Method, device and equipment for predicting hydrogen distribution in storage and oil refining production equipment

Abstract

The present invention discloses a method and device for predicting hydrogen distribution of an oil refining production device, the method comprising the following steps: A. Determine the device category, operating conditions and key operating parameters, determine the raw materials, product types and material parameter information; B. Establish a device model through process simulation software to obtain simulated calculation data of hydrogen content of raw materials and products under different operating conditions; C. Perform sampling experiments and analysis on raw materials and products under a limited set of operating conditions to obtain experimental test data of hydrogen content; D. For oil phase products in the products, perform fitting and correction on the experimental test data of hydrogen content and simulated calculation data of hydrogen content under a limited set of operating conditions to obtain corrected hydrogen content data; E. For oil phase products, perform regression fitting using the corrected hydrogen content data, material parameter information of crude oil and key operating parameters to obtain a prediction model for hydrogen distribution of the device. The method and device of the present invention achieve the purpose of improving the accuracy of model calculation, extrapolation and optimizing device operation.

Description

Technical Field

[0001] The present invention relates to the technical field of oil refining and processing, and in particular to a method, device and equipment for predicting hydrogen distribution in a storage device and an oil refining production device. Background Art

[0002] The crude oil processing process is actually a process of rebalancing and redistributing hydrogen. Crude oil passes through the atmospheric and vacuum distillation device and is divided into distillates with different hydrogen distributions according to the difference in boiling points. Both atmospheric distillation and vacuum distillation are physical processes. After the desalted and dehydrated mixed crude oil is heated in the distillation tower, it is divided into oil products with different boiling points from the top to the bottom of the tower according to their different boiling points, which are called fractions. Some of these distillates are shipped out of the factory in the form of products after blending and adding additives. Most of them are used as raw materials for secondary processing devices. Therefore, atmospheric and vacuum distillation is also called the primary processing of crude oil. Each distillate enters the corresponding secondary processing device. The secondary processing device (i.e., heavy oil lightening) is mainly divided into two categories: decarbonization device and hydrogenation device. Coking and catalysis are typical decarbonization processes, and various hydrogenation devices are hydrogenation processes. Studies have shown that the feed hydrogen content has an important influence on the operation economy of the device, product distribution, etc., and the product hydrogen content is also directly related to the key properties of the product. Therefore, the study of hydrogen distribution model of oil refining units is of great significance to optimize the overall processing flow of the whole plant, improve the operation of the device, and improve the economic benefits.

[0003] At present, the method for calculating hydrogen distribution in oil refining equipment is to measure the hydrogen content in raw oil and products respectively. There are mainly the following methods for measuring the hydrogen content of each fraction: First, the laboratory uses hydrogen element analyzers, mass spectrometry and other tests to obtain the hydrogen content of raw materials and products. This method is highly dependent on experimental tests and has high costs; second, an empirical formula is used to calculate the hydrogen content of raw materials and products (i.e. dry gas, gasoline, jet fuel, diesel, wax oil, and residual oil). Since the empirical formula has application range requirements, the hydrogen content calculated by this method sometimes has a large deviation.

[0004] There are also schemes in the prior art to optimize hydrogen systems by establishing algorithm models. For example, Chinese patent application CN106485341A discloses a method for optimizing a hydrogen system in a refinery. This method takes the maximum benefit of the hydrogen system in the refinery as the goal, optimizes the constraints, and forms a new mathematical programming algorithm including light hydrocarbon resource optimization. However, this method is not coupled with laboratory analysis and test data, and the constraints are limited and have no correlation with some key operating parameters of the device.

[0005] From the above analysis, it can be seen that there is no complete theoretical mathematical model for the calculation of hydrogen distribution in existing oil refining equipment. It is only a material balance accounting of hydrogen elements by calculating the hydrogen content of raw materials and products. The calculation process is highly dependent on experience, laboratory analysis and testing, and has no correlation with the key operating parameters of the equipment. There are many constraints in the actual production promotion and application, and it is difficult to play the role of model optimization guidance.

[0006] The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the invention and should not be regarded as an acknowledgment or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art. Summary of the invention

[0007] The purpose of the present invention is to provide a method and device for predicting hydrogen distribution of an oil refining production unit, which couples the mathematical modeling of commercial process simulation software and necessary laboratory analysis, combines the actual production of the refinery, associates with the main operating conditions of the unit, and makes full use of the production and operation data of the unit for model training, so as to achieve the purpose of improving the accuracy and extrapolation of model calculations and optimizing the operation of the unit.

