF-6, Planet, and Rededge-M) and the hyperspectral data, the corresponding broad-bandspectral data was simulated. According to a thorough band combination and sensitivity analysis, two novel spectralindices for monitoring RBLB that can be effective across multiple sensors (i.e., RBBRI and RBBDI) weredeveloped. An optimal feature set that includes the two novel indices and a classical vegetation index was *** capability of such a feature set in monitoring RBLB was assessed via FLDA and SVM algorithms. The resultdemonstrated that both constructed novel indices exhibited high sensitivity to the disease across multiple ***, the feature set yielded an overall accuracy above 90% for all sensors, which indicates its cross-sensorgenerality in monitoring RBLB. The outcome of this research permits disease monitoring with different remotesensing data over a large scale.

关键词： Rice bacterial leaf blight analysis of spectral response multispectral data simulation vegetation indices cross-sensor disease monitoring

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硅烷改性钛石膏沥青胶浆性能研究

森林工程 2025年第1期41卷 185-194页

作者：董营营苏纪壮张朝魏毛元张惠勤王凯山东高速集团四川乐宜公路有限公司成都610041 山东高速工程检测有限公司济南250002 大宗固废材料在交通领域行业循环利用行业研发中心济南250002

为降低钛石膏堆积带来的环境问题,同时缓解道路材料短缺现状,采用钛石膏和硅烷偶联剂处理后的钛石膏部分或全部代替石灰岩矿粉,研究钛石膏和改性钛石膏对沥青胶浆常规性能和流变性能的影响。首先通过激光粒度仪和扫描电子显微镜分析钛... 详细信息

为降低钛石膏堆积带来的环境问题,同时缓解道路材料短缺现状,采用钛石膏和硅烷偶联剂处理后的钛石膏部分或全部代替石灰岩矿粉,研究钛石膏和改性钛石膏对沥青胶浆常规性能和流变性能的影响。首先通过激光粒度仪和扫描电子显微镜分析钛石膏、改性钛石膏和石灰岩矿粉的粒度分布情况及微观形貌。然后制备不同钛石膏和改性钛石膏掺量的沥青胶浆,采用常规试验方法、温度扫描试验、频率扫描试验、多重应力蠕变恢复(mutiple stress creep recovery,MSCR)试验、线性振幅扫描(linear amplitude sweep,LAS)试验和弯曲梁流变(bending beam rheometer,BBR)试验测试沥青胶浆的三大指标(针入度、延度和软化点)、高温流变性能、抗疲劳性能和低温流变性能。结果表明,钛石膏和改性钛石膏的粒度范围更广,表面更为粗糙;钛石膏和改性钛石膏的加入降低了沥青胶浆的延度、针入度、不可恢复蠕变柔量(Jnr)、疲劳寿命(Nf)、蠕变速率(m)和相位角(δ),软化点、复数模量(G*)、弹性恢复率(R)和蠕变劲度模量(S)有所提高,即高温性能有所提高,低温性能和疲劳性能有所下降。相同温度下,改性钛石膏的高温性能和弹性恢复指标要优于钛石膏,低温性能有所降低。研究证实使用钛石膏进行沥青混合料生产的可行性,为钛石膏的资源化利用开拓方向。

关键词：钛石膏沥青胶浆改性钛石膏矿粉流变性能

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Rationally pairing photoactive materials for high-performance polymer solar cells with efficiency of 16.53%

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Science China Chemistry 2020年第2期63卷 265-271页

作者： Yue Wu Yan Zheng Hang Yang Chenkai Sun yingying dong Chaohua Cui He Yan Yongfang Li Laboratory of Advanced Optoelectronic Materials College of ChemistryChemical Engineering and Materials ScienceSoochow UniversitySuzhou 215123China CAS Research/Education Center for Excellence in Molecular Sciences CAS Key Laboratory of Organic SolidsInstitute of ChemistryChinese Academy of SciencesBeijing 100190China Department of Chemistry Energy Institute and Hong Kong Branch of Chinese National Engineering Research Center for Tissue Restoration and ReconstructionHong Kong University of Science and TechnologyClear Water Bay KowloonHong KongChina

