CN106407648A - Rapid batch forecast method for key property of gasoline - Google Patents
Rapid batch forecast method for key property of gasoline Download PDFInfo
- Publication number
- CN106407648A CN106407648A CN201610740854.2A CN201610740854A CN106407648A CN 106407648 A CN106407648 A CN 106407648A CN 201610740854 A CN201610740854 A CN 201610740854A CN 106407648 A CN106407648 A CN 106407648A
- Authority
- CN
- China
- Prior art keywords
- gasoline
- sample
- key property
- forecasting methodology
- rapid batch
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 40
- 238000002329 infrared spectrum Methods 0.000 claims abstract description 12
- 238000012937 correction Methods 0.000 claims abstract description 11
- 238000001228 spectrum Methods 0.000 claims abstract description 10
- 238000012549 training Methods 0.000 claims abstract description 10
- 238000010606 normalization Methods 0.000 claims abstract description 7
- 239000011159 matrix material Substances 0.000 claims abstract description 5
- 239000013598 vector Substances 0.000 claims abstract description 5
- 238000002835 absorbance Methods 0.000 claims description 11
- 230000006870 function Effects 0.000 claims description 10
- 230000007935 neutral effect Effects 0.000 claims description 8
- 238000001704 evaporation Methods 0.000 claims description 7
- 230000005284 excitation Effects 0.000 claims description 6
- 238000011156 evaluation Methods 0.000 claims description 3
- 230000001537 neural effect Effects 0.000 claims description 3
- TVMXDCGIABBOFY-UHFFFAOYSA-N octane Chemical compound CCCCCCCC TVMXDCGIABBOFY-UHFFFAOYSA-N 0.000 claims description 3
- 238000011160 research Methods 0.000 claims description 3
- 239000013604 expression vector Substances 0.000 claims description 2
- 238000003062 neural network model Methods 0.000 claims description 2
- 238000004611 spectroscopical analysis Methods 0.000 claims description 2
- 238000013528 artificial neural network Methods 0.000 abstract description 5
- 238000002156 mixing Methods 0.000 abstract description 2
- 238000007781 pre-processing Methods 0.000 abstract 1
- 238000001514 detection method Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 230000000875 corresponding effect Effects 0.000 description 2
- 238000004821 distillation Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000007670 refining Methods 0.000 description 2
- 150000001335 aliphatic alkanes Chemical class 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000009835 boiling Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 210000004218 nerve net Anatomy 0.000 description 1
- 238000003012 network analysis Methods 0.000 description 1
- 230000009329 sexual behaviour Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3577—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing liquids, e.g. polluted water
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Z—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR
- G16Z99/00—Subject matter not provided for in other main groups of this subclass
Landscapes
- Physics & Mathematics (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The invention discloses a rapid batch forecast method for key property of gasoline. The method is used for a refinery enterprise and comprises the steps of firstly, performing spectrum pre-processing by employing a baseline correction, cutout and vector normalization method on the basis of near-infrared spectrum of the gasoline; secondly, obtaining a coefficient matrix by employing fast Fourier transform, and performing BP neural network training; and finally, performing batch forecast on the key property of the gasoline. Compared with a general nonlinear modeling method, the method has the advantages that the batch forecast of a plurality of properties is achieved, the forecast accuracy is ensured, meanwhile, the real-time performance of forecasting is improved, real-time control on on-line blending of the gasoline of the refinery enterprise is facilitated, and the economic benefit of the enterprise is further improved.
Description
Technical field
The present invention relates to the gasoline property context of detection of petrochemical industry, specifically a kind of based on nerual network technique
Gasoline key property Forecasting Methodology.
Background technology
During oil refining processing and gasoline concoction, need to analyze multiple properties of gasoline product, such as research octane number (RON)
(RON), anti-knock index, boiling range etc..Quick and precisely prediction gasoline property plays an important role in oil refining processing and gasoline concoction.
For improve gasoline property prediction rapidity, current someone on the basis of conventional offset minimum binary (PLS), using many
The PLS of dependent variable is predicted, can the multiple property of batch forecast.However, this method based on PLS is only to P
In matter, the prediction of the linearity preferable property has preferable precision.
In the key property of gasoline, property and the near infrared spectrum such as 10% evaporating temperature, 50% evaporating temperature, end point of distillation
Between there is stronger non-linear relation, the non-linear modeling method therefore with artificial neural network as representative have started to apply
Quick detection in gasoline property.This method improves the precision of prediction of model to a certain extent, but what the method was set up
Model is normally only used for predicting single gasoline property.The neutral net of this single output, needs when predicting multiple property to build
Found multiple models, the operation and maintenance work of each model is very numerous and diverse, brings difficulty to engineering real-time application.
