WO2020155865A1 - Procédé d'intégration d'un modèle de cokéfaction retardée - Google Patents

Procédé d'intégration d'un modèle de cokéfaction retardée Download PDF

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WO2020155865A1
WO2020155865A1 PCT/CN2019/124316 CN2019124316W WO2020155865A1 WO 2020155865 A1 WO2020155865 A1 WO 2020155865A1 CN 2019124316 W CN2019124316 W CN 2019124316W WO 2020155865 A1 WO2020155865 A1 WO 2020155865A1
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data
model
delayed coking
coking
fingerprint data
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Chinese (zh)
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钱锋
杨明磊
钟伟民
杜文莉
李智
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East China University of Science and Technology
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C10/00—Computational theoretical chemistry, i.e. ICT specially adapted for theoretical aspects of quantum chemistry, molecular mechanics, molecular dynamics or the like
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/10—Analysis or design of chemical reactions, syntheses or processes
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/70—Machine learning, data mining or chemometrics
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Definitions

  • the invention relates to the industrialization optimization of a refinery and chemical process, in particular to a delayed coking model integration method based on fingerprint data.
  • Delayed coking is the main device for processing residual oil, asphalt, slop oil and other heavy oil products in the refining and chemical process.
  • the common point of delayed coking and other forms of coking is the process of using thermal cracking to deeply react the residual oil into gas, gasoline, diesel, wax oil and solid product coke.
  • the difference between delayed coking and other coking methods is that the residual oil flows through the furnace tube of the heating furnace at a high flow rate, is heated to the temperature required for the reaction of 490-510°C, and then enters the coke drum, where it is carried by itself. Heat, cracking, condensation and other reactions.
  • the residence time in the furnace tube is very short, which delays the cracking, condensation, and decomposition reactions to the coke tower, avoiding (reducing) the coking of the furnace tube.
  • the coke produced by the reaction is focused in the tower, and the high-temperature oil vapor generated from the vapor volatilization line enters the fractionation tower and exchanges heat with the raw materials.
  • the heavy oil enters the heating furnace with the raw materials. After the components of the light oil are separated, gas is obtained. , Gasoline, diesel, wax oil and other products. What is commonly used in the technological process is one heating furnace with two coke towers, as shown in Figure 1.
  • the hot residual oil enters one of the coke towers, and when the generated coke accumulates to a certain height, it is decoked. At the same time, the hot residual oil is switched to another coke tower to ensure the continuous operation of the coke tower and the subsequent fractionation process.
  • the key to delayed coking process simulation is to simulate its reaction process and fractionation process.
  • the reaction kinetics of lumping raw materials and products is generally used to describe the reaction kinetics, and there are 6 lumps, 10 lumps, 12 lumps, and so on.
  • This kind of kinetic model requires more detailed analysis of raw materials, and accurate four-component information in addition to macroscopic properties.
  • there are often only a few properties analysis data for residual oil and the key four-component analysis is not listed in the routine analysis items, and the frequency is often half a month to once a month.
  • the present invention aims to provide a delayed coking model integration.
  • a delayed coking model integration method including the steps:
  • step (3) According to the delayed coking product data and the yield obtained in step (2), the fingerprint data of the full fraction delayed coking product is formed by fitting;
  • the integrated fractionation model realizes the separation of gas, liquefied gas, gasoline, diesel and wax oil.
  • the macroscopic properties of the raw material in step (1) include distillation range, density, sulfur content, residual carbon and nitrogen content; the components in the raw material are four components, which are saturated and aromatic. Points, gum and asphaltene.
  • step (2) is to use the kinetic model to calculate the yield of the coking product according to the macroscopic properties of the raw material and the operating conditions; a 10 lumped kinetic model is more preferred.
  • the operating conditions include reaction temperature and reaction pressure.
  • the coking product property data in step (3) includes gas composition, liquefied gas composition, gasoline real boiling point distillation data, diesel real boiling point distillation data, and wax oil real boiling point distillation data; the fitting uses ASPEN Crude oil characterization tool.
  • step (4) is to use the full fraction delayed coking product generated in step (3) as a raw material and input it to the rectification tower model to realize the separation of gas, liquefied gas, gasoline, diesel and wax oil.
  • the method further includes the step of correcting the fingerprint data described in step (1).
  • the macroscopic properties of the raw materials and the four-component data are combined with the differential evolution algorithm with triangular mutation to correct the fingerprint data.
  • the simulation and/or optimization includes delayed coking process model development, device size optimization, and production plan optimization model verification.
  • the present invention provides a reliable delayed coking device model.
  • Figure 1 is a simplified flow chart of the delayed coking process.
  • Figure 2 shows the correlation between the macroscopic properties and the four components.
  • Figure 3 is a simplified flow chart of fingerprint database calibration.
  • the inventor provides a delayed coking model integration method based on fingerprint data.
  • the method is based on 10 lumped delayed coking reaction kinetic models, actual industrial operating data (macro properties, four components, operating conditions, etc.), and distillation tower models.
