CN117594144A - Modeling method of crude oil direct catalytic cracking process at molecular level - Google Patents
Modeling method of crude oil direct catalytic cracking process at molecular level Download PDFInfo
- Publication number
- CN117594144A CN117594144A CN202311632639.7A CN202311632639A CN117594144A CN 117594144 A CN117594144 A CN 117594144A CN 202311632639 A CN202311632639 A CN 202311632639A CN 117594144 A CN117594144 A CN 117594144A
- Authority
- CN
- China
- Prior art keywords
- model
- catalytic cracking
- crude oil
- direct catalytic
- reaction
- 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
- 238000004523 catalytic cracking Methods 0.000 title claims abstract description 96
- 238000000034 method Methods 0.000 title claims abstract description 96
- 239000010779 crude oil Substances 0.000 title claims abstract description 87
- 230000008569 process Effects 0.000 title claims abstract description 64
- 238000006243 chemical reaction Methods 0.000 claims abstract description 63
- 239000003921 oil Substances 0.000 claims abstract description 30
- 238000005516 engineering process Methods 0.000 claims abstract description 16
- 238000012795 verification Methods 0.000 claims abstract description 7
- 238000009826 distribution Methods 0.000 claims description 50
- 238000004821 distillation Methods 0.000 claims description 37
- 239000000295 fuel oil Substances 0.000 claims description 23
- 238000004458 analytical method Methods 0.000 claims description 22
- 238000000926 separation method Methods 0.000 claims description 21
- 238000011020 pilot scale process Methods 0.000 claims description 19
- 238000005457 optimization Methods 0.000 claims description 14
- 238000005520 cutting process Methods 0.000 claims description 10
- 239000007789 gas Substances 0.000 claims description 10
- 239000000126 substance Substances 0.000 claims description 9
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 claims description 6
- 238000005315 distribution function Methods 0.000 claims description 6
- 238000004519 manufacturing process Methods 0.000 claims description 6
- 238000005070 sampling Methods 0.000 claims description 6
- 238000012360 testing method Methods 0.000 claims description 6
- 238000012614 Monte-Carlo sampling Methods 0.000 claims description 5
- 239000000571 coke Substances 0.000 claims description 5
- 238000012937 correction Methods 0.000 claims description 5
- 239000003502 gasoline Substances 0.000 claims description 5
- 239000002002 slurry Substances 0.000 claims description 4
- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 claims description 3
- 230000003247 decreasing effect Effects 0.000 claims description 3
- -1 diesel Substances 0.000 claims description 3
- 229910052757 nitrogen Inorganic materials 0.000 claims description 3
- 229910052717 sulfur Inorganic materials 0.000 claims description 3
- 239000011593 sulfur Substances 0.000 claims description 3
- 230000004044 response Effects 0.000 claims description 2
- 239000000047 product Substances 0.000 description 58
- 239000002283 diesel fuel Substances 0.000 description 10
- 238000005336 cracking Methods 0.000 description 9
- 239000003350 kerosene Substances 0.000 description 7
- 239000002994 raw material Substances 0.000 description 7
- 150000001336 alkenes Chemical class 0.000 description 6
- 239000002245 particle Substances 0.000 description 6
- 229930195733 hydrocarbon Natural products 0.000 description 5
- 150000002430 hydrocarbons Chemical class 0.000 description 5
- 239000004215 Carbon black (E152) Substances 0.000 description 4
- 241000183024 Populus tremula Species 0.000 description 4
- 238000004088 simulation Methods 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 239000012263 liquid product Substances 0.000 description 3
- 238000004230 steam cracking Methods 0.000 description 3
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 2
- 230000004913 activation Effects 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 2
- 150000004945 aromatic hydrocarbons Chemical class 0.000 description 2
- 239000003054 catalyst Substances 0.000 description 2
- 238000012512 characterization method Methods 0.000 description 2
- 239000007795 chemical reaction product Substances 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000001704 evaporation Methods 0.000 description 2
- 230000008020 evaporation Effects 0.000 description 2
- 239000007791 liquid phase Substances 0.000 description 2
- 241000196324 Embryophyta Species 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 230000002776 aggregation Effects 0.000 description 1
- 150000001335 aliphatic alkanes Chemical class 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 229910052799 carbon Inorganic materials 0.000 description 1
- 238000004517 catalytic hydrocracking Methods 0.000 description 1
- 239000010724 circulating oil Substances 0.000 description 1
- 150000001924 cycloalkanes Chemical class 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006386 neutralization reaction Methods 0.000 description 1
- JRZJOMJEPLMPRA-UHFFFAOYSA-N olefin Natural products CCCCCCCC=C JRZJOMJEPLMPRA-UHFFFAOYSA-N 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 239000003208 petroleum Substances 0.000 description 1
- 239000012071 phase Substances 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 230000008929 regeneration Effects 0.000 description 1
- 238000011069 regeneration method Methods 0.000 description 1
Classifications
-
- 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
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computing arrangements based on specific mathematical models
- G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Computing Systems (AREA)
- Crystallography & Structural Chemistry (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Life Sciences & Earth Sciences (AREA)
- General Physics & Mathematics (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Algebra (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Health & Medical Sciences (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Probability & Statistics with Applications (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Computational Mathematics (AREA)
- Analytical Chemistry (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Production Of Liquid Hydrocarbon Mixture For Refining Petroleum (AREA)
Abstract
The invention provides a modeling method of a crude oil direct catalytic cracking process at a molecular level, which comprises the following steps: s110, establishing a structure-oriented lumped model of the mixed molecular level of the raw oil based on a molecular structure reconstruction technology; s120, establishing a crude oil direct catalytic cracking process model of a molecular level corresponding to the pilot plant based on the lumped reaction dynamics model; s130, carrying out repeatability and accuracy verification on a crude oil direct catalytic cracking process model; and S140, optimizing key process parameters by using a crude oil direct catalytic cracking process model. The invention establishes a lumped dynamic model of a mixed molecular level, and establishes a crude oil direct catalytic cracking model of a molecular level by utilizing a two-section riser catalytic cracking technology, thereby researching and optimizing the relationship between key process parameters of the crude oil direct catalytic cracking, and solving the problems that the accurate crude oil direct catalytic cracking model with high solving efficiency is difficult to establish at present.
