CN104568821A - Near-infrared method for simultaneously and rapidly detecting multiple quality items in coal - Google Patents
Near-infrared method for simultaneously and rapidly detecting multiple quality items in coal Download PDFInfo
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- CN104568821A CN104568821A CN201410579286.3A CN201410579286A CN104568821A CN 104568821 A CN104568821 A CN 104568821A CN 201410579286 A CN201410579286 A CN 201410579286A CN 104568821 A CN104568821 A CN 104568821A
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Abstract
The invention discloses a near-infrared method for simultaneously and rapidly detecting multiple quality items in coal. The method comprises the following steps: determining seven indexes including internal moisture and the like of each sample respectively, collecting spectral data and curves of the indexes, performing treatment on the spectral data, obtaining a calibration equation, performing correction and verification, then establishing a detection model, directly filling a sample injector of an NIR (near-infrared reflectance) instrument with the coal samples to be detected sequentially, starting a scanning key, recording and storing spectra of the samples, determining the spectral type to which the samples belong, selecting the corresponding detection model, obtaining detection results and the like. The accuracy of the detection method of the seven key indexes in the coal established in the invention is in line with the requirement on reproducibility tolerance of a current standard method, the operation is very simple, convenient and rapid, and the scanning and the detection of the seven indexes, including data output, of one sample can be completed by several minutes. In the whole detection operation process, sample weighing is not required, no chemical reagents need to be added, and the novel rapid detection method has the characteristics of high speed, convenience and no pollution.
Description
Technical field
The present invention relates to coal analysis detection technique field, more specifically, relate to one and to detect in coal sample 7 simultaneously and refer to calibration method.
Background technology
Coal resources are the first energy of China, are the basic energy resource materials needed for industrial development.Coal resources in China is very abundant, accounts for about 70% of China's total energy.
According to the coal examination criteria of the world, coal needs to detect the quality projects such as interior water usually.By me
country of stateregulation and trade need, coal need detect thermal value usually, full sulphur, interior water, volatile matter, ash content, fixed carbon, the indexs such as flammable body, the standard method of described 7 indexs of existing detection is mainly: interior water has GB/T 212, ISO11722, ASTMD 3173, ash content has GB/T 212, ISO 1171, ASTMD 3174, volatile matter has GB/T 212, ISO 562, ASTMD 3175, full sulphur has GB/T 214, ISO 351, ASTMD 4239, thermal value has GB/T 213, ISO 1928, ASTMD 5865, fixed carbon and flammable body carry out with reference to the requirement in the Industry Analysis Method of coal.
The chemical method in the classics that above-mentioned standard method generally adopts or modern times and physical-chemical process: interior water, volatile matter, ash content, fixed carbon, flammable body are all classical gravimetric method, relate to the equipment such as balance, baking oven, high temperature furnace, carbonization, ashing, constant weight, weighing, calculating are the steps often needing to carry out, quite loaded down with trivial details time-consuming; Even if use more advanced instrument at present, a technician completes these projects and also obtains 5 ~ 6 days, and the work such as operation, maintenance, demarcation, verification of modern physical chemistry instrument is also very heavy simultaneously.The traditional detection method of coal is numerous and diverse owing to working, and cost cost compare is high, and spended time is long.The classification of coal is sold, the quick demand etc. of the export trade and power plant, and traditional coal round of visits is oversize, is unfavorable for the sale of coal, needs the proving time shortening coal for this reason, needs to find the new method of inspection.
NIRS is the english abbreviation of near infrared spectrum.NIRS technology is one of high-new analytical technology with the fastest developing speed nearly ten years.NIRS analytical technology application of spectral section wavelength coverage is approximately 3 ~ 0.70mm, belong to dry infrared range of spectrum, the same with visible ray, be all an electromagnetic ingredient, the general characteristic shown when having electromagnetic wave and object effect, as transmission, diffuse reflection, absorption etc.In addition, frequency multiplication and the sum of fundamental frequencies uptake zone of its most outstanding feature to be this SPECTRAL REGION be hydric group (OH, SH, CH, NH).The near infrared spectrum of material is the frequency multiplication of wherein each group vibration and the comprehensive absorption performance of combination frequency.
NIRS develops rapidly nearly ten years, China is mainly used in the attributional analysis of agricultural product from the eighties in last century, now be applied to every field, expand to the field such as petrochemical complex and basic organic chemical industry, macromolecule chemical industry, pharmacy and clinical medicine, biochemical industry, environmental science, textile industry and food industry from traditional agricultural byproducts analysis.But the research report of current NIRS technology in coal seldom.
Data shows, the existence that external report utilizes NIRS technology to detect coal is much difficult, domestic have employing Fourier transform near infrared spectrum method to set up coal volatile matter, determination of moisture model, but do not propose the concrete technical scheme adopting NIRS technology quantitative test Coal ' moisture, volatile matter.The applicant is through long-term large quantifier elimination, it is feasible for summing up the correlation merit project method for quick utilizing NIR technology to set up coal, but the technical matters that following emphasis will solve effectively is solved always: (1) finds the suitable mathematics transition form of coal NIRS spectroscopic data; (2) NIRS finding suitable mathematical conversion detects reliable funtcional relationship between data and the detection data of coal quality composition conventional method; (3) gained funtcional relationship revised further and verify.Have no the technology report utilizing NIR technology to set up the method for quick simultaneously detecting coal sample 7 indexs.
Summary of the invention
The technical problem to be solved in the present invention is the detection for water in coal, the suitable mathematics transition form of concrete NIRS spectroscopic data is provided, sums up reliable funtcional relationship between the NIRS detection data of suitable mathematical conversion and the detection data of coal 7 quality component conventional methods, and gained funtcional relationship is revised further and verifies, provide one to detect in coal sample 7 simultaneously and refer to calibration method.