  • the fingerprint database method is used to correlate the macro properties with four component analysis. , To form a practical kinetic model for industrial sites, and to delump the products at the same time, so that the delayed coking reaction can be lumped up to the full fraction data, and it can be used as a fractionation tower to simulate operation and realize the integration of delayed coking device models. Level optimization applications provide reliable model support.
  • the method for integrating delayed coking models based on fingerprint data includes the following steps:
  • Raw material fingerprint data Establish the fingerprint data association between the macroscopic properties of the raw materials in the delayed coking unit of the refinery and the content of the four components in the raw materials. Input the macroscopic properties of the raw materials to obtain the four-component information of the raw materials;
  • step 2 On the basis of the correlation obtained in step 1, use the ten-lumped kinetic model to calculate the yield of coking products according to the properties of raw materials and operating conditions;
  • Product fingerprint data According to the coking product data collected on site, it mainly includes gas composition, liquefied gas composition, gasoline real boiling point distillation data, diesel real boiling point distillation data, and wax oil real boiling point distillation data, using the ASPEN crude oil characterization tool to perform fitting, combined with the second step to get The yield of the coking reaction is formed to form fingerprint data from the total product to the whole fraction of the coking reaction;
  • step 4 Integrated fractionation model.
  • the full fraction delayed coking product generated in step 3 is used as a raw material and input to the rectification tower model to realize the separation simulation of gas, liquefied gas, gasoline, diesel and wax oil.
  • the method further includes the steps:
  • Delayed coking raw materials are residual oil, asphalt and oil slurry, etc.
  • the reaction is mostly based on cracking, which converts heavy, long-chain hydrocarbons into gas, liquefied gas, gasoline, diesel, wax oil and coke. Since most of the raw materials are above C40, the cracking reaction mechanism is extremely complicated.
  • a lumped method is used to divide the raw materials into saturated components, aromatic components, gums and asphaltenes. Its response characteristics.
  • the actual industrial process generally only has macro characteristics analysis, which mainly includes distillation range, density, sulfur content, residual carbon, and nitrogen content. A large number of studies have shown that there is a quantitative relationship between the macroscopic properties of the material and its four components.
  • the main ones are: (1) The higher the boiling point of the raw material, the higher the proportion of gum and asphaltene. On the contrary, the higher the proportion of saturation. (2) Residual carbon mainly exists in asphaltenes and gums; (3) Density reflects the heaviness of raw materials, and the relationship with the four components is similar to the boiling point; (4) Sulfur content is more heavily distributed in asphaltenes; (5) The nitrogen content is similar to the sulfur content.
  • the invention adopts a fingerprint data method to establish a quantitative correlation between distillation range, density, sulfur content, residual carbon, nitrogen content and the four components.
  • the fingerprint data of the present invention is a vector processing method, which expresses the relationship between the output in a complex system and one or more variables in a linear array, as shown in formula (1).
  • y is the four-component value
  • y 0 is the four-component reference value
  • k is the rate of change
  • ⁇ x is the macroscopic property change value
  • the general formula (1) is the general formula for calculating the four-component based on the macroscopic property.
  • Figure 2 shows the correlation between the macroscopic properties and the four components in the fingerprint data.
  • the quantitative correlation formula of the four components in the fingerprint data is as follows:
  • Sat, Aro, Res, and Asp represent saturated components, aromatic components, gums and asphaltenes, respectively.
  • the subscript 0 represents the reference value
  • ⁇ represents the deviation value from the reference property.
  • the main purpose of this step is to use the fingerprint data established in the previous step to realize the integration of industrial field macro data and lumped dynamics models.
  • the main contents include: field data collection, data reconciliation, model interface development and model calculation.
  • On-site data collection In the actual production process, most factories will use laboratory analysis data to record material property data, and provide the corresponding data points to collect data.
  • the invention uses an industrial field database system to collect raw material property data and store it in a local database.
  • the data to be collected mainly include the distillation range, density, nitrogen content, sulfur content and residual carbon data of the delayed coking mixed raw material.
  • Model interface development After sorting out the sample data, it is necessary to develop a corresponding interface to send the raw material property data to the kinetic model to realize automatic data transmission.
  • the ten ensemble delayed coking kinetic model of the present invention is developed using the aspen platform, and the interface program is developed using vb.net.
  • Each data is transmitted by compiling specific fields and using the data table in the aspen software. The data fields of various properties are shown in Table 1. .
  • n 10 ⁇ 5; preferably 10 ⁇ 3
  • the product fingerprint data component table the data of gas and liquefied gas can be directly obtained from the average of the composition; gasoline, diesel and wax oil are fitted to the distillation range curve through polynomials to obtain the proportions of the components in different temperature ranges, and then the corresponding Fingerprint data.
  • T 30% (T 10% + T 50% )/2 (12)
  • T 70% (T 50% + T 90% )/2 (13)
  • Y is the cumulative volume yield
  • A, B, C, D, E, F are polynomial parameters (no specific meaning)
  • x is the temperature.
