Molecular atmospheric and vacuum corrosion simulation method and device

Abstract

The invention discloses a molecular-level atmospheric and vacuum corrosion simulation method and device, and the method comprises the steps: obtaining first macroscopic property data of current crude oil according to a crude oil physical property analysis model; according to a preset corrosion molecule library and the first macroscopic property data, obtaining a first molecule concentration of corrosion molecules in the current crude oil; acquiring virtual components of corrosion molecules in the current crude oil according to a preset physical property database and the first molecular concentration; and according to preset DCS data and the virtual components of the corrosion molecules, carrying out corrosion iteration simulation on the atmospheric and vacuum distillation unit, and outputting a simulation result of the distribution of the corrosion molecules at each part in the atmospheric and vacuum distillation unit. Compared with the prior art, the accuracy and timeliness of corrosion molecule distribution prediction of each part of the atmospheric and vacuum distillation unit can be improved.

Description

Technical Field

[0001] The present invention relates to the field of corrosion simulation of petroleum refining units, and particularly to a molecular-level atmospheric and vacuum corrosion simulation method and device. Background Art

[0002] In the process of petroleum refining, distillation is the first process of crude oil processing. Atmospheric and vacuum distillation fractionates crude oil into components such as gasoline, kerosene, diesel, wax oil, and residue, which is a key process for subsequent processing. However, with the deep processing of crude oil, the proportion of heavy, high-sulfur, high-nitrogen, and high-salt crude oil has been increasing year by year, resulting in enhanced corrosiveness of the acidic water in the atmospheric and vacuum units, serious salt fouling in equipment such as heat exchangers and air coolers, and affecting the safe and stable operation of the units. To improve enterprise efficiency and reduce unplanned shutdowns, it has become an urgent problem to carry out research on the safe corrosion of atmospheric and vacuum units. The corrosion of atmospheric and vacuum units mainly stems from the thermal decomposition or hydrolysis of chlorides and sulfides in crude oil during distillation, generating corrosive media such as hydrogen chloride, hydrogen sulfide, and organic acids. These media react with the metal materials of equipment and pipelines, resulting in corrosion. Chlorine in crude oil exists in two forms: inorganic chlorine and organic chlorine. Although the electro-de-salting unit can remove 70% - 100% of inorganic chlorides, there are still a small amount of inorganic chlorides and some organic chlorides entering the downstream refining units. During the processing, the hydrolysis of inorganic chlorides and the decomposition of organic chlorides will produce hydrogen chloride gas, causing serious corrosion to production units and pipelines. In addition, nitrogen-containing compounds in crude oil are easily oxidized to form gums and sediments, affecting the oxidation stability of oil products, causing catalyst poisoning, and generating ammonium salts to cause under-deposit corrosion of the unit. Sulfides generate hydrogen sulfide during processing, causing chemical corrosion and stress corrosion of equipment. Petroleum acids can also cause serious corrosion damage to the unit. Therefore, it is of great significance to understand the distribution of corrosion media such as chlorine, nitrogen, sulfur, and acids in the unit.

[0003] Due to the complexity of crude oil components, current atmospheric and vacuum distillation simulations usually perform characterization calculations through pseudo-components. The construction of pseudo-components is mainly based on boiling points and does not involve the molecular level. If information at the molecular level needs to be processed simultaneously, a molecular boiling point mapping method needs to be used. However, molecular boiling point mapping only considers the single-dimensional information of boiling points. Moreover, since corrosive molecules have characteristics such as a wide carbon number distribution, a wide boiling point range, a regular, concentrated, and trace content distribution, using the molecular boiling point mapping method is likely to introduce errors that are difficult to capture in physical property calculations, making it difficult to accurately depict the impact of the interaction between corrosive molecules and other molecules on the prediction of concentration distribution, and thus posing an obstacle to subsequent description of the pyrolysis process of corrosive molecules during atmospheric and vacuum distillation. Specifically, traditional pseudo-component physical property calculations are mainly based on a large number of empirical regression formulas. The input is usually only the macroscopic density and distillation range temperature of the crude oil, and the output is the content and corresponding properties of pseudo-components in a certain distillation range temperature segment. Due to the low content of corrosive molecules, the regression empirical formulas generally rarely use the concentration distribution of corrosive molecules as the correlation input. In the distillation fraction interval with a high concentration of corrosive molecules, the calculation of pseudo-component physical properties will be greatly affected. In addition, the results of traditional pseudo-component division mainly give the content and properties of each component changing with the boiling point. However, due to the very low concentration of corrosive molecules, the content ratio mapped to each pseudo-component is even less. If the corrosive molecules are mapped to the corresponding pseudo-components according to the traditional division method and then separated and calculated, and then the concentration distribution is calculated through inverse mapping, due to the fixity of the mapping and the subjectivity of property calculation, the distribution of corrosive molecules will be affected by the accompanying main hydrocarbon components.

[0004] In the actual industrial production process, there are certain difficulties in achieving molecular-level simulation. One of the reasons is the inability to perform real-time and detailed characterization of crude oil. Traditional atmospheric and vacuum distillation simulation technologies usually rely on daily crude oil detections at a relatively low frequency and lack the robustness of imported crude oil detection data, resulting in a large deviation between the process calculation results and the actual results, and it is difficult to cope with the dynamically changing crude oil and detection means of enterprises. Specifically, in the production process, the frequency of adding, modifying, and detecting crude oil property items often cannot keep up with the changes in crude oil. With the improvement of the processing capacity of atmospheric and vacuum distillation units, the properties of crude oil processed in the same batch or even on the same day may vary. If one wants to monitor the changes in crude oil properties in real time and simulate its impact on production, it will not only increase the detection cost but also be difficult to ensure timeliness. Summary of the Invention

[0005] The present invention provides a molecular-level atmospheric and vacuum corrosion simulation method and device, which improve the accuracy and timeliness of predicting the distribution of corrosive molecules in various parts of atmospheric and vacuum distillation units.

[0006] In a first aspect, an embodiment of the present invention provides a molecular-level atmospheric and vacuum corrosion simulation method, including: Obtaining first macroscopic property data of the current crude oil according to a crude oil physical property analysis model; Obtain the first molecular concentration of the corrosive molecules in the current crude oil according to the preset corrosive molecule library and the first macroscopic property data; Obtain the virtual components of the corrosive molecules in the current crude oil according to the preset physical property database and the first molecular concentration; Perform corrosion iterative simulation on the atmospheric and vacuum distillation unit according to the preset DCS data and the virtual components of the corrosive molecules, and output the simulation results of the distribution of corrosive molecules in each part of the atmospheric and vacuum distillation unit; wherein, the preset DCS data is obtained in real time by the DCS system for the entire technological process of atmospheric and vacuum distillation.

[0007] In the embodiment of the present invention, by obtaining the first macroscopic property data of the current crude oil, it provides a basis for oil sample matching for subsequent obtaining the first molecular concentration of the corrosive molecules; by obtaining the first molecular concentration of the corrosive molecules in the current crude oil, it provides a data basis for subsequent obtaining the virtual components of the corrosive molecules; by obtaining the virtual components of the corrosive molecules, it can more precisely reflect the characteristics and distribution of the corrosive molecules, thereby improving the accuracy of the simulation; by running the corrosion iterative simulation, it can adjust the molecular concentration of the virtual components in real time, thereby accurately predicting the distribution of corrosive molecules in each part of the atmospheric and vacuum distillation unit. Compared with the prior art, the present application can improve the accuracy and timeliness of predicting the distribution of corrosive molecules in each part of the atmospheric and vacuum distillation unit.