Description
Technical Field
The invention belongs to the technical field of petrochemical production, and particularly relates to a modeling method of a molecular-level crude oil direct catalytic cracking process.
Background
In recent years, with the decline of domestic fuel oil demand, more and more petroleum is turned to production of chemical raw materials. The current most representative "oil conversion" process path mainly surrounds two core technologies: steam cracking and catalytic cracking. Most new refineries convert heavy oil into naphtha through a hydrocracking unit as a raw material for producing olefins by steam cracking, and some refineries produce olefins and aromatic hydrocarbons by deploying a heavy oil catalytic cracking unit. But in either way, the process route is greatly prolonged, and the investment cost and the operation cost are increased. The direct catalytic cracking (one-step conversion of crude oil, OSCO for short) of crude oil is an emerging process route, the crude oil is converted into chemicals in one step through a catalytic cracking device, and the reaction temperature is 200 ℃ lower than that of steam cracking under the condition of maintaining the same olefin yield, so that the energy consumption and the equipment investment are obviously reduced. In the future, direct catalytic cracking of crude oil will be one of the important ways of achieving the aim of carbon neutralization in China.
The raw materials of common catalytic cracking are mostly heavy oil such as vacuum wax oil, the molecules have longer carbon chains, the cracking activation energy is lower, and the cracking can be carried out under the condition of about 540 ℃ to generate gasoline and diesel oil. Compared with the reaction process of ordinary catalytic cracking, the direct catalytic cracking of crude oil produces more light hydrocarbon fractions in the raw materials, namely naphtha, kerosene and diesel oil fractions. The light hydrocarbons are mainly composed of small molecular alkanes and cycloalkanes, are relatively stable, have high cracking activation energy and are difficult to react under the common catalytic cracking reaction condition. Therefore, how to solve the problem of cracking reaction of the raw material is one of the current bottleneck problems.
Chinese patent CN1118539C discloses a two-stage riser catalytic cracking technology, which mainly combines a two-stage riser with a conventional catalyst, so that fresh raw materials react with circulating oil which is easier to coke, and the average performance and single pass conversion rate of the catalyst in the riser are improved, which provides a concept for solving the problem of cracking reaction of crude oil direct catalytic cracking raw materials. The light components and the heavy components can be well separated by two-stage preheating of crude oil, so that the light components which are difficult to crack independently enter the second section of lifting pipe for reaction, the cracking performance of the light components is improved by severe cracking conditions (such as higher reaction temperature, higher catalyst-oil ratio and the like), and the heavy components enter the first section of lifting pipe for reaction under conventional reaction conditions. Because the direct catalytic cracking of crude oil is aimed at mostly small molecular olefins and aromatics.
Although the conventional lumped dynamics model can achieve higher accuracy in product distribution, it is difficult to meet the requirement of fully describing light hydrocarbon fractions such as naphtha, kerosene and diesel, which means that if the conventional lumped dynamics model is used for direct catalytic cracking of crude oil, the process of generating small molecular olefins and aromatic hydrocarbons is difficult to describe. In contrast, the catalytic cracking reaction kinetic model at the molecular level has more excellent capabilities in predicting crude oil cracking product distribution, calculating crude oil cracking product properties and the like, but as the fraction becomes heavier, the corresponding molecules are at geometric level, which greatly improves the complexity of the model and reduces the solving efficiency. How to build a hybrid molecular lumped kinetic model with the predictive capability of existing molecular level while retaining higher solving efficiency is one of the current bottleneck problems.
Disclosure of Invention
The invention aims to provide a modeling method of a crude oil direct catalytic cracking process at a molecular level, and the modeling method provided by the invention has the advantages that the accuracy of a crude oil direct catalytic cracking model established by the modeling method is high, and the solving efficiency is high.
