CN111103257A - Method for rapidly predicting yield of monocyclic aromatic hydrocarbon in steam cracking liquid-phase oil product - Google Patents
Method for rapidly predicting yield of monocyclic aromatic hydrocarbon in steam cracking liquid-phase oil product Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 54
- -1 monocyclic aromatic hydrocarbon Chemical class 0.000 title claims abstract description 48
- 239000007791 liquid phase Substances 0.000 title claims abstract description 47
- 238000004230 steam cracking Methods 0.000 title claims abstract description 34
- 238000002329 infrared spectrum Methods 0.000 claims abstract description 17
- 239000003921 oil Substances 0.000 claims description 67
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 61
- 229910052799 carbon Inorganic materials 0.000 claims description 61
- 150000004945 aromatic hydrocarbons Chemical class 0.000 claims description 39
- 238000009835 boiling Methods 0.000 claims description 21
- 238000005336 cracking Methods 0.000 claims description 9
- 239000000295 fuel oil Substances 0.000 claims description 8
- 238000002790 cross-validation Methods 0.000 claims description 7
- 238000012628 principal component regression Methods 0.000 claims description 5
- 238000012360 testing method Methods 0.000 claims description 4
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- 239000002283 diesel fuel Substances 0.000 claims description 3
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- 230000003595 spectral effect Effects 0.000 claims description 3
- 235000013405 beer Nutrition 0.000 claims description 2
- 238000012417 linear regression Methods 0.000 claims description 2
- 238000004458 analytical method Methods 0.000 description 19
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- 238000009499 grossing Methods 0.000 description 12
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- 229930195733 hydrocarbon Natural products 0.000 description 9
- 150000002430 hydrocarbons Chemical class 0.000 description 9
- UHOVQNZJYSORNB-UHFFFAOYSA-N Benzene Chemical compound C1=CC=CC=C1 UHOVQNZJYSORNB-UHFFFAOYSA-N 0.000 description 6
- YXFVVABEGXRONW-UHFFFAOYSA-N Toluene Chemical compound CC1=CC=CC=C1 YXFVVABEGXRONW-UHFFFAOYSA-N 0.000 description 6
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- VGGSQFUCUMXWEO-UHFFFAOYSA-N Ethene Chemical compound C=C VGGSQFUCUMXWEO-UHFFFAOYSA-N 0.000 description 5
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- 239000002994 raw material Substances 0.000 description 4
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- LXWWXSOAZUEXTE-UHFFFAOYSA-N C=CCCCCCC.[C] Chemical compound C=CCCCCCC.[C] LXWWXSOAZUEXTE-UHFFFAOYSA-N 0.000 description 2
- YNQLUTRBYVCPMQ-UHFFFAOYSA-N Ethylbenzene Chemical compound CCC1=CC=CC=C1 YNQLUTRBYVCPMQ-UHFFFAOYSA-N 0.000 description 2
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Abstract
The invention belongs to the field of petrochemical industry, and relates to a method for rapidly predicting yield of monocyclic aromatic hydrocarbon in a steam cracking liquid-phase oil product. The method comprises the following steps: step 1: establishing a near-infrared prediction model of the steam cracking liquid-phase oil product, and step 2: collecting near infrared spectrum data of a steam cracking liquid-phase oil product to be detected; and step 3: respectively obtaining a predicted value Bn of a fraction mass percentage content model of the steam cracking liquid-phase oil product to be detected and a predicted value Ai of a monocyclic aromatic hydrocarbon mass percentage content model based on the near infrared prediction model established in the step 1 according to the near infrared spectrum data acquired in the step 2; and 4, step 4: and calculating to obtain a predicted value Yi of the yield of the monocyclic aromatic hydrocarbon in the steam cracking liquid-phase oil product to be detected, wherein Yi is Bn multiplied by Ai. The method can quickly and accurately predict the yield of the monocyclic aromatic hydrocarbon in the steam cracking liquid-phase oil product.
Description
Technical Field
The invention belongs to the field of petrochemical industry, and particularly relates to a method for rapidly predicting yield of monocyclic aromatic hydrocarbon in a steam cracking liquid-phase oil product.