[0008] Further, the obtaining of the first macroscopic property data of the current crude oil according to the crude oil physical property analysis model is specifically: Obtain the first mid-infrared spectrogram of the current crude oil; wherein, the first mid-infrared spectrogram is obtained by measuring the attenuated total reflection accessory with a mid-infrared spectrometer, and the current crude oil is provided on the attenuated total reflection accessory; Based on the first mid-infrared spectrogram, obtain the first macroscopic property data of the current crude oil through the crude oil physical property analysis model.

[0009] In the embodiment of the present invention, through the crude oil physical property analysis model, the accurate calculation of the first macroscopic property data of the crude oil can be achieved in a short time.

[0010] Further, the crude oil physical property analysis model is constructed based on historical processed oil samples, specifically: Obtain the second mid-infrared spectrogram of the historical processed oil samples; wherein, the second mid-infrared spectrogram is obtained by measuring the attenuated total reflection accessory with a mid-infrared spectrometer, and the historical processed oil samples are provided on the attenuated total reflection accessory; Determine the macroscopic property data corresponding to each preset interval in the second mid-infrared spectrogram, and construct a crude oil physical property analysis model according to the correlation relationship between each preset interval and the macroscopic property data.

[0011] In the embodiments of the present invention, by using a mid-infrared spectrometer, it is possible to better capture the vibrations of complex molecules and chemical bonds, which is more suitable for the analysis of the physical properties of crude oil; by selecting the interval of the mid-infrared spectrogram, the model can be simplified.

[0012] Further, the obtaining of the first molecular concentration of the corrosion molecules in the current crude oil according to the preset corrosion molecule library and the first macroscopic property data is specifically as follows: According to the first macroscopic property data, search for the second macroscopic property data matching the first macroscopic property data in the corrosion molecule library; wherein, each second macroscopic property data corresponds to a historical processed oil sample. Compare the first macroscopic property data with the second macroscopic property data, and adjust the second molecular concentration of the corrosion molecules in the historical processed oil sample according to the error, and use the adjusted second molecular concentration as the first molecular concentration of the corrosion molecules in the current crude oil.

[0013] In the embodiments of the present invention, through the preset corrosion molecule library and the first macroscopic property data of the current crude oil, the first molecular concentration of the corrosion molecules in the current crude oil can be quickly matched, reducing the molecular detection frequency of the crude oil and meeting the actual production requirements.

[0014] Further, the corrosion molecule library is constructed based on historical processed oil samples, specifically as follows: Obtain the second molecular concentration of all corrosion molecules in the historical processed oil sample; wherein, the second molecular concentration is obtained through crude oil molecular detection technology. Based on the second macroscopic property data and the second molecular concentration of the historical processed oil sample, construct a corrosion molecule library.

[0015] In the embodiments of the present invention, by constructing a corrosion molecule library, the first macroscopic property data and the first molecular concentration of the current crude oil can be quickly matched, providing a data basis for the subsequent molecular characterization of the corrosion medium of the crude oil.

[0016] Further, the obtaining of the virtual components of the corrosion molecules in the current crude oil according to the preset physical property database and the first molecular concentration is specifically as follows: Classify the corrosion molecules of the current crude oil according to different species, and classify the corrosion molecules of each species according to one or more physical property data to obtain the lumps to which each corrosion molecule belongs; wherein, the physical property data is obtained from the physical property database. Based on the first molecular concentration and the physical property mixing calculation rule, obtain the first physical property data of each lump. Based on the first molecular concentration and the first physical property data, obtain the virtual components of the corrosion molecules in the current crude oil.

[0017] In the embodiments of the present invention, by classifying corrosion molecules according to genus, the independence of the distribution of each individual type of corrosion molecule can be ensured; by dividing the corrosion molecules of each genus through one or more physical property data, corrosion molecules with different characteristics in the same type can be obtained, and the chemical reaction process in the atmospheric and vacuum distillation process can be described through their mutual transformation.

[0018] Further, the physical property database is constructed based on historical processed oil samples, specifically as follows: Obtain the second physical property data of all corrosion molecules in the historical processed oil samples; wherein, the second physical property data is obtained through the group physical property calculation rules in the group contribution method; Construct a physical property database based on the second physical property data.

[0019] In the embodiments of the present invention, by constructing a physical property database, the physical property data of different genus corrosion molecules can be quickly obtained, providing a data basis for subsequent virtual component physical property calculations.

[0020] Further, the corrosion iterative simulation of the atmospheric and vacuum distillation unit is performed according to the preset DCS data and the virtual components of the corrosion molecules, and the simulation results of the distribution of corrosion molecules in each part of the atmospheric and vacuum distillation unit are output, specifically as follows: Perform corrosion iterative simulation on the atmospheric and vacuum distillation unit according to the preset calculation sequence, the virtual components of the corrosion molecules in the current iteration, and the DCS data in the current iteration until the DCS data error reaches the preset range, and output the DCS data in the current iteration and the simulation results of the distribution of corrosion molecules in each part of the atmospheric and vacuum distillation unit in the current iteration; wherein, in each iteration, the virtual components of the corrosion molecules are updated according to the DCS data error in the previous iteration; the DCS data error is calculated based on the DCS data in the current iteration and the preset DCS data; the calculation sequence is determined according to the equipment flow of the atmospheric and vacuum distillation process.

[0021] In the embodiments of the present invention, through the preset calculation sequence, it can be ensured that the calculation of each device does not depend on the devices that have not been calculated yet, thereby reducing calculation errors and improving calculation efficiency; by continuously adjusting the virtual component concentration of the crude oil through the DCS data error, it can be ensured that the simulation results are closer to the actual production situation.

[0022] Further, updating the virtual components of the corrosion molecules according to the DCS data error in the previous iteration includes taking the molecular concentration of the virtual components as the independent variable and the DCS data error as the dependent variable, and obtaining the optimal value of the independent variable through the SQP optimization algorithm, specifically as follows: ; Wherein, is the DCS data error; is the Error weight corresponding to the DCS data of No. For the error corresponding to the DCS data of No., specifically: ; Wherein, For the error mapping equation of No.; For the molecular concentration of the virtual component of No.

[0023] In the embodiment of the present invention, the SQP optimization algorithm is used to minimize the error between the simulation result and the actual operation data, obtain the optimal molecular concentration of the virtual component, and thus obtain a more accurate simulation result.

[0024] In a second aspect, an embodiment of the present invention provides a molecular-level atmospheric and vacuum corrosion simulation device, including: A crude oil quick evaluation module, configured to obtain first macroscopic property data of the current crude oil according to a crude oil physical property analysis model; A molecular analysis module, configured to obtain a first molecular concentration of corrosion molecules in the current crude oil according to a preset corrosion molecular library and the first macroscopic property data; A component mapping module, configured to obtain virtual components of corrosion molecules in the current crude oil according to a preset physical property database and the first molecular concentration; A simulation operation module, configured to perform corrosion iterative simulation on the atmospheric and vacuum distillation unit according to preset DCS data and the virtual components of the corrosion molecules, and output a simulation result of the distribution of corrosion molecules in each part of the atmospheric and vacuum distillation unit.