The invention provides a modeling method of a crude oil direct catalytic cracking process at a molecular level, which comprises the following steps:
s110, establishing a structure-oriented lumped model of the mixed molecular level of the raw oil based on a molecular structure reconstruction technology;
the level of the mixed molecules is as follows: describing the light fraction by using real molecules, and describing the heavy oil fraction by using virtual components;
the distillation point of the light fraction is between the initial distillation point and the staged preheating cutting temperature, and the distillation point of the heavy oil fraction is between the staged preheating cutting temperature and the final distillation point;
s120, establishing a crude oil direct catalytic cracking process model of a molecular level corresponding to the pilot plant based on the lumped reaction dynamics model;
s130, carrying out repeatability and accuracy verification on the crude oil direct catalytic cracking process model;
and S140, optimizing key process parameters by using the crude oil direct catalytic cracking process model.
Preferably, the step S110 includes:
s111, generating raw oil molecules according to the raw oil chemical analysis data based on a Monte Carlo sampling algorithm;
s112, determining the distribution type and parameters of probability density functions of different molecules based on the properties of different groups;
s113, sampling and generating a group of virtual component groups based on the probability density function, and calculating the properties of the virtual component groups;
s114, when the difference value between the property of the virtual molecular set and the analysis data result does not meet the preset condition, the distribution function is continuously optimized through a global optimization algorithm until the difference value between the property of the virtual molecular set and the analysis data result meets the preset condition;
and S115, when the difference between the nature of the virtual molecular set and the analysis data result meets the preset condition, retaining the virtual molecular set to obtain a structure-oriented lumped model of the mixed molecular level of the raw oil mixed fraction.
Preferably, in the step S111, the chemical analysis data of the raw oil includes one or more of elemental composition, molar mass, distillation range, PONA, sulfur content and nitrogen content.
Preferably, in the step S113, the properties of the virtual fraction set include one or more of density, initial distillation point, final distillation point, 10% distillation point, 30% distillation point, 50% distillation point, 70% distillation point, and 90% distillation point;
the preset conditions in step S114 and step S115 are 3 to 10%.
Preferably, in the step S120, the lumped reaction kinetic model uses twenty-one lumped reaction kinetic model.
Preferably, the step S120 specifically includes:
s121, separating the light fraction and the heavy oil fraction of the raw oil based on a staged preheating treatment technology, and feeding the light fraction and the heavy oil fraction into a corresponding reactor reaction area for reaction;
s122, establishing a reaction model based on direct catalytic cracking pilot-scale data of the raw oil;
s123, establishing a product separation model based on the product characteristics of the reaction model;
s124, when the difference value between the product distribution result of the separation model and the pilot scale data result meets a preset condition, reserving a correction factor set;
and S125, correcting the reaction model when the difference value between the product distribution result of the separation model and the pilot test data result does not meet the preset condition, until the difference value between the product distribution result of the separation model and the pilot test data result meets the preset condition.
Preferably, the reactor in the step S121 is a two-stage riser catalytic cracking reactor;
the separated light fraction enters a first section of riser for reaction, and the heavy oil fraction enters a second section of riser for reaction.
Preferably, the direct catalytic cracking pilot-scale data of the raw oil in step S122 includes an initial working condition adopted in the actual production process and a distribution of a flow product corresponding to the initial working condition, where the flow product includes one or more of dry gas, liquefied gas, gasoline, diesel oil, slurry oil and coke;
the preset conditions in step S124 and step S125 are 3 to 10%.
Preferably, the correction of the positive response model in step S125 specifically includes:
based on the value in the empirical formula, adjusting the reaction kinetic parameters in the crude oil direct catalytic cracking model of the molecular level in a preset value increasing or decreasing mode;
the preset increment or decrement range is 0.02-0.08.
Preferably, in step S140, the model key process parameters are optimized based on the life cycle optimization strategy and the multi-objective optimization algorithm.
The invention provides a modeling method of a crude oil direct catalytic cracking process at a molecular level, which comprises the following steps: s110, establishing a structure-oriented lumped model of the mixed molecular level of the raw oil based on a molecular structure reconstruction technology; the level of the mixed molecules is as follows: describing the light fraction by using real molecules, and describing the heavy oil fraction by using virtual components; the distillation point of the light fraction is between the initial distillation point and the staged preheating cutting temperature, and the distillation point of the heavy oil fraction is between the staged preheating cutting temperature and the final distillation point; the method comprises the steps of carrying out a first treatment on the surface of the S120, establishing a crude oil direct catalytic cracking process model of a molecular level corresponding to the pilot plant based on the lumped reaction dynamics model; s130, carrying out repeatability and accuracy verification on the crude oil direct catalytic cracking process model; and S140, optimizing key process parameters by using the crude oil direct catalytic cracking process model. The invention establishes a lumped dynamic model of a mixed molecular level based on the molecular reconstruction technology, and establishes a crude oil direct catalytic cracking model of a molecular level by utilizing a two-section riser catalytic cracking technology, thereby researching and optimizing the relationship between key technological parameters of crude oil direct catalytic cracking, and solving the problem that an accurate and high-solving-efficiency crude oil direct catalytic cracking model of a mixed molecular level is difficult to establish at present.