Object of the present invention is achieved by the following technical programs:
There is provided one to detect in coal sample 7 simultaneously and refer to calibration method, comprise the following steps:
S1. collect and prepare several coal samples, conventional method measures the interior water of each sample, ash content, volatile matter, the content of full sulphur, fixed carbon and flammable body and thermal value 7 indexs respectively;
S2. spectroscopic data and the curve of described coal sample is collected with the scanning of NIRS analyser;
S3. the spectroscopic data of S2 gained sample is processed, through returning the calibration equation calculating acquisition 7 indexs, revising and setting up detection model after water in checking;
S4. coal sample to be measured is directly filled successively the injector (not needing to sample) of NIR instrument, start scanning key, NIR instrument records storage sample spectra automatically.Determine sample ownership spectrum
figuretype, selects corresponding detection model, obtains testing result.
Scanning with NIRS analyser the spectroscopic data collecting described sample described in S2 is by sample (not needing to sample), directly fills the injector of NIR instrument successively, adopts digital raster system to scan, is automatically recorded and store sample spectra by NIRS instrument.The sample original spectrum collected is classified by its different variation tendency, identical with variation tendency or close according to peak type
figurespectrum is combined, and sorts out, and obtains Y type original spectrum, W type original spectrum, P type original spectrum and X-type original spectrum respectively.
Carrying out process to the spectroscopic data of S2 gained sample described in S3 is adopt WinISI software carry out spectral analysis and set up detection model, the spectroscopic data of S2 gained sample is imported NIR instrument, determines detection model, print testing result.Pretreated spectra adopts one or more of the methods such as the correction of trend converter technique, standard normal variable transformation approach, Multivariate Discrete, anti-phase Multivariate Discrete correction respectively, finally determines optimization process method; Regression correction method adopts Stepwise Regression Method (SMLR), principal component analysis (PCA) (PCA) and minimal error analytical method (PLS), by Data Dimensionality Reduction, to eliminate message part overlapped in numerous information co-exist and finally to accomplish the quantification to spectrum.
Utilize detection model to detect liquid water content in the sample of one group of unknown component content to be measured, then the detected value of NIR method gained and Typical physical chemical method detected value are compared and evaluate.The comparative result of two kinds of methods is with predicting that standard deviation (SEP, Standard Error of Prediction) and the corresponding coefficient of determination (RSQp) or coefficient R p are weighed.
The method for building up of described detection model comprises the following steps:
S31.GH value is analyzed, and the sample described GH value being greater than 3.0 is rejected, and the sample sets being less than 3.0 by GH value sets up respective class type spectrum respectively
figurecalibration (detection) model;
S32. by calculating SEC value and the RSQ value of calibration (detection) model described in S31;
S33. evaluation experimental determination optimum detection model is carried out to detection model.The present invention adopts validation-cross error (Standard Error of cross validation, SECV) and the validation-cross coefficient of determination (1minus the variance ratio, 1-VR) or coefficient R v to weigh detection model.Can the prediction accuracy of Efficient Evaluation detection model by this two indices.Adopt and there is low SECV value and high (1-VR) or the Rv value good detection model of evaluation and all detection models carry out validation-cross test, select the detection model of minimum SECV value and the highest (1-VR) or Rv value, be defined as optimum detection model.
Beneficial effect of the present invention is as follows:
The principle of the existing examination criteria of coal is all classical or modern chemical method and physical-chemical process: interior water, volatile matter, ash content, fixed carbon, flammable body are all classical gravimetric method, relate to the equipment such as balance, baking oven, high temperature furnace, carbonization, ashing, constant weight, weighing, calculating are the steps often needing to carry out, quite loaded down with trivial details time-consuming; Thermal value, full sulphur, state-of-the-art is at present adopt High Temperature High Pressure combustion method, but the operation of modern physical chemistry instrument, maintenance, demarcation are also very heavy.
For a long time, near-infrared spectrum technique is used for analyzing pure organism.Because the wave number of near infrared spectrum is at 4000cm
-1above (i.e. below 2500nm), therefore, vibration frequency is only had at 2000cm
-1above vibration, just may produce one-level frequency multiplication in near-infrared region, and can at 2000cm
-1what more than produce fundamental vibration mainly contains hydrogen functional group, as the stretching vibration of C-H, N-H, S-H and O-H.Almost the information of all hydric groups in organism, can be reflected near infrared spectrum.
Coal is a kind of flammable rock.Near-infrared spectrum technique is applied to this compounding substances system be made up of most of organic substance and part mineral matter and moisture of coal by the present invention first, and successfully set up the method that in coal sample, 7 key indexs detect simultaneously, thus prove that near-infrared spectrum technique can be applied to analysis dead matter, overcomes prior art prejudice to a certain extent.
The present invention establishes the near infrared detection method of water in coal, fill up prior art blank, and solve a following focus technology difficult problem: sum up the mathematics transition form that coal NIRS spectroscopic data is suitable, the NIRS providing suitable mathematical conversion detects reliable funtcional relationship between data and the detection data of coal quality composition conventional method, provides practical, reliable and stable correction and verification method to gained funtcional relationship.
Based on the inventive method, 4 detection models of each index in the content of water, ash content, volatile matter, full sulphur, fixed carbon and flammable body and thermal value 7 indexs in obtaining respectively in coal, and set up and can be applied to new detection method in daily practice examining work.7 indexs that the present invention detects in coal sample only need a few minutes, and without the need to sampling, without the need to using the test condition such as chemical reagent or high temperature, high pressure, big current, chemistry, biology or electromagnetic pollution can not be produced, harmful effect can not be caused to operating personnel and environment, continuous detecting can be realized, to mass detection task, its superiority is more outstanding.