Content of the invention
For solving the problems, such as prior art, the present invention proposes a kind of rapid batch Forecasting Methodology of gasoline key property,
Gasoline sample in library of spectra is carried out conventional pretreatment Fast Fourier Transform (FFT) by the method first, then sets up the BP god of multi output
Through network analysis model, finally according to this model, batch forecast is carried out to multiple properties of sample to be tested.Specifically include following steps：
(1) it is based on gasoline atlas of near infrared spectra, first Pretreated spectra is carried out to library of spectra sample and gasoline sample to be measured；
(2) pretreated spectroscopic data is carried out Fast Fourier Transform (FFT), obtain the coefficient matrices A of Fast Fourier Transform (FFT)；
(3) choose the front m row of A as the input of neutral net and Configuration network parameter；
(4) pretreated library of spectra gasoline sample, as training sample, carries out neural metwork training；
(5) using the neural network model training, batch forecast is carried out to multiple properties of sample to be tested.
The key property that this method is predicted include research octane number (RON), anti-knock index, density, 10% evaporating temperature,
50% evaporating temperature and the end point of distillation.
Preferably, Pretreated spectra includes baseline correction, intercepting and vector normalization.
Preferably, this method chooses 6400cm-1And 9200cm-12 wave number points are as two basic points of baseline correction.
Baseline correction is passed through formula (1) and is calculated：
In formula, xiFor gasoline near infrared spectrum wave number；kxi+ b is through 6400cm-1And 9200cm-12 points straight
Line equation, wherein k are this straight slope, and b is this Linear intercept；yiRepresent former spectrogram in wave number xiUnder absorbance；Represent base
After line correction, spectrogram is in wave number xiUnder absorbance.
Preferably, this method chooses 4000cm-1～4800cm-1Spectrogram modeling in wave number section.
This method, when spectrogram is carried out with vector normalization, is calculated using formula (2)：
In formula, XijRefer to i-th sample absorbance under wave number j；Refer to the absorbance values of i-th sample；M is
The number of wave number point；Xij *Absorbance under wave number j for i-th sample after expression vector normalization.
After pretreatment, gasoline near-infrared spectrogram high fdrequency component is less, and the coefficient matrices A after FFT typically exists
Amplitude very little after 20th Frequency point, therefore this method choose the input as neutral net for front 20 row of A, i.e. m=20.
The parameter of neutral net is configured using following：The number of hidden nodes is 30, and output node number is 6, i.e. gasoline to be measured
The number of key property, hidden layer excitation function tansig, output layer excitation function purelin, train function trainlm, performance
Function mse, performance arget value 0.05, learning coefficient 0.02.
The present invention adopts root-mean-square error for the evaluation of property j final result, i.e. RMSEj.Root-mean-square error is in engineering
It is widely adopted in measurement, it is sensitive to the especially big or special little error reflection in one group of measurement, can reflect measurement well
Precision.RMSEjCalculated by formula (3)：
In formula, n is the number of gasoline to be measured；Refer to the predicted value of property j of i-th gasoline to be measured；xijRefer to i-th to be measured
The actual value of property j of gasoline.RMSEjValue is less, illustrates higher to the accuracy of property j prediction, prediction effect is better.
Beneficial effect：
Detection method provided by the present invention is based on gasoline near infrared spectrum, combines nerve net using Fast Fourier Transform (FFT)
Network technology, realizes the batch quick detection of gasoline key property.The present invention is to the baseline correction of spectrogram, intercepting and neutral net
Input has carried out being directed to Sexual behavior mode, ensure that precision of prediction while reducing amount of calculation.With general non-linear modeling method
Compare, this method energy is quick, Accurate Prediction gasoline key property, contributes to the real-time control of the gasoline on-line blending of Petrochemical Enterprises
System, and then improve the economic benefit of enterprise.
In conjunction with Fig. 2, it is the typical atlas of near infrared spectra of 92# product oil, it can be found that in 6000cm-1～10000cm-1Ripple
In section, spectrogram is relatively steady, 6400cm-1And 9200cm-12 points of absorbance is relatively low, and therefore this method chooses 6400cm-1With
9200cm-1, as two basic points of baseline correction, amount of calculation is few and accuracy is high for 2 wave number points.