[0025] In the embodiment of the present invention, the first macroscopic property data of the current crude oil is obtained through the crude oil quick evaluation module, providing a basis for oil sample matching for subsequent obtaining of the first molecular concentration of corrosion molecules; the first molecular concentration of corrosion molecules in the current crude oil is obtained through the molecular analysis module, providing a data basis for subsequent obtaining of virtual components of corrosion molecules; the virtual components of corrosion molecules are obtained through the component mapping module, which can more finely reflect the characteristics and distribution of corrosion molecules, thereby improving the accuracy of simulation; by running the corrosion iterative simulation through the simulation operation module, the molecular concentration of the virtual component can be adjusted in real time, so as to accurately predict the distribution of corrosion molecules in each part of the atmospheric and vacuum distillation unit. Compared with the prior art, the present application can improve the accuracy and timeliness of predicting the distribution of corrosion molecules in each part of the atmospheric and vacuum distillation unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic flowchart of the molecular-level atmospheric and vacuum corrosion simulation method provided by the embodiment of the present invention; Figure 2Schematic flow chart of corrosion iteration simulation provided by an embodiment of the present invention; Figure 3 Schematic flow chart of device calculation algorithm provided by an embodiment of the present invention; Figure 4 Schematic flow chart of calculating error propagation path provided by an embodiment of the present invention; Figure 5 High-resolution mass spectrometry of historical processed oil samples - detection result graph of sulfur-containing compounds provided by an embodiment of the present invention; Figure 6 Mid-infrared spectrogram of current crude oil provided by an embodiment of the present invention; Figure 7 Flow chart of a certain refinery unit provided by an embodiment of the present invention; Figure 8 Flow topology network graph of a certain refinery unit provided by an embodiment of the present invention; Figure 9 Comparison graph before and after virtual component adjustment provided by an embodiment of the present invention; Figure 10 Schematic structural diagram of a molecular-level atmospheric and vacuum corrosion simulation device provided by an embodiment of the present invention. Detailed implementation manners

[0027] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0028] Please refer to Figure 1 , a molecular-level atmospheric and vacuum corrosion simulation method provided by an embodiment of the present invention, includes steps S101 to S104, which are described in detail as follows: Step S101, according to the crude oil physical property analysis model, obtain the first macroscopic property data of the current crude oil.

[0029] In this embodiment, the obtaining of the first macroscopic property data of the current crude oil according to the crude oil physical property analysis model is specifically as follows: Obtain the first mid-infrared spectrogram of the current crude oil; wherein, the first mid-infrared spectrogram is obtained by measuring the attenuated total reflection accessory with a mid-infrared spectrometer, and the current crude oil is provided on the attenuated total reflection accessory; Based on the first mid-infrared spectrogram, obtain the first macroscopic property data of the current crude oil through the crude oil physical property analysis model; optionally, the first macroscopic property data includes specific gravity, water content, sulfur content, residual carbon, acid value, basic nitrogen, viscosity, etc., and specific details can be seen in Table 1.

[0030] Table 1 - Table of Partial First Macroscopic Property Data and Reference Standards In this embodiment, through the crude oil physical property analysis model, the accurate calculation of the first macroscopic property data of crude oil can be achieved in a short time.

[0031] In this embodiment, the crude oil physical property analysis model is constructed based on historical processed oil samples, specifically as follows: Obtain the second mid-infrared spectrogram of the historical processed oil sample; wherein, the second mid-infrared spectrogram is obtained by measuring the attenuated total reflection accessory with a mid-infrared spectrometer, and the historical processed oil sample is set on the attenuated total reflection accessory; Determine the macroscopic property data corresponding to each preset interval in the second mid-infrared spectrogram, and construct a crude oil physical property analysis model according to the correlation relationship between each preset interval and the macroscopic property data; optionally, the interval selection uses the correlation coefficient method; optionally, the model selection includes partial least squares, neural network, similarity matching, or kernel partial least squares method, etc., and the optimal crude oil physical property analysis model is automatically established and selected through the developed optimal algorithm.

[0032] In this embodiment, the mid-infrared spectrometer can better capture the vibrations of complex molecules and chemical bonds, and is more suitable for crude oil physical property analysis; the model can be simplified by selecting intervals of the mid-infrared spectrogram.

[0033] Step S102, according to the preset corrosion molecule library and the first macroscopic property data, obtain the first molecular concentration of the corrosion molecules in the current crude oil.

[0034] In this embodiment, the obtaining of the first molecular concentration of the corrosion molecules in the current crude oil according to the preset corrosion molecule library and the first macroscopic property data is specifically as follows: According to the first macroscopic property data, search for the second macroscopic property data matching the first macroscopic property data in the corrosion molecule library; wherein, each second macroscopic property data corresponds to a historical processed oil sample; optionally, the matching method uses clustering and similarity matching; Compare the first macroscopic property data with the second macroscopic property data, and adjust the second molecular concentration of the corrosive molecules in the historical processed oil sample according to the error. Take the adjusted second molecular concentration as the first molecular concentration of the corrosive molecules in the current crude oil. Optionally, use the first macroscopic property data of a single stream of crude oil before blending to match in the corrosive molecule library, obtain the second molecular concentration of the corrosive molecules in the single stream of crude oil, and then mix according to the crude oil blending ratio to obtain the second molecular concentration of the corrosive molecules in the blended crude oil. Optionally, the second macroscopic property data corresponds to the blended crude oil and is calculated based on the second molecular concentration of the corrosive molecules in the blended crude oil and a physical property calculation algorithm.

[0035] In this embodiment, through the preset corrosive molecule library and the first macroscopic property data of the current crude oil, the first molecular concentration of the corrosive molecules in the current crude oil can be quickly matched, reducing the molecular detection frequency of the crude oil and meeting the actual production requirements.

[0036] In this embodiment, the corrosive molecule library is constructed based on historical processed oil samples, specifically as follows: Obtain the second molecular concentration of all corrosive molecules in the historical processed oil sample; where the second molecular concentration is obtained through crude oil molecular detection technology. Optionally, the crude oil molecular detection technology includes four-component analysis, elemental analysis, high-temperature simulated distillation, gas chromatography, or high-resolution mass spectrometry combined with various derivatization methods. Optionally, the corrosive molecules include sulfur (elemental sulfur, hydrogen sulfide, disulfides, mercaptans, etc. in active sulfur, thioethers, thiophenes, etc. in inactive sulfur), nitrogen (nitrogen element, pyridine, pyrrole, and their derivatives, etc.), and acids (alkanoic acid and naphthenic acid molecules). Construct a corrosive molecule library based on the second macroscopic property data and the second molecular concentration of the historical processed oil sample. Optionally, constructing the corrosive molecule library includes entering the SOL structure strings of all corrosive molecules in the historical processed oil sample, disassembling the SOL structure strings based on a proprietary algorithm, and the proprietary algorithm rules include disassembling the molecules into group types and numbers according to the group recognition priority. Optionally, the corrosive molecule library contains more than 20,000 corrosive molecules.

[0037] In this embodiment, by constructing a corrosive molecule library, the first macroscopic property data and the first molecular concentration of the current crude oil can be quickly matched, providing a data basis for the subsequent molecular characterization of the crude oil corrosion medium.

[0038] Step S103, obtain the virtual components of the corrosive molecules in the current crude oil according to the preset physical property database and the first molecular concentration.

[0039] In this embodiment, the obtaining of the virtual components of the corrosive molecules in the current crude oil according to the preset physical property database and the first molecular concentration is specifically as follows: Classify the corrosion molecules of the current crude oil according to different species, and cluster and classify the corrosion molecules of each species according to one or more physical property data to obtain the lumps to which each corrosion molecule belongs; wherein, the physical property data is obtained from a physical property database; optionally, the species includes saturated hydrocarbons, aromatic hydrocarbons, sulfur-containing molecules, nitrogen-containing molecules, acid-containing molecules, etc.; optionally, the physical property data includes boiling point, critical properties, density, etc.; optionally, the number of clusters for the clustering classification can be given in advance; Based on the first molecular concentration and the physical property mixing calculation rules, obtain the first physical property data of each of the lumps; optionally, the first physical property data includes acentric factor (w), critical properties (Tc, Pc, Vc, and Zc), saturation vapor pressure (Psat), gas-liquid phase heat capacity, and other physical property data required for process simulation; Based on the first molecular concentration and the first physical property data, obtain the virtual components of the corrosion molecules in the current crude oil.

[0040] Exemplarily, when the corrosion molecules of each species are classified according to the boiling point and the boiling point classification method is unified, the first physical property data of each lump can be corrected. Specifically: Use the true boiling point curve (V-Tb) of the crude oil or D86 experimental data, and the measured average density value of the crude oil to cut the current crude oil by temperature section to obtain the volume, density, and boiling point of the crude oil components (i.e., virtual components) in each temperature section; Based on the boiling point (Tb) and density of each section of crude oil components, use empirical formulas for calculating crude oil physical properties, such as the TWU method or the LK method, etc., to obtain the first physical property data of each section of crude oil components, and the first physical property data includes acentric factor (w), critical properties (Tc, Pc, Vc, and Zc), saturation vapor pressure (Psat), gas-liquid phase heat capacity, and other physical property data required for process simulation; Compare the first physical property data of the virtual lumps in the same temperature section with the first physical property data obtained by regression of the empirical formula, and evaluate a specific temperature range that significantly deviates from the empirical calculation law. If the non-hydrocarbon content in this range is significantly higher or lower than the adjacent temperature range, it can be considered that this result is reasonable. If the non-hydrocarbon content in this range is relatively close to the adjacent temperature range, the first physical property data of the virtual lumps needs to be corrected to a certain extent; Finally, the lump flow direction corresponding to each molecule, the content of each lump, the first physical property data of each lump, and the first molecular concentration of each lump can be obtained.

[0041] In this embodiment, classifying corrosion molecules by species can ensure the independence of the distribution of each individual type of corrosion molecules; classifying the corrosion molecules of each species according to one or more physical property data can obtain corrosion molecules with different characteristics in the same type, and can describe the chemical reaction process in the atmospheric and vacuum distillation process through the conversion between each other.

[0042] In this embodiment, the physical property database is constructed based on historical processed oil samples, specifically as follows: Obtain the second physical property data of all corrosive molecules in the historical processed oil samples; wherein, the second physical property data is obtained through the group physical property calculation rules in the group contribution method; optionally, the second physical property data includes boiling point (Tb), acentric factor (w), gas-liquid phase heat capacity, molecular enthalpy of formation, and molecular Gibbs free energy, etc. Construct a physical property database based on the second physical property data; optionally, the physical property database contains more than 100,000 pieces of second physical property data.

[0043] In this embodiment, by constructing a physical property database, the physical property data of different species of corrosive molecules can be quickly obtained, providing a data basis for subsequent virtual component physical property calculations.

[0044] Step S104: Perform corrosion iterative simulation on the atmospheric and vacuum distillation unit according to the preset DCS data and the virtual components of the corrosive molecules, and output the simulation results of the distribution of corrosive molecules in each part of the atmospheric and vacuum distillation unit.

[0045] In this embodiment, the performing corrosion iterative simulation on the atmospheric and vacuum distillation unit according to the preset DCS data and the virtual components of the corrosive molecules, and outputting the simulation results of the distribution of corrosive molecules in each part of the atmospheric and vacuum distillation unit is specifically as follows: According to the preset calculation sequence, the virtual components of the corrosion molecules in the current iteration, and the DCS data in the current iteration, perform corrosion iteration simulation on the atmospheric and vacuum distillation unit until the DCS data error reaches the preset range, and output the DCS data in the current iteration and the simulation results of the distribution of corrosion molecules in each part of the atmospheric and vacuum distillation unit in the current iteration; wherein, in each iteration, update the virtual components of the corrosion molecules according to the DCS data error in the previous iteration; the DCS data error is calculated based on the DCS data in the current iteration and the preset DCS data; the calculation sequence is determined according to the equipment process of the atmospheric and vacuum distillation process; optionally, the DCS data includes the steady-state data of temperature, pressure, and flow control instruments at the inlet and outlet pipelines of each equipment, as well as parameters for corrosion iteration simulation such as the reflux ratio and draw-off rate of the tower equipment, and other non-simulation parameters for error comparison; optionally, before running the corrosion iteration simulation, it is also necessary to obtain the equipment data of the atmospheric and vacuum distillation process, and the equipment data includes the design parameters of the tower equipment, the number of trays and tray types of the plate column, the packing properties, specifications, and distribution of the packed column, the feed position, the tray numbers of the middle section reflux draw-off, the reflux tray numbers of the middle section reflux, the tray numbers of the side line steam stripping draw-off, the reflux tray numbers of the side line steam stripping, and the tray numbers of the side line draw-off, etc.; optionally, the equipment data is obtained from the equipment design drawings; optionally, the DCS data is obtained in real time by the DCS system for the entire process of the atmospheric and vacuum distillation process; optionally, when obtaining the DCS data and equipment data, it is necessary to sort out the variable set required for stream calculation according to the equipment-stream relationship table, and link the variable set required by the unit with the algorithm variable names, and finally form a large json file, which also needs to additionally add the variable names corresponding to the DCS data used for error comparison and their DCS data; optionally, before running the corrosion iteration simulation, it is necessary to receive two files, namely the physical property data global variable json and the process input parameter json.

[0046] Exemplarily, the calculation order is determined according to the equipment process of the atmospheric and vacuum distillation process. Specifically: according to the actual atmospheric and vacuum distillation process flow, draw the unit flow chart; when drawing the unit flow chart, continuous heat exchange equipment and continuous pressure equipment need to be simplified. The top condenser, tank, shunt, and intermediate reflux, and side line stripping module need to be incorporated into the corresponding tower as one equipment. The packed vacuum tower needs to be converted into a plate tower. The number of theoretical plates can be obtained by calculating the height equivalent to a theoretical plate according to the packing type; according to the unit flow chart and the method for identifying indivisible subsystems, construct an algorithm connection topology structure and obtain the equipment-stream relationship table. There will be no completely unrelated equipment and streams in the topology structure; the equipment-stream relationship table records the stream subordination relationship with 1 and 0, describes the mutual relationship between the units of the system, and the input and output relationship of the material flow. Its nodes represent equipment units, and the directed lines represent the material and information flow; convert the equipment-stream relationship table into an equipment adjacency matrix. The equipment adjacency matrix is composed of 0 and 1. The cell corresponding to 1 represents the equipment in the abscissa flowing to the equipment in the ordinate; perform matrix multiplication on the equipment adjacency matrix. After obtaining the new matrix, extract the rows corresponding to the columns all of 0 as the equipment to be calculated first, and remove the rows and columns to form a new matrix. Repeat the calculation until the last equipment; one iteration of the corrosion simulation will obtain the name of the currently calculated equipment and the number of equipment that still needs to be calculated. Determine whether it is the last equipment according to the number, and extract the corresponding parameters of the equipment and pass them into the equipment calculation algorithm for calculation to obtain the result.

[0047] Exemplarily, the specific process of the corrosion iteration simulation is as Figure 2 shown. Determine whether the calculation is successful according to the status information. If it is successful and not the last equipment, continue to calculate the next equipment; if it fails and is not the last equipment, directly report an error and exit the simulation; if it is successful and is the last equipment, perform the DCS data error judgment; if it fails and is the last equipment, report an error and exit; after the DCS data error judgment, if the error is less than the threshold, end the calculation to obtain the simulation result, otherwise call the crude oil adjustment algorithm, obtain a new crude oil composition again, and repeat the above steps for verification; according to the equipment type, use the corresponding equipment calculation algorithm to obtain the output data of each equipment. The internal operation logic of the equipment calculation algorithm is as Figure 3 shown; among them, the mechanism models of the equipment calculation algorithm include a distillation mechanism model, a pressure transformation mechanism model, a heat exchange mechanism model, and a simple treatment mechanism model according to different equipment units. All models are based on the mass conservation equation, energy conservation equation, phase equilibrium equation, and component equation to form their own numerical solution methods for nonlinear equations; the solution algorithms include the self-developed basic flash iteration solution algorithm, distillation iteration solution algorithm, flash initial value given algorithm, and distillation initial value given method, etc.

[0048] In this embodiment, through the preset calculation order, it is possible to ensure that the calculation of each device does not depend on the devices that have not been calculated yet, thereby reducing calculation errors and improving calculation efficiency; by continuously adjusting the virtual component concentration of crude oil based on the DCS data error, it is possible to ensure that the simulation results are closer to the actual production situation.

[0049] In this embodiment, updating the virtual components of the corrosion molecules according to the DCS data error of the last iteration includes taking the molecular concentration of the virtual components as the independent variable and the DCS data error as the dependent variable, and obtaining the optimal value of the independent variable based on the principle of minimum error through the idea of mathematical iterative solution. This embodiment believes that the main ways in which the crude oil error affects the propagation of the final calculation error are mainly as Figure 4 shown.

[0050] Exemplarily, it is assumed that a non-linear equation system is propagated in each calculation layer, and its variables are the results of the previous layer. In subsequent calculations, the error mapping equation of the DCS data is defined according to the level to which the DCS data belongs, and finally a complex non-linear equation system is formed. The calculation formula is as follows: ; where, is the DCS data error; is the error weight corresponding to the rd DCS data, which is specified by the user; is the error corresponding to the th DCS data, specifically: .

[0051] In the process of corrosion iterative simulation, the physical property calculation layer is the most basic layer. Since highly non-linear mathematical calculations such as square root and logarithm are used in the strict calculation of physical properties, the error equation should include square root and logarithm terms. Since the input range of the logarithm term is greater than 0 and does not include 0, the exponential term is directly used instead. In addition, quadratic terms, linear terms, and constant terms are added. The error mapping equation of the physical property calculation layer is calculated as follows: ; where, are the weights of each term of the polynomial respectively; and are the temperature and pressure; is the weight of the temperature and pressure, and this weight is 0 when the input is the crude oil temperature and pressure; and are the enthalpy and equilibrium constant.

[0052] In the flash calculation layer and the unit calculation layer, since the core principle is the MESH equation (material balance, energy balance, normalization, and energy balance equations), that is, in equals out, it can be considered a system of linear equations. Therefore, the error mapping equation is also simplified to a system of linear equations in this embodiment. The error mapping equation of the flash calculation layer has the following calculation formula: ; The error mapping equation of the unit calculation layer has the following calculation formula: ; where is the molar flow rate.

[0053] The outermost error mapping equation has the following calculation formula: .

[0054] For , the specific parameters of its system of nonlinear equations, such as are unknown. Therefore, the SQP optimization algorithm is used, that is, an error feedback method is constructed. By continuously adjusting the values of the unknown variables, the error is calculated. By reducing the difference between and , the optimal parameters of the system of nonlinear equations are obtained; through error weight setting, is calculated; through the SQP optimization algorithm, the virtual component molecular concentration under the minimum is obtained.

[0055] Optionally, in this embodiment, for the specific corrosion behavior and actual requirements, the strict mechanism model also combines a basic algorithm to construct an algorithm for calculating corrosion parameters of the stream, improving the universality and accuracy of the corrosion model and giving the corrosion result.

[0056] In this embodiment, the SQP optimization algorithm is used to minimize the error between the simulation result and the actual operation data, obtaining the optimal virtual component molecular concentration, thereby obtaining a more accurate simulation result.

[0057] To better demonstrate the beneficial effects of the present invention, this embodiment provides a specific example, taking a refinery in Shandong as the analysis object to show the specific implementation process of the molecular-level atmospheric and vacuum corrosion simulation method.

[0058] In this specific embodiment, a corrosion molecule library and a physical property database are constructed based on historical processed oil samples. High-resolution mass spectrometry is performed on each of the historical processed oil samples to obtain the second molecular concentration of each historical processed oil sample, and the simulated distillation results are recorded; after the high-resolution mass spectrometry is processed, asFigure 5 As shown, the second macroscopic property data of the historical processed oil samples for building the library are shown in Table 2, and the results calculated based on the group contribution method for the historical processed oil samples for building the library are shown in Table 3.

[0059] Table 2 - Data Table of the Second Macroscopic Properties of Some Historical Processed Oil Samples for Building the Library Table 3 - Calculation Result Table of Some Group Contribution Methods In this specific embodiment, the blending ratio of the current crude oil is crude oil a: crude oil b = 30:70. The mid-infrared spectrum scan results of the blended current crude oil are as Figure 6 shown. The first macroscopic property data in Table 4 are obtained through the crude oil physical property analysis model, and the crude oil physical property analysis model is constructed based on the historical processed oil samples.

[0060] Table 4 - Data Table of the First Macroscopic Properties of Some Current Crude Oils In this specific embodiment, the first macroscopic property data of crude oil a and crude oil b have been measured when the crude oil is purchased and put into the factory, as shown in Table 5 respectively; the second macroscopic property data matching the first macroscopic property data are searched from the corrosion molecule library; among them, each second macroscopic property data corresponds to a historical processed oil sample; the second macroscopic property data of the historical processed oil samples are shown in Table 6; the second molecular concentrations of the historical processed oil samples are shown in Table 7, and the second molecular concentrations of the historical processed oil samples are mixed according to the blending ratio to obtain the initial blended second molecular concentration.

[0061] Table 5 - Data Table of the First Macroscopic Properties of Some Crude Oils a and b Table 6 - Data Table of the Second Macroscopic Properties of Some Reference Historical Processed Oil Samples Table 7 - Data Table of the Second Molecular Concentrations of Some Reference Historical Processed Oil Samples In this specific embodiment, the first macroscopic property data of the crude oil a and the crude oil b are compared with the second macroscopic property data of the reference historical processed oil sample, and the second molecular concentration of the corrosion molecules in the reference historical processed oil sample is adjusted according to the error. The adjusted second molecular concentration is used as the first molecular concentration of the corrosion molecules in the current crude oil. The first molecular concentrations before and after the adjustment are shown in Table 8; the comparison results of the second macroscopic property data of the reference historical processed oil sample and the first macroscopic property data of the current crude oil are shown in Table 9; by virtually dividing and mapping different species of corrosion molecules in combination with the boiling point, and calculating the first molecular concentration and the first physical property data of each virtual component according to the molecular concentration and the physical property mixing calculation rules, the final output is the first molecular concentration and the first physical property data of the virtual component as shown in Tables 10-11.

[0062] Table 8 - Table of the first molecular concentrations before and after partial adjustment Table 9 - Table of the comparison results of the second macroscopic property data of the partial reference historical processed oil sample and the first macroscopic property data of the current crude oil Table 10 - Table of the first molecular concentration and the first physical property data of partial virtual components Table 11 - Table of the first molecular concentrations of partial corrosion molecules In this specific embodiment, the equipment data of the refinery are obtained according to the equipment design drawings; the DCS data of the refinery are obtained according to the DCS system; according to the actual process of the refinery simplified as Figure 7The unit flow chart shown, where the packed tower VAC is mapped to 30 plates by the height equivalent to a theoretical plate, and the number of plates in the CDU tower is rounded up to 33 (including the top condenser) according to a 50% overall tower efficiency; the tag numbers of the DCS system are corresponded to the input parameters in the unit flow chart to form the result shown in Table 12; according to the requirements of the corrosion iteration simulation input variables, the feed IN, STEAM, STEAM2, S11, S13, S15 and the algorithm variables of equipment B1, B2, B3, CDU, VAC are sorted and integrated to form the stream setting table and the unit setting table shown in Tables 13 - 15; the preset DCS data for the crude oil adjustment algorithm is determined to form the result shown in Table 16.

[0063] Table 12 - Table of DCS System Tag Numbers, Process Relationships and Physical Variable Values Table 13 - Table of Stream Setting Variable Values Table 14 - Table of Heat Exchange and Simple Unit Setting Variable Values Table 15 - Table of Tower Unit Setting Variable Values Table 16 - Table of Preset DCS Data for Crude Oil Adjustment In this specific embodiment, the calculation algorithm for each device is configured according to the corresponding tag numbers, equipment data and virtual components, the overall input stream of the process is configured according to the virtual components and steam data, and the preset DCS data table for crude oil adjustment is configured with the corresponding data, and finally two json - format files are formed with the global physical properties parameters and transmitted to the mechanism algorithm interface; the threshold of the crude oil adjustment algorithm is set to 5 °C, that is, it is required that the absolute value of the absolute error between the simulated values and the actual values of the seven temperature variables is within 5 °C.

[0064] In this specific embodiment, an algorithm scheduling topology network as shown in Figure 8 is constructed and converted into a device - stream relationship table shown in Table 17, and the device - stream relationship table is converted into a json file and passed into the mechanism algorithm interface; the device - stream relationship table is input into the outermost calculation sequence algorithm to generate an adjacent matrix shown in Table 18, and after matrix operations, the calculation sequence for the corrosion iteration simulation is obtained as IN, STEAM, S11, S13, S15, STEAM2, B1, B2, B3, CDU, VAC.

[0065] Table 17 - Device - Stream Relationship Table Table 18 - Equipment - Stream Adjacent Matrix Table In this specific embodiment, according to the calculation sequence, the stream data of IN, STEAM, S11, S13, S15, and STEAM2 are retrieved by the stream algorithm; according to the calculation sequence, the algorithms for the operating equipment of B1, B2, B3, CDU, and VAC are calculated in sequence, specifically as follows: Separate the data of B1 equipment, and form the input parameters of B1 with the virtual components and the stream data of IN stream; the scheduling algorithm calls the heat exchange equipment algorithm according to the equipment type to process the input parameters; call the flash algorithm to obtain the key parameters of the outlet stream IN2, such as temperature, pressure, flow rate, and the composition of each molecule; call the stream algorithm to calculate and obtain the specific data of the outlet stream IN2, such as temperature, pressure, density, enthalpy, and volume, etc.; Separate the data of B2 equipment, and form the input parameters of B2 with the virtual components and the data of the outlet stream IN2 of B1; the scheduling algorithm calls the simple equipment algorithm according to the equipment type to process the input parameters; call the flash calculation to obtain the temperature, pressure, and load conditions in the separation tank under the set conditions; according to the gas - liquid phase relationship, separate the fluid in the tank into phases, and calculate the key parameters of each phase, such as temperature, pressure, flow rate, and the composition of each molecule; call the stream calculation algorithm to obtain the specific data of the outlet streams IN5 and IN3; Separate the data of B3 equipment, and form the input parameters of B3 with the virtual components and the data of the outlet stream IN3 of B2; the scheduling algorithm calls the heat exchange equipment algorithm according to the unit type to process the input parameters; call the flash algorithm to obtain the key parameters of the outlet stream IN4, such as temperature, pressure, flow rate, and the composition of each molecule; call the stream calculation algorithm to obtain the specific data of the outlet stream IN4; Form the input parameters of CDU with the input data such as the CDU equipment data, the data of the outlet stream IN5, the data of the outlet stream IN4, the virtual components, the main tower steam stream STEAM data, and the side tower steam stream S11, etc.; the scheduling algorithm calls the distillation equipment algorithm according to the unit type for distributed calculation. The specific calculation process is as follows: call the initial value calculation algorithm to obtain the initial values of the CDU temperature, pressure, flow rate, the composition of each molecule, and the tray distribution; transfer the tray distribution initial value and the input parameters to the solution algorithm for iteration until the square error of the tray enthalpy between two iterative calculations is less than 0.0002; call the result sorting and output algorithm to form the CDU output data, including equipment results such as the main tower distribution of the atmospheric tower, the stripping tower distribution, the middle section reflux result, and the condenser result, as well as the product stream data of V, LIQUID, SP1, SP2, SP3, and AR. The CDU corrosion molecule distribution is shown in the corresponding data before adjustment in Table 19; The VAC device data, product stream AR data, product stream VAC data, virtual components, and steam stream STEAM2 data are used to form the input parameters of VAC; the scheduling algorithm calls the distillation equipment algorithm for distribution calculation according to the unit type. The specific calculation process is the same as that of CDU. Finally, the middle reflux result, tower distribution result, and product stream SIDE1, SIDE2, SIDE3, SIDE4, VR, and VP data are obtained. The VAC corrosion molecule distribution is as shown in the corresponding data before adjustment in Table 20. Since VAC is the last device and the calculation is successful, this corrosion iteration simulation ends, and all results are summarized into a json file; Calculate the relevant DCS data error. The error result is as shown in the corresponding data before adjustment in Table 21; the mean absolute error is greater than the threshold, and the crude oil adjustment algorithm is called to obtain the virtual components as shown Figure 9 after adjustment; Perform the next corrosion iteration simulation again to obtain the CDU and VAC corrosion molecule distribution data as shown in the corresponding data after adjustment in Tables 19 and 20, and obtain the error values as shown in the corresponding data after adjustment in Table 21; the error meets the requirements, and the corrosion iteration simulation ends.