Compared with the prior art, the invention has the following beneficial effects.
1. The invention is based on a molecular reconstruction technology, and utilizes crude oil fractional preheating to establish structure-oriented lumped of a mixed molecular level, and uses molecules for representing naphtha, kerosene and diesel fractions, thereby solving the problem that the light hydrocarbon fraction can not be fully described by the traditional lumped, simultaneously retaining the traditional lumped form of the heavy oil fraction, and solving the problems of complex dynamic model and low solving efficiency of the molecular level lumped.
2. The invention establishes a crude oil direct catalytic cracking device model based on a two-section riser catalytic cracking technology and twenty-one lumped reaction dynamics model, carries out partition reaction on different fractions of crude oil, utilizes the model to explore the influence of different reaction conditions of different reaction areas on product distribution, and solves the problem of time and labor consumption when exploring complex variable relationships in the traditional experimental mode.
3. Based on a life cycle strategy, the optimal parameter combination of the operable variables for realizing the optimization target is obtained through automatic searching and manual intervention of the optimization algorithm, and the method has great significance in assisting engineers in operation and improving enterprise benefits.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic modeling diagram of the molecular level of an crude oil fraction catalytic cracking process in example 1 of the present invention;
FIG. 2 is a schematic flow diagram of a modeling method of a crude oil direct catalytic cracking process at a molecular level in the present invention;
FIG. 3 is a flow chart of step S110 in the present invention;
fig. 4 is a flow chart of step S120 of the present invention.
Detailed Description
The invention provides a modeling method of a crude oil direct catalytic cracking process at a molecular level, which comprises the following steps:
s110, establishing a structure-oriented lumped model of the mixed molecular level of the raw oil based on a molecular structure reconstruction technology;
the level of the mixed molecules is as follows: describing the light fraction by using real molecules, and describing the heavy oil fraction by using virtual components;
in the invention, the distillation point of the light fraction is between the initial distillation point and the staged preheating cutting temperature, and the distillation point of the heavy oil fraction is between the staged preheating cutting temperature and the final distillation point; the dividing temperatures of the staged preheating are different, and the dividing ranges of the light fraction and the heavy fraction are different. The light fraction is a gas phase generated in the staged preheating system and covers naphtha, kerosene and diesel oil fractions, the heavy oil fraction is a liquid phase generated in the final stage of the staged preheating system and is a fraction larger than diesel oil; in the example of FIG. 1, a two-stage preheating system is adopted, and the cutting temperature is 210 ℃ and 290 ℃, so that the light fraction ranges from an initial distillation point to 290 ℃ and the heavy fraction ranges from 290 ℃ to a final distillation point.
S120, establishing a crude oil direct catalytic cracking process model of a molecular level corresponding to the pilot plant based on the lumped reaction dynamics model;
s130, carrying out repeatability and accuracy verification on the crude oil direct catalytic cracking process model;
and S140, optimizing key process parameters by using the crude oil direct catalytic cracking process model.
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other. In addition, unless explicitly defined or contradicted by context, the particular steps included in the methods described herein need not be limited to the order described, but may be performed in any order or in parallel.
FIG. 2 illustrates a flow of a modeling method 100 for a molecular level crude oil direct catalytic cracking process according to an exemplary embodiment of the invention.
As shown in fig. 2, the execution of the modeling method 100 of the crude oil direct catalytic cracking process at the molecular level includes the steps of:
s110, building a structure-oriented lumped model of a hybrid molecular level based on a molecular structure reconstruction technology;
s120, establishing a crude oil direct catalytic cracking process model of a molecular level corresponding to the pilot plant based on the lumped dynamics model;
s130, carrying out repeatability and accuracy verification on the crude oil direct catalytic cracking process model; and
and S140, optimizing key process parameters by using the crude oil direct catalytic cracking process model.
It should be understood that the steps illustrated in modeling method 100 for a molecular level crude oil direct catalytic cracking process are not exclusive and that modeling method 100 for a molecular level crude oil direct catalytic cracking process may also include additional steps not illustrated and/or may omit illustrated steps, as the scope of the invention is not limited in this respect. Step S110 to step S140 are described in detail below with reference to fig. 3 to 4.
S110
For a reaction dynamics model of catalytic cracking, light hydrocarbon fractions such as naphtha, kerosene and diesel oil cannot be fully described by traditional lumped dynamics, and the chemical properties and distribution of products can be accurately predicted by adopting the lumped dynamics at a molecular level, but the model has high complexity and low solving efficiency. The invention adopts the structure-oriented lumped of the mixed molecular level, namely, the description of lighter fractions such as naphtha, diesel oil and the like is carried out by real molecules, while the heavy oil fraction is still described according to traditional virtual components, and a crude oil direct catalytic cracking device model corresponding to a pilot plant is established according to twenty-one lumped reaction dynamics model divided by chemical property composition and product distribution of the product.