The emergence of non-fullerene acceptors(NFA) offers a promising opportunity to develop high-performance donor/acceptor pairs with high power conversion efficiency,as NFAs offer tunable energy levels,broad absorption and suitable aggregation *** order to enhance light-harvesting capability of active layers,we choose a wide bandgap polymer PTQ10 as the donor to blend with a narrow bandgap NFAY6 as the *** comparison with PTQ10:IDIC blend,~130 nm red-shifted absorption spectrum is observed in the PTQ10:Y6 blend,which potentially enhance the short-circuit current density(Jsc) for the *** addition,the optimal PTQ10:Y6 blend shows higher photoluminescence quenching efficiency and more efficient charge separation,higher charge mobilities,as well as weaker bimolecular recombination over the PTQ10:IDIC blend,which leads to an outstanding power conversion efficiency(PCE) of 16.53%,with a notable Jsc of 26.65 mA cm^-2 and fill factor(FF) of 0.751.

关键词： polymer solar cells polymer donor nonfullerene acceptor power conversion efficiency

维普期刊数据库

Differentiation of Wheat Diseases and Pests Based on Hyperspectral Imaging Technology with a Few Specific Bands

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Phyton-International Journal of Experimental Botany 2023年第2期92卷 611-628页

作者： Lin Yuan Jingcheng Zhang Quan Deng yingying dong Haolin Wang Xiankun Du School of Information Engineering Zhejiang University of Water Resources and Electric PowerHangzhou310018China School of Automation Hangzhou Dianzi UniversityHangzhou310018China Aerospace Information Research Institute Chinese Academy of SciencesBeijing100094China

Hyperspectral imaging technique is known as a promising non-destructive way for detecting plants diseases and *** most previous studies,the utilization of the whole spectrum or a large number of bands as well as the complexity of model structure severely hampers the application of the technique in *** a detection system can be established with a few bands and a relatively simple logic,it would be of great significance for *** study established a method for identifying and discriminating three commonly occurring diseases and pests of wheat,i.e.,powdery mildew,yellow rust and aphid with a few specific *** a comprehensive spectral analysis,only three bands at 570,680 and 750 nm were selected.A novel vegetation index namely Ratio Triangular Vegetation Index(RTVI)was developed for detecting anomalous areas on ***,the Support Vector Machine(SVM)method was applied to construct the discrimination model based on the spectral ratio *** validating results suggested that the proposed method with only three spectral bands achieved a promising accuracy with the Overall Accuracy(OA)of 83%.With three bands from the hyperspectral imaging data,the three wheat diseases and pests were successfully detected and discriminated.A stepwise strategy including background removal,damage lesions recognition and stresses discrimination was *** present work can provide a basis for the design of low cost and smart instruments for disease and pest detection.

关键词： Winter wheat diseases pests hyperspectral imaging discriminant analysis

Remote sensing retrieval of winter wheat leaf area index and canopy chlorophyll density at different growth stages

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Big Earth Data 2022年第4期6卷 580-602页

作者： Naichen Xing Wenjiang Huang Huichun Ye yingying dong Weiping Kong Yu Ren Qiaoyun Xie Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Earth Observation Aerospace Information Research InstituteChinese Academy of SciencesSanyaChina Key Laboratory of Quantitative Remote Sensing Information Technology Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina University of Technology Sydney SydneyAustralia

Leaf area index(LAI)and canopy chlorophyll density(CCD)are key indicators of crop growth *** this study,we compared several vegetation indices and their red-edge modified counterparts to evaluate the optimal red-edge bands and the best vegetation index at different growth *** indices were calculated with Sentinel-2 MSI data and hyperspectral *** performances were validated against ground measurements using R2,RMSE,and *** results suggest that indices computed with hyperspectral data exhibited higher R2 than multispectral data at the late jointing stage,head emergence stage,and filling ***,rededge modified indices outperformed the traditional indices for both data *** models indicated that the indices with short red-edge wavelengths showed better estimation at the early joint-ing and milk development stage,while indices with long red-edge wavelength estimate the sought variables better at the middle three *** results were consistent with the red-edge inflec-tion point shift at different growth *** best indices for Sentinel-2 LAI retrieval,Sentinel-2 CCD retrieval,hyperspectral LAI retrieval,and hyperspectral CCD retrieval at five growth stages were determined in the *** results are beneficial to crop trait monitoring by providing references for crop biophysical and bio-chemical parameters retrieval.