The present invention, compared with current methods, has significant superiority.(1) lack a kind of simple ground at present disposablely can analyze the detection method obtaining 7 key indexs simultaneously, the present invention can successfully solve this technical barrier.New method detection provided by the invention obtains 7 key components in coal sample only needs a few minutes, and without the need to sampling, without the need to using the test condition such as chemical reagent or high temperature, high pressure, big current, chemistry, biology or electromagnetic pollution can not be produced, harmful effect can not be caused to operating personnel and environment.(2) with regard to each single Indexes Comparison:
The detection of moisture, compared with existing Oven Method: Y type original spectrum
figurecalibration standard deviation is 0.14%, and calibration related coefficient is 0.9969; Validation-cross standard deviation 0.15%, validation-cross related coefficient 0.9965; The standard deviation of Preliminary Applications is 0.17%, and related coefficient is 0.995.W type original spectrum
figurecalibration standard deviation is 0.25%, and calibration related coefficient is 0.9021; Validation-cross standard deviation 0.28%, validation-cross related coefficient 0.8756; The standard deviation of Preliminary Applications is 0.23%, and related coefficient is 0.875.P type original spectrum
figurecalibration standard deviation is 0.41%, and calibration related coefficient is 0.9814; Validation-cross standard deviation 0.47%, validation-cross related coefficient 0.9746; The standard deviation of Preliminary Applications is 0.83%, and related coefficient is 0.944.X-type original spectrum
figurecalibration standard deviation is 0.12%, and calibration related coefficient is 0.9981; Validation-cross standard deviation 0.31%, validation-cross related coefficient 0.9874; The standard deviation of Preliminary Applications is 0.13%, and related coefficient is 0.997.
The detection of ash content, compared with existing high temperature oven process: Y type original spectrum
figurecalibration standard deviation is 0.34%, and calibration related coefficient is 0.9955; Validation-cross standard deviation 0.35%, validation-cross related coefficient 0.9954; The standard deviation of Preliminary Applications is 0.77%, and related coefficient is 0.980.W type original spectrum
figurecalibration standard deviation is 0.51%, and calibration related coefficient is 0.9851; Validation-cross standard deviation 0.74%, validation-cross related coefficient 0.9687; The standard deviation of Preliminary Applications is 0.57%, and related coefficient is 0.982.P type original spectrum
figurecalibration standard deviation is 1.29%, and calibration related coefficient is 0.9785; Validation-cross standard deviation 1.62%, validation-cross related coefficient 0.9659; The standard deviation of Preliminary Applications is 1.32%, and related coefficient is 0.970.X-type original spectrum
figurecalibration standard deviation is 1.48%, and calibration related coefficient is 0.9879; Validation-cross standard deviation 2.05%, validation-cross related coefficient 0.9763; The standard deviation of Preliminary Applications is 3.88%, and related coefficient is 0.926.
Volatile matter detects, compared with existing high temperature oven process: Y type original spectrum
figurecalibration standard deviation is 0.27%, and calibration related coefficient is 0.9922; Validation-cross standard deviation 0.29%, validation-cross related coefficient 0.9911; The standard deviation of Preliminary Applications is 0.37%, and related coefficient is 0.986.W type original spectrum
figurecalibration standard deviation is 0.22%, and calibration related coefficient is 0.9618; Validation-cross standard deviation 0.24%, validation-cross related coefficient 0.9521; The standard deviation of Preliminary Applications is 0.25%, and related coefficient is 0.928.P type original spectrum
figurecalibration standard deviation is 0.93%, and calibration related coefficient is 0.9889; Validation-cross standard deviation 1.05%, validation-cross related coefficient 0.9858; The standard deviation of Preliminary Applications is 0.89%, and related coefficient is 0.975.X-type original spectrum
figurecalibration standard deviation is 0.59%, and calibration related coefficient is 0.9991; Validation-cross standard deviation 0.65%, validation-cross related coefficient 0.9989; The standard deviation of Preliminary Applications is 1.61%, and related coefficient is 0.994.
The detection of full sulphur, compared with high-temp combustion infrared absorption method: Y type original spectrum
figurecalibration standard deviation is 0.06%, and calibration related coefficient is 0.9838; Validation-cross standard deviation 0.07%, validation-cross related coefficient 0.9822; The standard deviation of Preliminary Applications is 0.08%, and related coefficient is 0.976.W type original spectrum
figurecalibration standard deviation is 0.04%, and calibration related coefficient is 0.9496; Validation-cross standard deviation 0.05%, validation-cross related coefficient 0.9379; The standard deviation of Preliminary Applications is 0.05%, and related coefficient is 0.917.P type original spectrum
figurecalibration standard deviation is 0.17%, and calibration related coefficient is 0.9572; Validation-cross standard deviation 0.21%, validation-cross related coefficient 0.9330; The standard deviation of Preliminary Applications is 0.22%, and related coefficient is 0.927.X-type original spectrum
figurecalibration standard deviation is 0.11%, and calibration related coefficient is 0.8988; Validation-cross standard deviation 0.16%, validation-cross related coefficient 0.7412; The standard deviation of Preliminary Applications is 0.12%, and related coefficient is 0.849.