  • the main purpose of this step is to use the fingerprint data established in the previous step to realize the integration of industrial field macro data with the main fractionation tower model.
  • the main contents include: field data collection, data reconciliation, model interface development and model calculation.
  • Field data collection use industrial field database system to collect real-time data and laboratory analysis data.
  • Real-time data includes fractionation tower operating conditions, such as reflux ratio, tower top pressure, sensitive plate temperature, etc.
  • laboratory analysis data includes gas and liquefied gas molecular composition data, gasoline, diesel and wax oil distillation data;
  • Model interface development After finishing the sample data, it is necessary to develop a corresponding interface to send the raw material property data to the distillation tower model to realize automatic data transmission. Refer to the delayed coking kinetic model using the interface program, and use the data table in the aspen software for transmission. Each component field is the component name.
  • Model calculation On the basis of the prepared real-time data and analysis data, it is automatically transmitted to the distillation tower model, and run in the aspen software to obtain the composition and flow of gas and liquefied gas; detailed fractions of gasoline, diesel and wax oil Composition of data and traffic.
  • it further includes:
  • Fingerprint data is the core of raw material characterization. After the oil refinery is switched to crude oil, the material properties are prone to large fluctuations. The fingerprint data needs to be re-calibrated to ensure the accuracy of the property correlation.
  • Fingerprint data correction is actually an optimization problem. Select the four-component prediction value and the minimum variance of the collected data from the industrial field as the goal, and convert the fingerprint data determination process into a function optimization problem to solve, namely:
  • the decision variable x includes the four-component correlation coefficients of various properties, and x actual and x calculate respectively represent the four-component data of raw materials calculated through actual industrial analysis and fingerprint database. Aiming at this type of optimization goal, the present invention uses a differential evolution algorithm with triangle mutation to solve the problem.
  • Differential evolution algorithm (differential evolution, DE) is a population-based random search algorithm, which has the characteristics of simple structure, fast convergence speed, and high robustness.
  • the mutation mechanism of the algorithm that is, the method of generating offspring is:
  • r' is the newly generated offspring individual
  • r 1 , r 2 , and r 3 are three different parent individuals randomly selected in the population
  • F is the differential evolution operator, which is generally a constant.
  • the present invention selects an improved differential evolution algorithm with triangular mutation. This method is proven to have significant effects in improving the convergence speed of the algorithm.
  • the improved mutation strategy can be expressed as:
  • r′ (r 1 +r 2 +r 3 )/3+(p 2 -p 1 )(r 1 -r 2 )+(p 3 -p 2 )(r 2 -r 3 )+(p 1- p 3 )(r 3 -r 1 ) (17)
  • the present invention provides an effective and reliable idea for the simulation and integration of the entire delayed coking process, and at the same time significantly improves the applicability of the model in industry.
  • the distillation range, density, sulfur content, residual carbon, nitrogen content and the four components are quantitatively correlated:
  • y 0 is the benchmark
  • k is the rate of change
  • ⁇ x is the amount of parameter change.
  • CCR Carbon Residue
  • Sat, Aro, Res, and Asp represent saturated components, aromatic components, gums and asphaltenes, respectively.
  • the subscript 0 represents the reference value
  • the reference values of Sat, Aro, Res and Asp are 0.5051, 0.1603, 0.1079 and 0.2266 respectively.
  • ⁇ represents the deviation value from the reference property (Table 6).
  • the gas mainly contains hydrogen sulfide, hydrogen, C1, C2 and C3, and the liquefied gas mainly contains C3 and C4.
  • the distillation range of gasoline is C5-230°C
  • the distillation range of diesel is 180°C-380°C
  • the distillation range of wax oil is 350°C- Final boiling point.
  • T 30% (T 10% + T 50% )/2 (12)
  • T 70% (T 50% + T 90% )/2 (13)
  • Y is the cumulative volume yield
  • A, B, C, D, E, F are polynomial parameters (no specific meaning)
  • x is the temperature.
  • the actual average data of raw materials for a certain week in the delayed coking unit of a refinery The macroscopic properties of the raw materials are: distillation range (IBP: 555; 5%: 559; 10%: 562; 30%: 584; 50%: 606; 70) %: 637; 90%: 735;), density (20° C.) is 0.9748, residual carbon is 20%, sulfur content is 1.9%, nitrogen content is 7920.1 ppm.
  • the corrected four-component output data obtained by the algorithm is shown in Table 5, and the corrected fingerprint data is shown in Table 6.
  • the above method is based on fingerprint data to establish the macroscopic properties of raw materials, including real boiling point distillation data, density, sulfur content and residual carbon, and quantitative correlation with saturated content, aromatic content, gum and asphaltene content, realizing industrial field data and delay
  • fingerprint data to establish the macroscopic properties of raw materials, including real boiling point distillation data, density, sulfur content and residual carbon, and quantitative correlation with saturated content, aromatic content, gum and asphaltene content, realizing industrial field data and delay
  • the integration of ten lumped coking models at the same time, combined with the analysis data of the coking product laboratory, the fingerprint data association between the reaction products in the lumped kinetics and the detailed composition of the actual plant products is established to realize the relationship between the kinetic model and the distillation model integrated.
  • the collected industrial field data needs to be processed by data reconciliation technology, combined with the improved differential evolution algorithm, to correct the raw material fingerprint data, so that the fingerprint data can accurately predict the four-component information on the basis of macroscopic properties.