[0008] To achieve the above-mentioned purpose, according to the first aspect of the present invention, the present invention provides a method for predicting hydrogen distribution of an oil refining production device, comprising the following steps: A. determining the device category, operating conditions and key operating parameters, determining the types of raw materials, products and material parameter information; B. establishing a device model through process simulation software to obtain simulated calculation data of hydrogen content of raw materials and products under different operating conditions; C. performing sampling experiments and analysis on raw materials and products under a limited set of operating conditions to obtain experimental test data of hydrogen content; D. fitting and correcting the experimental test data of hydrogen content and the simulated calculation data of hydrogen content under a limited set of operating conditions for oil phase products in the products to obtain corrected hydrogen content data; E. performing regression fitting using the corrected hydrogen content data, material parameter information of crude oil and key operating parameters for oil phase products to obtain a prediction model of hydrogen distribution of the device as follows:

[0009]

[0010]

[0011]

[0012] Among them, F f，i is the feed flow rate of the device, t / h; f Hf，i is the hydrogen content of feed stream i, m%; F p，j is the flow rate of device product j, t / h; f Hp，j is the hydrogen content of product stream j, m%; T vis the average boiling point of the feed volume, K; ρ is the feed density, kg / m3; S is the feed sulfur content, m%; N is the feed nitrogen content, m%; T is the reaction temperature of the device, K; P is the reaction pressure of the device, MPa; a j 、b j 、c j ,d j 、e j , A 1，j , A 2，j , B 1，j is the model coefficient.

[0013] Furthermore, in the above technical solution, the correction in step D specifically adopts the following correction model:

[0014]

[0015] Among them, f H，实际 is the experimental determination of the hydrogen content in the stream, m%; i is the power index; f H，模拟 is the hydrogen content in the stream calculated by simulation, m%; A i , b is a coefficient; n≤6 in the formula (4);

[0016] The f obtained by simulation calculation under a limited set of working conditions H，模拟 And the experimentally determined f H，实际 Calculate A i and b, and then calculate the corrected hydrogen content data after obtaining a universal correction model for the same device.

[0017] Furthermore, in the above technical solution, the device in step A includes a constant pressure reduction device, a continuous reforming device, a catalytic cracking device, a delayed coking device, a solvent deasphalting device, various hydrotreating devices, various hydromodification devices and various hydrocracking devices.

[0018] Furthermore, in the above technical solution, the working condition in step A is the corresponding production condition of the device when the main crude oil types processed are the same within a period of time.

[0019] Furthermore, in the above technical solution, the key operating parameters in step A mainly include reaction temperature and pressure.

[0020] Furthermore, in the above technical solution, the material parameter information in step A mainly includes: density, distillation range, S content, N content of the liquid phase material and molecular composition of the gas phase material.

[0021] Furthermore, in the above technical solution, the process simulation software in step B is Hysys or PetroSim software; and the method also includes the step of calibrating the device model established by the software.

[0022] Furthermore, in the above technical solution, the sampling experimental analysis in step C is specifically as follows: extracting 1 to 10 groups of samples within the same time period under each working condition; using a mass spectrometer or a hydrogen element analyzer to analyze the liquid phase material, and using molecular composition analysis for the gas phase material.

[0023] Furthermore, in the above technical solution, the method for fitting the hydrogen content experimental test data and the hydrogen content simulation calculation data under a limited set of working conditions in step D adopts the least squares method; the fitting tool is Excel, Matlab or Origin.

[0024] Furthermore, in the above technical solution, formula (1) in step E is a material balance formula, and the material balance is corrected by calculating the flow rate of device product j and comparing it with the known flow rate of device feed i.

[0025] Furthermore, in the above technical solution, formula (2) in step E is a hydrogen balance formula, and the hydrogen balance is corrected by calculating the hydrogen content of the product and comparing it with the known hydrogen content of the feed.

[0026] Furthermore, in the above technical solution, the model coefficients in formula (3) are calculated by regression fitting in step E, thereby obtaining the hydrogen distribution prediction model of the general structure.

[0027] To achieve the above-mentioned purpose, according to the second aspect of the present invention, there is provided a device for predicting hydrogen distribution of an oil refining production device, comprising: a basic information preparation module, which is used to determine the device category, operating conditions and key operating parameters, and to determine the types of raw materials, products and material parameter information; a simulation calculation data acquisition module, which is used to establish a device model through process simulation software, and obtain the simulated calculation data of the hydrogen content of raw materials and products under different operating conditions; an experimental test data acquisition module, which is used to perform sampling experimental analysis on raw materials and products under a limited set of operating conditions, and obtain hydrogen content experimental test data; a correction data acquisition module, which is used to fit and correct the hydrogen content experimental test data and hydrogen content simulation calculation data under a limited set of operating conditions for oil-phase products in the products, and obtain corrected hydrogen content data; a hydrogen distribution prediction model acquisition module, which is used to perform regression fitting on oil-phase products using the corrected hydrogen content data, material parameter information of crude oil and key operating parameters, and obtain a device hydrogen distribution prediction model.

[0028] To achieve the above objective, according to a third aspect of the present invention, the present invention provides a memory including an instruction set, wherein the instruction set is suitable for a processor to execute the steps in any of the aforementioned methods for predicting hydrogen distribution in an oil refinery production unit.