The detection of thermal value, compared with existing oxygen bomb combustion: Y type original spectrum
figurecalibration standard deviation is 0.12MJ/Kg, and calibration related coefficient is 0.9960; Validation-cross standard deviation 0.14MJ/Kg, validation-cross related coefficient 0.9951; The standard deviation of Preliminary Applications is 0.26MJ/Kg, and related coefficient is 0.983.W type original spectrum
figurecalibration standard deviation is 0.22MJ/Kg, and calibration related coefficient is 0.9815; Validation-cross standard deviation 0.31MJ/Kg, validation-cross related coefficient 0.9610; The standard deviation of Preliminary Applications is 0.27MJ/Kg, and related coefficient is 0.972.P type original spectrum
figurecalibration standard deviation is 0.48MJ/Kg, and calibration related coefficient is 0.9734; Validation-cross standard deviation 0.60MJ/Kg, validation-cross related coefficient 0.9594; The standard deviation of Preliminary Applications is 0.88MJ/Kg, and related coefficient is 0.971.X-type original spectrum
figurecalibration standard deviation is 0.45%, and calibration related coefficient is 0.9830; Validation-cross standard deviation 0.72MJ/Kg, validation-cross related coefficient 0.9582; The standard deviation of Preliminary Applications is 1.20MJ/Kg, and related coefficient is 0.901.
The detection of fixed carbon, compared with existing baking oven, high temperature oven process: Y type original spectrum
figurecalibration standard deviation is 0.29%, and calibration related coefficient is 0.9948; Validation-cross standard deviation 0.30%, validation-cross related coefficient 0.9931; The standard deviation of Preliminary Applications is 0.43%, and related coefficient is 0.986.W type original spectrum
figurecalibration standard deviation is 2.20%, and calibration related coefficient is 0.8499; Validation-cross standard deviation 2.49%, validation-cross related coefficient 0.8036; The standard deviation of Preliminary Applications is 2.11%, and related coefficient is 0.835.P type original spectrum
figurecalibration standard deviation is 1.34%, and calibration related coefficient is 0.9731; Validation-cross standard deviation 1.65%, validation-cross related coefficient 0.9587; The standard deviation of Preliminary Applications is 3.10%, and related coefficient is 0.941.X-type original spectrum
figurecalibration standard deviation is 1.66%, and calibration related coefficient is 0.9883; Validation-cross standard deviation 2.16%, validation-cross related coefficient 0.9802, the standard deviation of Preliminary Applications is 2.64%, and related coefficient is 0.972.
The detection of flammable body, compared with existing baking oven, high temperature oven process: Y type original spectrum
figurecalibration standard deviation is 0.34%, and calibration related coefficient is 0.9954; Validation-cross standard deviation 0.37%, validation-cross related coefficient 0.9948; The standard deviation of Preliminary Applications is 0.60%, and related coefficient is 0.988.W type original spectrum
figurecalibration standard deviation is 2.35%, and calibration related coefficient is 0.8414; Validation-cross standard deviation 2.62%, validation-cross related coefficient 0.7981; The standard deviation of Preliminary Applications is 2.19%, and related coefficient is 0.819.P type original spectrum
figurecalibration standard deviation is 1.32%, and calibration related coefficient is 0.9685; Validation-cross standard deviation 1.51%, validation-cross related coefficient 0.9586; The standard deviation of Preliminary Applications is 4.67%, and related coefficient is 0.867.X-type original spectrum
figurecalibration standard deviation is 1.34%, and calibration related coefficient is 0.9874; Validation-cross standard deviation 1.92%, validation-cross related coefficient 0.9738; The standard deviation of Preliminary Applications is 3.32%, and related coefficient is 0.930.
The present invention only needs employing near-infrared spectrometers, just can replace that prior art is multiple, multiple stage analytical instrument, only need grind away equipment, not Water demand balance, usually people's operation is only needed, and within a few minutes, by gathering the spectrum of primary measured sample, just can complete the mensuration of interior liquid water content simultaneously.In spectra collection process except consumption electric energy, do not need to consume any reagent and standard substance, the purchasing of a large amount of instrument and equipment can be saved like this, operate, the expense such as maintenance, save a large amount of time and manpower, greatly reduce analysis cost, significantly improve the efficiency of testing.
Accompanying drawing explanation
fig. 1the near-infrared diffuse reflectance DDS original spectrum of 509 anthracite samples is total
figure.
fig. 2the near-infrared diffuse reflectance DDS original spectrum of 134 bituminous coal samples is total
figure.
fig. 3the near-infrared diffuse reflectance DDS original spectrum of 155 steam coal samples is total
figure.
fig. 4the near-infrared diffuse reflectance DDS original spectrum of 60 meager lean coal samples is total
figure.
fig. 5the near-infrared diffuse reflectance DDS original spectrum of 134 steam coal samples is total
figure.
fig. 6the near-infrared diffuse reflectance DDS original spectrum of 179 samples clearly do not divided into groups is total
figure.
fig. 7the near-infrared diffuse reflectance DDS original spectrum of 202 anthracite samples is total
figure (sample in 2008).
fig. 8the near-infrared diffuse reflectance DDS original spectrum of 235 samples clearly do not divided into groups is total
figure (sample in 2008).
fig. 9standard specimen original spectrum
figure (upper is 103f stone coal, and lower is 101L bituminous coal).
fig. 10 certified reference coal original spectrum
figure.
fig. 11 2008 years stone coals and the anthracitic original spectrum of standard specimen
figure.
fig. 1the original spectrum of 2 2005-2008 bituminous coal and standard specimen bituminous coal
figure.
fig. 13 2008 years stone coals and standard specimen stone coal original spectrum are through (NONE+0011) process
figure.
fig. 14 2008 years stone coals and standard specimen stone coal original spectrum are through (NONE+1441) process
figure.
fig. 15 2008 years stone coals and standard specimen stone coal original spectrum are through (D+1441) process
figure.
fig. 16 2008 years stone coals and standard specimen stone coal original spectrum are through (D+1441) process
figure.
fig. 1the original spectrum of 7 2005-2008 bituminous coal and standard specimen bituminous coal is through (NONE+0011) process
figure.
