Heavy Oil Dirt Deposition Rate Prediction Method, Device, Electronic Equipment and Storage Medium

Abstract

The present invention discloses a method for predicting the fouling deposition rate of heavy oil, comprising the steps of: acquiring on-site monitoring data and equipment parameters; performing regression processing on a fouling thermal resistance deposition rate model based on the acquired on-site monitoring data and equipment parameters; and predicting the fouling deposition rate of heavy oil based on the fouling thermal resistance deposition rate model after regression processing, wherein the fouling thermal resistance deposition rate model includes the influence of mass transfer rate and reaction probability coefficient correction. The present invention also discloses a device for predicting the fouling deposition rate of heavy oil, an electronic device, and a non-transient computer-readable storage medium. By considering the influence of mass transfer rate and reaction probability coefficient correction in the fouling thermal resistance deposition rate model, the present invention makes the prediction result closer to reality and can fully adapt to the situation where the fouling thermal resistance development rate fluctuates greatly due to large changes in asphaltene mass transfer rate and large changes in asphaltene gelation of oil products at different temperatures and pressures, thereby providing a more reliable theoretical reference for production.

Description

Technical Field

[0001] The present invention relates to the technical field of heavy oil processing, and in particular to a method, device, electronic equipment and storage medium for predicting the deposition rate of heavy oil dirt. Background Art

[0002] Heavy oil is prone to scaling during refinery processing, severely impacting the stable, long-term operation of the unit. Changes in process operating parameters and feed composition significantly influence the rate of scaling deposition. Therefore, understanding the scaling characteristics of heavy oil is crucial for efficient heavy oil processing. Accurate scaling models can guide heavy oil processing. In recent years, numerous researchers have systematically studied the scaling characteristics of heavy oil, some of which have been applied to actual production, achieving considerable progress.

[0003] Heavy oil itself has a complex composition, and scaling is caused by a variety of factors, including suspended impurity particles, oxidized colloids, and asphaltenes. However, asphaltenes deposition is generally considered the primary factor. Besides being influenced by its composition, high-temperature scaling of heavy oil is also affected by operating conditions such as temperature and flow rate. In the process of generating thermal resistance to fouling, mass transfer, heat transfer, and chemical reactions interact, collectively determining the rate of fouling development.

[0004] Although there is currently a lack of characterization standards for heavy oil fouling, a lot of experimental work has been carried out to study heavy oil fouling, and various factors affecting fouling have been identified and studied. Among them, heavy oil composition, flow rate, surface temperature and bulk temperature are important factors affecting the fouling rate.

[0005] Based on experimental research, mathematical models for predicting fouling in various complex situations have been established, including theoretical, semi-empirical, and empirical models. Ebert and Panchal introduced the concept of threshold fouling, below which the fouling growth rate is zero or very low. Ebert and Panchal proposed a semi-empirical fouling thermal resistance deposition rate model:

[0006]

[0007] Among them, R f is the fouling thermal resistance, Re is the Reynolds number, E is the reaction activation energy, α, β, γ are model parameters (regression coefficients), R is the gas constant, T f is the film temperature, τ w is the shear stress. The first term in the model (M1) represents the deposition rate of fouling on the heat transfer surface, while the second term represents the removal rate due to the wall shear stress. The net fouling rate is the deposition rate minus the removal rate.

[0008] Panchal and colleagues introduced the Prandtl number (P r) Considering the thermal properties of heavy oil, the model (M1) is improved as follows:

[0009]

[0010] However, the above models still have some problems. For example, according to Model (M1) and Model (M2), as the film temperature T f Or surface temperature T s As the temperature T increases, the fouling rate increases monotonically. This has been very effectively supported by many experimental studies, but there are also cases where the opposite is true. Data published in existing literature have observed that the fouling rate does increase with the membrane temperature T. f The first term in the above model, representing the effect of reaction kinetics on the thermal resistance of fouling, only incorporates the Arrhenius equation and does not fully consider mass transfer, which in many cases controls fouling. These issues lead to inaccurate predictions of heavy oil fouling deposition rates in existing models, making it impossible to accurately guide actual production processes such as heavy oil processing based on these predictions.

[0011] The information disclosed in this background technology section is only intended to enhance understanding of the overall background of the invention and should not be regarded as an admission 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

[0012] One of the objectives of the present invention is to provide a method for predicting the deposition rate of heavy oil fouling, thereby improving the accuracy of heavy oil fouling deposition rate prediction in the prior art, so as to provide support for early warning and response to heavy oil fouling in actual production.

[0013] To achieve the above-mentioned objectives, according to a first aspect of the present invention, a method for predicting the fouling deposition rate of heavy oil is provided, comprising the steps of: acquiring field monitoring data and equipment parameters; performing regression processing on a fouling thermal resistance deposition rate model based on the acquired field monitoring data and equipment parameters; and predicting the fouling deposition rate of heavy oil based on the fouling thermal resistance deposition rate model after the regression processing, wherein the fouling thermal resistance deposition rate model includes the influence of mass transfer rate and reaction probability coefficient correction.

[0014] Furthermore, in the above technical solution, the fouling thermal resistance deposition rate model is

[0015]

[0016] Among them, C W is the concentration of fouling precursors on the fouling deposition wall, F p is the reaction probability coefficient, Pr is the Prandtl number, E is the reaction activation energy, R is the gas constant, T f is the film temperature, τ wis the shear stress, and α, β, ε and γ are regression coefficients.

[0017] Furthermore, in the above technical solution, the concentration of the fouling precursor on the fouling deposition wall is

[0018]

[0019] Among them, C b is the concentration of fouling precursor in the concentration boundary layer, k is the first-order rate constant of the fouling precursor coking kinetics, K m is the characteristic mass transfer rate, and A is the dirt deposition area.

[0020] Furthermore, in the above technical solution, the characteristic mass transfer rate

[0021]

[0022] Where z is the wetted radius, C is the asphaltene colloid concentration, and C sm is the logarithmic mean of asphaltene colloid concentration and boundary layer precursor concentration, and D is the asphaltene diffusion coefficient.

[0023] Furthermore, in the above technical solution, the reaction probability coefficient

[0024]

[0025] Among them, ASP is asphaltene content, FPI is fouling tendency index, R a =1-P a is the asphaltene melting demand, P a Indicates the ability of asphaltene to maintain dispersion, P o Indicates the melting ability of the oil phase.

[0026] Furthermore, in the above technical solution, P a and P o Obtained by the P value method of heavy oil compatibility test, P=P o / (1-P a ).

[0027] Furthermore, in the above technical solution, the fouling tendency index

[0028]

[0029] Among them, S BN is the solubility mixing number, I N is the insoluble number, ASP is the asphaltene content, TBN is the total base number, Ni is the nickel content in heavy oil, and V is the vanadium content in heavy oil.

[0030] Furthermore, in the above technical solution, the on-site monitoring data is obtained through the refinery's DCS system.

[0031] Furthermore, in the above technical solution, the method for predicting the heavy oil fouling deposition rate further includes the step of: performing data coordination on the obtained on-site monitoring data.

[0032] Furthermore, in the above technical solution, the method for predicting the heavy oil fouling deposition rate further includes the steps of: obtaining the physical properties of the fluid and the heat exchange process parameters through heat exchange network simulation calculation based on the acquired on-site monitoring data and equipment parameters.

[0033] Furthermore, in the above technical solution, after the heavy oil fouling deposition rate is predicted to reach a preset time according to the fouling thermal resistance deposition rate model after regression processing, the fouling thermal resistance deposition rate model is re-regressed according to the acquired field monitoring data and equipment parameters.

[0034] According to a second aspect of the present invention, a device for predicting the fouling deposition rate of heavy oil is provided, which comprises: an acquisition unit for acquiring on-site monitoring data and equipment parameters; a calculation unit for performing regression processing on a fouling thermal resistance deposition rate model according to the acquired on-site monitoring data and equipment parameters; a prediction unit for predicting the fouling deposition rate of heavy oil according to the fouling thermal resistance deposition rate model after regression processing, wherein the fouling thermal resistance deposition rate model is

[0035]

[0036] Among them, C W is the concentration of fouling precursors on the fouling deposition wall, F p is the reaction probability coefficient, Pr is the Prandtl number, E is the reaction activation energy, R is the gas constant, T f is the film temperature, τ w is the shear stress, and α, β, ε and γ are regression coefficients.

[0037] Furthermore, in the above technical solution, the heavy oil fouling deposition rate prediction device further comprises: an early warning unit, which is used to issue an early warning signal according to the predicted heavy oil fouling deposition rate.

[0038] According to a third aspect of the present invention, the present invention provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute a method for predicting a heavy oil fouling deposition rate as described in any one of the above technical solutions.

[0039] According to a fourth aspect of the present invention, the present invention provides a non-transitory computer-readable storage medium, which stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute a heavy oil fouling deposition rate prediction method as described in any one of the above technical solutions.

[0040] Compared with the prior art, the present invention has one or more of the following beneficial effects:

[0041] 1. By considering the influence of mass transfer rate and the correction of reaction probability coefficient in the fouling thermal resistance deposition rate model, the present invention makes the prediction results closer to reality. It can fully adapt to the situation where the fouling thermal resistance development rate fluctuates greatly due to large changes in asphaltene mass transfer rate and asphaltene gelation under different temperatures and pressures of oil products. This provides a more reliable theoretical reference for production, facilitating timely prevention and adjustment, and avoiding production accidents caused by fouling.

[0042] 2. The fouling thermal resistance deposition rate model proposed in this invention is controlled by the mass transfer rate and reaction kinetics. Based on the existing model, the fouling precursor concentration C on the fouling deposition wall is considered to be affected by the mass transfer rate. W To replace the Reynolds number Re, it more accurately reflects the impact of dirt deposition.

[0043] 3. As the temperature of heavy oil increases, the medium participating in the reaction to generate dirt increases or decreases, and the reaction activation energy E is calculated through the reaction probability coefficient F. p The revision takes into account the oil ash content, oil asphaltene content and oil gelling ability, so as to better reflect the deposition changes of dirt in complex oil systems over a wide temperature range.

[0044] 4. By coordinating the acquired on-site monitoring data and using mature commercial software to simulate the heat exchange network, the fouling thermal resistance deposition rate model is optimized on a regular or irregular basis to ensure accurate prediction of the long-term changes in fouling thermal resistance.

[0045] 5. The fouling thermal resistance deposition rate model of the present invention can also evaluate the correlation between the feed conditions and the process operation conditions, and check the influence of the feed composition and process operating parameters on the development of fouling thermal resistance.

[0046] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and to implement it according to the contents of the specification, and to make the above and other purposes, technical features and advantages of the present invention easier to understand, one or more preferred embodiments are listed below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1FIG. 4 is a flow chart of a method for predicting a heavy oil fouling deposition rate according to an embodiment of the present invention.

[0048] Figure 2 It is a distribution diagram of the fouling thermal resistance prediction curve and the actual fouling thermal resistance value obtained by using different fouling thermal resistance deposition rate models.

[0049] Figure 3 FIG. 1 is a schematic diagram of a device for predicting a heavy oil fouling deposition rate according to an embodiment of the present invention.

[0050] Figure 4 4 is a schematic diagram of the hardware structure of an electronic device for executing a method for predicting a heavy oil fouling deposition rate according to an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings, but it should be understood that the protection scope of the present invention is not limited by the specific embodiments.

[0052] Unless expressly stated otherwise, throughout the specification and claims, the term “comprise” or variations such as “include” or “comprising” will be understood to include the stated elements or components but not to exclude other elements or components.

[0053] In this document, for ease of description, spatially relative terms such as "below," "beneath," "down," "above," "above," etc. may be used to describe the relationship of one element or feature to another element or feature in the accompanying drawings. It should be understood that the spatially relative terms are intended to encompass different orientations of an object in use or operation in addition to the orientation depicted in the drawings. For example, if the object in the drawings is turned over, the element described as being "below" or "below" other elements or features will be oriented "above" the element or feature. Therefore, the exemplary term "below" can include both below and above directions. Objects may also have other orientations (rotated 90 degrees or other orientations) and the spatially relative terms used herein should be interpreted accordingly.

[0054] In this document, the terms "first", "second", etc. are used to distinguish two different elements or parts, and are not used to limit specific positions or relative relationships. In other words, in some embodiments, the terms "first", "second", etc. can also be interchangeable with each other.

[0055] Scaling in heavy oil processing, particularly in its heat exchange networks, has become one of the most prominent issues in the refining industry. Accurately predicting heavy oil fouling could mitigate fouling by controlling heat exchanger tube temperature, wall shear stress, or other process parameters. This could also provide early warning and enable appropriate measures before significant changes in thermal resistance disrupt production, thereby preventing safety accidents caused by fouling.

[0056] According to a specific embodiment of the present invention, a method for predicting the fouling deposition rate of heavy oil includes the following steps: obtaining on-site monitoring data and equipment parameters; performing regression processing on a fouling thermal resistance deposition rate model based on the obtained on-site monitoring data and equipment parameters; and predicting the fouling deposition rate of heavy oil based on the fouling thermal resistance deposition rate model after the regression processing. The fouling thermal resistance deposition rate model proposed in the present invention is as follows:

[0057]

[0058] Among them, C W is the concentration of fouling precursors on the fouling deposition wall, F p is the reaction probability coefficient, Pr is the Prandtl number, E is the reaction activation energy, R is the gas constant, T f is the film temperature, τ w is the shear stress, and α, β, ε and γ are all regression coefficients. The fouling thermal resistance deposition rate model (M3) of the present invention is controlled by the mass transfer rate and reaction kinetics. On the basis of the existing model, the fouling precursor concentration C on the fouling deposition wall is considered to be affected by the mass transfer rate. W Instead of the Reynolds number Re, it can more accurately reflect the influence of dirt deposition; the reaction activation energy E is determined by the reaction probability coefficient F considering the ash content, asphaltene content and gelling ability of the oil. p Correction to better adapt to the changes in dirt deposition in a large temperature range of complex oil systems.

[0059] Furthermore, in one or more exemplary embodiments of the present invention, a method for describing the mass transfer rate of asphaltene deposition on a dirt deposit wall is provided. In the process of asphaltene transforming into dirt, asphaltene colloids first diffuse from the oil product to the boundary layer of the deposit wall, and then diffuse from the boundary layer to the dirt deposit wall. Some asphaltene colloids adhere to the wall surface and undergo a coking chemical reaction to become dirt. The concentration of dirt precursors C on the dirt deposit wall is W It can be expressed as:

[0060]

[0061] Among them, C b is the concentration of fouling precursor in the concentration boundary layer, k is the first-order rate constant of the fouling precursor coking kinetics, K mis the characteristic mass transfer rate, and A is the dirt deposition area.

[0062] Furthermore, in one or more exemplary embodiments of the present invention, the characteristic mass transfer rate K m It can be expressed as:

[0063]

[0064] Where z is the wetted radius, C is the asphaltene colloid concentration, and C sm is the logarithmic mean of asphaltene colloid concentration and boundary layer precursor concentration, and D is the asphaltene diffusion coefficient.

[0065] Furthermore, in one or more exemplary embodiments of the present invention, the reaction probability coefficient characterizes the probability of asphaltene colloids depositing on the deposition wall to react, and takes into account the solubility of asphaltene colloids in oil products and the dirt tendency of oil products. The reason why asphaltene is separated from oil products may be that the protective effect of the colloid on the asphaltene is weakened due to the change in intermolecular forces when different oil products are mixed, and the asphaltene cannot be maintained in a dispersed state in the oil products. It may also be that the content of colloids and aromatic components that play a protective role on asphaltene in the system after mixing is relatively small. In addition, in actual production, polar components such as heteroatoms and metals contained in oil products are more likely to react to form coke at high temperatures and deposit on equipment. Dissolved oxygen in heavy oil will promote the oxidation reaction of oil products at high temperatures. In addition, the iron ions and copper ions in heavy oil have a certain dehydrogenation effect, and dissolved oxygen will trigger the dehydrogenation reaction of metal ions. Taking the above-mentioned influencing factors into consideration, the reaction probability coefficient F p It can be expressed as:

[0066]

[0067] Among them, ASP is asphaltene content, FPI is fouling tendency index, R a =1-P a is the asphaltene melting demand, P a Indicates the ability of asphaltene to maintain dispersion, P o Indicates the melting ability of the oil phase.

[0068] Further, in one or more exemplary embodiments of the present invention, P a and P o It can be obtained by the heavy oil compatibility test P value method. The basic steps of this method are to dissolve the residual oil to be tested in an aromatic solvent (e.g., toluene), and then titrate with a normal alkane (e.g., n-heptane) until phase separation occurs. Calculate the amount of aromatic solvent, normal alkane, and oil sample consumed in this process. Calculate the P value, Pa value, and P value of each oil sample based on the calculation results. o Value. P=P o / R a =P o / (1-P a ), where P represents the ratio between the peptization capacity of the oil phase in the system and the peptization demand of the asphaltene. The P value can be understood as an indicator of the probability of fouling. As the P value decreases, heavy oil becomes more susceptible to fouling. However, the presence of other foulants (clay, dirt, sand, or olefins) or foulant precursors (olefins, sulfur compounds, etc.) can also reduce or enhance fouling potential.

[0069] Further, in one or more exemplary embodiments of the present invention, the fouling tendency of the heavy oil component is related to the S BN -I N , ASP, TBN, and the concentrations of Ni and V. These properties are summarized into a single fouling tendency index FPI:

[0070]

[0071] Among them, S BN is the solubility mixing number, I N is the insoluble number, ASP is the asphaltene content, TBN is the total base number, Ni is the nickel content in heavy oil, and V is the vanadium content in heavy oil. FPI is dimensionless, in which the values of all attributes are normalized and the value of each attribute is less than or equal to 1. FPI includes two competing terms, the first bracketed term represents fouling, and the second bracketed term represents fouling inhibition. FPI is S BN -I N A strongly nonlinear decreasing function of .

[0072] The following describes in more detail the method, apparatus, memory, and device for predicting the heavy oil fouling deposition rate of the present invention by way of specific embodiments. It should be understood that the embodiments are merely exemplary and the present invention is not limited thereto.

[0073] Example 1

[0074] The following is an example of the crude oil preheating network of a domestic company's 1 million tons / year ethylene and supporting project's 10 million tons / year atmospheric and vacuum distillation unit. The unit is designed to process a mixture of 20% each of Saudi light, Saudi medium, Saudi heavy, Basra light, and Kuwait crude oil, with a processing capacity of 10 million tons / year and an annual operating hours of 8,400 hours.

[0075] The crude oil is pumped into the unit from the crude oil tank area outside the unit and is divided into two routes: the crude oil before degassing enters the crude oil-normal top circulation heat exchanger (501-E-107E), crude oil-normal top oil and gas heat exchanger (501-E-101A), crude oil-reduced top circulation (1) heat exchanger (501-E-201A), crude oil-normal third line heat exchanger (501-E-106G / H), crude oil-normal first line heat exchanger (501-E-104A), crude oil-normal first middle line heat exchanger (501-E-108D), crude oil-reduced second line heat exchanger (501-E-202N / O) to exchange heat to 130℃. The crude oil heat exchange route 2 before degassing is further divided into two routes. The first route enters the crude oil-normal line 1 heat exchanger (501-E-104B) and the crude oil-normal top oil and gas heat exchanger (501-E-101B) in succession; the second route enters the crude oil-reduced top circulation heat exchanger (501-E-201B) and the crude oil-normal top oil and gas heat exchanger (501-E-101C) in succession and then merges with the first route, and enters the crude oil-normal line 2 (3) heat exchanger (501-E-105C), the crude oil-normal top circulation heat exchanger (501-E-107A~D), the crude oil-reduced line 3 heat exchanger (501-E-203G), and the crude oil-reduced slag heat exchanger (501-E-205K / L) in succession to exchange heat to 136℃. After the two crude oil streams are combined, the heat exchange temperature reaches 134°C before entering the electric desalter (501-D-101A / B) for desalting and dehydration. The desalted crude oil is then divided into three streams and enters the desalted crude oil heat exchange system. The desalted crude oil heat exchange system enters the desalted oil-minimized first-line heat exchanger (501-E-202G~J), the desalted oil-normalized second-line heat exchanger (501-E-105A), and the desalted oil-minimized second-line heat exchanger (501-E-202K~M) for heat exchange to 217°C. The desalted crude oil enters the desalted oil-normal second line heat exchanger (501-E-105B), the desalted oil-normal third line heat exchanger (501-E-106E / F), the desalted oil-normal first middle heat exchanger (501-E-108A), the desalted oil-mineralization first middle heat exchanger (501-E-202E / F), and the desalted oil-mineralization slag heat exchanger (501-E-205F~H) for heat exchange to 221℃. The three heat exchange routes for the desalted crude oil are sequentially fed into the desalted oil-to-Changyizhong heat exchanger (501-E-108B / C), the desalted oil-to-reduction line heat exchanger (501-E-203F), the desalted oil-to-reduction slag heat exchanger (501-E-205I / J), the desalted oil-to-reduction line heat exchanger (501-E-202C / D), and the desalted oil-to-Changyizhong heat exchanger (501-E-106C / D) for heat exchange to 225°C. The three heat exchange routes for the desalted crude oil are then combined and fed into the desalted oil-to-reduction slag heat exchanger (501-E-205D / E) for heat exchange to 232°C, completing crude oil preheating.

[0076] In the crude oil preheating network of this atmospheric and vacuum distillation unit, the desalted oil-residue heat exchanger (501-E-205D / E) has the highest temperature and the most severe fouling, significantly impacting the energy consumption of the entire unit. This example uses the heavy oil fouling rate prediction method of the present invention to predict the heavy oil fouling rate in the desalted oil-residue heat exchanger (501-E-205D / E).

[0077] Combine Figure 1 As shown, the process of the heavy oil fouling deposition rate prediction method according to this embodiment is as follows:

[0078] S110 acquires on-site monitoring data and equipment parameters. The on-site monitoring data is acquired by accessing the DCS system of the crude oil preheating network of the atmospheric and vacuum distillation unit.

[0079] S120 performs data reconciliation on the acquired field monitoring data. Data reconciliation can use optimization methods to minimize the measurement error of each process variable. Mass and energy balances, as well as additional process requirements, can be used as constraints. For example, through the covariance matrix The difference between the measured value and the harmonic value Weighted constraints are then weighted and, depending on the linearity of the constraints, the optimization problem is solved using techniques such as nonlinear programming, successive linearization, or principal component analysis. Successive linearization is a viable approach due to its faster computational time than nonlinear programming solvers and the simplicity of the matrix-based simulation model. When all process variables are measured, the solution to the linearized data coordination problem is expressed as follows:

[0080]

[0081] Among them, J y is the Jacobian matrix of the equality constraint, at the decision point and b y The evaluation is defined as It uses an iterative process to solve the data reconciliation problem until a certain tolerance is met, but it only works if the data does not contain grotesque errors. When these errors are taken into account, the above solution is combined with a grotesque detection process that identifies and estimates the size and location of single and multiple grotesque errors by searching all possible combinations and selecting the one with the smallest measurement error.

[0082] S130 uses the coordinated on-site monitoring data and equipment parameters to simulate the heat exchange network and obtain the fluid's physical properties and heat exchange process parameters. This step requires the input of actual equipment parameters such as on-site equipment specifications. The simulation can be performed using either a custom model or established commercial software. In this example, Aspen HYSYS software was used to build the crude oil preheating network for the atmospheric and vacuum distillation unit, and Aspen EDR was used to calculate the specific heat exchange parameters of the heat exchangers.

[0083] S140 uses Aspen EDR to calculate the heat exchange process of the heat exchanger based on the calculated physical properties of the fluid, heat exchange process parameters, and equipment parameters. It also calculates the real-time fouling thermal resistance value of the heat exchanger. The fouling thermal resistance values obtained in real time at different times are saved as an actual fouling thermal resistance value sequence. The fouling thermal resistance deposition rate model is regressed to obtain the regression coefficient in the fouling thermal resistance deposition rate model. This regression coefficient should have the smallest error relative to the actual fouling thermal resistance value sequence over a certain period of time (usually about one year).

[0084] S150 predicts the heavy oil fouling deposition rate based on the fouling thermal resistance deposition rate model after regression processing.

[0085] In this embodiment, field monitoring data and equipment parameters are collected every 3 seconds, Aspen HYSYS software and Aspen EDR heat exchange calculations are called every 30 seconds, and the regression coefficients in the fouling thermal resistance deposition rate model are regressed every 2 hours. After obtaining the regression coefficients of the fouling thermal resistance deposition rate model, the fouling thermal resistance deposition rate model is used to predict the fouling situation of the heat exchanger in the next 1 to 12 months.

[0086] In this embodiment, the fouling thermal resistance deposition rate model (M3) is used:

[0087]

[0088] like Figure 2 As shown, the fouling thermal resistance curve of the desalted oil-residue heat exchanger (501-E-205D / E) predicted by the fouling thermal resistance deposition rate model (M3) in this embodiment is relatively consistent with the actual situation; the fouling thermal resistance curves of the desalted oil-residue heat exchanger (501-E-205D / E) predicted by the models (M1) and (M2) in Comparative Examples 1 and 2 deviate greatly from the actual situation, and are particularly unable to predict the long-term changes in the fouling thermal resistance.

[0089] Comparative Example 1

[0090] This comparative example adopts the same process as Example 1, except that the model (M1) is used.

[0091] Comparative Example 2

[0092] This comparative example adopts the same process as Example 1, except that the model (M2) is used.

[0093] Example 2

[0094] Combine Figure 3As shown, the heavy oil fouling deposition rate prediction device of this embodiment includes: an acquisition unit 10, which is used to acquire on-site monitoring data and equipment parameters; a calculation unit 20, which is used to perform regression processing on the fouling thermal resistance deposition rate model based on the acquired on-site monitoring data and equipment parameters; a prediction unit 30, which is used to predict the heavy oil fouling deposition rate based on the fouling thermal resistance deposition rate model after the regression processing; and an early warning unit 40, which is used to issue an early warning signal based on the predicted heavy oil fouling deposition rate.

[0095] The fouling thermal resistance deposition rate model used by the heavy oil fouling deposition rate prediction device of this embodiment is:

[0096]

[0097] Among them, C W is the concentration of fouling precursors on the fouling deposition wall, F p is the reaction probability coefficient, Pr is the Prandtl number, E is the reaction activation energy, R is the gas constant, T f is the film temperature, τ w is the shear stress, and α, β, ε and γ are regression coefficients.

[0098] Example 3

[0099] This embodiment provides a non-transitory (non-volatile) computer storage medium, which stores computer-executable instructions. The computer-executable instructions can execute the method in any of the above method embodiments and achieve the same technical effects.

[0100] Example 4

[0101] This embodiment provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the methods described in the above aspects and achieves the same technical effects.

[0102] Example 5

[0103] Figure 4 6 is a schematic diagram of the hardware structure of an electronic device for executing the heavy oil fouling deposition rate prediction method according to this embodiment. 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.

[0104] 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 4 The bus connection is taken as an example.

[0105] Memory 620, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer executable programs, and modules. Processor 610 executes the non-transitory software programs, instructions, and modules stored in memory 620 to execute various functional applications and data processing of the electronic device, thereby implementing the processing method of the above-mentioned method embodiment.

[0106] 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-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 620 may optionally include a memory remotely located 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.

[0107] 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.

[0108] The one or more modules are stored in the memory 620 and, when executed by the one or more processors 610, perform:

[0109] Obtain on-site monitoring data and equipment parameters;

[0110] Regressing the fouling thermal resistance deposition rate model based on the acquired field monitoring data and equipment parameters; and

[0111] The heavy oil fouling deposition rate is predicted based on the fouling thermal resistance deposition rate model after regression processing.

[0112] Among them, the fouling thermal resistance deposition rate model includes the influence of mass transfer rate and reaction probability coefficient correction.

[0113] 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 fully described in this embodiment, please refer to the methods provided by other embodiments of the present invention.

[0114] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0115] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a general hardware platform, or of course, by hardware. Based on this understanding, the above technical solution, in essence, or the part that contributes to the relevant technology, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiment.

[0116] The foregoing descriptions of specific exemplary embodiments of the present invention are for purposes of illustration and description. These descriptions are not intended to limit the invention to the precise form disclosed, and it is apparent that many changes and variations are possible in light of the foregoing teachings. The exemplary embodiments are selected and described for the purpose of explaining the specific principles of the invention and their practical application, thereby enabling those skilled in the art to realize and utilize a variety of exemplary embodiments of the invention and various options and variations. Any simple modifications, equivalent variations, and modifications made to the exemplary embodiments described above are intended to fall within the scope of protection of the present invention.

Claims

1. A method for predicting the deposition rate of heavy oil fouling, characterized in that: Including steps: Obtain on-site monitoring data and equipment parameters; Regressing the fouling thermal resistance deposition rate model based on the acquired field monitoring data and equipment parameters; and The heavy oil fouling deposition rate is predicted based on the fouling thermal resistance deposition rate model after regression processing. The fouling thermal resistance deposition rate model includes the influence of mass transfer rate and reaction probability coefficient correction; the fouling thermal resistance deposition rate model is Among them, C W is the concentration of fouling precursors on the fouling deposition wall, F p is the reaction probability coefficient, Pr is the Prandtl number, E is the reaction activation energy, R is the gas constant, T f is the film temperature, τ w is the shear stress, α, β, ε and γ are regression coefficients; The response probability coefficient Among them, ASP is asphaltene content, FPI is fouling tendency index, R a =1-P a is the asphaltene melting demand, P a Indicates the ability of asphaltene to maintain dispersion, P o Indicates the melting ability of the oil phase.

2. The method for predicting the heavy oil fouling deposition rate according to claim 1, characterized in that: The concentration of fouling precursor on the fouling deposition wall Among them, C b is the concentration of fouling precursor in the concentration boundary layer, k is the first-order rate constant of the fouling precursor coking kinetics, K m is the characteristic mass transfer rate, and A is the dirt deposition area.

3. The method for predicting the heavy oil fouling deposition rate according to claim 2, characterized in that: The characteristic mass transfer rate Where z is the wetted radius, C is the asphaltene colloid concentration, and C sm is the logarithmic mean of asphaltene colloid concentration and boundary layer precursor concentration, and D is the asphaltene diffusion coefficient.

4. The method for predicting the heavy oil fouling deposition rate according to claim 1, wherein: The P a and P o Obtained by the P value method of heavy oil compatibility test, P=P o / (1-P a ).

5. The method for predicting the heavy oil fouling deposition rate according to claim 1, characterized in that: The fouling tendency index Among them, S BN is the solubility mixing number, I N is the insoluble number, TBN is the total base number, Ni is the content of metallic nickel in heavy oil, and V is the content of metallic vanadium in heavy oil.

6. The method for predicting the heavy oil fouling deposition rate according to claim 1, characterized in that: The on-site monitoring data is obtained through the refinery's DCS system.

7. The method for predicting the heavy oil fouling deposition rate according to claim 1, characterized in that: Also includes the steps: Perform data coordination on the acquired field monitoring data.

8. The method for predicting the heavy oil fouling deposition rate according to claim 1, characterized in that: Also includes the steps: Based on the acquired on-site monitoring data and equipment parameters, the physical properties of the fluid and the heat transfer process parameters are obtained through heat exchange network simulation calculation.

9. The method for predicting the heavy oil fouling deposition rate according to claim 1, characterized in that: After the heavy oil fouling deposition rate predicted by the fouling thermal resistance deposition rate model after the regression processing reaches a preset time, the fouling thermal resistance deposition rate model is re-regressed according to the acquired on-site monitoring data and equipment parameters.

10. A device for predicting the deposition rate of heavy oil fouling, characterized in that: include: An acquisition unit, which is used to acquire on-site monitoring data and equipment parameters; A calculation unit, which is used to perform regression processing on the fouling thermal resistance deposition rate model based on the acquired on-site monitoring data and equipment parameters; A prediction unit is used to predict the heavy oil fouling deposition rate based on the fouling thermal resistance deposition rate model after regression processing, Among them, the fouling thermal resistance deposition rate model is Among them, C W is the concentration of fouling precursors on the fouling deposition wall, F p is the reaction probability coefficient, Pr is the Prandtl number, E is the reaction activation energy, R is the gas constant, T f is the film temperature, τ w is the shear stress, α, β, ε and γ are regression coefficients; The response probability coefficient Among them, ASP is asphaltene content, FPI is fouling tendency index, R a =1-P a is the asphaltene melting demand, P a Indicates the ability of asphaltene to maintain dispersion, P o Indicates the melting ability of the oil phase.

11. The heavy oil fouling deposition rate prediction device according to claim 10, characterized in that: Also includes: An early warning unit is used to issue an early warning signal according to the predicted heavy oil fouling deposition rate.

12. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the heavy oil fouling deposition rate prediction method according to any one of claims 1 to 9.

13. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable the computer to execute the heavy oil fouling deposition rate prediction method according to any one of claims 1 to 9.

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