[0029] To achieve the above-mentioned purpose, according to the fourth aspect of the present invention, the present invention provides a hydrogen distribution prediction device for an oil refining production unit, comprising a bus, an input device, an output device, a processor and a memory as described above; the bus is used to connect the memory, the input device, the output device and the processor; the input device and the output device are used to realize interaction with the user; the processor is used to execute the instruction set in the memory.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1) The present invention calculates the hydrogen content of gas phase products and liquefied gas based on the actual molecular composition; the hydrogen content of liquid phase products is calculated using a hydrogen distribution model, and relevant calculations can be performed by simply knowing the raw material properties and operating conditions of the device; laboratory measurement data are used to characterize solid phase products or losses, and the hydrogen content of gas, liquid and solid products can be more accurately predicted by combining experimental and model calculation data;

[0032] 2) The present invention adds material balance judgment and correction and hydrogen balance judgment and correction to the hydrogen distribution prediction model, which effectively avoids the large deviation caused by the unbalanced amount of the device due to the direct use of real-time data of production operation, and more effectively ensures the accuracy of the prediction;

[0033] 3) The present invention couples the mathematical modeling of commercial process simulation software and necessary laboratory analysis, combines it with the actual production of the refinery, mathematically associates it with conventional analysis items and the main operating conditions of the device, and makes full use of the production and operation data of the device for model training, so as to improve the accuracy of model calculation, extrapolation and optimize the operation of the device.

[0034] Further features and aspects of the present invention will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] One or more embodiments are exemplarily described by pictures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0036] Figure 1 This is a schematic diagram of the steps of a method for predicting hydrogen distribution in an oil refining production device according to Example 1 of the present invention;

[0037] Figure 2 It is a schematic diagram of a process of calculating hydrogen distribution by using a hydrogen distribution prediction model after obtaining the coefficients of the hydrogen distribution prediction model in a method for predicting hydrogen distribution of an oil refining production device of the present invention;

[0038] Figure 3 This is a schematic diagram of the structure of a hydrogen distribution prediction device for an oil refining production device according to Example 2 of the present invention;

[0039] Figure 4 This is a schematic diagram of the structure of a hydrogen distribution prediction device for an oil refining production device according to Example 4 of the present invention;

[0040] Figure 5 It is a schematic diagram of the relationship between the hydrogen content data obtained by simulation calculation and the hydrogen content data measured experimentally in the correction model of Example 1 of the present invention. DETAILED DESCRIPTION

[0041] The specific implementation modes of the present invention are described in detail below in conjunction with the accompanying drawings, but it should be understood that the protection scope of the present invention is not limited by the specific implementation modes.

[0042] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Unless otherwise expressly stated, throughout the specification and claims, the term "including" or its variations such as "comprising" or "including" will be understood to include the stated elements or components, without excluding other elements or other components.

[0043] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0044] In addition, in order to better illustrate the present invention, numerous specific details are provided in the following specific embodiments. It should be understood by those skilled in the art that the present invention can also be implemented without certain specific details. In some examples, methods, means, and elements well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present invention.

[0045] Example 1

[0046] like Figure 1 As shown, the method for predicting hydrogen distribution of a refinery production device according to Example 1 of the present invention comprises the following steps:

[0047] Step S101, prepare basic information, namely: determine the device type, operating conditions and key operating parameters, determine the raw materials, product types and material parameter information.

[0048] Among them, the unit mainly refers to the main unit in the oil refining process, and the unit categories include atmospheric and vacuum units, continuous reforming units, catalytic cracking units, delayed coking units, solvent deasphalting units, various hydrofining units, various hydro-upgrading units and various hydrocracking units, etc. The operating conditions mainly refer to the corresponding production conditions of the unit when the refinery processes several major types of crude oil in a period of time (for example, within a year).

[0049] The key operating parameters can be divided into the following categories: for atmospheric and vacuum devices, they mainly refer to the atmospheric heating furnace outlet temperature, device extraction rate, etc.; for continuous reforming devices, they mainly refer to reforming reaction temperature, pressure, etc.; for catalytic cracking devices, they mainly refer to reaction temperature, space velocity, residence time, etc.; for various types of hydrogenation devices, they mainly refer to reaction temperature, hydrogen partial pressure, etc.; for delayed coking devices, they mainly refer to reaction temperature, pressure, circulation ratio, etc. The key operating parameters used in this embodiment are mainly reaction temperature and pressure.

[0050] Raw materials and product types mainly refer to the device feed, including single feed or multiple feeds, and product types include one or more of dry gas, liquefied gas, gasoline, jet fuel, diesel, wax oil, residual oil, and coke.

[0051] The material parameter information mainly includes: the density, distillation range, S content, N content, etc. of the liquid phase material and the molecular composition of the gas phase material. The raw material and product information of the atmospheric and vacuum devices, catalysis, coking and other devices are all universal, that is, the oil phase material involves flow rate, relative density, distillation range, sulfur content, nitrogen content (except for liquefied gas, the liquefied gas fraction is generally analyzed by molecular composition data), the gas phase material involves flow rate, molecular composition analysis data, and the solid phase product involves output and hydrocarbon content data. For example, the raw material of the atmospheric and vacuum device is crude oil or a mixed feed of several crude oils. In this embodiment, the flow rate, relative density, distillation range, sulfur content, and nitrogen content of the raw material need to be determined. The products mainly include gas phase products (including initial top gas, normal top gas, and reduced top gas, and the flow rate and molecular composition need to be determined), liquid distillate oil (the flow rate, relative density, distillation range, sulfur content, and nitrogen content need to be determined). For another example, the feed of the delayed coking unit includes vacuum residue oil and catalytic oil slurry, and the products include dry gas, liquefied gas, coking gasoline and diesel, coking wax oil and coke. Therefore, it is necessary to determine the material balance of the unit, the relative density, distillation range, sulfur content, nitrogen content of the oil phase material, the molecular composition of the gas phase material, and the hydrocarbon element composition of the solid phase material.