fig. 1the original spectrum of 8 2005-2008 bituminous coal and standard specimen bituminous coal is through (D+1441) process
figure.
fig. 1the original spectrum of 9 2005-2008 bituminous coal and standard specimen bituminous coal is through (D+0011) process
figure.
fig. 20 509 anthracite sample original spectrums are through (NONE+1441) process
figure.
fig. 21 509 anthracite sample original spectrums are through (NONE+0011) process
figure.
fig. 22 509 anthracite sample original spectrums are through (D+1441) process
figure.
fig. 23Y type (374) original spectrum
figure.
fig. 24W type (1367) original spectrum
figure.
fig. 25P type (436) original spectrum
figure.
fig. 26X type (52) original spectrum.
fig. 27Y type original spectrum
figuregH Distribution value
figure.
fig. 28W type original spectrum
figuregH Distribution value
figure.
fig. 29P type original spectrum
figuregH Distribution value
figure.
fig. 30X type original spectrum
figuregH Distribution value
figure.
fig. 31 in the Y type original spectrum of validation-cross water detection model (MPLS+D+1441).
fig. 32 through validation-cross W type original spectrum inspection in water survey model (PLS+D+0011).
fig. 33 in the P type original spectrum of validation-cross water detection model (MPLS+D+0011).
fig. 34 through validation-cross X-type original spectrum inspection in water survey model (MPLS+NONE+2441).
Embodiment
Below in conjunction with
accompanying drawingthe present invention is further illustrated with specific embodiment.The equipment of embodiment of the present invention employing can refer to listed equipment and reagent in " the standard sample method that high temperature process furnances combustion method analyzes sulfur content in coal and coke analysis sample " (ASTMD-4239-2010e1), " proximate analysis of coal " (GB/T212-2008), " heat output determining method of coal " (GB/T213-2008).Other unless stated otherwise, the embodiment of the present invention adopt reagent and equipment be this area routine use reagent and equipment.
Embodiment 1
The one that provides the present embodiment to detect in coal sample 7 simultaneously and refers to calibration method, comprises the following steps:
S1. collect and prepare several coal samples, conventional method measures the interior water of each sample, ash content, volatile matter, the content of full sulphur, fixed carbon and flammable body and thermal value 7 indexs respectively;
S2. spectroscopic data and the curve of described coal sample is collected with the scanning of NIRS analyser;
S3. the spectroscopic data of S2 gained sample is processed, through returning the calibration equation calculating acquisition 7 indexs, revising and setting up detection model after water in checking;
S4. coal sample to be measured is directly filled successively the injector (not needing to sample) of NIR instrument, start scanning key, NIR instrument records storage sample spectra automatically.Determine sample ownership spectrum
figuretype, selects corresponding detection model, obtains testing result.
Wherein, to collect described in S1 and the method for preparing coal sample is carried out with reference to the requirement in " sample for commercial coal takes method " (GB475-2008) and " preparation method of coal sample " (GB474-2008).The method of the interior liquid water content of described conventional method working sample is undertaken by " proximate analysis of coal " (GB/T212-2008), the method of described conventional method working sample thermal value is undertaken by the requirement in " heat output determining method of coal " (GB/T213-2008), the method of the fixed carbon in described conventional method working sample is undertaken by the requirement in " proximate analysis of coal " (GB/T212-2008), the method of the ash content of described conventional method working sample is undertaken by " proximate analysis of coal " (GB/T212-2008), the method of the volatile content of described conventional method working sample is undertaken by " proximate analysis of coal " (GB/T212-2008), the method of the flammable body in described conventional method working sample is that flammable body carries out with reference to the requirement in " proximate analysis of coal " (GB/T212-2008), the method of the total sulphur content of described conventional method working sample is undertaken by " high temperature process furnances combustion method analyzes the standard sample method of sulfur content in coal and coke analysis sample " (ASTMD-4239-2010e1).
Scanning with NIRS analyser the spectroscopic data collecting described sample described in S2 is by sample (not needing to sample), directly fills the injector of NIR instrument successively, adopts digital raster system to scan, is automatically recorded and store sample spectra by NIRS instrument.
The SY-3650-2 type near-infrared analyzer that the present embodiment adopts FOSSANALYTICALA/S company of Denmark to produce, monochromator: the holographic digital raster of single beam, wavelength coverage 1100 ~ 2500nm; Light source: tungsten lamp; Detecting device: PBS, mobile conveying multipoint positioning detects automatically, and check point is greater than 32; Operating temperature: 15 ~ 32 DEG C; Analysis time: continuous spectrum per minute scans more than 32 subsamples and completes its spectral analysis; System noise: noise signal value is less than 2 × 10
-5aU; Report the test: show the sample that result and display " surmount correcting range " on a terminal screen, is reported the result by printer simultaneously, is also connected by RS-232C interface and external electrical computing machine.
Method of operating: carefully stir coal sample, dress gets appropriate sample in the sample cell of quartz, thickness of sample must be made even, cover little cardboard, press gently, make sample tight distribution in filling process.After filling, observe the sample situation on sample cell surface, if find that there is gap or sample has loosening phenomenon, again should load sample.The sample cell filled is placed on sample introduction track, click " scanning ", by the input mode pumped, complete Scanning Detction and according to output.In the process of whole detection operation, without the need to sampling, without the need to adding any chemical reagent.