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Abstract

L'invention concerne un procédé d'intégration d'un modèle de cokéfaction retardée. Le procédé comporte les étapes suivantes : (1) l'association des données d'empreinte digitale des propriétés macroscopiques d'une charge d'alimentation avec des données d'empreinte digitale de contenu en composants d'une charge d'alimentation utilisée dans une unité de cokéfaction retardée ; (2) l'intégration dans un modèle de réaction de cokéfaction pour obtenir une production de produits résultant d'une cokéfaction ; (3) la mise en œuvre d'un ajustement, en fonction des données de produit de coke et de la production obtenue à l'étape (2), pour obtenir des données d'empreinte digitale relatives à une gamme complète de produits résultant d'une cokéfaction retardée ; et (4) l'intégration dans un modèle de fractionnement pour réaliser la séparation de gaz, de gaz liquéfiés, de gazole, de mazout et d'huile de paraffine.
PCT/CN2019/124316 2019-02-01 2019-12-10 Procédé d'intégration d'un modèle de cokéfaction retardée Ceased WO2020155865A1 (fr)

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CN109817287B (zh) * 2019-02-01 2023-08-11 华东理工大学 一种延迟焦化模型集成方法
CN115831250B (zh) * 2023-02-20 2023-06-06 新疆独山子石油化工有限公司 一种延迟焦化反应模型构建方法及装置、存储介质及设备
CN115862759B (zh) * 2023-02-20 2023-06-06 新疆独山子石油化工有限公司 一种延迟焦化反应优化方法及装置、存储介质及设备
CN116543851B (zh) * 2023-05-05 2025-05-27 西南石油大学 一种基于四组分模型的稠油裂解转化生焦预测方法
CN116609161A (zh) * 2023-05-24 2023-08-18 山东联化新材料有限责任公司 一种研究焦化不完全焦的分析方法

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