[0052] Step S102, establish a device model through process simulation software, and obtain simulated calculation data of hydrogen content of the raw materials and the products under different working conditions. The process simulation software can use software such as Hysys or PetroSim. After the device model is established by the simulation software, it is necessary to conduct model investigation and verification. The model investigation mainly refers to the use of the model to investigate the impact of changes in main operating parameters on product distribution and hydrogen content distribution under different working conditions. Establishing a device model through simulation software is a conventional technology in the field and will not be described in detail here. After the device model is established and verified, the model can be used to obtain simulated calculation data of hydrogen content.

[0053] Step S103, sampling and experimental analysis are performed on the raw materials and products under a limited set of working conditions to obtain experimental test data on hydrogen content. Sampling mainly refers to sampling the raw materials and products of each device corresponding to the main types of oil processed by the refinery. 1-10 groups of samples are taken in the same time period for each working condition, and 2-3 groups of samples are preferably taken. The experimental analysis mainly includes: the gas phase material is analyzed by molecular composition to determine the hydrogen content therein, and the liquid phase material is analyzed by mass spectrometry, hydrogen element analyzer, etc., and preferably the hydrogen element analyzer is used to determine the hydrogen content. For the product, for the oil phase product, the hydrogen content, sulfur content, nitrogen content, density, distillation range and other information are determined by experimental analysis; for the gas phase product and liquefied gas product, the laboratory determines its molecular composition; for the solid phase product, the laboratory determines its carbon and hydrogen content.

[0054] Step S104, for the oil phase product in the product, the hydrogen content experimental test data and the hydrogen content simulation calculation data under a limited set of working conditions are fitted and corrected to obtain corrected hydrogen content data. The specific correction model used is as follows:

[0055]

[0056] Among them, f H，实际 is the experimental determination of the hydrogen content in the stream, m%; i is the power index; f H，模拟 is the hydrogen content in the stream calculated by simulation, m%; A i , b is a coefficient; n≤6 in the formula; f obtained by simulation calculation under a limited set of working conditions in this embodiment H，模拟 And the experimentally determined f H，实际 Calculate A i and b, and then calculate the corrected hydrogen content data after obtaining a universal correction model for the same device.

[0057] In this step, the method for fitting the hydrogen content experimental test data and the hydrogen content simulation calculation data under a limited set of working conditions can adopt the least square method; the fitting tool can use Excel, Matlab or Origin.

[0058] Step S105, for the oil phase product in step S104, regression fitting is performed using the corrected hydrogen content data, material parameter information of the crude oil, and key operating parameters to obtain a device hydrogen distribution prediction model. The corrected hydrogen content data mainly refers to the hydrogen content of the raw materials and products under different operating conditions simulated and calculated by step S102, and then the corrected actual content of hydrogen in the raw materials and products is obtained by the correction model obtained in step S104. The device hydrogen distribution prediction model is as follows:

[0059]

[0060] The formula (1) is a material balance formula, and the material balance can be corrected by calculating the flow rate of the device product j and comparing it with the known flow rate of the device feed i.

[0061]

[0062] This formula (2) is a hydrogen balance formula. By calculating the product hydrogen content and comparing it with the known feed hydrogen content, the hydrogen balance can be corrected.

[0063]

[0064] Among them, F f，i is the feed flow rate of the device, t / h; f Hf，i is the hydrogen content of feed stream i, m%; F p，j is the flow rate of device product j, t / h; f Hp，j is the hydrogen content of product stream j, m%; T v is the average boiling point of the feed volume, K; ρ is the feed density, kg / m3; S is the feed sulfur content, m%; N is the feed nitrogen content, m%; T is the reaction temperature of the device, K; P is the reaction pressure of the device, MPa; a j 、b j 、c j ,d j 、e j , A 1，j , A 2，j , B 1，j is the model coefficient.

[0065] T in the hydrogen distribution prediction model of the above device v , ρ, S, N parameters can be calculated by the following formula:

[0066]

[0067]

[0068]

[0069] S=∑(F f，i ·S i ) / ∑F f，i

[0070] N=∑(F f，i N i ) / ∑F f，i

[0071] t 10 ,t 30 ,t 50 ,t 70 ,t 90 The gas phase temperature of 10%, 30%, 50%, 70%, and 90% of the distilled mixed feed, °C; ω i is the mass fraction of the device feed i; S i is the mass fraction of S in the device feed i; N i is the mass fraction of N in the device feed i.

