CN102759521B - On-line detection system and method for performance parameters of propylene copolymer - Google Patents
On-line detection system and method for performance parameters of propylene copolymer Download PDFInfo
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- QQONPFPTGQHPMA-UHFFFAOYSA-N propylene Natural products CC=C QQONPFPTGQHPMA-UHFFFAOYSA-N 0.000 title claims abstract description 61
- 229920001577 copolymer Polymers 0.000 title claims abstract description 46
- 238000000034 method Methods 0.000 title claims abstract description 37
- 238000001514 detection method Methods 0.000 title claims abstract description 29
- CTQNGGLPUBDAKN-UHFFFAOYSA-N O-Xylene Chemical compound CC1=CC=CC=C1C CTQNGGLPUBDAKN-UHFFFAOYSA-N 0.000 claims abstract description 50
- VGGSQFUCUMXWEO-UHFFFAOYSA-N Ethene Chemical compound C=C VGGSQFUCUMXWEO-UHFFFAOYSA-N 0.000 claims abstract description 40
- 239000008096 xylene Substances 0.000 claims abstract description 38
- 239000005977 Ethylene Substances 0.000 claims abstract description 34
- 238000001069 Raman spectroscopy Methods 0.000 claims abstract description 22
- 238000001237 Raman spectrum Methods 0.000 claims abstract description 21
- 238000001228 spectrum Methods 0.000 claims description 34
- 239000011159 matrix material Substances 0.000 claims description 30
- -1 polyethylene Polymers 0.000 claims description 21
- 238000004422 calculation algorithm Methods 0.000 claims description 18
- 238000004364 calculation method Methods 0.000 claims description 17
- 238000007334 copolymerization reaction Methods 0.000 claims description 16
- 125000004805 propylene group Chemical group [H]C([H])([H])C([H])([*:1])C([H])([H])[*:2] 0.000 claims description 16
- 239000004743 Polypropylene Substances 0.000 claims description 15
- 230000002068 genetic effect Effects 0.000 claims description 15
- 229920001155 polypropylene Polymers 0.000 claims description 11
- 239000004698 Polyethylene Substances 0.000 claims description 10
- 229920000573 polyethylene Polymers 0.000 claims description 10
- 230000003595 spectral effect Effects 0.000 claims description 7
- 239000012633 leachable Substances 0.000 claims description 5
- 238000010998 test method Methods 0.000 claims description 5
- 230000007704 transition Effects 0.000 claims description 5
- 239000002699 waste material Substances 0.000 claims description 5
- HGAZMNJKRQFZKS-UHFFFAOYSA-N chloroethene;ethenyl acetate Chemical group ClC=C.CC(=O)OC=C HGAZMNJKRQFZKS-UHFFFAOYSA-N 0.000 claims description 4
- 238000005079 FT-Raman Methods 0.000 claims description 3
- 238000003860 storage Methods 0.000 claims description 3
- 238000005070 sampling Methods 0.000 abstract description 3
- 239000000126 substance Substances 0.000 abstract description 3
- 238000004519 manufacturing process Methods 0.000 abstract description 2
- 238000012544 monitoring process Methods 0.000 abstract description 2
- 238000005457 optimization Methods 0.000 abstract 1
- 238000010183 spectrum analysis Methods 0.000 abstract 1
- 235000000332 black box Nutrition 0.000 description 11
- 244000085682 black box Species 0.000 description 11
- 229920001971 elastomer Polymers 0.000 description 8
- 239000007789 gas Substances 0.000 description 8
- 238000010926 purge Methods 0.000 description 8
- 239000005060 rubber Substances 0.000 description 8
- 238000004611 spectroscopical analysis Methods 0.000 description 8
- 239000000463 material Substances 0.000 description 7
- 238000012360 testing method Methods 0.000 description 7
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- 238000013461 design Methods 0.000 description 4
- 238000000926 separation method Methods 0.000 description 4
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- 238000009826 distribution Methods 0.000 description 3
- 238000011156 evaluation Methods 0.000 description 3
- 238000002474 experimental method Methods 0.000 description 3
- 229920001684 low density polyethylene Polymers 0.000 description 3
- 239000004702 low-density polyethylene Substances 0.000 description 3
- 239000002245 particle Substances 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 238000007654 immersion Methods 0.000 description 2
- 238000012417 linear regression Methods 0.000 description 2
- 229920003023 plastic Polymers 0.000 description 2
- 239000004033 plastic Substances 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 101100433727 Caenorhabditis elegans got-1.2 gene Proteins 0.000 description 1
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N Silicium dioxide Chemical compound O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000003556 assay Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
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- 230000005540 biological transmission Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 229920001038 ethylene copolymer Polymers 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 239000004700 high-density polyethylene Substances 0.000 description 1
- 229920001519 homopolymer Polymers 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 238000009659 non-destructive testing Methods 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
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- Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)
Abstract
The invention discloses an on-line detection system for performance parameters of propylene copolymer. The on-line detection system comprises an on-line sampler, a detection tank for containing samples collected by the on-line sampler, a Raman spectrometer used for collecting the Raman spectrum signals of the samples in the detection tank and a processor for calculating the collected Raman spectrum signals to obtain the performance parameters of the propylene copolymer. In addition, the invention also relates to an application method for the system. The method mainly comprises an on-line sampling method and a spectral analysis method. According to the invention, the blank in the field of industrial rapid and accurate on-line detection of contents of xylene soluble and ethylene in the propylene copolymer as well as content of ethylene in a soluble substance is filled up, and a strong basis is provided for the monitoring and optimization of propylene copolymer production.
Description
Technical field
The present invention relates to detection system and the method for propylene copolymer performance parameter, refer more particularly to a kind of system and method that utilizes Raman spectrum to detect propylene copolymer performance parameter.
Background technology
Polypropylene (Polypropylene) plastics are since nineteen fifty-seven commercially produces, and tendency is powerful, in the mid-90 in 20th century, become a kind of plastics of thermoplastics market demand maximum.Yet traditional HOPP rigidity is weak, shock resistance is poor etc., and shortcoming affects its processing characteristics and usability always.Thereby heterogeneous crushing-resistant copolymerization polypropylene is a kind of ethylene copolymer of introducing in polymerization process forms the Novel polypropylene that EP rubbers improves traditional HOPP shock resistance.Because it has good shock resistance and fabulous processing characteristics, heterogeneous crushing-resistant copolymerization polypropylene is widely used in fields such as automobile special material, household electrical appliance.
Crushing-resistant copolymerization polypropylene (calling propylene copolymer in the following text) is a kind of potpourri being comprised of homopolymer (comprising HOPP and homopolymerisation polyethylene) and bipolymer (EP rubbers is called for short EPR rubber).There are three important Index Influences propylene copolymer product quality, are respectively ethylene contents in xylene soluble part content (be the potpourri of EPR rubber and minute quantity random polypropylene, be called for short XS), ethylene contents and solvend.Wherein the content of xylene soluble part is the content of EPR rubber in product, and it has determined the shock resistance of product; Ethylene contents is the total content of ethene in product, the namely ethylene contents in EPR rubber and homopolymerisation polyethylene content sum, and it affects the physical property such as fusing point, breaking elongation, flexural strength, tensile strength of product; And ethylene contents in solvend is the ethylene contents in EPR rubber, crystallinity of itself and rubber etc. has close contacting.
At present, in detection propylene copolymer, the method for xylene soluble part content mainly contains chemical extraction method (GB/T 24282-2009), infra-red sepectrometry (CN 1124483C) and Raman spectroscopy (CN 101498667A) etc.; The method in ethylene contents in propylene copolymer that detects mainly contains infra-red sepectrometry (CN 1152245C) and Raman spectroscopy (CN 101498667A); In solvend, the assay method of ethylene contents is first dimethylbenzene stripping solvend from multipolymer, and then adopts the means detections such as infrared.Chemical extraction method toxicity is large, operation steps is complicated; And can not realize online detection.Infra-red sepectrometry needs the pre-service such as press mold, also should not realize online detection.Raman spectrum, due to the feature of its Non-Destructive Testing, can be realized online detection, but also there is no the on-line detecting system based on Raman spectrum at present.In addition, the computing method degree of accuracy that adopts at present the method deal with data of xylene soluble part content and ethylene contents in Raman detection propylene copolymer to adopt is not high.
Summary of the invention
The invention provides a kind of on-line detecting system and method for performance parameter of propylene copolymer, realized the online detection of the performance parameter of propylene copolymer, testing result degree of accuracy is high.
An on-line detecting system for the performance parameter of propylene copolymer, comprising:
The detection groove of on-line sampler institute collected specimens described in on-line sampler, splendid attire, be used for collecting the Raman spectrometer of sample raman spectral signal in described detection groove and the raman spectral signal of collecting calculated to the processor of the performance parameter of propylene copolymer;
The performance parameter of described propylene copolymer is ethylene contents in xylene soluble part content, ethylene contents and xylene soluble part in propylene copolymer.
In the present invention, described after thread detector sampling, by propylene copolymer sample delivery to be measured to detecting groove, Raman spectrometer is analyzed the copolymerization of propylene matter sample to be measured detecting in groove, obtain the raman spectral signal of described copolymerization of propylene matter sample to be measured, after the Raman signal obtaining is analyzed through processor, obtain the performance parameter of copolymerization of propylene matter sample to be measured.
As preferably, described on-line sampler is cyclone separator, described cyclone separator sidewall has sample inlet, top and has gas vent, bottom has and the sample export that detects groove and be connected, cuvette used when detecting groove and being similar to off-line Raman detection, surrounding material is quartz glass.Described sample inlet is connected with the product conveyance conduit of copolymerization of propylene device, described cyclone separator from product delivery air isolated copolymer powder through solid outlet enter described detection groove for detection of, waste gas emits from top gas outlet.
As preferably, described gas vent is connected with threeway, two outlets of all the other of threeway are connected with respectively vacuum system and fan blower, described detection trench bottom is with operation valve, under this operation valve, be provided with waste material storage tank, after detection completes, fan blower is blown into waste material storage tank by the waste material after detecting, ready for detect next time, improved the efficiency detecting.
The outlet of cyclone separator lower end and the changeover joint section detecting between groove should be made without the interface end that seamlessly transits that purges dead angle, in case the existence due to dead angle purges unclean in the process that purges waste material, pollute and detect material next time, as preferably, between described cyclone separator and detection groove, be connected with transition member, described transition member is shaped as back taper or bowl-type.
In the present invention, described Raman spectrometer can be FT-Raman Spectrometer, also can be color dispersion-type Raman spectrometer, wherein Fourier draws the graceful spectrometer of conversion to have minute shorter, the advantage that sensitivity is higher, as preferably, described Raman spectrometer is FT-Raman Spectrometer.
The present invention also provides a kind of online test method of performance parameter of the propylene copolymer that uses described on-line detecting system, comprising:
(1) described on-line sampler obtains copolymerization of propylene matter sample to be measured, is stored in and detects in groove;
(2) described Raman spectrometer detects the copolymerization of propylene matter sample to be measured detecting in groove, obtains the Raman spectrum of copolymerization of propylene matter sample to be measured;
(3) Raman spectrum that described processor obtains step (2) calculates, and obtains the performance parameter of described propylene copolymer.
As preferably, the resolution of described Raman spectrometer is better than 8cm
-1, sweep limit is 50～3600cm
-1, the raising of resolution makes the result of calculating more accurate.
While utilizing the Raman spectrum data of copolymerization of propylene matter sample to calculate the performance parameter of described propylene copolymer, can be calculated by the blackbox model of setting up according to the spectroscopic data of known sample, also can by method of superposition, be calculated according to the spectroscopic data of tygon spectroscopic data, polypropylene spectroscopic data and xylene soluble part, the algorithm core that wherein method of superposition is calculated is genetic algorithm.
Wherein, described blackbox model is set up with reference to the Raman spectrum of sample by least 5 known propylene copolymers of performance parameter.
The method of setting up blackbox model is modeling method well known to those skilled in the art, can be the method for the linear regressions such as partial least square method (PLS) recurrence, major component (PCR) recurrence, can be also the method for the non-linear regressions such as artificial neural network; While setting up blackbox model, can select 50～3600cm
-1the long range of spectra data of all-wave, also can select 50～600cm
-1, 600～1600cm
-1or 2700～3100cm
-1the spectroscopic data of a certain section, in the present invention, preferred 2700～3100cm
-1the spectroscopic data of this section is used for matching forecast model, in this wavelength coverage, and signal cross less pollution.
The present invention also provides a kind of new method of utilizing spectroscopic data to calculate the performance parameter of propylene copolymer, i.e. method of superposition, and calculation process and genetic algorithm flow process are shown in respectively Fig. 4 and Fig. 5.If the mole fraction of homopolymerisation polyethylene is x in described propylene copolymer, the mole fraction of HOPP is y, and the mole fraction of xylene soluble part is z, and in xylene soluble part, the mole fraction of ethene is x
1, in xylene soluble part, the mole fraction of propylene is y
1, wherein x, y, z, x
1and y
1meet formula (I) and formula (II);
x+y+z＝1 (I)
x
1+y
1＝1 (II)
Calculation procedure is as follows:
(1) obtain respectively homopolymerisation polyethylene with reference to sample, HOPP with reference to sample and dimethylbenzene leachable the Raman spectrum with reference to sample;
(2) utilize the Raman spectrum data of x, y, z and step (1) to obtain fitness function Fitness by genetic algorithm, formula is as follows:
Wherein, S is a number of getting for spectrum matrix;
C '
jspectrum matrix for sample;
C
jfor synthetic spectrum matrix, C
j=xC
lDPE+ yC
pP+ zC
xS, C
lDPEfor the spectrum matrix of homopolymerisation polyethylene with reference to sample, C
pPfor the spectrum matrix of HOPP with reference to sample, C
pPfor the spectrum matrix of dimethylbenzene leachable with reference to sample;
J is subscript, represents the data that in spectrum matrix, j is ordered;
The fitness function Fitness < Pm that makes genetic algorithm, Pm gets 0.5, obtains the value of x, y, z;
(3) from sample spectra matrix, deduct tygon part and polypropylene and partly obtain xylene soluble part spectrum matrix in sample
formula is as follows:
Then utilize x
1, y
1by genetic algorithm, to obtain fitness function as follows with Raman spectrum data:
Wherein, C
xSjfor the spectrum matrix of synthetic xylene soluble part, C
xSj=x
1c
lDPE+ y
1c
pP;
spectrum matrix for the xylene soluble part in sample;
The fitness function Fitness < Pm of the genetic algorithm making, Pm gets 0.5, obtains x
1and y
1value;
(4) according to the x, y, z, the x that obtain
1and y
1value, calculate xylene soluble part content, the ethylene contents in ethylene contents and solvend, wherein content represents by mass percent;
Wherein, ρ
1for the density of homopolymerisation polyethylene, ρ
2for the density of HOPP, ρ
3density for copolymerization of propylene matter sample to be measured.
Compared with the existing technology, beneficial effect of the present invention is embodied in:
(1) use cyclone separator to carry out separation to propylene copolymer sample to be measured, separation efficiency is high, has realized the online detection to sample Raman spectrum;
(2) use Raman spectrometer to detect propylene copolymer sample to be measured, avoided the preparation process to sample, and minute is short, highly sensitive;
(3) adopted the method for calculating the performance parameter of propylene copolymer, do not needed a large amount of known sample data, result of calculation accurately, can realize monitoring industrial processes faster more fast, to producing to optimize, provides important guiding.
Accompanying drawing explanation
Fig. 1 is the on-line detecting system structured flowchart of the performance parameter of propylene copolymer;
The structural drawing that Fig. 2 (a) is cyclone separator;
Fig. 2 (b) is transition member and the structural representation that detects groove;
Fig. 3 is the calculation flow chart of blackbox model;
Fig. 4 is the calculation flow chart of method of superposition;
Fig. 5 is the basic flow sheet of genetic algorithm;
The predicted value of xylene soluble part content and the comparison diagram of actual value of Fig. 6 (a) for obtaining according to blackbox model;
The predicted value of ethylene contents and the comparison diagram of actual value of Fig. 6 (b) for obtaining according to blackbox model;
Fig. 6 (c) is the predicted value of ethylene contents and the comparison diagram of actual value in the xylene soluble part obtaining according to blackbox model;
The predicted value of xylene soluble part content and the comparison diagram of actual value of Fig. 7 (a) for calculating according to method of superposition;
The predicted value of ethylene contents and the comparison diagram of actual value of Fig. 7 (b) for calculating according to method of superposition;
Fig. 7 (c) is the predicted value of ethylene contents and the comparison diagram of actual value in the xylene soluble part calculating according to method of superposition.
Embodiment
Embodiment 1
Cold mould experimental simulation sampling and sample purge: the copolymerization of propylene matter sample that experiment is used is provided by Qilu Petrochemical, and density is 900kg/m
3, size-grade distribution is recorded by screen experiments (in Table 1), and heavy of the material that testing result demonstration particle diameter is less than 0.09mm accounts for 0.4% of gross weight.In industry, product is carried and is adopted air to carry, and tolerance is 7000m
3/ h, cold mould experimental gas volumetric flow rate is contracted to 1000m
3/ h, cyclone design critical separation particles used is 0.0065mm, is less than 0.09mm, to guarantee isolating all material from delivery air.
Table 1 size-grade distribution
Lab design cyclone separator material is organic glass.Design major parameter: barrel diameter D is 300mm, import A=D/2, B=D/4, gas within it rotating cycle N is 5, inlet gas speed u
ifor 20m/s, the viscosity of air when gas viscosity is got 20 ℃.Through theory, calculate its critical separated diameter:
Detect Reynolds number:
meet Stokes formula service condition.
Above design conditions is industrial reduction condition.It is 500～700m that cold mould experiment changes purge gas volumetric flow rate scope
3/ h, purge time is 5s.Experimental result is in Table 2.
Table 2 samples and purges cold mould experimental result
Experimental result shows, adopts cyclone separator can complete separation; As shown in experimental result, purge tolerance and reach 700m
3/ h, can be by sampler and detect all material in groove and blow out.Certainly, in use and commercial plant, need to amplify sampler Optimum Operation parameter according to certain ratio.
Embodiment 2
Set up the performance parameter that blackbox model calculates propylene copolymer: the MultiRam FTRaman spectrometer that adopts German Bruker company to produce, detected parameters is as follows: sweep limit is 50～3600cm
-1, laser intensity is 500mW, resolution is 4cm
-1, scanning times 32 times.Non-invasive is measured, and detects some propylene copolymers with reference to the Raman spectrum of sample, and predicts the ethylene contents in xylene soluble part content, ethylene contents and the solvend in propylene copolymer testing sample.Ethylene contents in xylene soluble part content, ethylene contents and the solvend of sample is in Table 3.
Table 3 sample indices
First, in order to reduce the diffuse transmission influence that distribution of particles is inhomogeneous and grain size is brought, and effectively strengthen the spectral information relevant to component content, and adopt polynary scatter correction to carry out pre-service to original spectrum data, then the intensity spectrum after pre-service is changed into relative intensity spectrum.
Then, employing offset minimum binary (PLS) Return Law respectively will be through pretreated propylene copolymer with reference to sample 2700～3100cm
-1in xylene soluble part content, ethylene contents and the solvend of spectral coverage spectroscopic data and respective sample, ethylene contents carries out associated modeling, and adopts the mode of getting 1 intersection prediction to make prediction to indices.
What is called is got 1 intersection prediction and is referred to and in 7 samples, take out 1 sample as testing sample at every turn, adopts the data of its 6 samples to set up model, then the index of the testing sample taking out is made prediction.Predict the outcome and see accompanying drawing 6.Predict the outcome and evaluate in Table 4.
Table 4 evaluation that predicts the outcome
From predicting the outcome, evaluate and can find out, adopt 2700～3100cm
-1predicting the outcome of three indexs of this region matching all can reach more than 0.9 with the related coefficient of actual value, and average relative error is respectively 1.05%, 1.00% and 1.16%, all much smaller than 5%, meets commercial Application requirement.
Embodiment 3
Utilize method of superposition to calculate the performance parameter of propylene copolymer: the MultiRam FTRaman spectrometer that adopts German Bruker company to produce, detected parameters is as follows: sweep limit is 50～3600cm
-1, laser intensity is 500mW, resolution is 4cm
-1, scanning times 32 times.Without immersion detection density, be 950kg/m
3high density polyethylene (be called for short LDPE) with reference to sample, density, be 890kg/m
3hOPP (be called for short PP) is the Raman spectrum with reference to sample with reference to sample and dimethylbenzene leachable (being called for short XS).Without immersion detection density, be 900kg/m
3the Raman spectrum of propylene copolymer testing sample (in embodiment 2, sample 1).By method of superposition, calculate in sample 1 ethylene contents in xylene soluble part content, ethylene contents and solvend.
Suppose that the homopolymerisation polyethylene molecule content containing in propylene copolymer testing sample is x, HOPP molecule content is y, and xylene soluble part molecule content is z; And ethylene molecule content is x in sample xylene soluble part
1, propylene molecules content is y
1.
x+y+z＝1 (I)
x
1+y
1＝1 (II)
Computation process following (the visible accompanying drawing 4 of flow process):
1) superposition calculation x, y and z, computing method are genetic algorithm.Namely by genetic algorithm, calculate x, y and the z of 1 group of optimum, make the fitness function Fitness < Pm of genetic algorithm, Pm gets 0.5.
Wherein, S-----spectrum matrix gets a number;
The spectrum matrix of C '-----sample;
The spectrum matrix that C-----is synthetic, C=xC
lDPE+ yC
pP+ zC
xS;
Under j-----, be designated as the data that in spectrum matrix, j is ordered.
The algorithm flow of genetic algorithm, at accompanying drawing 5, is calculated by Matlab programming.For sample 1, calculate: x=0.0084, y=0.8324, z=0.1592.
2) from sample spectra matrix, deduct tygon part and polypropylene and partly obtain xylene soluble part spectrum matrix in sample
computing method are as follows:
Then superposition calculation x
1and y
1, being similarly superposition algorithm, fitness function is as follows:
Wherein, C
xSjfor the spectrum matrix of synthetic xylene soluble part, C
xSj=x
1c
lDPE+ y
1c
pP;
spectrum matrix for the xylene soluble part in sample;
Can be calculated thus x
1=0.0541 and y
1=0.9469.
3) calculate xylene soluble part content (W/W%), the ethylene contents (W/W%) in ethylene contents (W/W%) and solvend.
Adopt the method to calculate the index of all samples in embodiment 2, it the results are shown in accompanying drawing 7.The evaluation of result of calculation is in Table 5.Contrast the result of calculation of these computing method and the result of calculation of blackbox model can be found, adopt method of superposition result of calculation to be better than blackbox model.
Table 5 evaluation that predicts the outcome
Claims (3)
1. an online test method for the performance parameter of propylene copolymer, is characterized in that, comprising:
(1) on-line sampler obtains copolymerization of propylene matter sample to be measured, is stored in and detects in groove;
(2) Raman spectrometer detects the copolymerization of propylene matter sample to be measured detecting in groove, obtains the Raman spectrum of copolymerization of propylene matter sample to be measured;
(3) Raman spectrum that processor obtains step (2) calculates, and obtains the performance parameter of described propylene copolymer;
Calculating described in step (3) adopts superposition calculation method, if the mole fraction of homopolymerisation polyethylene is x in described propylene copolymer, the mole fraction of HOPP is y, and the mole fraction of xylene soluble part is z, and in xylene soluble part, the mole fraction of ethene is x
1, in xylene soluble part, the mole fraction of propylene is y
1, wherein x, y, z, x
1and y
1meet formula (I) and formula (II);
x+y+z＝1 (I)
x
1+y
1＝1 (II)
Calculation procedure is as follows:
(1) obtain respectively homopolymerisation polyethylene with reference to sample, HOPP with reference to sample and dimethylbenzene leachable the Raman spectrum with reference to sample;
(2) utilize the Raman spectrum data of x, y, z and step (1) to obtain fitness function Fitness by genetic algorithm, formula is as follows:
Wherein, S is a number of getting for spectrum matrix;
C'
jspectrum matrix for sample;
C
jfor synthetic spectrum matrix, C
j=xC
lDPE+ yC
pP+ zC
xS, C
lDPEfor the spectrum matrix of homopolymerisation polyethylene with reference to sample, C
pPfor the spectrum matrix of HOPP with reference to sample, C
pPfor the spectrum matrix of dimethylbenzene leachable with reference to sample;
J is subscript, represents the data that in spectrum matrix, j is ordered;
The fitness function Fitness<Pm that makes genetic algorithm, Pm gets 0.5, obtains the value of x, y, z;
(3) from sample spectra matrix, deduct tygon part and polypropylene partly obtain xylene soluble part spectrum Matrix C in sample '
xs, formula is as follows:
C'
XS＝C'-xC
LDPE-yC
PP (IV)
Then utilize x
1, y
1by genetic algorithm, to obtain fitness function as follows with Raman spectrum data:
Wherein, C
xSjfor the spectrum matrix of synthetic xylene soluble part, C
xSj=x
1c
lDPE+ y
1c
pP;
C'
xsjspectrum matrix for the xylene soluble part in sample;
The fitness function Fitness<Pm that makes genetic algorithm, Pm gets 0.5, obtains x
1and y
1value;
(4) according to the x, y, z, the x that obtain
1and y
1value, calculate xylene soluble part content, the ethylene contents in ethylene contents and solvend;
Wherein, the on-line detecting system that described online test method is used, comprising:
The detection groove of the sample that described in on-line sampler, splendid attire, on-line sampler gathers, be used for collecting the Raman spectrometer of sample raman spectral signal in described detection groove and the raman spectral signal of collecting calculated to the processor of the performance parameter of propylene copolymer;
The performance parameter of described propylene copolymer is ethylene contents in xylene soluble part content, ethylene contents and xylene soluble part in propylene copolymer;
Described on-line sampler is cyclone separator (1), and described cyclone separator sidewall has sample inlet, top and has gas vent, bottom has and the sample export that detects groove (4) and be connected;
Described gas vent is connected with threeway (2), and two outlets of all the other of threeway are connected with respectively vacuum system and fan blower, and described detection groove (4) bottom, with operation valve, is provided with waste material storage tank under this operation valve.
2. the online test method of the performance parameter of propylene copolymer according to claim 1, is characterized in that, between described cyclone separator and detection groove, is connected with transition member (3), and described transition member is shaped as back taper or bowl-type.
3. the online test method of the performance parameter of propylene copolymer according to claim 1, is characterized in that, described Raman spectrometer is FT-Raman Spectrometer.
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| CN104089941B (en) * | 2014-06-11 | 2018-01-05 | 华南理工大学 | A kind of Raman spectrum on-line measurement apparatus and method of polymer melt property |
| CN104267018B (en) * | 2014-10-27 | 2018-01-23 | 武汉四方光电科技有限公司 | The processing method of gas concentration signal in a kind of Raman gas analyzer |
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| CN101498667A (en) * | 2009-02-16 | 2009-08-05 | 浙江大学 | Method for detecting ethylene or ethylene propylene rubber content in ethylene-propylene copolymerization polypropylene |
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Non-Patent Citations (6)
| Title |
|---|
| A.S.Nielsen et al.Estimation of crystallinity of isotactic polypropylene using Raman spectroscopy.《polymer》.2002,第43卷2671-2676. * |
| Estimation of crystallinity of isotactic polypropylene using Raman spectroscopy;A.S.Nielsen et al;《polymer》;20021231;第43卷;2671-2676 * |
| 丙烯聚合卧式搅拌床反应器中持料量的检测;董克增等;《化工学报》;20110630;第62卷(第6期);1509-1514 * |
| 利用拉曼光谱检测乙丙共聚物中乙烯含量;陈美娟等;《光谱学与光谱分析》;20110331;第31卷(第3期);709-713 * |
| 董克增等.丙烯聚合卧式搅拌床反应器中持料量的检测.《化工学报》.2011,第62卷(第6期),1509-1514. * |
| 陈美娟等.利用拉曼光谱检测乙丙共聚物中乙烯含量.《光谱学与光谱分析》.2011,第31卷(第3期),709-713. * |
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