关键词： Growth stages hyperspectral red-edge band Sentinel-2 vegetation index

Impact of spectral interval on wavelet features for detecting wheat yellow rust with hyperspectral data

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International Journal of Agricultural and Biological Engineering 2018年第6期11卷 138-144页

作者： Jingcheng Zhang Bin Wang Xuexue Zhang Peng Liu yingying dong Kaihua Wu Wenjiang Huang College of Life Information Science and Instrument Engineering Hangzhou Dianzi UniversityHangzhou 310018China Key laboratory of Digital Earth Science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijing 100094 China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China

Detection of yellow rust using hyperspectral data is of practical importance for disease control and *** an emerging spectral analysis method,continuous wavelet analysis(CWA)has shown great potential for the detection of plant diseases and *** the spectral interval of airborne or spaceborne hyperspectral sensor data differ greatly,it is important to understand the impact of spectral interval on the performance of CWA in detecting yellow rust in winter wheat.A field experiment was conducted which obtained spectral measurements of both healthy and disease-infected *** impacts of the mother wavelet type and spectral interval on disease detection were *** results showed that spectral features derived from all four mother wavelet types exhibited sufficient sensitivity to the occurrence of yellow *** Mexh wavelet slightly outperformed the others in estimating disease *** the detecting accuracy generally declined with decreasing of spectral interval,relatively high accuracy levels were maintained(R^(2)>0.7)until a spectral interval of 16 ***,it is recommended that the spectral interval of hyperspectral data should be no larger than 16 nm for the detection of yellow *** relatively loose spectral interval requirement permits extensive applications for disease detection with hyperspectral imagery.

关键词： continuous wavelet analysis spectral interval hyperspectral data wheat yellow rust

Identification of banana fusarium wilt using supervised classification algorithms with UAV-based multi-spectral imagery

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International Journal of Agricultural and Biological Engineering 2020年第3期13卷 136-142,I0001页

作者： Huichun Ye Wenjiang Huang Shanyu Huang Bei Cui yingying dong Anting Guo Yu Ren Yu Jin Key Laboratory of Digital Earth Science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijing 100094China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China Key Laboratory of Earth Observation Hainan ProvinceSanya 572029China Chinese Academy of Agricultural Engineering Planning&Design Beijing 100125China University of Chinese Academy of Sciences Beijing 100049China School of Electronics and Information Engineering Anhui UniversityHefei 230601China

The disease of banana Fusarium wilt currently threatens banana production areas all over the *** and large-area monitoring of Fusarium wilt disease is very important for the disease treatment and crop planting *** objective of this study was to evaluate the performance of supervised classification algorithms such as support vector machine(SVM),random forest(RF),and artificial neural network(ANN)algorithms to identify locations that were infested or not infested with Fusarium *** unmanned aerial vehicle(UAV)equipped with a five-band multi-spectral sensor(blue,green,red,red-edge and near-infrared bands)was used to capture the multi-spectral imagery.A total of 139 ground sample-sites were surveyed to assess the occurrence of banana Fusarium *** results showed that the SVM,RF,and ANN algorithms exhibited good performance for identifying and mapping banana Fusarium wilt disease in UAV-based multi-spectral *** overall accuracies of the SVM,RF,and ANN were 91.4%,90.0%,and 91.1%,respectively for the pixel-based *** RF algorithm required significantly less training time than the SVM and ANN *** maps generated by the SVM,RF,and ANN algorithms showed the areas of occurrence of Fusarium wilt disease were in the range of 5.21-5.75 hm2,accounting for 36.3%-40.1%of the total planting area of bananas in the study *** results also showed that the inclusion of the red-edge band resulted in an increase in the overall accuracy of 2.9%-3.0%.A simulation of the resolutions of satellite-based imagery(i.e.,0.5 m,1 m,2 m,and 5 m resolutions)showed that imagery with a spatial resolution higher than 2 m resulted in good identification accuracy of Fusarium *** results of this study demonstrate that the RF classifier is well suited for the identification and mapping of banana Fusarium wilt disease from UAV-based remote sensing *** results provide guidance for disease treatment and crop planting adjustments.

关键词： banana fusarium wilt UAV-based multi-spectral remote sensing support vector machine artificial neural network random forest