[0072] Specifically, 20-50 groups of samples can be selected in this step to obtain the basic property data of the crude oil (i.e., distillation range, density, S content, N content, flow rate, etc.) as well as temperature T and pressure P. For the oil phase product, the actual hydrogen content f of each product has been calculated by the calibration model in the above step S104. H，实际 In this step, the actual hydrogen content data of the oil phase product is used to fit the basic property data of the feed oil, temperature T and pressure P, so as to obtain a j 、b j 、c j ,d j 、e j , A 1，j , A 2，j , B 1，j These model coefficients form a general structure hydrogen distribution prediction model, so that the f on the left side of the equation can be calculated by formula (3) Hp，j , to achieve a more accurate prediction of hydrogen distribution. It should be noted here that if the real-time data of production operation is directly used, the imbalance of the device will lead to a large deviation. Therefore, the hydrogen distribution prediction model in this step adds the material balance judgment and correction of formula (1) and the hydrogen balance judgment and correction of formula (2). For the detailed process of hydrogen distribution calculation using the hydrogen distribution prediction model, please refer to Figure 2 .

[0073] It should also be noted that: under a certain working condition, the final presentation form of the hydrogen distribution prediction model in this step is: gas phase products and liquefied gas - calculate the hydrogen content according to the actual molecular composition; liquid phase product hydrogen content - the hydrogen distribution model determined by the model coefficient obtained in this step S105, only the raw material properties and operating conditions of the device need to be known to perform relevant calculations; solid phase products or losses - represented by the laboratory measurement data of step S103. Therefore, under this working condition, if the feed properties of a device (including: flow rate, hydrogen content, density, distillation range, S content, N content), the main operating parameters of the device (temperature T, pressure P), and product distribution (product type, flow rate, density, distillation range, S content, N content of each product) are known, the hydrogen content of each product can be calculated using the final presentation form of the aforementioned hydrogen distribution prediction model. If a working condition is changed (for example, a crude oil with a large property change is changed), it is necessary to repeat step S102 of this embodiment and subsequent steps to predict hydrogen distribution.

[0074] The present invention is described in detail below by taking the development and application calculation of a hydrogen distribution model of a 1.8 million tons / year hydrocracking unit as an example:

[0075] 1) Basic information of the device

[0076] The device is designed to process straight-run wax oil raw materials. The products mainly include dry gas (all gaseous streams), liquefied gas, light naphtha, heavy naphtha, jet fuel, diesel and tail oil. The device process is existing technology and will not be described in detail here.

[0077] 2) Establishing device model through process simulation software

[0078] Using Hysys software, a plant process simulation model was established according to the characteristics of the plant process. The relative deviation between the simulation calculation results and the plant operation data is less than ±5%. It can be considered that the simulation model can accurately simulate and calculate the operation data of the plant under this working condition, see Table 2-1.

[0079] Table 2-1

[0080]

[0081]

[0082] 3) Obtain hydrogen content correction model

[0083] Five groups of samples were taken from light naphtha, heavy naphtha, jet fuel, diesel and tail oil respectively, and the hydrogen content data of each sample was obtained by two methods: laboratory measurement and model calculation in step S102. Then, the data was fitted by using "experimental measurement of hydrogen content VS simulated calculation of hydrogen content" to obtain the product hydrogen content correction calculation model (i.e., the correction model in step S104).

[0084] (a) Light naphtha

[0085] The simulated calculated hydrogen content is calculated by using the experimentally measured hydrogen content, and the correction formula for light naphtha is obtained as Formula 1 (the dependent variable y in Formula 1 represents the actual hydrogen content, and the independent variable x represents the simulated hydrogen content). The simulated calculated hydrogen content data and the experimentally measured hydrogen content data are shown in Table 3-1. The relationship between the two in the correction model can also be seen in Figure 5 .

[0086] y=44995333079040.0000x 4 -30318585199744.8000x 3 +7660932835747.3700

[0087] x 2 -860343448267.2070x+36232084454.7147 (Formula 1)

[0088] Table 3-1

[0089]

[0090] Similarly, we can get:

[0091] (b) Correction formula for calculating hydrogen content in heavy naphtha:

[0092] y=12576597868544.0000x 4 -7149679555718.1600x 3 +1524197375759.5300x 2 -144415326118.7810x+5131172210.3161 (Formula 2)

[0093] (c) Correction formula for calculating hydrogen content in aviation fuel:

[0094] y=-3301309448192.0000x 4 +1790133338523.6800x 3 -364012015045.4000x 2 +32897542988.8125x-1114916645.6957 (Formula 3)

[0095] (d) Diesel hydrogen content calculation correction formula:

[0096] y=-31467167023104.0000x 4+17453938803957.0000x 3 -3630449924075.4300x 2 +335617706284.6860x-11634841660.3959 (Formula 4)

[0097] (e) Correction formula for calculating hydrogen content in tail oil:

[0098] y=287699009536.0000x 4 -164178380825.7870x 3 +35133765566.6956x 2 -3341571618.0162x+119181261.4740 (Formula 5)

[0099] 4) Obtain hydrogen distribution model

[0100] 20 groups of raw material samples were selected, and the hydrogen content of each product was calculated using the model established in step S102. Then, the actual hydrogen content of each product was calculated according to the correction model of each product obtained in step S104, as shown in Table 4-1.