Because gasoline near-infrared spectrogram contains much noise in high frequency region, the spectrogram information of low frequency range is less, therefore can not
Using whole near infrared spectrums as modeling wave band, need to carry out spectrogram intercepting.This method finds through test, 4000cm-1~
4800cm-1Spectrogram modeling effect in wave number section is best.
Brief description
Fig. 1 predicts procedural block diagram for gasoline property rapid batch
Fig. 2 is the typical atlas of near infrared spectra of 92# product oil
Specific embodiment
The invention will be further described with case study on implementation below in conjunction with the accompanying drawings.
The present invention, introduces the gasoline key property Forecasting Methodology based on nerual network technique taking certain 92# product oil as a example.Table
1 is the numbering of certain all sample of 92# product oil and its corresponding property.
Certain 92# product oil sample number of table 1 and corresponding property
In Table 1, the sample of numbering 92#-1～99 is Sample Storehouse sample, and the sample of numbering 92#-100～108 is to be measured
Sample.The near infrared spectrum data of all gasoline samples is carried out, after conventional pretreatment, completing using Matlab function fft ()
Fast Fourier Transform (FFT), obtains transform coefficient matrix A.To obtaining atlas of near infrared spectra after matrix A delivery under each Frequency point
Amplitude, intercepts front 20 row.Table 2 gives the coefficient amplitude of part sample (92#-1～8).
The amplitude of the Fast Fourier Transform (FFT) coefficient of table 2 part 92# product oil sample
Complete the training of multi output BP network, first Configuration network parameter using Matlab Neural Network Toolbox：Hidden layer
Nodes 30, output node number 6, hidden layer excitation function tansig, output layer excitation function purelin, train function
Trainlm, performance function mse, performance arget value 0.05, learning coefficient 0.02.Then the gasoline sample being 92#-1～99 by numbering
Front 20 row of this coefficient matrices A, as network inputs, carry out BP network training.
After the completion of training, before the gasoline sample coefficient matrix that numbering is 92#-100～109,20 row are defeated as network
Enter, carry out the prediction of BP network, predicting the outcome of 92#-100～109 is as shown in table 3.
The predicting the outcome of table 3 92# product oil sample to be tested property
Table 4 is the root-mean-square error of 92# product oil sample to be tested property.
The predicated error of table 4 92# product oil sample to be tested
In order to contrast, the test experiments that conventional neural networks predict the single property of gasoline, test result such as table 5 institute are carried out
Show.
The experimental result of the single property of table 5 neural network prediction
Contrast table 4 and table 5 are it is found that in addition to indivedual property such as 10% evaporating temperature, the organon of this method prediction is pungent
The root-mean-square error of the properties such as alkane value, anti-knock index is superior to the neural net prediction method of routine.This shows, this method for
The prediction effect of gasoline key property is preferable, and energy batch forecast, and real-time is more excellent.
Claims (9)
1. a kind of rapid batch Forecasting Methodology of gasoline key property is it is characterised in that the method comprises the following steps：
(1) it is based on gasoline atlas of near infrared spectra, first Pretreated spectra is carried out to library of spectra sample and gasoline sample to be measured；
(2) pretreated spectroscopic data is carried out Fast Fourier Transform (FFT), obtain the coefficient matrices A of Fast Fourier Transform (FFT)；
(3) choose the front m row of A as the input of neutral net and Configuration network parameter；
(4) pretreated library of spectra gasoline, as training sample, carries out neural metwork training；
(5) using the neural network model training, batch forecast is carried out to multiple properties of sample to be tested and obtain the final of property
Evaluation of result.
2. a kind of rapid batch Forecasting Methodology of gasoline key property according to claim 1 is it is characterised in that step (1)
Described in Pretreated spectra include baseline correction, intercepting and vector normalization.
3. a kind of rapid batch Forecasting Methodology of gasoline key property according to claim 2 is it is characterised in that described base
Two basic points of line correction choose 6400cm-1And 9200cm-12 wave number points, baseline correction is calculated by following formula：
In formula, xiFor gasoline near infrared spectrum wave number；kxi+ b is 6400cm in spectrogram-1And 9200cm-1Two wave numbers
The linear equation of point, wherein k is this straight slope, and b is this Linear intercept；yiRepresent former spectrogram in wave number xiUnder absorbance；
After representing baseline correction, spectrogram is in wave number xiUnder absorbance.
4. a kind of rapid batch Forecasting Methodology of gasoline key property according to claim 2 is it is characterised in that intercept
4000cm-1～4800cm-1Interior spectrogram is modeling.
5. a kind of rapid batch Forecasting Methodology of gasoline key property according to claim 2 is it is characterised in that to spectrogram
When carrying out vector normalization, calculated by following formula：
In formula, XijRefer to i-th sample absorbance under wave number j；Refer to the absorbance values of i-th sample；M is wave number
The number of point；Xij *Absorbance under wave number j for i-th sample after expression vector normalization.
6. the rapid batch Forecasting Methodology of a kind of gasoline key property according to any one of Claims 1 to 5, its feature exists
Parameter in neutral net is configured using following：The number of hidden nodes is 30, and output node number is 6, hidden layer excitation function tansig,
Output layer excitation function purelin, trains function trainlm, performance function mse, performance arget value 0.05, learning coefficient
0.02.
7. the rapid batch Forecasting Methodology of a kind of gasoline key property according to any one of Claims 1 to 5, its feature exists
Before the coefficient matrix of the method selection Fast Fourier Transform (FFT), 20 row are as the input of neutral net, i.e. m=20.
8. the rapid batch Forecasting Methodology of a kind of gasoline key property according to any one of Claims 1 to 5, its feature exists
In the method, root-mean-square error is adopted for the final result evaluation of property j, i.e. RMSEj, calculated by following formula：
In formula, n is the number of gasoline sample to be measured；Refer to the predicted value of property j of i-th sample to be tested；xijRefer to i-th to be measured
The actual value of property j of sample.
9. the rapid batch Forecasting Methodology of a kind of gasoline key property according to any one of Claims 1 to 5, its feature exists
Include in described key property：Research octane number (RON), anti-knock index, density, 10% evaporating temperature, 50% evaporating temperature and end
Evaporate a little.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610740854.2A CN106407648A (en) | 2016-08-26 | 2016-08-26 | Rapid batch forecast method for key property of gasoline |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610740854.2A CN106407648A (en) | 2016-08-26 | 2016-08-26 | Rapid batch forecast method for key property of gasoline |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106407648A true CN106407648A (en) | 2017-02-15 |
Family
ID=58003411
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610740854.2A Pending CN106407648A (en) | 2016-08-26 | 2016-08-26 | Rapid batch forecast method for key property of gasoline |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106407648A (en) |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108415246A (en) * | 2018-02-06 | 2018-08-17 | 南京富岛信息工程有限公司 | A kind of crude oil nonlinear optimization blending method based on expansion initialisation range |
| CN113702328A (en) * | 2021-08-20 | 2021-11-26 | 广东省惠州市石油产品质量监督检验中心 | Method, device, equipment and storage medium for analyzing properties of product oil |
| US11702600B2 (en) | 2021-02-25 | 2023-07-18 | Marathon Petroleum Company Lp | Assemblies and methods for enhancing fluid catalytic cracking (FCC) processes during the FCC process using spectroscopic analyzers |
| US11802257B2 (en) | 2022-01-31 | 2023-10-31 | Marathon Petroleum Company Lp | Systems and methods for reducing rendered fats pour point |
| US11835450B2 (en) | 2021-02-25 | 2023-12-05 | Marathon Petroleum Company Lp | Methods and assemblies for determining and using standardized spectral responses for calibration of spectroscopic analyzers |
| US11891581B2 (en) | 2017-09-29 | 2024-02-06 | Marathon Petroleum Company Lp | Tower bottoms coke catching device |
| US11898109B2 (en) | 2021-02-25 | 2024-02-13 | Marathon Petroleum Company Lp | Assemblies and methods for enhancing control of hydrotreating and fluid catalytic cracking (FCC) processes using spectroscopic analyzers |
| US11905468B2 (en) | 2021-02-25 | 2024-02-20 | Marathon Petroleum Company Lp | Assemblies and methods for enhancing control of fluid catalytic cracking (FCC) processes using spectroscopic analyzers |
| US11905479B2 (en) | 2020-02-19 | 2024-02-20 | Marathon Petroleum Company Lp | Low sulfur fuel oil blends for stability enhancement and associated methods |
| US11970664B2 (en) | 2021-10-10 | 2024-04-30 | Marathon Petroleum Company Lp | Methods and systems for enhancing processing of hydrocarbons in a fluid catalytic cracking unit using a renewable additive |
| US11975316B2 (en) | 2019-05-09 | 2024-05-07 | Marathon Petroleum Company Lp | Methods and reforming systems for re-dispersing platinum on reforming catalyst |
| US12000720B2 (en) | 2018-09-10 | 2024-06-04 | Marathon Petroleum Company Lp | Product inventory monitoring |
| US12031676B2 (en) | 2019-03-25 | 2024-07-09 | Marathon Petroleum Company Lp | Insulation securement system and associated methods |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103728267A (en) * | 2014-01-17 | 2014-04-16 | 华东理工大学 | Method for correcting baseline of spectrogram in near infrared analysis of gasoline |
| CN104990893A (en) * | 2015-06-24 | 2015-10-21 | 南京富岛信息工程有限公司 | Gasoline octane number detecting method based on similar discriminance |
| CN105891141A (en) * | 2016-03-30 | 2016-08-24 | 南京富岛信息工程有限公司 | Method for rapidly measuring gasoline property data |
-
2016
- 2016-08-26 CN CN201610740854.2A patent/CN106407648A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103728267A (en) * | 2014-01-17 | 2014-04-16 | 华东理工大学 | Method for correcting baseline of spectrogram in near infrared analysis of gasoline |
| CN104990893A (en) * | 2015-06-24 | 2015-10-21 | 南京富岛信息工程有限公司 | Gasoline octane number detecting method based on similar discriminance |
| CN105891141A (en) * | 2016-03-30 | 2016-08-24 | 南京富岛信息工程有限公司 | Method for rapidly measuring gasoline property data |
Non-Patent Citations (4)
| Title |
|---|
| JUAN LI ET AL: ""Neural-network-based composite disturbance rejection control for a distillation column"", 《TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL》 * |
| 刘翠玲 等: "《近红外光谱技术在食品品质检测方法中的研究》", 31 January 2016 * |
| 李为民 等: ""基于红外光谱-神经网络预测汽油辛烷值"", 《炼油技术与工程》 * |
| 李智 等: ""基于傅立叶变换的人工神经网络近红外光谱定量分析法"", 《分析测试学报》 * |
Cited By (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11891581B2 (en) | 2017-09-29 | 2024-02-06 | Marathon Petroleum Company Lp | Tower bottoms coke catching device |
| CN108415246B (en) * | 2018-02-06 | 2020-12-15 | 南京富岛信息工程有限公司 | Crude oil nonlinear optimization blending method based on expanded initialization range |
| CN108415246A (en) * | 2018-02-06 | 2018-08-17 | 南京富岛信息工程有限公司 | A kind of crude oil nonlinear optimization blending method based on expansion initialisation range |
| US12000720B2 (en) | 2018-09-10 | 2024-06-04 | Marathon Petroleum Company Lp | Product inventory monitoring |
| US12031676B2 (en) | 2019-03-25 | 2024-07-09 | Marathon Petroleum Company Lp | Insulation securement system and associated methods |
| US11975316B2 (en) | 2019-05-09 | 2024-05-07 | Marathon Petroleum Company Lp | Methods and reforming systems for re-dispersing platinum on reforming catalyst |
| US11920096B2 (en) | 2020-02-19 | 2024-03-05 | Marathon Petroleum Company Lp | Low sulfur fuel oil blends for paraffinic resid stability and associated methods |
| US11905479B2 (en) | 2020-02-19 | 2024-02-20 | Marathon Petroleum Company Lp | Low sulfur fuel oil blends for stability enhancement and associated methods |
| US11905468B2 (en) | 2021-02-25 | 2024-02-20 | Marathon Petroleum Company Lp | Assemblies and methods for enhancing control of fluid catalytic cracking (FCC) processes using spectroscopic analyzers |
| US11898109B2 (en) | 2021-02-25 | 2024-02-13 | Marathon Petroleum Company Lp | Assemblies and methods for enhancing control of hydrotreating and fluid catalytic cracking (FCC) processes using spectroscopic analyzers |
| US11885739B2 (en) | 2021-02-25 | 2024-01-30 | Marathon Petroleum Company Lp | Methods and assemblies for determining and using standardized spectral responses for calibration of spectroscopic analyzers |
| US11860069B2 (en) | 2021-02-25 | 2024-01-02 | Marathon Petroleum Company Lp | Methods and assemblies for determining and using standardized spectral responses for calibration of spectroscopic analyzers |
| US11906423B2 (en) | 2021-02-25 | 2024-02-20 | Marathon Petroleum Company Lp | Methods, assemblies, and controllers for determining and using standardized spectral responses for calibration of spectroscopic analyzers |
| US11921035B2 (en) | 2021-02-25 | 2024-03-05 | Marathon Petroleum Company Lp | Methods and assemblies for determining and using standardized spectral responses for calibration of spectroscopic analyzers |
| US11835450B2 (en) | 2021-02-25 | 2023-12-05 | Marathon Petroleum Company Lp | Methods and assemblies for determining and using standardized spectral responses for calibration of spectroscopic analyzers |
| US11702600B2 (en) | 2021-02-25 | 2023-07-18 | Marathon Petroleum Company Lp | Assemblies and methods for enhancing fluid catalytic cracking (FCC) processes during the FCC process using spectroscopic analyzers |
| US12031094B2 (en) | 2021-02-25 | 2024-07-09 | Marathon Petroleum Company Lp | Assemblies and methods for enhancing fluid catalytic cracking (FCC) processes during the FCC process using spectroscopic analyzers |
| CN113702328B (en) * | 2021-08-20 | 2024-05-17 | 广东省惠州市石油产品质量监督检验中心 | Method, device, equipment and storage medium for analyzing properties of finished oil |
| CN113702328A (en) * | 2021-08-20 | 2021-11-26 | 广东省惠州市石油产品质量监督检验中心 | Method, device, equipment and storage medium for analyzing properties of product oil |
| US11970664B2 (en) | 2021-10-10 | 2024-04-30 | Marathon Petroleum Company Lp | Methods and systems for enhancing processing of hydrocarbons in a fluid catalytic cracking unit using a renewable additive |
| US11802257B2 (en) | 2022-01-31 | 2023-10-31 | Marathon Petroleum Company Lp | Systems and methods for reducing rendered fats pour point |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106407648A (en) | Rapid batch forecast method for key property of gasoline | |
| CN104990894B (en) | A kind of gasoline property detection method based on weighting absorbance and similar sample | |
| CN103364364B (en) | Crude oil property rapid detection method based on recombination prediction technology | |
| CN105388123A (en) | Method for predicting crude oil characteristic through near infrared spectrum | |
| CN104990893B (en) | A kind of gasoline octane value detection method based on similar differentiation | |
| He et al. | A novel adaptive algorithm with near-infrared spectroscopy and its application in online gasoline blending processes | |
| CN107958267A (en) | A kind of oil property Forecasting Methodology represented based on linear | |
| CN106529008B (en) | A kind of double integrated offset minimum binary modeling methods based on Monte Carlo and LASSO | |
| CN108416439B (en) | Oil refining process product prediction method and system based on variable weighted deep learning | |
| Khanmohammadi et al. | Feature selection strategies for quality screening of diesel samples by infrared spectrometry and linear discriminant analysis | |
| CN107632010B (en) | Method for quantifying steel sample by combining laser-induced breakdown spectroscopy | |
| CN102954946B (en) | By the method for infrared spectrum measurement sulfur content in crude oil | |
| CN116559110A (en) | Self-adaptive near infrared spectrum transformation method based on correlation and Gaussian curve fitting | |
| Alvarez et al. | On the application of petroleum feedstock modeling techniques for developing molecule-based models of hydrocarbon conversion processes | |
| EP3861320B1 (en) | Systems and methods for implicit chemical resolution of vacuum gas oils and fit quality determination | |
| CN106529680B (en) | A kind of multiple dimensioned extreme learning machine integrated modelling approach based on empirical mode decomposition | |
| Chen et al. | Rapid determination of viscosity and viscosity index of lube base oil based on near-infrared spectroscopy and new transformation formula | |
| Doicin et al. | Octane number estimation using neural networks | |
| CN109580918B (en) | Method for predicting molecular composition of naphtha | |
| CN102539370B (en) | Filtering method for fourier transform infrared spectrum online analysis of multi-component gas | |
| CN113782112A (en) | Method and device for determining petroleum fraction composition model | |
| CN105911016A (en) | Non-linear modeling method for spectral properties of crude oil | |
| CN104462751B (en) | Near infrared spectrum modeling method based on multi-element Gaussian fitting | |
| CN109724939B (en) | Method for predicting hydrogenated tail oil property by near infrared spectrum | |
| CN106644977A (en) | Spectral variable selection method based on bat algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170215 |
|
| RJ01 | Rejection of invention patent application after publication |