In some embodiments, step S110 includes:
s111, generating raw oil molecules according to the raw oil chemical analysis data (element composition, molar mass, distillation range and the like) based on a Monte Carlo sampling algorithm;
s112, determining the distribution type and parameters of probability density functions of different molecules based on the properties of different groups, so as to determine the occurrence probability of the different molecules, and sampling through orthogonal analysis;
s113, sampling based on the distribution function to generate a group of virtual component groups and calculating the properties of the virtual component groups;
s114, when the difference value between the virtual molecular set property and the analysis data result does not meet the preset condition, the distribution function is continuously optimized through a global optimization algorithm until the difference value between the virtual molecular set property and the analysis data result meets the preset condition;
and S115, when the difference between the nature of the virtual molecular set and the analysis data result meets the preset condition, reserving the virtual molecular set to obtain a structure-oriented lumped model of the raw oil mixed fraction at the molecular level.
Specifically, in step S111, crude oil analysis data such as elemental composition, molar mass, distillation range, PONA, sulfur content, nitrogen content, etc. are obtained through a laboratory, molecular sampling and molecular reconstruction are performed through a monte carlo sampling algorithm, and thousands of molecules are finally generated. The algorithm samples based on probability density functions for each molecule, compressing the thousands of molecules generated to hundreds, while ensuring that the relevant properties remain consistent with laboratory data.
In step S112, the distribution type and parameters of the probability distribution function of the molecules are initialized by the radical properties of the molecules.
In step S113, sampling is performed using the initialized probability density function parameter, the monte carlo sampling algorithm and the orthogonal analysis, to obtain a set of virtual component sets, and detailed properties are calculated.
In step S114, when the difference between the properties of the virtual molecular set and the analysis data result does not satisfy the preset condition, it is indicated that the initialized probability density function parameter cannot keep the properties before and after the molecular reconstruction consistent. The distribution function can be continuously optimized through a global optimization algorithm until the difference between the properties of the virtual molecular set and the analysis data result meets the preset condition.
Specifically, the properties of the molecules before and after reconstruction may be density, initial distillation point, final distillation point, 10% distillation point, 30% distillation point, 50% distillation point, 70% distillation point, 90% distillation point, etc. As an example, the difference satisfying the preset condition means that the difference is smaller than a preset value, and the preset value ranges from about 3% to about 10%. It should be noted that the above data are only for explaining the technical solution of the present invention, and do not limit the protection scope of the present invention.
In step S115, when the difference between the properties of the virtual molecular set and the analysis data results meets the preset condition, it is indicated that the current probability density function parameter can keep the properties of the molecular reconstruction before and after the reconstruction consistent. Data can be summarized to obtain a structure-oriented lumped model at a molecular level.
S120
After the structure-oriented lumped model of the hybrid molecular level is established based on the molecular structure reconstruction technique in step S110, a crude oil direct catalytic cracking process model of the molecular level corresponding to the pilot plant is established based on the lumped dynamics model in step S120.
In some embodiments, step S120 includes:
s121, feeding different fractions of raw oil into corresponding reactor reaction areas for reaction based on a staged preheating treatment technology;
s122, establishing a reaction model based on initial working conditions of the crude oil direct catalytic cracking pilot-scale data and default reaction kinetic parameters;
s123, establishing a product separation model based on the product characteristics of the reaction model;
s124, when the difference value between the product distribution result of the separation model and the pilot scale data result meets a preset condition, reserving a correction factor set;
and S125, correcting the reaction model when the difference value between the product distribution result of the separation model and the pilot test data result does not meet the preset condition, until the difference value between the product distribution result of the separation model and the pilot test data result meets the preset condition.
Specifically, industrial data or pilot plant data of direct catalytic cracking of crude oil may be collected during actual production activities of an industrial plant, including initial conditions employed during actual production and distribution of flow products corresponding to the initial conditions. The flow products may include dry gas, liquefied gas, gasoline, diesel, slurry oil, coke, and the like.
In step S121, the staged preheating treatment technique may be a three stage preheating treatment technique, the first stage preheating to 180 ℃, separating out the naphtha fraction, the second stage preheating to 230 ℃, separating out the kerosene fraction, the third stage preheating to 260 ℃, separating out the diesel fraction; naphtha, kerosene and diesel oil fractions enter a first section of riser for reaction, and heavy oil fractions enter a second section of riser for reaction.
In step S122, the crude oil direct catalytic cracking model is input with the initial conditions in the crude oil catalytic cracking industrial data or pilot-scale data, and default kinetic parameters are set, so as to obtain the unseparated reaction product.
In step S123, a corresponding product separation model may be established according to the characteristics of the reaction product, for example, a product of the direct catalytic cracking process of crude oil is mostly small molecular olefins, and different product separation models such as sequential separation, pre-deethanization, pre-depropanization, etc. may be established.
In step S124, when the difference between the distribution result of the flow product predicted by the product separation model and the distribution result of the flow product in the industrial data or pilot plant data satisfies the preset condition, it means that the established crude oil direct catalytic cracking model can accurately predict the flow product of the crude oil direct catalytic cracking reaction. At this time, a preformed simulation working condition can be input into the crude oil direct catalytic cracking model, so that the distribution of flow products under the simulation working condition can be predicted.