[0101] Take 20 groups of raw material samples

[0102] Table 4-1 Raw material samples

[0103]

[0104]

[0105] The average boiling point temperature of 20 groups of raw material samples was calculated, as shown in Table 4-2. Since there is only one raw material, the calculation of mixed density, sulfur and nitrogen content, etc. is not involved.

[0106] Table 4-2 Average boiling point temperature

[0107] sample TV / K 1 677.45 2 679.75 3 682.65 4 679.59 5 680.31 6 679.67 7 680.57 8 680.03 9 679.09 10 680.31 11 680.25 12 680.19 13 680.09 14 680.19 15 680.37 16 679.73 17 680.61 18 680.65 19 680.47 20 681.29

[0108] The model established in step S102 is used to calculate the data of various products, including gas phase, liquefied gas, and oil phase products. The main purpose is to calculate the properties of the oil phase products. The calculation process of light naphtha is taken as an example, as shown in Table 4-3.

[0109] Table 4-3 Light naphtha simulation data

[0110]

[0111] The light naphtha hydrogen content correction model (Formula 1) obtained in step S104 is used to calculate the actual hydrogen content data of the light naphtha of the 20 groups of samples, as shown in Table 4-4 below:

[0112] Table 4-4 Corrected hydrogen content data

[0113]

[0114]

[0115] The data is summarized as shown in Table 4-5:

[0116] Table 4-5 Data summary

[0117]

[0118] Using the data in Table 4-4, the properties of the crude oil (T V , ρ, S, N), operating parameters (T, P) and light naphtha flow rate (data in Table 4-5) are fitted, and the data fitting formula of Excel is used for fitting to obtain the hydrogen distribution model coefficients of the light naphtha unit as shown in Table 4-6.

[0119] Table 4-6 Light naphtha hydrogen distribution model coefficients

[0120]

[0121]

[0122] Similarly, the hydrogen distribution model coefficients of heavy naphtha, jet fuel, diesel and tail oil are obtained, as shown in Table 4-7.

[0123] Table 4-7 Summary of Hydrogen Distribution Model Coefficients

[0124] coefficient Light naphtha Heavy naphtha Jet fuel diesel fuel tail oil <![CDATA[e j ]]> -0.736379877 0.140185 0.135085 0.138442 0.309237 <![CDATA[a j ]]> 3202.180424 240.1278 83.8405 -78.9883 -2363.34 <![CDATA[b j ]]> 725.633915 -292.362 -83.555 87.67873 1067.5 <![CDATA[c j ]]> -0.00456456 0.000445 0.004925 0.002657 0.086317 <![CDATA[d j ]]> -1.591942854 0.181303 -0.19349 0.687076 4.979296 <![CDATA[A 1，j ]]> 1.5719E-05 -5.2E-07 8.12E-07 -6E-07 -1.3E-05 <![CDATA[A 2，j ]]> -0.003825067 0.000331 -0.00047 -4.3E-05 0.007114 <![CDATA[B 1，j ]]> 0.062394384 -0.00468 0.006589 0.001038 -0.10552

[0125] 5) Application of hydrogen distribution model

[0126] The operating status of the device at a certain moment is shown in Table 5-1:

[0127] Table 5-1 Material balance of the device

[0128] Entering party: Feed rate, t / h Straight run wax oil 212.80 New Hydrogen 5.57 total 218.37 Source: Output, t / h Dry gas 9.09 Liquefied gas 6.11 Light naphtha 10.39 Heavy naphtha 43.92 Jet fuel 50.67 diesel fuel 27.50 tail oil 70.58 loss 0.11 total 218.37

[0129] Table 5-2 Raw oil properties

[0130] project Crude oil <![CDATA[Density at 20 °C, g / cm 3 > 0.9142 D86 distillation range, ℃ IBP 218 10％ 346 30％ 380 50％ 407 70％ 434 90％ 466 EBP 500 Sulfur content / % 2.35 Nitrogen content, mg / kg 513 Hydrogen content / % 12.1241

[0131] Table 5-3 Device operating temperature

[0132] Temperature, K 663.15 Pressure, MPa 15.273

[0133] Table 5-4 Dry gas composition

[0134] Composition / %(v) hydrogen 27.66 Methane 40.31 Ethane 20.14 Propane 3.83 n-Butane 0.46 Isobutane 0.04 C5 and above 0.04 Hydrogen sulfide 2.25 Air 5.27 total 100

[0135] Table 5-5 Composition of liquefied gas

[0136] Composition / % (volume) hydrogen Methane Ethane Propane 35.79 n-Butane 41.06 Isobutane 22.57 C5 and above 0.58 total 100

[0137] The hydrogen distribution prediction model of the present invention (i.e., formula (3) in step S105, where the model coefficients are known) is used to calculate the hydrogen distribution of the device. As shown in Table 5-6, the relative deviation of the total amount of hydrogen element in the device product compared with the feed is (31.8258-31.3701) / 31.3701=1.45%<5%, which meets the engineering calculation requirements.

[0138] Table 5-6 Calculation of hydrogen balance of the device

[0139]

[0140]

[0141] The method for predicting hydrogen distribution of oil refining production equipment of the present invention is coupled with the mathematical modeling of commercial process simulation software, necessary laboratory test analysis, combined with the actual production of the refinery, mathematically associated with conventional analysis items and main operating conditions of the equipment, and fully utilizes the production and operation data of the equipment for model training, so as to achieve the purpose of improving the accuracy and extrapolation of model calculation and optimizing the operation of the equipment. In the calculation process of the hydrogen distribution prediction model, the judgment and correction of material balance and hydrogen balance are carried out, which more effectively ensures the accuracy of the prediction.

[0142] Example 2

[0143] like Figure 3 As shown, the hydrogen distribution prediction device for oil refining production equipment of this embodiment includes: a basic information preparation module 201, a simulation calculation data acquisition module 202, an experimental test data acquisition module 203, a correction data acquisition module 204 and a hydrogen distribution prediction model acquisition module 205. Among them, the basic information preparation module 201 is used to determine the device category, working conditions and key operating parameters, determine the raw materials, product types and material parameter information; the simulation calculation data acquisition module 202 is used to establish a device model through process simulation software, and obtain the hydrogen content simulation calculation data of raw materials and products under different working conditions; the experimental test data acquisition module 203 is used to perform sampling experimental analysis on raw materials and products under a limited set of working conditions to obtain hydrogen content experimental test data; the correction data acquisition module 204 is used to fit and correct the hydrogen content experimental test data and hydrogen content simulation calculation data under a limited set of working conditions for oil phase products in the products, and obtain the corrected hydrogen content data; the hydrogen distribution prediction model acquisition module 205 is used to perform regression fitting with the corrected hydrogen content data, the material parameter information of the crude oil and the key operating parameters for the oil phase products, and obtain the device hydrogen distribution prediction model.

[0144] Example 3

[0145] This embodiment provides a memory, which can be a non-transitory (non-volatile) computer storage medium, which stores computer executable instructions. The computer executable instructions can execute the various steps of the hydrogen distribution prediction method for an oil refining production unit in any of the above-mentioned method embodiments and achieve the same technical effect.

[0146] Example 4

[0147] The present embodiment provides a device for predicting hydrogen distribution of an oil refining production unit. The device includes a memory including a corresponding computer program product. When the program instructions included in the computer program product are executed by a computer, the computer can execute the above-mentioned various aspects of the method for predicting hydrogen distribution of an oil refining production unit and achieve the same technical effect.

[0148] Figure 4 FIG. 1 is a schematic diagram of the hardware structure of the electronic device in this embodiment. Figure 4 As shown, the device includes one or more processors 610 and a memory 620. Taking one processor 610 as an example, the device may also include: an input device 630 and an output device 640.

[0149] The processor 610, the memory 620, the input device 630 and the output device 640 may be connected via a bus or other means. Figure 3 The example of connecting through bus is taken in the following.

[0150] The memory 620 is a non-transitory computer-readable storage medium that can be used to store non-transitory software programs, non-transitory computer executable programs, and modules. The processor 610 executes various functional applications and data processing of the electronic device by running the non-transitory software programs, instructions, and modules stored in the memory 620, that is, the processing method of the above method embodiment is implemented.

[0151] The memory 620 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data, etc. In addition, the memory 620 may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 620 may optionally include a memory remotely arranged relative to the processor 610, and these remote memories may be connected to the processing device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0152] The input device 630 can receive input digital or character information and generate signal input. The output device 640 can include a display device such as a display screen.

[0153] The one or more modules are stored in the memory 620, and when executed by the one or more processors 610, the following are executed: The method for predicting hydrogen distribution of a refinery production device according to the present invention. The above-mentioned product can execute the method provided by the embodiment of the present invention, and has the functional modules and beneficial effects corresponding to the execution method. For technical details not described in detail in this embodiment, please refer to the method provided by the embodiment of the present invention.

[0154] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting hydrogen distribution in an oil refining production unit, characterized in that: The steps include: A. Determine the equipment type, operating conditions and key operating parameters, and determine the raw materials, product types and material parameter information; B. Establishing a device model through process simulation software to obtain simulation calculation data of hydrogen content of the raw materials and the products under different working conditions; C. Sampling and experimentally analyzing the raw materials and products under the limited set of working conditions to obtain experimental test data on hydrogen content; D. For the oil phase product in the product, fitting and correcting the hydrogen content experimental test data and the hydrogen content simulation calculation data under the limited set of working conditions to obtain corrected hydrogen content data; E. For the oil phase product, regression fitting is performed using the corrected hydrogen content data, the material parameter information of the feedstock oil, and the key operating parameters to obtain a device hydrogen distribution prediction model as follows: Among them, F f，i is the feed flow rate of the device, t / h; f Hf，i is the hydrogen content of feed stream i, m%; F p，j is the flow rate of device product j, t / h; f Hp，j is the hydrogen content of product stream j, m%; T v is the average boiling point of the feed volume, K; ρ is the feed density, kg / m3; S is the feed sulfur content, m%; N is the feed nitrogen content, m%; T is the reaction temperature of the device, K; P is the reaction pressure of the device, MPa; a j 、b j 、c j d j 、e j , A 1，j , A 2，j , B 1，j is the model coefficient.

2. The method for predicting hydrogen distribution in an oil refining production unit according to claim 1, characterized in that: The correction in step D specifically adopts the following correction model: Among them, f H，实际 is the experimental determination of the hydrogen content in the stream, m%; i is the power index; f H，模拟 is the hydrogen content in the stream calculated by simulation, m%; A i , b is a coefficient; n≤6 in the formula (4); f obtained by the simulation calculation under the finite set of working conditions H，模拟 And the experimentally determined f H，实际 Calculate the A i and b, and then obtaining a universal correction model for the same device to calculate the corrected hydrogen content data.

3. The method for predicting hydrogen distribution in an oil refining production unit according to claim 1, characterized in that: The devices in step A include atmospheric and vacuum devices, continuous reforming devices, catalytic cracking devices, delayed coking devices, solvent deasphalting devices, various hydrofining devices, various hydromodification devices and various hydrocracking devices.

4. The method for predicting hydrogen distribution in an oil refining production unit according to claim 1, characterized in that: The working condition in step A is the production condition corresponding to the device when processing the same main crude oil type within a period of time.

5. The method for predicting hydrogen distribution in an oil refining production unit according to claim 1, characterized in that: The key operating parameters in step A mainly include reaction temperature and pressure.

6. The method for predicting hydrogen distribution in an oil refining production unit according to claim 1, characterized in that: The material parameter information in step A mainly includes: density, distillation range, S content, N content of liquid phase material and molecular composition of gas phase material.

7. The method for predicting hydrogen distribution in an oil refining production unit according to claim 1, characterized in that: The process simulation software in step B is Hysys or PetroSim software; and the process also includes a step of verifying the device model established by the software.

8. The method for predicting hydrogen distribution in an oil refining production unit according to claim 1, characterized in that: The sampling experimental analysis in step C is specifically as follows: extracting 1 to 10 groups of samples within the same time period under each working condition; analyzing the liquid phase material by mass spectrometry or hydrogen element analyzer, and analyzing the gas phase material by molecular composition analysis.

9. The method for predicting hydrogen distribution in an oil refinery production unit according to claim 1, characterized in that: The method for fitting the hydrogen content experimental test data and the hydrogen content simulation calculation data under the limited set of working conditions in step D adopts the least squares method; the fitting tool is Excel, Matlab or Origin.

10. The method for predicting hydrogen distribution in an oil refinery production unit according to claim 1, characterized in that: Formula (1) in step E is a material balance formula, and the material balance is corrected by calculating the flow rate of the device product j and comparing it with the known flow rate of the device feed i.

11. The method for predicting hydrogen distribution in an oil refinery production unit according to claim 1, characterized in that: Formula (2) in step E is a hydrogen balance formula, and the hydrogen balance is corrected by calculating the hydrogen content of the product and comparing it with the known hydrogen content of the feed.

12. The method for predicting hydrogen distribution in an oil refinery production unit according to claim 1, characterized in that: The model coefficients in the formula (3) are calculated by the regression fitting in the step E, thereby obtaining the hydrogen distribution prediction model of the general structure.

13. A hydrogen distribution prediction device for an oil refining production device, characterized in that: include: Basic information preparation module, which is used to determine the equipment type, working conditions and key operating parameters, and determine the raw materials, product types and material parameter information; A simulation calculation data acquisition module, which is used to establish a device model through process simulation software to obtain simulation calculation data of hydrogen content of the raw material and the product under different working conditions; An experimental test data acquisition module, which is used to perform sampling and experimental analysis on the raw materials and products under a limited set of working conditions to obtain hydrogen content experimental test data; A correction data acquisition module, which is used to fit and correct the hydrogen content experimental test data and the hydrogen content simulation calculation data under the limited set of working conditions for the oil phase product in the product, and obtain corrected hydrogen content data; The hydrogen distribution prediction model acquisition module is used to perform regression fitting on the oil phase product using the corrected hydrogen content data, the material parameter information of the feedstock oil, and the key operating parameters to obtain the device hydrogen distribution prediction model as follows: Among them, F f，i is the feed flow rate of the device, t / h; f Hf，i is the hydrogen content of feed stream i, m%; F p，j is the flow rate of device product j, t / h; f Hp，j is the hydrogen content of product stream j, m%; T v is the average boiling point of the feed volume, K; ρ is the feed density, kg / m3; S is the feed sulfur content, m%; N is the feed nitrogen content, m%; T is the reaction temperature of the device, K; P is the reaction pressure of the device, MPa; a j 、b j 、c j d j 、e j , A 1，j , A 2，j , B 1，j is the model coefficient.

14. A memory, characterized in that: The method comprises an instruction set, wherein the instruction set is suitable for a processor to execute the steps of the method for predicting hydrogen distribution in an oil refinery production device as claimed in any one of claims 1 to 12.

15. A hydrogen distribution prediction device for an oil refining production unit, characterized in that: comprising a bus, an input device, an output device, a processor and a memory as claimed in claim 14; The bus is used to connect the memory, the input device, the output device and the processor; The input device and the output device are used to implement interaction with a user; The processor is configured to execute an instruction set in the memory.

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