First, digital raster system (Digital Dispersive System, DDS) is adopted to carry out scanning collection sample spectra.The present invention scientifically classifies, and is divided into groups respectively by coal sample according to stone coal, bituminous coal, steam coal, meager lean coal, steam coal, is then classified as separately one group to the sample of not concrete classification.Coal sample does not need to sample, and directly fills the injector of NIR instrument successively, then the sample of the injector that overfills is scalped, and near-infrared analyzer records the DDS spectrum storing sample automatically, obtains original spectrum
figure, see
accompanying drawing 1extremely
accompanying drawing 8shown in.By
accompanying drawing 1extremely
accompanying drawing 8shown original spectrum
figureknown, only have
accompanying drawing 4the variation tendency of the near-infrared diffuse reflectance DDS original spectrum of the meager lean coal sample of display relatively or unanimously, other coals are as the original spectrum of stone coal, bituminous coal, steam coal, steam coal
figureall there is the variation tendency of obvious difference.Due to
accompanying drawing 1extremely
fig. 6for the spectrum of nearly three months concentrated collection
figure, consider the impact analyzed Sample Storage time and environment and exist its original spectrum, the present invention collects coal sample in 2008 and gathers spectrum in time
figure,
fig. 7extremely
fig. 8, can see,
fig. 7with
fig. 1it is basically identical,
fig. 8the near-infrared diffuse reflectance DDS original spectrum of various coal sample covers the original spectrum of aforementioned stone coal, bituminous coal, steam coal, steam coal substantially
figurevariation tendency.This illustrates that our storage of samples conditioned disjunction Spectral acquisition times is not obvious on original spectrum impact, can meet the present invention and study needs.For better investigating coal original spectrum, we adopt the same manner to gather original spectrum to coal standard model,
fig. 9extremely
fig. 10.And by the original spectrum of gained standard model original spectrum and corresponding coal
figurebe combined and observe,
fig. 11 to
fig. 12.By
fig. 11 He
fig. 12 can see that spectrum change trend is basically identical to aforementioned corresponding coal.
Then, the present invention couple
fig. 11 to
fig. 12 carry out Pretreated spectra respectively,
fig. 13 to
fig. 19.Pretreated spectra mode is respectively: (wherein, NONE represents and do not carry out Pretreated spectra, is original spectrum for NONE+0011, NONE+1441, D+1441, D+1441, NONE+0011, D+1441, D+0011
figure, D represents trend converter technique, and 1441 represent with every 4 spectrum points for the mathematics manipulation of first order derivative is made at interval, and 0011 represents and do not make any derivative processing.Such as, D+1441 is to original spectrum
figureafter carrying out the pre-service of trend converter technique, then with every 4 spectrum points for carrying out mathematics manipulation as first order derivative in interval).By
fig. 13 to
fig. 19 can see spectrum after pretreatment
figurevariation tendency is more obvious, clear, but the various Different treatments income effects equal ubiquity visibly different peak type of same coal and change.Totally it seems, Pretreated spectra also fails to obtain more consistent spectrum
figure.Carry out pre-service to the aforementioned various coal original spectrums collected, gained situation is substantially identical, with 509 anthracite sample original spectrums through (NONE+1441) process
figurefor example, result is asked for an interview
accompanying drawing 20 to
accompanying drawing 2shown in 2, other sample pretreatments
figurethis do not repeat one by one (
figureslightly).
The sample original spectrum collected is classified by its different variation tendency by S2, identical with variation tendency or close according to peak type
figurespectrum is combined, and sorts out, and obtains Y type original spectrum, W type original spectrum, P type original spectrum and X-type original spectrum respectively.The present invention by the aforementioned original spectrum collected by its different variation tendency grouping, then peak type is identical with variation tendency or relatively
figurespectrum is combined, and sorts out, and obtains spectrum
fig. 23 to
fig. 26.
fig. 2the spectrum of 3
figurebe labeled as Y type original spectrum, represent most steam coal and the coal such as part bituminous coal, partial power coal, have 374 samples.
fig. 24 are marked as W type original spectrum, represent the coals such as most stone coal, part bituminous coal, partial power coal, have 1367 samples.
fig. 25 are marked as P type original spectrum, represent the coals such as most meager lean coal, part bituminous coal, partial power coal, have 436 samples.
fig. 26 are marked as X-type original spectrum, represent non-common coal, have 195 samples.
Carrying out mathematics manipulation to the spectroscopic data of S2 gained sample described in S3 is adopt WinISI software carry out spectral analysis and set up detection model, the spectroscopic data of S2 gained sample is imported NIR instrument, determines detection model, print testing result.Pretreated spectra adopts one or more of the methods such as the correction of trend converter technique, standard normal variable transformation approach, Multivariate Discrete, anti-phase Multivariate Discrete correction respectively, in conjunction with coal sample specificity analysis, finally determines optimization process method; Regression correction method adopts Stepwise Regression Method (SMLR), principal component analysis (PCA) (PCA) and minimal error analytical method (PLS), by Data Dimensionality Reduction, to eliminate message part overlapped in numerous information co-exist and finally to accomplish the quantification to spectrum.
Utilize detection model to detect liquid water content in the sample of one group of unknown component content to be measured, then the detected value of NIR method gained and Typical physical chemical method detected value are compared and evaluate.The comparative result of two kinds of methods is with predicting that standard deviation (SEP, Standard Error of Prediction) and the corresponding coefficient of determination (RSQp) or coefficient R p are weighed.
The method for building up of described detection model comprises the following steps:
S31.GH value is analyzed: GH value and mahalanobis distance, is that the score of spectroscopic data regression correction is three-dimensional
in figure, the distance of each sample distance center sample spot.GH value is usually set to 3.0 near infrared spectrum data analysis, and implication is 3 times of standard variation unit, is namely exactly 2.84 times that are similar to standard deviation (SD), this means the GH value of the sample having about 10% to be greater than 3.0.If the GH value of which sample is greater than 3.0, this sample needs to reject, and separately performs an analysis.The present invention adopts principal component analysis (PCA) (PCR) to carry out cluster analysis respectively to Y type original spectrum, W type original spectrum, P type original spectrum and X-type original spectrum; The results are shown in
accompanying drawing 27 to
accompanying drawing 3shown in 0, by
accompanying drawing 27 to 30 analyses are known, Four types original spectrum
figurethe GH value overwhelming majority be less than 3.0, the sample that Y type is greater than 3.0 has 8, the sample that W type is greater than 3.0 has 10, the sample that P type is greater than 3.0 has 13, the sample that X is greater than 3.0 has 9, after described GH value is greater than the sample rejecting of 3.0 by the present invention, the sample sets being less than 3.0 by GH value sets up respective class type spectrum respectively
figurecalibration (detection) model, wherein 366, Y type, 1357, W type, 423, P type, X-type 186.
S32. by calculating SEC value and the RSQ value of calibration (detection) model described in S31;
Spectral manipulation and regression correction method in WinISI software is utilized to carry out spectral manipulation and data analysis to above-mentioned four class original spectrums.Pretreated spectra adopts one or more of the methods such as the correction of trend converter technique, standard normal variable transformation approach, Multivariate Discrete, anti-phase Multivariate Discrete correction respectively, in conjunction with coal sample specificity analysis, finally determines optimization process method; Mathematics manipulation adopts and makes first order derivative (1441) or second derivative (2441) method with the interval (Gap) of every 4 spectrum points, and any derivative processing is not done in (0011) expression.Regression correction method adopts Stepwise Regression Method (Stepwise Mutiple Linear Regression, SMLR), principal component analysis (PCA) (Principal Component Analysis, PCR), minimal error analytical method (Partial Least Squares Regression, PLS) and inclined minimal error analytical method (Modified Partial Least Squares Regression, MPLS) calculate SEC value and the RSQ value of described calibration (detection) model.
SEC is calibration standard deviation (Standard Error of Calibration, SEC), referring to that the calibration model by setting up predicts obtained near-infrared analysis value and the standard deviation of conventional chemical methods assay value to calibration sample collection, is the mark returning reading and actual read number degree of agreement.SEC is lower, illustrate near-infrared analysis result and traditional analysis result more identical, confidence level is higher.RSQ (R squared) is the calibration coefficient of determination, be related coefficient (Rc) square, refer to that calibration model make a variation the percent that can describe out to calibration sample collection, expression near-infrared analysis value and the close degree of conventional method assay value linear relationship.A good detection model requires low SEC and high RSQ (Rc).According to the existing common practise in this area, in theory, the model possessing minimum SEC value and the highest RSQ value should be just best detection model.But the present invention finds through experimental summary long-term in a large number, and the model of not least SEC value and the highest RSQ value is exactly the detection model of the best, but finally can be decided to be optimum detection model necessarily there is lower SEC value and higher RSQ value.The present invention carries out evaluation test one by one to the whole detection models set up for this reason.With Y type original spectrum
figurefor example, other can refer to Y type and carry out experimental summary, do not repeat one by one.The interior water spectral manipulation of coal sample and regression correction result are shown in respectively
table 1shown (Y type original spectrum
figure):
table 1y type original spectrum
figurespectral manipulation and regression correction effect
table 2the validation-cross result of Y type detection model
S33. evaluation experimental determination optimum detection model is carried out to detection model.By
table 1with
table 2can find out, no matter the coal spectrum of which kind of type, the detection model of each coal index, SEC value, the RSQ value of the eigenwert of its validation-cross result--SECV value, (1-VR) value and corresponding model are all different, SECV value is all slightly larger than SEC value, and (1-VR) value is all slightly less than RSQ value.SECV value, (1-VR) value more can reflect detection model in the future for predicting the accuracy of unknown sample than SEC value, RSQ value, this is because SEC value, RSQ value reflect to be detection model predict to calibration sample collection the degree that the standard deviation of obtained near-infrared analysis value and conventional chemical methods assay value, near-infrared analysis value and conventional method assay value linear relationship are close; And the reflection of SECV value, (1-VR) value is when validation-cross calculates, detection model predicts to the sample not participating in calibration modeling the degree that the standard deviation of obtained near-infrared analysis value and conventional chemical methods assay value, the sample near-infrared analysis value of not participating in calibration modeling and conventional method assay value linear relationship are close.As can be seen here, a good detection model not only requires low SEC and high RSQ (Rc), more requires low SECV value and high (1-VR) or Rv value.
No matter the coal spectrum of which kind of type, the detection model of each coal index, a detection model having minimum SECV value and the highest (1-VR) or a Rv value, necessarily has lower SEC value and higher RSQ value.The present invention is based on the knot of summing up in more kinds of sample detection research except coal sample consistent with result of study of the present invention, the detection model that each coal index of the present invention's four kinds of spectrum types has minimum SECV value and the highest (1-VR) value is judged to be optimum detection model, and by main information analysis and summary in
table 3.
table 3the main characteristic parameters of optimum detection model and validation-cross result thereof
By
table 3visible, utilize the near-infrared diffuse reflectance original spectrum of WinISI software to coal to carry out multiple spectrum process and regression correction, result to adopt standard normal variable to change (SNV), trend conversion (D), without scattering (NONE) process and smoothing (0011), make first order derivative (1441) with the interval (Gap) of 4 spectrum points, second derivative (2441) processes and combined effect the best of MPLS, PLS regression correction.The principal character value of gained optimum detection model and corresponding homing method, spectral manipulation mode refer to
table 3listed by.The optimum N IR detection model of each coal index has 692 parameters, wherein comprises a constant term, and within the present embodiment, water is example, and parameter and the equation curve of detection model refer to
table 4extremely
table 7, equation curve (detection model)
as Fig. 31 to
fig. 3shown in 4, the parameter of other detection model and equation curve
figureslightly.
table 4y type original spectrum detection model parameter (interior water %)
table 5w type original spectrum detection model parameter (interior water %)
table 6p type original spectrum detection model parameter (interior water %)
table 7x-type original spectrum detection model parameter (interior water %)
Embodiment 2 applicating evaluating is tested
The detection model that the present embodiment utilizes embodiment to establish detects liquid water content in the coal sample of one group of unknown component content to be measured, then the detected value of NIR method gained and Typical physical chemical method detected value are compared and evaluated.The comparative result of two kinds of methods is with predicting that standard deviation (SEP, Standard Error of Prediction) and the corresponding coefficient of determination (RSQp) or coefficient R p are weighed.
Select some coal samples at random, do not need to sample, directly fill the injector of NIR instrument successively, start scanning key, NIR instrument records storage sample spectra automatically.In judgement sample ownership spectrum
figureafter type, click corresponding detection model, just can print testing result, complete the interior water of coal sample, ash content, volatile matter, the full content of sulphur, fixed carbon and flammable body and the testing of thermal value 7 indexs fast.The scan values (average and standard deviation) that the experiment value (average and standard deviation) that the sample number that this application test Y, W, P, X Four types adopts, conventional method obtain, Near-Infrared Absorption Method obtain, SEP and corresponding RSQp, Rp value are shown in respectively
table 8shown in.
table 8various coal adopts the inventive method to analyze testing result
By
table 8visible, in coal of the present invention, the index such as the prediction standard deviation of the content of water, ash content, volatile matter, full sulphur, fixed carbon and flammable body and thermal value 7 index detection method, the coefficient of determination and related coefficient is all close with the result with corresponding calibration.The repeatability limit difference of concrete conventional method Physical Chemistry Experiment value, NIR method scan values and both differences, current standard methods is shown in
table 9extremely
table 12, the every project of every type only lists the result of 10 samples, within water, thermal value, fixed carbon be example, other abundant experimental results can not repeat one by one at this.
table 9y type detection model testing result-interior water (%)
table 10 W type detection model Preliminary Applications result-interior water (%)
table 11 P type detection model Preliminary Applications result--interior water (%)
table 12 X-type detection model Preliminary Applications result-interior water (%)
table 13 Y type detection models Preliminary Applications result-thermal value (MJ/Kg)
table 14 W type detection models Preliminary Applications result-thermal value (MJ/Kg)
table 15 P type detection models Preliminary Applications result-thermal value (MJ/Kg)
table 16 X-type detection model Preliminary Applications result-thermal value (MJ/Kg)
table 17 Y type detection models Preliminary Applications result-fixed carbon (%)
table 18 W type detection models Preliminary Applications result-fixed carbon (%)
table 19 P type detection models Preliminary Applications result-fixed carbon (%)
table 20 X-type detection model Preliminary Applications result-fixed carbon (%)
By above-mentioned representational
table 9extremely
table 20 is visible, and in the coal that the present invention sets up, the accuracy of index detection method conforms to current standards the requirement of method repeatability limit difference.Operate very fast easy, within about 50 seconds, just can complete the scanning of a sample and interior water, ash content, volatile matter, the content of full sulphur, fixed carbon and flammable body and thermal value 7 Indexs measure, comprise the output of data.In the process of whole detection operation, without the need to sampling, without the need to adding any chemical reagent, there is quick, convenient, free of contamination feature.
Claims (5)
1. to detect in coal sample 7 simultaneously and refer to a calibration method, it is characterized in that, comprise the following steps:
S1. collect and prepare several coal samples, conventional method measures the interior water of each sample, ash content, volatile matter, the content of full sulphur, fixed carbon and flammable body and thermal value 7 indexs respectively;
S2. spectroscopic data and the curve of described coal sample is collected with the scanning of NIRS analyser;
S3. the spectroscopic data of S2 gained sample is processed, through returning the calibration equation calculating acquisition 7 indexs, revising and setting up detection model after checking;
S4. coal sample to be measured is directly filled successively the injector of NIR instrument, start scanning key, NIR instrument records storage sample spectra automatically, determines sample ownership spectrogram type, selects corresponding detection model, obtain the testing result of 7 indexs.
2. to detect in coal sample 7 according to claim 1 simultaneously and refer to calibration method, it is characterized in that, the method for building up of described detection model comprises the following steps:
S31. GH value is analyzed, and described GH value is greater than the sample rejecting of 3.0, sets up the detection model of respective type spectrogram by the sample sets that GH value is less than 3.0 respectively;
S32. by calculating SEC value and the RSQ value of detection model described in S31;
S33. evaluation experimental determination optimum detection model is carried out to detection model.
3. to detect in coal sample 7 according to claim 2 simultaneously and refer to calibration method, it is characterized in that, described in S33, determine that optimum detection model adopts validation-cross error and the validation-cross coefficient of determination or related coefficient to weigh detection model.
4. to detect in coal sample 7 according to claim 3 simultaneously and refer to calibration method, it is characterized in that, that employing has the good detection model of low validation-cross error amount and the high validation-cross coefficient of determination or correlation coefficient value evaluation and all detection models carry out validation-cross test, select the detection model of minimum validation-cross error amount and the highest validation-cross coefficient of determination or correlation coefficient value, be defined as optimum detection model.
5. to detect in coal sample 7 according to claim 1 simultaneously and refer to calibration method, it is characterized in that, S2 also comprises and being combined according to the collection of illustrative plates that peak type is identical or close with variation tendency by the sample original spectrum collected, sort out, obtain Y type original spectrum, W type original spectrum, P type original spectrum and X-type original spectrum respectively.
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