As an example, the difference satisfying the preset condition means that the difference between them is smaller than a preset value, which is approximately 3% to 10% according to different components. For example, for dry gas, the preset value is approximately 5%, for liquefied gas, the preset value is approximately 10%, for gasoline, the preset value is approximately 1%, for diesel, the preset value is approximately 0.5%, for slurry oil, the preset value is approximately 2%, for coke, the preset value is approximately 3%. It should be noted that the above data are only for explaining the technical solution of the present invention, and do not limit the protection scope of the present invention.
The parameters included in the pre-formed simulated conditions may be the same as the parameters included in the initial conditions in the industrial or pilot plant data. In addition, the value of the parameter in the simulation working condition can be expanded on the basis of the value of the parameter in the initial working condition. Taking the temperature of the upper chamber of the reactor of the industrial device as an example, in the initial working condition in industrial data, the temperature range of the upper chamber of the reactor is 500-505 ℃, and then the value range of the temperature of the upper chamber of the reactor in the simulation working condition can be expanded to 480-530 ℃.
In step S125, if the difference between the distribution result of the flow product predicted by the product separation model and the distribution result of the flow product in the industrial or pilot-scale data does not meet the preset condition, it means that the established crude oil direct catalytic cracking model is still unable to accurately predict the flow product of the crude oil direct catalytic cracking reaction. At this time, the reaction kinetic parameters may be readjusted until the difference between the distribution result of the flow product predicted by the product separation model and the distribution result of the flow product in the industrial or pilot plant data satisfies the preset condition.
Specifically, when the difference between the distribution result of the process product and the distribution result of the process product in the industrial data or pilot-scale data does not meet a preset condition, for example, exceeds the preset value, the crude oil direct catalytic cracking model is corrected until the difference between the distribution result of the process product and the distribution result of the process product in the industrial big data meets the preset condition, and then the simulated working condition is input into the crude oil direct catalytic cracking model.
In some embodiments, the step of correcting the crude oil direct catalytic cracking model in step S125 comprises: and adjusting the reaction kinetic parameters in the crude oil direct catalytic cracking model.
As an example, in the above crude oil direct catalytic cracking model, the value of the reaction kinetic parameter may be a value in an empirical formula, and when the difference between the distribution result of the flow product and the distribution result of the flow product in the industrial data or pilot-scale data does not meet the preset condition, it means that the value in the empirical formula cannot be well fit to the current industrial data or pilot-scale data for the reaction kinetic parameter, so that the reaction kinetic parameter in the crude oil direct catalytic cracking model needs to be adjusted to achieve the purpose of correcting the crude oil direct catalytic cracking model.
In some embodiments, after the reaction kinetic parameters are adjusted in a preset value increasing or decreasing mode based on the values in the empirical formula, the initial working conditions in the crude oil direct catalytic cracking industrial data or pilot plant data are input into the crude oil direct catalytic cracking model to predict again to obtain the distribution of the flow products. When the difference between the distribution result of the flow product obtained by re-prediction and the distribution result of the flow product in the industrial data or pilot plant data still does not meet the preset condition, the reaction kinetic parameter needs to be adjusted again until the difference between the distribution result of the flow product obtained by re-prediction and the distribution result of the flow product in the industrial data or pilot plant data meets the preset condition.
As an example, the preset increment or decrement value may range from 0.02 to 0.08.
S130
After establishing a molecular level crude oil direct catalytic cracking process model corresponding to the pilot plant in step S120, repeatability and accuracy verification is performed using the process model in step S130.
In some embodiments, the operation conditions in the industrial data or pilot-scale data are used for direct catalytic cracking of multiple groups of crude oil, the crude oil direct catalytic cracking process model established in the step S120 is used for predicting the distribution of the process products, and the comparison error analysis is performed with the distribution results of the process products in the industrial data or pilot-scale data. If the differences between the distribution results of the flow products obtained by prediction under the multiple groups of operation conditions and the distribution results of the flow products in the industrial data or the pilot scale meet the preset conditions, the process model established in the step S120 is repeatable and accurate. If the difference between the distribution result of the flow product obtained by prediction under the multiple groups of operation conditions and the distribution result of the flow product in the industrial data or the pilot scale cannot meet the preset condition, it is indicated that the flow model established in the step S120 has no repeatability and accuracy. Step S120 is repeated until the established flow model has repeatability and accuracy.
S140
After the repeatability and accuracy of the crude oil direct catalytic cracking process model is verified in step S130, the operating conditions of the industrial or pilot plant are optimized by the crude oil direct catalytic cracking process model in step S140.
In some embodiments, the crude oil direct catalytic cracking process model is utilized, and the optimal parameter combination of the operable variables for achieving the optimization objective is obtained through automatic searching and manual intervention of an optimization algorithm in the safety range of the operable variables.