[0066] Table 19 - CDU Corrosion Molecule Distribution Data Table Table 20 - VAC Corrosion Molecule Distribution Data Table Table 21 - DCS Data Error Result Table for Crude Oil Adjustment In this specific embodiment, according to the corrosion iteration simulation results, the corrosion dew points of the CDU and VAC top, bottom, and side products are calculated to obtain the results shown in Table 22 (reference pressure is 101.325 kPa).

[0067] Table 22 - CDU and VAC Corrosion Dew Point Calculation Result Table In this embodiment, by obtaining the first macroscopic property data of the current crude oil, a basis for oil sample matching is provided for subsequently obtaining the first molecular concentration of the corrosion molecules; by obtaining the first molecular concentration of the corrosion molecules in the current crude oil, a data basis is provided for subsequently obtaining the virtual components of the corrosion molecules; by obtaining the virtual components of the corrosion molecules, the characteristics and distribution of the corrosion molecules can be more finely reflected, thereby improving the accuracy of the simulation; by presetting the DCS data and the virtual components of the corrosion molecules, the molecular concentration of the virtual components can be adjusted in real time, so as to accurately predict the distribution of the corrosion molecules in each part of the atmospheric and vacuum distillation unit. Compared with the prior art, the present application can improve the accuracy and timeliness of predicting the distribution of the corrosion molecules in each part of the atmospheric and vacuum distillation unit.

[0068] Please refer to Figure 10 , a molecular-level atmospheric and vacuum corrosion simulation device provided by an embodiment of the present invention, includes a crude oil quick evaluation module 1001, a molecular analysis module 1002, a component mapping module 1003, and a simulation operation module 1004, which are described in detail as follows: The crude oil quick evaluation module 1001 is used to obtain the first macroscopic property data of the current crude oil according to the crude oil physical property analysis model; The molecular analysis module 1002 is used to obtain the first molecular concentration of the corrosion molecules in the current crude oil according to the preset corrosion molecule library and the first macroscopic property data; The component mapping module 1003 is used to obtain the virtual components of the corrosion molecules in the current crude oil according to the preset physical property database and the first molecular concentration; The simulation operation module 1004 is used to perform corrosion iterative simulation on the atmospheric and vacuum distillation unit according to the preset DCS data and the virtual components of the corrosion molecules, and output the simulation results of the distribution of the corrosion molecules in each part of the atmospheric and vacuum distillation unit.

[0069] In this embodiment, the crude oil quick evaluation module 1001 includes a spectral scanning unit and a spectral diagram analysis unit, specifically: The spectral scanning unit is used to obtain the first mid-infrared spectral diagram of the current crude oil; wherein, the first mid-infrared spectral diagram is obtained by measuring the attenuated total reflection accessory with a mid-infrared spectrometer, and the current crude oil is arranged on the attenuated total reflection accessory; The spectral diagram analysis unit is used to obtain the first macroscopic property data of the current crude oil through the crude oil physical property analysis model based on the first mid-infrared spectral diagram.

[0070] In this embodiment, through the crude oil physical property analysis model, the accurate calculation of the first macroscopic property data of the crude oil can be realized in a short time.

[0071] In this embodiment, the molecular analysis module 1002 includes a crude oil matching unit and an error adjustment unit, specifically: The crude oil matching unit searches for the second macroscopic property data matching the first macroscopic property data in the corrosion molecule library according to the first macroscopic property data; wherein, each second macroscopic property data corresponds to a historical processed oil sample. The error adjustment unit is used to compare the first macroscopic property data with the second macroscopic property data, and adjust the second molecular concentration of the corrosion molecules in the historical processed oil sample according to the error, and use the adjusted second molecular concentration as the first molecular concentration of the corrosion molecules in the current crude oil.

[0072] In this embodiment, through the preset corrosion molecule library and the first macroscopic property data of the current crude oil, the first molecular concentration of the corrosion molecules in the current crude oil can be quickly matched, the molecular detection frequency of the crude oil can be reduced, and the actual production requirements can be met.

[0073] In this embodiment, the component mapping module 1003 includes a physical property partitioning unit and a physical property calculation unit, specifically: The physical property partitioning unit is used to partition the corrosion molecules of the current crude oil according to different species, and partition the corrosion molecules of each species according to one or more physical property data to obtain the lumps to which each corrosion molecule belongs; wherein, the physical property data is obtained from the physical property database. The physical property calculation unit is used to obtain the first physical property data of each lump based on the first molecular concentration and the physical property mixing calculation rule. The component construction unit is used to obtain the virtual components of the corrosion molecules in the current crude oil based on the first molecular concentration and the first physical property data.

[0074] In this embodiment, partitioning the corrosion molecules by species can ensure the independence of the distribution of each individual type of corrosion molecule; partitioning the corrosion molecules of each species by one or more physical property data can obtain corrosion molecules with different characteristics in the same type, and the chemical reaction process in the atmospheric and vacuum distillation process can be described by the conversion between them.

[0075] In this embodiment, the simulation operation module 1004 includes a corrosion iteration unit, specifically: An erosion iteration unit is used to perform erosion iteration simulation on the atmospheric and vacuum distillation unit according to a preset calculation sequence, the virtual components of the erosion molecules in the current iteration, and the DCS data in the current iteration until the DCS data error reaches a preset range, and output the DCS data in the current iteration and the simulation results of the distribution of erosion molecules in each part of the atmospheric and vacuum distillation unit in the current iteration; wherein, in each iteration, the virtual components of the erosion molecules are updated according to the DCS data error in the previous iteration; the DCS data error is calculated based on the DCS data in the current iteration and the preset DCS data; the calculation sequence is determined according to the equipment process of the atmospheric and vacuum distillation process.

[0076] In this embodiment, through the preset calculation sequence, it can be ensured that the calculation of each device does not depend on the devices that have not been calculated yet, thereby reducing calculation errors and improving calculation efficiency; by continuously adjusting the virtual component concentration of the crude oil through the DCS data error, it can be ensured that the simulation results are closer to the actual production situation.

[0077] The above-mentioned molecular-level atmospheric and vacuum corrosion simulation device can implement the molecular-level atmospheric and vacuum corrosion simulation method of the above method embodiment. The optional items in the above method embodiment are also applicable to this embodiment and will not be elaborated here. The remaining content of the embodiment of the present application can refer to the content of the above method embodiment and will not be repeated in this embodiment.

[0078] In this embodiment, the first macroscopic property data of the current crude oil is obtained through the crude oil quick evaluation module, providing a basis for oil sample matching for subsequent obtaining of the first molecular concentration of the erosion molecules; the first molecular concentration of the erosion molecules in the current crude oil is obtained through the molecular analysis module, providing a data basis for subsequent obtaining of the virtual components of the erosion molecules; the virtual components of the erosion molecules are obtained through the component mapping module, which can more precisely reflect the characteristics and distribution of the erosion molecules, thereby improving the accuracy of the simulation; the erosion iteration simulation is run through the simulation operation module, which can adjust the molecular concentration of the virtual components in real time, thereby accurately predicting the distribution of erosion molecules in each part of the atmospheric and vacuum distillation unit. Compared with the prior art, the present application can improve the accuracy and timeliness of predicting the distribution of erosion molecules in each part of the atmospheric and vacuum distillation unit.

[0079] The specific embodiments described above further elaborate the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above description is only for the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. In particular, it is pointed out that for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A molecular-level atmospheric and vacuum corrosion simulation method, characterized in that Applicable to atmospheric and vacuum distillation units, including: Obtain the first macroscopic property data of the current crude oil according to the crude oil physical property analysis model; Obtain the first molecular concentration of corrosive molecules in the current crude oil according to the preset corrosive molecule library and the first macroscopic property data; Obtain the virtual components of corrosive molecules in the current crude oil according to the preset physical property database and the first molecular concentration; Perform corrosion iterative simulation on the atmospheric and vacuum distillation unit according to the preset DCS data and the virtual components of the corrosive molecules, and output the simulation results of the distribution of corrosive molecules in each part of the atmospheric and vacuum distillation unit; wherein, the preset DCS data is obtained in real time by the DCS system for the entire technological process of atmospheric and vacuum distillation.

2. The molecular-level atmospheric and vacuum corrosion simulation method according to claim 1, wherein The step of obtaining the first macroscopic property data of the current crude oil according to the crude oil physical property analysis model is specifically: Obtain the first mid-infrared spectrogram of the current crude oil; wherein, the first mid-infrared spectrogram is obtained by measuring the attenuated total reflection accessory with a mid-infrared spectrometer, and the current crude oil is arranged on the attenuated total reflection accessory; Based on the first mid-infrared spectrogram, obtain the first macroscopic property data of the current crude oil through the crude oil physical property analysis model.

3. The molecular-level atmospheric and vacuum corrosion simulation method according to claim 2, characterized in that The crude oil physical property analysis model is constructed based on historical processed oil samples, specifically: Obtain the second mid-infrared spectrogram of the historical processed oil sample; wherein, the second mid-infrared spectrogram is obtained by measuring the attenuated total reflection accessory with a mid-infrared spectrometer, and the historical processed oil sample is arranged on the attenuated total reflection accessory; Determine the macroscopic property data corresponding to each preset interval in the second mid-infrared spectrogram, and construct a crude oil physical property analysis model according to the correlation relationship between each preset interval and the macroscopic property data.

4. The molecular-level atmospheric and vacuum corrosion simulation method according to claim 1, wherein The step of obtaining the first molecular concentration of corrosive molecules in the current crude oil according to the preset corrosive molecule library and the first macroscopic property data is specifically: According to the first macroscopic property data, search for the second macroscopic property data matching the first macroscopic property data in the corrosive molecule library; wherein, each second macroscopic property data corresponds to a historical processed oil sample; Compare the first macroscopic property data with the second macroscopic property data, and adjust the second molecular concentration of corrosive molecules in the historical processed oil sample according to the error, and use the adjusted second molecular concentration as the first molecular concentration of corrosive molecules in the current crude oil.

5. The molecular-level atmospheric and vacuum corrosion simulation method according to claim 4, wherein The corrosive molecule library is constructed based on historical processed oil samples, specifically: Obtain the second molecular concentration of all corrosive molecules in the historical processed oil sample; wherein, the second molecular concentration is obtained through crude oil molecular detection technology; Construct a corrosive molecule library based on the second macroscopic property data and the second molecular concentration of the historical processed oil sample.

6. The molecular-level atmospheric and vacuum corrosion simulation method according to claim 1, characterized in that The step of obtaining the virtual components of corrosive molecules in the current crude oil according to the preset physical property database and the first molecular concentration is specifically: Classify the corrosive molecules of the current crude oil according to different species, and classify the corrosive molecules of each species according to one or more physical property data to obtain the lumps to which each corrosive molecule belongs; wherein, the physical property data is obtained from the physical property database; Obtain the first physical property data of each lumping based on the first molecular concentration and the physical property mixing calculation rules; Obtain the virtual components of the corrosion molecules in the current crude oil based on the first molecular concentration and the first physical property data.

7. The molecular-level atmospheric and vacuum corrosion simulation method according to claim 6, wherein The physical property database is constructed based on historical processed oil samples, specifically: Obtain the second physical property data of all corrosion molecules in the historical processed oil samples; wherein, the second physical property data is obtained through the group physical property calculation rules in the group contribution method; Construct a physical property database based on the second physical property data.

8. The molecular-level atmospheric and vacuum corrosion simulation method according to claim 1, characterized in that Perform corrosion iterative simulation on the atmospheric and vacuum distillation unit according to the preset DCS data and the virtual components of the corrosion molecules, and output the simulation results of the distribution of corrosion molecules in each part of the atmospheric and vacuum distillation unit, specifically: Perform corrosion iterative simulation on the atmospheric and vacuum distillation unit according to the preset calculation sequence, the virtual components of the corrosion molecules in the current iteration, and the DCS data in the current iteration until the DCS data error reaches the preset range, and output the DCS data in the current iteration and the simulation results of the distribution of corrosion molecules in each part of the atmospheric and vacuum distillation unit in the current iteration; wherein, in each iteration, update the virtual components of the corrosion molecules according to the DCS data error in the previous iteration; the DCS data error is calculated based on the DCS data in the current iteration and the preset DCS data; the calculation sequence is determined according to the equipment process of the atmospheric and vacuum distillation process.

9. The molecular-level atmospheric and vacuum corrosion simulation method according to claim 8, characterized in that, Updating the virtual components of the corrosion molecules according to the DCS data error in the previous iteration includes using the molecular concentration of the virtual components as the independent variable and the DCS data error as the dependent variable, and obtaining the optimal value of the independent variable through the SQP optimization algorithm, specifically: ； Among them, is the DCS data error; is the error weight corresponding to the DCS data of No. is the error corresponding to the DCS data of No. , specifically: ； Among them, is the th error mapping equation; [[ID=⑥]]is the th molecular concentration of the virtual component. It should be noted that there is a mislabeling in your original text where "⑥" is used instead of "6" in the Chinese text. I have translated it as "th" which is a placeholder for the correct ordinal number in the context. You may want to correct the original text for more accurate translation.

10. A molecular-level atmospheric and vacuum corrosion simulation device, characterized in that, Include: A crude oil quick review module for obtaining the first macroscopic property data of the current crude oil according to the crude oil physical property analysis model; A molecular analysis module for obtaining the first molecular concentration of the corrosion molecules in the current crude oil according to the preset corrosion molecule library and the first macroscopic property data; A component mapping module for obtaining the virtual components of the corrosion molecules in the current crude oil according to the preset physical property database and the first molecular concentration; A simulation operation module for performing corrosion iterative simulation on the atmospheric and vacuum distillation unit according to the preset DCS data and the virtual components of the corrosion molecules, and outputting the simulation results of the distribution of corrosion molecules in each part of the atmospheric and vacuum distillation unit.

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