资源目录
压缩包内文档预览：
编号：122377386
类型：共享资源
大小：3.27MB
格式：ZIP
上传时间：2021-04-19
上传人：221589****qq.com
认证信息
个人认证
李**（实名认证）
湖南
IP属地：湖南
40
积分
- 关 键 词：
-
粉煤
含水量
自动检测
加湿
系统
设计
- 资源描述：
-
粉煤机含水量自动检测及加湿系统设计,粉煤,含水量,自动检测,加湿,系统,设计
- 内容简介：
-
APPLICATION OF NEAR INFRAREDSPECTROSCOPY TO RAPID ANALYSISOF COALSMikio Kaihara,* Tadayuki Takahashi,Takahiro Akazawa, Toshihiro Sato,and Satoshi TakahashiIchinoseki National College of Technology, Hagisyo,Ichinoseki 021-8511, JapanABSTRACTComparing near infrared (NIR) spectra with infrared spectrain case of coal samples, we generally notice NIR spectra haveless characteristic absorbance bands. However,utilizingmerits of NIR spectroscopy (non destructive, high penetrationability, simple sample preparation, rapid analysis) and partialleast squares regression (PLS) model, we found that severalmajor properties of coals could be conveniently and rapidlyestimated. For examples, multiple correlation coefficient be-tween the measured values vs. the predicted values from ob-tained spectra, R2, of moisture, volatile matter, oxygencontent, maximum fluidity temperature, solidification tem-perature were 0.9736, 0.9774, 0.8996, 0.8849 and 0.9282 inrespectively although the precision of obtained results were*Corresponding author. E-mail: mkaiharaichinoseki.ac.jp369Copyright # 2002 by Marcel Dekker, ISPECTROSCOPY LETTERS, 35(3), 369376 (2002)not superior to those by methods of American Standard ofTesting Materials.Key Words:Coals;Nearinfrared(NIR) spectroscopy;Rapid analysis; PLSINTRODUCTIONNIR spectroscopy is quite useful as a non destructive and a rapidanalysis method, which is expanding the application field.1Much more,recent trend of compacting the spectrometer and the computer makes itmore convenient and appropriate for the analysis at mining fields ofresources or the on-line analysis at factories.The properties of coals are usually estimated by a lot of index, carboncontent (C), hydrogen content (H), oxygen content (O), maximum fluiditytemperature (MFT) and so on. For example, C, H, O are now measuredwith an elemental analysis apparatus (using the combustion method) andMFT, SOT are measured under high temperature (about 4007550 degreecentigrade) with the gieseler-plastmeter. Still more, testing the moisturecontent is necessary for more than one hour by the other instrument, themoisture oven. Such measurements of coal properties need plural equip-ments, much time and energy (heating).Infrared spectroscopy is widely used for the analysis2,3,4of coal andso on because it has a lot of characteristic bands in the wavenumber region(40007400cm?1, see Fig. 1). On the contrary, NIR spectra of coals are notso prominent in the region (110072500nm, see Fig. 1). However, NIRFigure 1.IR and NIR spectra of a representative coal (No.7).370KAIHARA ET AL.spectroscopy has some characteristic merits. Especially, it can penetratesamples much more than other spectroscopy (infrared, ultra violet, visible)can, which could be suitable for inhomogeneous samples such as coalsbecause it can measure much more samples at once.In this report, we tried estimation of such properties of coals by theNIR spectra although we generally took less characteristic absorbancebands than those obtained by infrared spectra. As a result, we found that wecould predict some important properties of coals by just measuring the NIRspectra and using the PLS regression model toward obtained NIR spectra.Apparatus and Sample PreparationWe got 15 coals5(powders under 100 mesh from different mines)from Pennsylvania State University coal bank via Dr. Gary Mitchell. Themajor properties of the coals are listed in Table 1.NIR 6500 (NIRSystems Co.) was used for obtaining the NIR spectraof coals with the diffuse reflection cup. Coal powders (from 2.99g to 3.01)were placed into the cup (35mm in diameter by 10mm deep) and coveredwith a quartz plate.Table 1.Major Properties of Sample CoalsProximate AnalysisUltimate AnalysisThermo PlasticPropertiesNo.MoAshVMFCCHOISMFSo110.4314.4734.1640.9358.664.087.3138541143222.6419.7630.7446.8664.604.386.8340644047336.8111.4238.3843.4064.664.747.2737642145645.9711.3040.4942.2465.364.807.9337942345455.2013.1739.2342.4066.154.809.0239642043862.009.2538.6350.1272.725.005.9137342847074.735.5642.4047.3172.915.609.4240041943882.4010.0035.1652.4472.994.997.3338744047793.746.3245.0044.9373.405.589.29387420442102.3210.6432.5254.5274.554.745.89404446483111.4610.3732.1156.0675.134.825.17376446485122.795.3236.4855.4177.335.376.92403439469132.446.2033.4657.8979.105.055.18397447487142.063.8129.5164.6282.675.044.16383458500151.454.1624.6269.7783.254.674.44404456493RAPID ANALYSIS OF COALS371NIR spectra were measured at interval of 2nm (from 1100nm to2500nm and scanned 32 times (about 2 minutes).Analysis and DiscussionAll the procedures of the spectral analysis using the pirouette ver.2.6are written below.1.Preprocessing of spectraMean centering all the spectra after multiplicative scatteringcorrection (MSC6) in some cases. In case of highly scatteringoptical samples, a multiplicative effect (differences in measure-ment path lengths) can occur. Therefore, MSC attempts toaccount for it. All the spectra are composed of 700 data (every2nm from 1100 to 2500nm).2.Fabricating first derivation and second derivation spectra afterSavitsky-Golay smoothing.3.Applying PLS regression model7to the relation between thepreprocessed spectra and measured values of moisture (Mo),volatile matter (VM), oxygen content (O), maximum fluiditytemperature (MFT) and solidification temperature (SOT) with across validation method (leave one out).4.A couple of the cross check of the obtained best model and theerror of prediction for Mo, VM, MFT, SOT and O were shown inFigs. 276, in respectively. Best regression models were determinedby the minimum value of standard error of cross validation(SECV). R2(multiple correlation coefficient) of SECV of Mo,VM, O, MFT and SOT were 0.9736, 0.9774, 0.8996, 0.8849 and0.9282 and the SECV of Mo, VM, O, MFT and SOT were 0.45,0.79, 0.51, 4.55 and 4.76 in respectively.From these figures (Figs. 276), we didnt find special displacements in thepredicted errors, i.e., they were random. Therefore, we expect that theprecision of this method could be improved much more. However, theprecision level by this method is now inferior to those of American Standardof Testing Material (ASTM).In Table 2, we compared the influence of preprocessing methods. Justin the case of oxygen content, MSC was useful for improving the precisionof the regression method. Judging from our results, MSC was not complete.We suppose that the errors are caused by three major factors blems on scattering effect372KAIHARA ET AL.2.prediction error by the PLS regression model3.errors by the ASTM methodExact estimation of scattering effect should be needed. Perhaps, thisrapid analysis could be potent in case of quality control for relativelyhomogeneous coals. We are now under investigation of the improvements ofthis method and the application to other kind of coals.Figure 2.Measured Mo vs. predicted Mo and residual values. The arrowed zonemeans the allowed error by the method of ASTM, D3173.Figure 3.Measured VM vs. predicted VM and residual values. The arrowed zonemeans the allowed error by the method of ASTM, D3175.RAPID ANALYSIS OF COALS373CONCLUSIONUsing NIR spectroscopy and PLS regression model, we found thatsome important properties of coals could be estimated with the quite simplesample preparation for getting NIR spectra. Still more, our method doesntneed much time, much energy (heating) and plural instruments. ThisFigure 4.Measured O vs. predicted O and residual values. The arrowed zone meansthe allowed error by the method of ASTM, D3176.Figure 5.Measured MFT vs. predicted MFT and residual values. The arrowedzone means the allowed error by the method of ASTM, D2639.374KAIHARA ET AL.method could be a rapid and convenient analysis in factories or miningplaces although it should be improved on the precision of the prediction.ACKNOWLEDGMENTSWe are grateful to the Hanaizumi Agricultural Development Center,Kentaro Takahashi, and Kenichi Tatsubayasi (Nireco) for giving advice andFigure 6.Measured SOT vs. predicted SOT and residual values. The arrowed zonemeans the allowed error by the method of ASTM, D2639.Table 2.Comparison of Preprocessing MethodsPropertiesPreprocessingSECVNumbers of PLS ComponentsMono use of MSC0.457MoMSC0.954VMno use of MSC0.799VMMSC1.298Ono use of MSC0.675OMSC0.516MFTno use of MSC4.558MFTMSC4.912SOTno use of MSC4.763SOTMSC7.632RAPID ANALYSIS OF COALS375taking care of us on the usage of NIR 6500. We are deeply grateful to IwatePromotion Center and the Regional Science Promotion Program by JapanScience and Technology Corporation which supported our study financially.REFERENCES1.Norris, K.H. Making Light Work, Advances in Near Infrared Spec-troscopy. In Murray, I., Cowe, I.A., Ed.; Ian Michael Publications:UK, 1992.2.Fredericks, Peter M.; Lee, James B.; Osborn, Paul R.; Swinkels, DomA.J. Material Characterization using Factor Ana
- 温馨提示:
1: 本站所有资源如无特殊说明，都需要本地电脑安装OFFICE2007和PDF阅读器。图纸软件为CAD,CAXA,PROE,UG,SolidWorks等.压缩文件请下载最新的WinRAR软件解压。
2: 本站的文档不包含任何第三方提供的附件图纸等，如果需要附件，请联系上传者。文件的所有权益归上传用户所有。
3.本站RAR压缩包中若带图纸，网页内容里面会有图纸预览，若没有图纸预览就没有图纸。
4. 未经权益所有人同意不得将文件中的内容挪作商业或盈利用途。
5. 人人文库网仅提供信息存储空间，仅对用户上传内容的表现方式做保护处理，对用户上传分享的文档内容本身不做任何修改或编辑，并不能对任何下载内容负责。
6. 下载文件中如有侵权或不适当内容，请与我们联系，我们立即纠正。
7. 本站不保证下载资源的准确性、安全性和完整性, 同时也不承担用户因使用这些下载资源对自己和他人造成任何形式的伤害或损失。
人人文库网所有资源均是用户自行上传分享，仅供网友学习交流，未经上传用户书面授权，请勿作他用。
川公网安备: 51019002004831号