In some embodiments, the objective of optimizing an optimal combination of parameters of the objective's operable variables is achieved using a supermultiple objective evolutionary algorithm based on maximum vector angle selection and reference vector adaptation. For example, the aggregation function in the algorithm dynamically balances the convergence and diversity of the population according to the number of targets and evolutionary algebra, where the convergence criterion is measured by the distance between the individual and the ideal point and the diversity criterion is measured by the vector angle between the individual and the reference vector. This can effectively promote the ability of the algorithm to solve the ultra-multi-objective optimization problem.
In other embodiments, the objective of optimizing the optimal combination of parameters of the objective operable variables may also be achieved using an adaptive angle-partitioned multi-objective particle swarm algorithm. For example, first, the uniformity of population distribution is improved by guiding boundary particles in the initial stage. Then, in the target space, the angle is adaptively adjusted based on the number of particles, and region division is performed. And then, according to the distribution condition of particles in the region, selecting optimal particles, maintaining an external archive set, maintaining good population diversity and improving the coverage of an optimal solution set. In addition, for the particle-free distribution area, the search is enhanced by particles in the adjacent areas, so that the uniformity of the optimal solution set is promoted.
Example 1
Taking crude oil fractional catalytic cracking process as an example (figure 1), crude oil enters a preheating system and is heated to about 170-210 ℃, and gas-liquid phase products enter a primary flash evaporation device to realize flash evaporation separation. The gaseous product (naphtha) is fed to the second riser reactor of the two-ended riser reactor unit and the liquid product is fed to a secondary preheating system to a temperature of 210-290 ℃. After the liquid product is flash-evaporated by the two-stage flash tank, gas-liquid separation is realized, and the gas product (part of light diesel oil) is sent into the two-stage riser reactor. Liquid products (including heavy diesel and heavy oil) enter the first riser reactor. And determining different molecular structures in the light fraction by adopting a molecular characterization method in Aspen HYSYS, starting a regression method, setting regression parameters, obtaining a real molecular model of naphtha and diesel after operation, cutting the distillation range into narrow fraction segments by adopting the Aspen HYSYS traditional characterization method, and carrying out lumped classification by taking the narrow fraction segments as a virtual component. And (3) building a preheating system, a reaction regeneration system and a separation system in Aspen HYSYS by combining pilot-scale data, inputting pilot-scale operation parameters, and adjusting model convergence to obtain a crude oil direct catalytic cracking model with a molecular level.
And carrying out case analysis on different working conditions by the model, comparing the product distribution with the product distribution of the actual working conditions, and verifying the repeatability and accuracy of the model. And (3) a multi-objective optimization algorithm program is established by applying Python programming, engineering operation parameters such as preheating temperature, reaction temperature and the like are taken as independent variables, a life cycle evaluation index is taken as a dependent variable, and the model is optimized by combining Aspen HYSYS, so that the operation parameters capable of generating the maximum life cycle benefit are obtained.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (10)
1. A modeling method of a crude oil direct catalytic cracking process at a molecular level, comprising the steps of:
s110, establishing a structure-oriented lumped model of the mixed molecular level of the raw oil based on a molecular structure reconstruction technology;
the level of the mixed molecules is as follows: describing the light fraction by using real molecules, and describing the heavy oil fraction by using virtual components;
the distillation point of the light fraction is between the initial distillation point and the staged preheating cutting temperature, and the distillation point of the heavy oil fraction is between the staged preheating cutting temperature and the final distillation point;
s120, establishing a crude oil direct catalytic cracking process model of a molecular level corresponding to the pilot plant based on the lumped reaction dynamics model;
s130, carrying out repeatability and accuracy verification on the crude oil direct catalytic cracking process model;
and S140, optimizing key process parameters by using the crude oil direct catalytic cracking process model.
2. The modeling method of claim 1, wherein the step S110 includes:
s111, generating raw oil molecules according to the raw oil chemical analysis data based on a Monte Carlo sampling algorithm;
s112, determining the distribution type and parameters of probability density functions of different molecules based on the properties of different groups;
s113, sampling and generating a group of virtual component groups based on the probability density function, and calculating the properties of the virtual component groups;
s114, when the difference value between the property of the virtual molecular set and the analysis data result does not meet the preset condition, the distribution function is continuously optimized through a global optimization algorithm until the difference value between the property of the virtual molecular set and the analysis data result meets the preset condition;
and S115, when the difference between the nature of the virtual molecular set and the analysis data result meets the preset condition, retaining the virtual molecular set to obtain a structure-oriented lumped model of the mixed molecular level of the raw oil mixed fraction.
3. The modeling method as claimed in claim 2, wherein in the step S111, the raw oil chemical analysis data includes one or more of elemental composition, molar mass, distillation range, PONA, sulfur content and nitrogen content.
4. A modeling method according to claim 3, wherein in the step S113, the properties of the virtual component group include one or more of density, initial point, final point, 10% point, 30% point, 50% point, 70% point, and 90% point;
the preset conditions in step S114 and step S115 are 3 to 10%.