Background
Ethylene cracking is a source of downstream chemical industry. The aromatic hydrocarbon components such as benzene, toluene, xylene and ethylbenzene, which are rich in the oil product, are important raw materials for preparing reagents such as BTX (benzene, toluene and xylene) aromatic hydrocarbon, and are also synthetic raw materials of fine chemical intermediates such as organic synthesis, medicines and dyes. With the annual increase in ethylene production capacity, the liquid-phase oil yield, which accounts for about 15% of the ethylene production capacity, is increasing. The comprehensive utilization of the resources is beneficial to improving the overall economic benefit of the ethylene device, and has important significance for national economic development.
In order to obtain the yield of monocyclic aromatic hydrocarbon in pyrolysis gasoline, the distillation range distribution data and the aromatic hydrocarbon content data of a steam pyrolysis liquid-phase oil product need to be obtained. The common Gas Chromatography (GC) analysis method needs different instruments with different configurations, the full distillation range simulation analysis of the cracked tar is completed in nearly 40min, and the quantitative analysis of the detailed aromatic hydrocarbon content of the cracked tar is completed in nearly 30 min. The GC method can obtain a large amount of qualitative and quantitative information through cutting, column separation, back flushing and the like, but needs a plurality of chromatographs with different configurations, and has a long analysis period.
In order to shorten the analysis time, improve the analysis efficiency, and reduce the analysis cost while accelerating the analysis speed, it is necessary to develop a new analysis method for the yield of monocyclic aromatic hydrocarbons in oil products.
Disclosure of Invention
The invention aims to provide a method for rapidly predicting the yield of monocyclic aromatic hydrocarbon in a steam cracking liquid-phase oil product, thereby shortening the analysis time, improving the analysis efficiency and reducing the analysis cost.
Specifically, the invention provides a method for rapidly predicting the yield of monocyclic aromatic hydrocarbon in a steam cracking liquid-phase oil product, which comprises the following steps:
step 1: the method for establishing the near-infrared prediction model of the steam cracking liquid-phase oil product comprises the following steps:
step 1-1: collecting the steam cracking liquid-phase oil product;
step 1-2: collecting near infrared spectrum data of the steam cracking liquid-phase oil product;
step 1-3: measuring the mass percentage content of fractions and the mass percentage content of monocyclic aromatic hydrocarbon of the steam cracking liquid-phase oil product;
step 1-4: correlating the near infrared spectrum data of the steam cracking liquid-phase oil product with the measured mass percentage content of the fractions and the mass percentage content of the monocyclic aromatic hydrocarbons by using a chemometrics method, and respectively establishing a fraction mass percentage content prediction model and a monocyclic aromatic hydrocarbon mass percentage content prediction model of the steam cracking liquid-phase oil product;
step 2: collecting near infrared spectrum data of a steam cracking liquid-phase oil product to be detected;
and step 3: respectively obtaining a fraction mass percentage content model predicted value Bn and a monocyclic aromatic hydrocarbon mass percentage content model predicted value Ai of the steam cracking liquid-phase oil product to be detected based on the fraction mass percentage content prediction model and the monocyclic aromatic hydrocarbon mass percentage content prediction model established in the step 1 according to the near infrared spectrum data acquired in the step 2;
and 4, step 4: and calculating to obtain a predicted value Yi of the yield of the monocyclic aromatic hydrocarbon in the steam cracking liquid-phase oil product to be detected, wherein Yi is Bn multiplied by Ai.
The method of the invention can be used for cracking liquid-phase oil products of light oil products and can also be used for cracking liquid-phase oil products of heavy oil products; the light oil can be at least one of naphtha, topping oil, raffinate oil and aviation kerosene, and the heavy oil can be diesel oil and/or hydrogenated tail oil.
According to a specific embodiment of the present invention, the near infrared spectrum data is 4000cm-1-12000cm-1Band near infrared spectral data.
According to the scheme of the invention, the technical personnel can understand thatThe mass percentage of the fraction means the mass percentage of the fraction obtained in the distillable range. For the steam cracking liquid-phase oil product, the fraction quality percentage content prediction model is preferably a cracking tar fraction quality percentage content prediction model. More specific distillation range can be determined according to a content analysis method, and preferably, the prediction model of the mass percentage content of the fraction is an initial boiling point-T1Fraction mass percentage content prediction model, T1160-220 ℃. According to a specific embodiment of the invention, the fraction mass percentage prediction model is a fraction mass percentage prediction model with an initial boiling point of-216 ℃.
According to one embodiment of the invention, the monocyclic aromatic hydrocarbons include at least one of carbon six aromatic hydrocarbons, carbon seven aromatic hydrocarbons, carbon eight aromatic hydrocarbons and total aromatic hydrocarbons. Correspondingly, the single-ring aromatic hydrocarbon mass percentage content prediction model comprises at least one of a carbon six-aromatic hydrocarbon mass percentage content prediction model, a carbon seven-aromatic hydrocarbon mass percentage content prediction model, a carbon eight-aromatic hydrocarbon mass percentage content prediction model and a total aromatic hydrocarbon mass percentage content prediction model. A specific prediction model of the mass percentage content of the monocyclic aromatic hydrocarbon can be established according to the requirement.
According to the present invention, the chemometric method described in steps 1-4 may employ various methods applicable in the art, including, but not limited to, Partial Least Squares (PLS), Principal Component Regression (PCR), Classical Least Squares (CLS), Stepwise Multiple Linear Regression (SMLR), Artificial Neural Network (ANN), or Beer's law. Preferably, partial least squares, principal component regression or classical least squares are used.
According to the present invention, during the model building process, optimization of the model performance is also preferably included, and the method for optimizing the model performance can be various methods for optimizing the model, which are conventional in the art. According to the invention, near infrared modeling bands can be optimized, and first-order derivative + Norris-Derivative smoothing preprocessing or second-order derivative + Norris-Derivative smoothing preprocessing can be performed on each prediction model.
The method of the present invention also preferably comprises: evaluating the fitting effect of the near infrared prediction model by adopting a correlation coefficient R,
wherein R is a correlation coefficient, C'iIs a predicted value of a sample monocyclic aromatic hydrocarbon mass percentage content prediction model, CiIs the measured value of the mass percentage content of the monocyclic aromatic hydrocarbon of the sample,
is CiI is an integer from 1 to n ', and n' is the number of samples.
The method of the present invention also preferably comprises: evaluating the prediction performance of the near infrared prediction model by adopting a cross validation mean square error RMSECV,
wherein RMSECV is cross-validation mean square error,
to cross-test the predicted values for samples, CiAnd i is an integer from 1 to n ', and n' is the number of samples.
Among them, the determination method of the cross-validation prediction value is well known to those skilled in the art.
The measured values of the mass percent single ring aromatics in the above samples can be determined by conventional analytical methods (e.g., GC).
According to the invention, after the mass percentage content model predicted value Bn of the pyrolysis tar fraction and the mass percentage content model predicted value Ai of the monocyclic aromatic hydrocarbon of the steam pyrolysis liquid-phase oil product to be detected are obtained, the yield data of the carbon hexaaromatic hydrocarbon, the carbon heptaaromatic hydrocarbon, the carbon octaaromatic hydrocarbon and the total aromatic hydrocarbon can be obtained through calculation by Excel.
The invention has the following beneficial effects:
(1) the method adopts a near-infrared method, can quickly predict the mass percentage content of monocyclic aromatic hydrocarbon and the mass percentage content of fractions in the oil product by using a single instrument, quickly obtains the yield data of the carbon hexa-aromatic hydrocarbon, the carbon hepta-aromatic hydrocarbon, the carbon octa-aromatic hydrocarbon and the total aromatic hydrocarbon through Excel reference calculation, greatly shortens the analysis time and reduces the analysis cost.
(2) The invention can predict the yield of the monocyclic aromatic hydrocarbon in the cracked liquid-phase oil products which take various light oil products such as naphtha, topping oil, raffinate oil, aviation kerosene and the like as raw materials and the cracked liquid-phase oil products which take various heavy oil products such as diesel oil, hydrogenated tail oil and the like as raw materials.
(3) The method is simple and convenient and is easy to realize; a prediction model can be flexibly established according to field conditions; the method can be used in various specific application scenes, such as oil refineries, ethylene plants, aromatic hydrocarbon plants and the like.
(4) The invention does not need to carry out pretreatment on the sample, does not damage the sample, does not use organic solvent, does not consume carrier gas and the like, has low investment and maintenance cost and has wide application prospect in the petrochemical field.
Additional features and advantages of the invention will be set forth in the detailed description which follows.
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Exemplary embodiments of the present invention will be described in more detail by referring to the accompanying drawings.
FIG. 1 is a schematic flow chart of the method for rapidly predicting the yield of monocyclic aromatic hydrocarbons in a steam cracking liquid-phase oil product according to the present invention.
FIG. 2 is a graph showing the correlation between the NIR predicted value and the GC analysis value of the mass percentage content of the fraction at-216 ℃ in the initial boiling point of an unknown sample to be measured in one embodiment of the present invention.
Fig. 3 is a graph showing correlation between NIR predicted values and GC analyzed values of mass percentage of carbon hexaarene in an unknown sample to be measured in an embodiment of the present invention.
Fig. 4 is a graph showing correlation between NIR predicted values and GC analyzed values of mass percentage of carbon heptaaromatics in an unknown sample to be measured in an embodiment of the present invention.
Fig. 5 is a graph showing the correlation between the NIR predicted value and the GC analyzed value of the mass percentage of carbon octaarene in the unknown sample to be measured in the embodiment of the present invention.
Fig. 6 is a graph showing correlation between NIR predicted values and GC analyzed values of the mass percentage of total aromatic hydrocarbons of the unknown sample to be measured in an embodiment of the present invention.
Detailed Description
The following describes in detail specific embodiments of the present invention. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
Example 1
This example is used to illustrate the near infrared analysis method of monocyclic aromatic hydrocarbons in liquid-phase oil products of light oil cracking, as shown in fig. 1:
(1) establishing a prediction model:
collecting the near infrared spectrum of the known light oil product; measuring the mass percentage content data (Bn) of the fractions by a gas chromatography method configured with simulated distillation; the monocyclic aromatic hydrocarbon content (Ai) is measured by a gas chromatography method for configuring aromatic hydrocarbon content analysis (initial boiling point-216 ℃) to obtain the mass percentage data of carbon hexa-aromatic hydrocarbon, carbon hepta-aromatic hydrocarbon, carbon octa-aromatic hydrocarbon, carbon nona-aromatic hydrocarbon and carbon deca-aromatic hydrocarbon. Associating the near infrared spectrum of the known oil product, the mass percentage content data of the fraction with the initial boiling point of-216 ℃ and the mass percentage content data of the monocyclic aromatic hydrocarbon by adopting a Partial Least Squares (PLS) method, establishing a prediction model of the mass percentage content of the fraction with the initial boiling point of-216 ℃ and a prediction model of the mass percentage content of the monocyclic aromatic hydrocarbon of the oil product, and optimizing the performance of the models; a prediction model of mass percentage content of fractions with initial boiling point of-216 ℃, preferably a modeling waveband of 6000cm-1-8000cm-1The second derivative + Norris-Derivative smoothing preprocessing method; a prediction model of the mass percentage content of the carbon hexaarene, preferably a modeling waveband of 6000cm-1-8000cm-1The second derivative + Norris-Derivative smoothing preprocessing method; a prediction model of the mass percentage content of the carbon heptaarene, preferably a modeling waveband of 6000cm-1-8000cm-1The first derivative + Norris-Derivative smoothing preprocessing method; a prediction model of the mass percentage content of the carbon octaene, preferably a modeling waveband of 6000cm-1-10000cm-1The second derivative + Norris-Derivative smoothing preprocessing method; a total aromatic hydrocarbon mass percentage content prediction model, preferably a modeling waveband of 6000cm-1-8000cm-1First-order derivative + Norris-Derivative smoothing preprocessing method. The results of the model evaluation are shown in table 1. Table 2 shows the results of measuring the mass percent of fractions with the initial boiling point of-216 ℃ and the mass percent of carbon hexaarene, carbon heptaarene, carbon octaarene and total arene of unknown samples.
(2) Collecting a sample spectrum: collecting light oil pyrolysis liquid phase oil such as naphtha, topped oil and the like as unknown samples; collecting a near infrared spectrum of a sample by adopting an Antaris II Fourier near infrared spectrometer with a transmission sampling module; the collection range is 12000cm-1-4000cm-1Scanning 32 times to obtain an average spectrum, and scanning a background before each sample experiment; the spectrum was collected using a 1mm cuvette.
Preparation work of the near-infrared spectrometer: when the system is started, firstly, turning on a power supply of the near infrared analyzer, and then, running application software of an operating system; completing instrument correction and detection; all are normal, can load the sample and test; the operation condition of the instrument is checked regularly, all items of the check result meet the technical requirements, and the instrument can be used continuously; if some projects can not meet the specification requirements, the instrument parameters are adjusted according to the conditions, or necessary maintenance is carried out according to the requirements.
(3) Model prediction: obtaining a predicted value Bn of the mass percentage content of the fraction at the initial boiling point of the unknown sample at the temperature of-216 ℃ by using a prediction model of the mass percentage content of the fraction at the initial boiling point of-216 ℃; and obtaining a predicted value Ai of the mass percentage content of the carbon hexaarene, the carbon heptaarene, the carbon octaarene and the total arene of the unknown sample by using a single-ring arene mass percentage content prediction model. FIGS. 2 to 6 are graphs respectively showing the correlation between the NIR predicted value and the GC analysis value of the mass percentage of fraction at the initial boiling point of an unknown sample, the NIR predicted value and the GC analysis value of the mass percentage of hexaaromatic hydrocarbon of the unknown sample, the NIR predicted value and the GC analysis value of the mass percentage of heptaaromatic hydrocarbon of the unknown sample, the NIR predicted value and the GC analysis value of the mass percentage of octaaromatic hydrocarbon of the unknown sample, and the NIR predicted value and the GC analysis value of the mass percentage of total aromatic hydrocarbon of the unknown sample.
(4) And (3) calculating yield: the mass percentage content predicted value Bn of the distillate of the unknown sample and the mass percentage content predicted value Ai of the monocyclic aromatic hydrocarbon are quoted by Excel, namely the mass percentage content predicted value A6 of the carbon hexaaromatic hydrocarbon, the mass percentage content predicted value A7 of the carbon heptaaromatic hydrocarbon, the mass percentage content predicted value A8 of the carbon octaaromatic hydrocarbon and the mass percentage content predicted value A0 of the total aromatic hydrocarbon are used for calculating the yield Yi of the monocyclic aromatic hydrocarbon of the unknown sample, namely Bn multiplied by Ai, as shown in Table 3.
TABLE 1
| Prediction model | Coefficient of correlation (R) | Cross validation mean square error (RMSECV) |
| Initial boiling point-216 deg.C fraction | 0.9927 | 0.998 |
| Carbon six aromatic hydrocarbon | 0.9811 | 1.82 |
| Carbon hepta-arene | 0.9897 | 1.42 |
| C-octa-aromatic hydrocarbon | 0.8936 | 2.06 |
| Total aromatic hydrocarbons | 0.9845 | 2.13 |
TABLE 3
| Sample numbering | Carbon six aromatic hydrocarbons (m%) | Carbon heptaarene (m%) | C eight aromatic hydrocarbons (m%) | Total aromatic hydrocarbons (m%) |
| 1 | 15.86 | 17.10 | 11.59 | 50.90 |
| 2 | 17.19 | 18.66 | 12.84 | 55.98 |
| 3 | 19.11 | 20.19 | 13.77 | 60.67 |
| 4 | 25.36 | 23.52 | 17.94 | 79.79 |
| 5 | 29.50 | 25.05 | 16.92 | 80.80 |
| 6 | 26.49 | 23.90 | 15.61 | 74.45 |
| 7 | 31.16 | 25.14 | 16.72 | 82.07 |
| 8 | 30.94 | 24.56 | 17.04 | 82.36 |
| 9 | 12.77 | 15.18 | 11.72 | 47.34 |
| 10 | 22.14 | 21.67 | 15.10 | 68.25 |
Example 2
This example is used to illustrate the near infrared analysis method for monocyclic aromatic hydrocarbons in liquid-phase oil products of heavy oil cracking according to the present invention:
(1) establishing a prediction model:
collecting the near infrared spectrum of the known heavy oil product; measuring the mass percentage content data (Bn) of the fractions by a gas chromatography method configured with simulated distillation; the monocyclic aromatic hydrocarbon content (Ai) is measured by a gas chromatography method for configuring aromatic hydrocarbon content analysis (initial boiling point-216 ℃) to obtain the mass percentage data of carbon hexa-aromatic hydrocarbon, carbon hepta-aromatic hydrocarbon, carbon octa-aromatic hydrocarbon, carbon nona-aromatic hydrocarbon and carbon deca-aromatic hydrocarbon. Associating the near infrared spectrum of the known oil product, the mass percentage content data of the fraction with the initial boiling point of-216 ℃ and the mass percentage content data of the monocyclic aromatic hydrocarbon by adopting a Partial Least Squares (PLS) method, establishing a prediction model of the mass percentage content of the fraction with the initial boiling point of-216 ℃ and a prediction model of the mass percentage content of the monocyclic aromatic hydrocarbon of the oil product, and optimizing the performance of the models; a prediction model of mass percentage content of fractions with initial boiling point of-216 ℃, preferably a modeling waveband of 6000cm-1-8000cm-1The second derivative + Norris-Derivative smoothing preprocessing method; a prediction model of the mass percentage content of the carbon hexaarene, preferably a modeling waveband of 6000cm-1-8000cm-1The first derivative + Norris-Derivative smoothing preprocessing method; a prediction model of the mass percentage content of the carbon heptaarene, preferably a modeling waveband of 6000cm-1-8000cm-1The first derivative + Norris-Derivative smoothing preprocessing method; a prediction model of the mass percentage content of the carbon octaene, preferably a modeling waveband of 6000cm-1-10000cm-1The second derivative + Norris-Derivative smoothing preprocessing method; a total aromatic hydrocarbon mass percentage content prediction model, preferably a modeling waveband of 6000cm-1-8000cm-1Second derivative + Norris-Derivative smoothing preprocessing method. The results of the model evaluation are shown in table 4. Table 5 shows the results of measuring the mass percent of fractions with the initial boiling point of-216 ℃ and the mass percent of carbon hexaarene, carbon heptaarene, carbon octaarene and total arene of unknown samples.
(2) MiningCollecting a sample spectrum: collecting heavy oil products such as hydrogenated tail oil and the like, namely cracking liquid-phase oil products as unknown samples; collecting a near infrared spectrum of a sample by adopting an Antaris II Fourier near infrared spectrometer with a transmission sampling module; the collection range is 12000cm-1-4000cm-1Scanning 32 times to obtain an average spectrum, and scanning a background before each sample experiment; the spectrum was collected using a 1mm cuvette.
Preparation work of the near-infrared spectrometer: when the system is started, firstly, turning on a power supply of the near infrared analyzer, and then, running application software of an operating system; completing instrument correction and detection; all are normal, can load the sample and test; the operation condition of the instrument is checked regularly, all items of the check result meet the technical requirements, and the instrument can be used continuously; if some projects can not meet the specification requirements, the instrument parameters are adjusted according to the conditions, or necessary maintenance is carried out according to the requirements.
(3) Model prediction: obtaining a predicted value Bn of the mass percentage content of the fraction at the initial boiling point of the unknown sample at the temperature of-216 ℃ by using a prediction model of the mass percentage content of the fraction at the initial boiling point of-216 ℃; and obtaining a predicted value Ai of the mass percentage content of the carbon hexaarene, the carbon heptaarene, the carbon octaarene and the total arene of the unknown sample by using a single-ring arene mass percentage content prediction model.
(4) And (3) calculating yield: the Excel refers to a predicted value Bn of the mass percentage of the distillate of the unknown sample and a predicted value Ai of the mass percentage of the monocyclic aromatic hydrocarbon, namely a predicted value A6 of the mass percentage of the carbon hexaarene, a predicted value A7 of the mass percentage of the carbon heptaarene, a predicted value A8 of the mass percentage of the carbon octaarene and a predicted value A0 of the mass percentage of the total arene, and calculates the yield Yi of the monocyclic aromatic hydrocarbon of the unknown sample, namely Bn multiplied by Ai, as shown in Table 6.
TABLE 4
| Prediction model | Coefficient of correlation (R) | Mean square cross validationDifference (RMSECV) |
| Initial boiling point-216 deg.C fraction | 0.9901 | 0.998 |
| Carbon six aromatic hydrocarbon | 0.9253 | 2.42 |
| Carbon hepta-arene | 0.9724 | 1.96 |
| C-octa-aromatic hydrocarbon | 0.8676 | 2.54 |
| Total aromatic hydrocarbons | 0.9800 | 3.25 |
TABLE 6
| Sample numbering | Carbon six aromatic hydrocarbons (m%) | Carbon heptaarene (m%) | C eight aromatic hydrocarbons (m%) | Total aromatic hydrocarbons (m%) |
| 1 | 9.91 | 11.50 | 11.31 | 38.48 |
| 2 | 8.20 | 8.45 | 6.40 | 27.76 |
| 3 | 9.11 | 5.32 | 3.52 | 19.87 |
| 4 | 12.85 | 8.20 | 5.38 | 29.46 |
| 5 | 10.70 | 6.50 | 4.20 | 23.98 |
| 6 | 15.00 | 8.99 | 5.79 | 33.06 |
| 7 | 18.46 | 12.37 | 8.08 | 43.14 |
| 8 | 21.78 | 17.87 | 18.82 | 77.75 |
| 9 | 24.62 | 19.14 | 18.61 | 78.49 |
| 10 | 25.73 | 19.61 | 18.80 | 80.06 |
The data show that the GC measured value of each content is close to the NIR predicted value, errors are small, the near infrared prediction model established by the method has good correlation coefficient and cross validation mean square deviation, and the fitting effect and the prediction performance of the model are good. The method can quickly and accurately predict the yield of the monocyclic aromatic hydrocarbon in the steam cracking liquid-phase oil product.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.
Claims (10)
1. A method for rapidly predicting the yield of monocyclic aromatic hydrocarbon in a steam cracking liquid-phase oil product is characterized by comprising the following steps:
step 1: the method for establishing the near-infrared prediction model of the steam cracking liquid-phase oil product comprises the following steps:
step 1-1: collecting the steam cracking liquid-phase oil product;
step 1-2: collecting near infrared spectrum data of the steam cracking liquid-phase oil product;
step 1-3: measuring the mass percentage content of fractions and the mass percentage content of monocyclic aromatic hydrocarbon of the steam cracking liquid-phase oil product;
step 1-4: correlating the near infrared spectrum data of the steam cracking liquid-phase oil product with the measured mass percentage content of the fractions and the mass percentage content of the monocyclic aromatic hydrocarbons by using a chemometrics method, and respectively establishing a fraction mass percentage content prediction model and a monocyclic aromatic hydrocarbon mass percentage content prediction model of the steam cracking liquid-phase oil product;
step 2: collecting near infrared spectrum data of a steam cracking liquid-phase oil product to be detected;
and step 3: respectively obtaining a fraction mass percentage content model predicted value Bn and a monocyclic aromatic hydrocarbon mass percentage content model predicted value Ai of the steam cracking liquid-phase oil product to be detected based on the fraction mass percentage content prediction model and the monocyclic aromatic hydrocarbon mass percentage content prediction model established in the step 1 according to the near infrared spectrum data acquired in the step 2;
and 4, step 4: and calculating to obtain a predicted value Yi of the yield of the monocyclic aromatic hydrocarbon in the steam cracking liquid-phase oil product to be detected, wherein Yi is Bn multiplied by Ai.
2. The method of claim 1, wherein the steam cracking liquid phase oil comprises light oil cracking liquid phase oil or heavy oil cracking liquid phase oil; the light oil is selected from at least one of naphtha, topping oil, raffinate oil and aviation kerosene, and the heavy oil is selected from diesel oil and/or hydrogenated tail oil.
3. The method of claim 1, wherein the near infrared spectral data is 4000cm-1-12000cm-1Band near infrared spectral data.
4. The method of claim 1, wherein the fraction mass percentage prediction model is a cracked tar fraction mass percentage prediction model.
5. The method of claim 4, wherein the fraction mass percentage prediction model is initial boiling point-T1Fraction mass percentage content prediction model, T1160-220 ℃.
6. The method of claim 1, wherein the single ring aromatics comprise at least one of carbon six aromatics, carbon seven aromatics, carbon eight aromatics, and total aromatics; the single-ring aromatic hydrocarbon mass percentage content prediction model comprises at least one of a carbon six-aromatic hydrocarbon mass percentage content prediction model, a carbon seven-aromatic hydrocarbon mass percentage content prediction model, a carbon eight-aromatic hydrocarbon mass percentage content prediction model and a total aromatic hydrocarbon mass percentage content prediction model.
7. The method of claim 1, wherein the chemometric method in steps 1-4 comprises partial least squares, principal component regression, classical least squares, stepwise multiple linear regression, artificial neural networks, or beer's law.
8. The method of claim 7, wherein the chemometric method comprises partial least squares, principal component regression, or classical least squares.
9. The method according to any one of claims 1-8, wherein the method further comprises: evaluating the fitting effect of the near infrared prediction model by adopting a correlation coefficient R,
wherein R is a correlation coefficient, C'iIs a predicted value of a sample monocyclic aromatic hydrocarbon mass percentage content prediction model, CiIs the measured value of the mass percentage content of the monocyclic aromatic hydrocarbon of the sample,
is CiI is an integer from 1 to n ', and n' is the number of samples.
10. The method according to any one of claims 1-8, wherein the method further comprises: evaluating the prediction performance of the near infrared prediction model by adopting a cross validation mean square error RMSECV,
wherein RMSECV is cross-validation mean square error,
to cross-test the predicted values for samples, CiAnd i is an integer from 1 to n ', and n' is the number of samples.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114707778A (en) * | 2022-06-08 | 2022-07-05 | 中汽研汽车检验中心（天津）有限公司 | Method for rapidly predicting automobile PN emission based on oil product aromatic hydrocarbon composition |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102998276A (en) * | 2011-09-15 | 2013-03-27 | 中国石油化工股份有限公司 | Method for determining crude oil true boiling point distillation curve by using infrared spectroscopy |
| CN105273745A (en) * | 2014-05-29 | 2016-01-27 | 中国石油化工股份有限公司 | Low-grade diesel oil selective hydrogenation aromatic hydrocarbon removing method |
| CN106610420A (en) * | 2015-10-21 | 2017-05-03 | 中国石油化工股份有限公司 | Method for analysis of oil product monocyclic aromatic hydrocarbon |
| CN106701181A (en) * | 2016-12-31 | 2017-05-24 | 西北大学 | Method and device for hydrogenating whole-fraction medium and low-temperature coal tar to prepare mononuclear aromatics |
| CN107460003A (en) * | 2016-06-03 | 2017-12-12 | 中国石油化工股份有限公司 | A kind of method for being hydrocracked increasing of aviation kerosene yield |
-
2018
- 2018-10-29 CN CN201811269278.3A patent/CN111103257B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102998276A (en) * | 2011-09-15 | 2013-03-27 | 中国石油化工股份有限公司 | Method for determining crude oil true boiling point distillation curve by using infrared spectroscopy |
| CN105273745A (en) * | 2014-05-29 | 2016-01-27 | 中国石油化工股份有限公司 | Low-grade diesel oil selective hydrogenation aromatic hydrocarbon removing method |
| CN106610420A (en) * | 2015-10-21 | 2017-05-03 | 中国石油化工股份有限公司 | Method for analysis of oil product monocyclic aromatic hydrocarbon |
| CN107460003A (en) * | 2016-06-03 | 2017-12-12 | 中国石油化工股份有限公司 | A kind of method for being hydrocracked increasing of aviation kerosene yield |
| CN106701181A (en) * | 2016-12-31 | 2017-05-24 | 西北大学 | Method and device for hydrogenating whole-fraction medium and low-temperature coal tar to prepare mononuclear aromatics |
Non-Patent Citations (1)
| Title |
|---|
| 李凤: "标定裂解炉时裂解气的采样与分析", 《齐鲁石油化工》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114707778A (en) * | 2022-06-08 | 2022-07-05 | 中汽研汽车检验中心（天津）有限公司 | Method for rapidly predicting automobile PN emission based on oil product aromatic hydrocarbon composition |
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