5. The modeling method of claim 1, wherein in the step S120, twenty-one lumped reaction dynamics model is used as the lumped reaction dynamics model.
6. The modeling method as defined in claim 1, wherein the step S120 specifically includes:
s121, separating the light fraction and the heavy oil fraction of the raw oil based on a staged preheating treatment technology, and feeding the light fraction and the heavy oil fraction into a corresponding reactor reaction area for reaction;
s122, establishing a reaction model based on direct catalytic cracking pilot-scale data of the raw oil;
s123, establishing a product separation model based on the product characteristics of the reaction model;
s124, when the difference value between the product distribution result of the separation model and the pilot scale data result meets a preset condition, reserving a correction factor set;
and S125, correcting the reaction model when the difference value between the product distribution result of the separation model and the pilot test data result does not meet the preset condition, until the difference value between the product distribution result of the separation model and the pilot test data result meets the preset condition.
7. The modeling method as defined in claim 6, wherein the reactor in step S121 is a two-stage riser catalytic cracking reactor;
the separated light fraction enters a first section of riser for reaction, and the heavy oil fraction enters a second section of riser for reaction.
8. The modeling method as claimed in claim 7, wherein the direct catalytic cracking pilot-scale data of the raw oil in step S122 includes an initial condition adopted in an actual production process and a distribution of a process product corresponding to the initial condition, the process product including one or more of dry gas, liquefied gas, gasoline, diesel, slurry oil and coke;
the preset conditions in step S124 and step S125 are 3 to 10%.
9. The modeling method as defined in claim 8, wherein the correction of the positive response model in step S125 specifically includes:
based on the value in the empirical formula, adjusting the reaction kinetic parameters in the crude oil direct catalytic cracking model of the molecular level in a preset value increasing or decreasing mode;
the preset increment or decrement range is 0.02-0.08.
10. The modeling method of claim 1, wherein in step S140, the model key process parameters are optimized based on a life cycle optimization strategy and a multi-objective optimization algorithm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311632639.7A CN117594144A (en) | 2023-11-30 | 2023-11-30 | Modeling method of crude oil direct catalytic cracking process at molecular level |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311632639.7A CN117594144A (en) | 2023-11-30 | 2023-11-30 | Modeling method of crude oil direct catalytic cracking process at molecular level |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117594144A true CN117594144A (en) | 2024-02-23 |
Family
ID=89918100
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311632639.7A Pending CN117594144A (en) | 2023-11-30 | 2023-11-30 | Modeling method of crude oil direct catalytic cracking process at molecular level |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117594144A (en) |
-
2023
- 2023-11-30 CN CN202311632639.7A patent/CN117594144A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104789256A (en) | Real-time yield predicting method for catalytic cracking device | |
| CN103713604B (en) | A kind of industrial pyrolysis furnace real time operation optimizing based on data-driven and control method | |
| US12049592B2 (en) | Predictive control systems and methods with hydrocracker conversion optimization | |
| Ho | Kinetic modeling of large‐scale reaction systems | |
| CN103524284A (en) | Forecasting and optimizing method for ethylene cracking material configuration | |
| Ivanchina et al. | Mathematical modeling of multicomponent catalytic processes of petroleum refining and petrochemistry | |
| WO2016048252A1 (en) | Reactor modelling method for fluidized-bed catalytic cracking unit | |
| CN102289198A (en) | Automatic online control method for production operation of industrial cracking furnace | |
| CN100517337C (en) | Modeling method for residual oil catalytic cracking reaction mechanism model | |
| CN117594144A (en) | Modeling method of crude oil direct catalytic cracking process at molecular level | |
| CN102289199B (en) | Automatic on-line control method for production and operation of industrial cracking furnace | |
| CN115312130A (en) | Mixed modeling method for simulation of yield-increasing catalytic cracking high-added-value products | |
| Radu et al. | Modelling and simulation of an industrial fluid catalytic cracking unit | |
| Shvets et al. | Oxygen-Induced Cracking Distillation of Oil in the Continuous Flow Tank Reactor. | |
| CN102289200B (en) | Automatic on-line control method for production operation of industrial cracking furnace | |
| CN115938502A (en) | Chemical product characteristic prediction method and system based on molecular-level reaction mechanism | |
| Alhajri | Integration of hydrogen and CO2 management within refinery planning | |
| Ranzi et al. | Detailed feedstock characterization for pyrolysis process | |
| Yan et al. | The study of molecular modeling for heavy oil thermal cracking | |
| Hiltunen et al. | NExCC™-Novel short contact time catalytic cracking technology | |
| Saihod et al. | Prediction of Reaction Kinetic of Al-Doura Heavy Naphtha Reforming Process Using Genetic Algorithm | |
| Padmavathi et al. | Multiple Objective Optimization of Industrial Naphtha Cracking Process by Box-Behnken Response Surface Methodology | |
| Nazarova et al. | Deactivation patterns of zeolite-containing catalysts of catalytic cracking | |
| Ellis | Supervisory optimization of a fluidized catalytic cracking unit | |
| Reyniers et al. | Modelling of deactivation by coke formation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |