Method and equipment for predicting bubble point pressure of solvent deasphalting system

Abstract

The invention discloses a method and equipment for predicting bubble point pressure of a solvent deasphalting system, and relates to the field of chemical separation simulation computation.The method comprises the steps that on the basis of a binary interaction parameter initial value of a target substance, the separation process of a substance to be separated is simulated, and a first liquid phase fugacity and a second liquid phase fugacity are calculated; determining a regression binary interaction parameter according to the first liquid phase fugacity and the second liquid phase fugacity; fitting binary interaction estimation model parameters according to the regressed binary interaction parameters; constructing a binary interaction estimation model suitable for the SRK or PR state equation according to the fitted binary interaction estimation model parameters; according to an SRK or PR state equation and the binary interaction estimation model, phase equilibrium calculation is conducted, the bubble point pressure of the solvent deasphalting system is predicted, the bubble point pressure can be accurately predicted, and more reliable thermodynamic support is provided for process design.

Description

Technical Field

[0001] This application relates to the field of chemical separation simulation calculation, and in particular to a method and equipment for predicting the bubble point pressure of a solvent deasphalting system. Background Technology

[0002] Chemical thermodynamics is fundamental to research, development, and design in the chemical industry. The Equation of State (EoS) is an analytical expression used to describe the P (pressure), V (volume), and T (temperature) relationship of a fluid (a general term for both gas and liquid) in equilibrium. It is widely used in simulations in chemical engineering (such as chemical thermodynamics and chemical processes). For example, in chemical separation simulations, the condition for ending the simulation is that the fugacity corresponding to the vapor phase composition is equal to the fugacity corresponding to the liquid phase composition. To calculate fugacity, the compressibility factor must first be calculated, then the fugacity coefficient based on the compressibility factor, and finally the fugacity itself. Currently, the fugacity coefficient is often calculated by solving the Peng-Robinson (PR) and Soave-Redlich-Kwong (SRK) equations of state.

[0003] The origins of equations of state can be traced back to 1873 when van der Waals proposed a cubic equation of state, but this cubic equation of state only applies to simple gases. In 1949, Redlich and Kwong modified the gravitational term of the VDW (Van Der Waals) equation of state, proposing the RK (Redlich-Kwong) equation of state, applicable to nonpolar symmetric molecular systems. In 1972, Soave introduced an eccentricity factor into the RK equation of state, proposing the SRK (Soave-Redlich-Kwong) equation of state, making it more widely applicable. In 1976, Peng and Robinson improved the gravitational term of the RK equation of state, proposing the PR (Peng-Robinson) equation of state. The PR and SRK equations of state provide relatively accurate predictions of phase equilibrium and thermodynamic properties for nonpolar and weakly polar molecular systems.

[0004] In the context of global energy supply and demand tensions, the efficient conversion and utilization of heavy oil products is of great significance. Solvent deasphalting is a major processing technology for heavy oil products, and accurate simulation calculations of this process can provide effective guidance for actual production. Existing equations of state used for simulation calculations of the deasphalting process still have the following problems: (1) Basic physical property data of asphaltenes are difficult to obtain, and necessary physical property data for simulation calculations are lacking; (2) The system is highly non-ideal, and the extraction conditions of extractant-asphaltenes are under high pressure, requiring the use of equations of state models for prediction, but the current equations of state models are not accurate enough. The above problems result in low accuracy of existing equations of state in predicting the bubble point pressure of solvent deasphalting systems. Since bubble point pressure is inseparable from process design, it cannot provide more reliable thermodynamic support for process design. Summary of the Invention

[0005] The purpose of this application is to provide a method and equipment for predicting the bubble point pressure of a solvent deasphalting system, so as to solve the problem of low accuracy in predicting the bubble point pressure of the solvent deasphalting system using existing equations of state.

[0006] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for predicting the bubble point pressure of a solvent deasphalting system, including: A method for predicting the bubble point pressure of a solvent deasphalting system, characterized by comprising: Based on the initial values ​​of the binary interaction parameters of the target substance, the separation process of the substance to be separated is simulated, and the first liquid phase fugacity and the second liquid phase fugacity are calculated. The target substance includes the extractant and the asphaltenes group components. The binary interaction parameters are the binary interaction parameters between the extractant and the asphaltenes group components. The first liquid phase fugacity and the second liquid phase fugacity are calculated using the SRK equation of state, or the first liquid phase fugacity and the second liquid phase fugacity are calculated using the PR equation of state.

[0007] The binary interaction parameters are regressed based on the first liquid phase fugacity and the second liquid phase fugacity.

[0008] The model parameters are estimated by fitting the binary interaction parameters of the regression binary interaction.

[0009] A binary interaction estimation model is constructed based on the fitted binary interaction estimation model parameters; the binary interaction estimation model is applicable to the SRK state equation or the PR state equation.

[0010] Phase equilibrium calculations are performed based on the binary interaction estimation model to predict the bubble point pressure of the solvent deasphalting system.

[0011] Secondly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for predicting the bubble point pressure of a solvent deasphalting system.

[0012] According to the specific embodiments provided in this application, this application has the following technical effects: This application utilizes the SRK or PR equation of state regression to obtain the binary interaction parameters between the extractant and asphaltenes components, fits the parameters of the binary interaction estimation model, and constructs a binary interaction estimation model applicable to the SRK or PR equation of state. Based on the SRK or PR equation of state and the binary interaction estimation model, phase equilibrium calculations are performed to predict the bubble point pressure of the solvent deasphalting system, correcting the prediction bias of the SRK or PR equation of state for non-ideal systems. It is not limited by the basic physical property data of asphaltenes components, and improves the prediction accuracy of the SRK or PR equation of state for the phase equilibrium of the solvent deasphalting system (extractant-asphaltenes complex system), so as to accurately predict the bubble point pressure and provide more reliable thermodynamic support for process design. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 An application environment diagram for a solvent deasphalting system bubble point pressure prediction method provided in an embodiment of this application; Figure 2 This is a schematic diagram illustrating the bubble point pressure prediction results of an ethane-asphaltite mixture system provided in an embodiment of this application; Figure 3 This is a schematic diagram illustrating the bubble point pressure prediction results of a propane-CO2-asphalt mixture system provided in an embodiment of this application. Detailed Implementation

[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0016] To make the objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0017] SRK or PR cubic equations of state are suitable for phase equilibrium calculations of nonpolar and weakly polar systems under medium to low pressure and high pressure, and can be used to calculate the bubble point pressure of extractant-asphaltene mixtures. Binary interaction parameters, as correction factors for non-ideal systems, are key parameters in thermodynamic models describing the interactions between different components. Their core function is to quantify the energy deviation between components in non-ideal mixtures. Equations of state typically use single-fluid mixing rules to correct the energy parameters of mixtures. In thermodynamic calculations involving extractant-asphaltene, the composition of asphaltene is complex and its structure is indeterminate, lacking a fixed composition. The binary interaction between extractant and asphaltene varies with the asphaltene content. Existing models for binary interaction parameters cannot provide effective estimation. Determining the binary interaction parameters is a critical issue in thermodynamic calculations of extractant-asphaltene mixtures. Existing research shows few general-purpose binary interaction parameter estimation models available for use with equations of state. To improve the application of equations of state in phase equilibrium calculations of non-ideal mixtures, a general estimation method capable of more accurately describing the binary interaction parameters of extractant-asphaltene mixtures is needed.

[0018] This application develops a binary interaction estimation model to obtain the interaction parameters between the extractant and the asphaltenes group components, corrects the prediction bias of the SRK or PR cubic equation of state for non-ideal systems, improves the prediction accuracy of the equation of state for the phase equilibrium of the extractant-asphaltene complex system, and provides more reliable thermodynamic support for process design.

[0019] Example 1 like Figure 1 As shown in the embodiments of this application, a method for predicting the bubble point pressure of a solvent deasphalting system is provided, including: Step 101: Based on the initial values ​​of the binary interaction parameters of the target substance, simulate the separation process of the substance to be separated and calculate the first liquid phase fugacity and the second liquid phase fugacity; the target substance includes the extractant and the asphaltenes group components; the binary interaction parameters are the binary interaction parameters between the extractant and the asphaltenes group components; the first liquid phase fugacity and the second liquid phase fugacity are calculated using the SRK equation of state, or the first liquid phase fugacity and the second liquid phase fugacity are calculated using the PR equation of state.

[0020] Step 102: Regress the binary interaction parameters based on the first liquid phase fugacity and the second liquid phase fugacity.

[0021] Step 103: Fit the binary interaction parameters based on the regression binary interaction parameters to estimate the model parameters.

[0022] Step 104: Construct a binary interaction estimation model based on the fitted binary interaction estimation model parameters; the binary interaction estimation model is applicable to the SRK state equation or the PR state equation.

[0023] Step 105: Perform phase equilibrium calculations based on the binary interaction estimation model to predict the bubble point pressure of the solvent deasphalting system.

[0024] In an exemplary embodiment, the SRK state equation is: in, P Standard atmospheric pressure; a ( T ) represents the gravitational term parameter; R It is the gas constant; T For temperature; V For volume; b For the repulsion term parameters.

[0025] In an exemplary embodiment, the first liquid phase fugacity of the i-th component : The second liquid phase fugacity of the i-th component for: in, P For pressure, and These represent the mole fractions of the components in the first and second liquid phases, respectively. and , respectively, are the fugacity coefficients of the first and second liquid phases.

[0026] Derived from the SRK state equation and The calculation formulas are the same, both being: in, for or ; b i These are the repulsion term parameters for each component in the first liquid phase L1 or the second liquid phase L2; b m For the repulsive term parameter of the first liquid phase L1 mixture or the second liquid phase L2 mixture; Z m The compressibility factor is the first liquid phase mixture L1 or the second liquid phase mixture L2. a ijThese are the gravitational parameters of each component in the first liquid phase L1 or the second liquid phase L2; a m For the gravitational term parameters of the first liquid phase L1 mixture or the second liquid phase L2 mixture; V m The molar volume of either the first liquid phase L1 mixture or the second liquid phase L2 mixture; x j The composition of each component in the first liquid phase L1 or the second liquid phase L2; j The component number in the first liquid phase L1 or the second liquid phase L2; n The total number of components in the first liquid phase L1 or the second liquid phase L2.

[0027] In an exemplary embodiment, the PR state equation is: in, P Standard atmospheric pressure; a ( T ) represents the gravitational term parameter; R It is the gas constant; T For temperature; V For volume; b For the repulsion term parameters.

[0028] The first liquid phase fugacity of the i-th component : The second liquid phase fugacity of the i-th component for: in, P For pressure, and These represent the mole fractions of the components in the first and second liquid phases, respectively. and These are the fugacity coefficients of the first liquid phase and the second liquid phase, respectively. Derived from the PR equation of state and The calculation formulas are the same, both being: in, for or ; b i These are the repulsion term parameters for each component in the first liquid phase L1 or the second liquid phase L2; b m For the repulsive term parameter of the first liquid phase L1 mixture or the second liquid phase L2 mixture; Zm The compressibility factor is the first liquid phase mixture L1 or the second liquid phase mixture L2. a ij These are the gravitational parameters of each component in the first liquid phase L1 or the second liquid phase L2; a m For the gravitational term parameters of the first liquid phase L1 mixture or the second liquid phase L2 mixture; V m The molar volume of either the first liquid phase L1 mixture or the second liquid phase L2 mixture; x j The composition of each component in the first liquid phase L1 or the second liquid phase L2; j The component number in the first liquid phase L1 or the second liquid phase L2; n The total number of components in the first liquid phase L1 or the second liquid phase L2.

[0029] In one exemplary embodiment, step 102 specifically includes: Determine whether the first liquid phase fugacity and the second liquid phase fugacity are equal to obtain the first determination result; If the first judgment result is yes, the initial value of the binary interaction parameter is used as the binary interaction parameter for regression; If the first judgment result is negative, the initial value of the binary interaction parameter is updated by combining the first nonlinear least squares formula, and the following is returned: "Based on the initial value of the binary interaction parameter of the target substance, the separation process of the substance to be separated is simulated, and the first liquid phase fugacity and the second liquid phase fugacity are calculated using the SRK equation of state"; it is then determined whether the first liquid phase fugacity and the second liquid phase fugacity satisfy the first iteration termination condition, and the second judgment result is obtained. If the second judgment result is yes, the initial value of the binary interaction parameter is used as the binary interaction parameter for regression; If the second judgment result is negative, the initial value of the binary interaction parameter is updated using the first nonlinear least squares formula, and the process of separating the substance to be separated is simulated based on the initial value of the binary interaction parameter of the target substance. The first liquid phase fugacity and the second liquid phase fugacity are calculated using the SRK equation of state until the first liquid phase fugacity and the second liquid phase fugacity meet the first iteration termination condition. The last updated regression binary interaction parameter is used as the regression binary interaction parameter. The first nonlinear least squares formula is: ;in, Let i be the first liquid phase fugacity of the i-th component; L1 is the fugacity of the second liquid phase for the i-th component; L2 is the first liquid phase; L1 is the second liquid phase; i is the component number; N is the total number of components; the first iteration termination condition is: .

[0030] In an exemplary embodiment, the binary interaction parameters of the regression are: in, k ij This represents the binary interaction parameter between the extractant and the asphaltenes group components; Estimate model parameters for binary interaction 1; To estimate the model parameters for the binary interaction; Estimate model parameters for binary interaction 3; T For temperature.

[0031] In one exemplary embodiment, step 103 specifically includes: Based on the regression binary interaction parameters, the initial values ​​of the binary interaction estimation model parameters of the target substance are determined, and the initial values ​​of the binary interaction estimation model parameters are input into the binary interaction estimation model to calculate the estimated binary interaction parameters. A third judgment result is obtained by determining whether the regression binary interaction parameter and the estimated binary interaction parameter are equal. If the third judgment result is yes, the initial value of the binary interaction estimation model parameter is used as the binary interaction estimation model parameter. If the third judgment result is negative, the initial value of the parameters of the binary interaction estimation model is updated in combination with the second nonlinear least squares formula, and the system returns "input the initial value of the parameters of the binary interaction estimation model into the binary interaction estimation model and calculate the estimated binary interaction parameters". The system then judges whether the regression binary interaction parameters and the estimated binary interaction parameters satisfy the second iteration termination condition, and obtains the fourth judgment result. If the fourth judgment result is yes, the initial value of the binary interaction estimation model parameter is used as the binary interaction estimation model parameter; If the fourth judgment result is negative, the initial values ​​of the binary interaction estimation model parameters are updated using the second nonlinear least squares formula, and the function is returned to "input the initial values ​​of the binary interaction estimation model parameters into the binary interaction estimation model and calculate the estimated binary interaction parameters" until the regression binary interaction parameters and the estimated binary interaction parameters satisfy the second iteration termination condition. The last updated binary interaction estimation model parameters are then used as the binary interaction estimation model parameters. The second nonlinear least squares formula is: ;in, The regression value of the binary interaction parameter between the j-th extractant and the asphaltenes group components; Here, represents the estimated value of the binary interaction parameter between the j-th extractant and the asphaltenes group components; j is the component pair number; M is the total number of component pairs; the second iteration termination condition is: .

[0032] In an exemplary embodiment, the binary interaction estimation model is: in, k ij This represents the binary interaction parameter between the extractant and the asphaltenes group components; T For temperature; Symbolic representation for estimating model parameters of binary interactions. MW Molecular weight SG denoted by , S represents solvent, VR represents asphaltene, and a, b, c, d, e, f, g, h represent parameters of the binary interaction estimation model.

[0033] Example 2 The embodiments of this application can be applied to the solution of the bubble point pressure of the extractant-asphalt mixture system.

[0034] S1: Set the initial values ​​of the binary interaction parameters of the target material system.

[0035] S2: By simulating the separation process of the substances to be separated, the first liquid phase fugacity and the second liquid phase fugacity are calculated using the SRK or PR equation of state with the first value as input.

[0036] S3: Based on the condition that the first liquid phase fugacity and the second liquid phase fugacity are equal, determine whether the first iteration termination condition is met; if yes, end the iteration and output the binary interaction parameter; if no, combine the nonlinear least squares method to determine the second value of the binary interaction parameter, use the second value as input, use the SRK or PR state equation to calculate the fugacity of the first liquid phase and the second liquid phase, and return to the step "Based on the condition that the first liquid phase fugacity and the second liquid phase fugacity are equal, determine whether the first iteration termination condition is met".

[0037] S4: Based on the binary interaction parameters obtained from regression, set the initial values ​​of the binary interaction estimation model parameters for the target material system.

[0038] S5: Using the initial values ​​of the estimated model parameters as input, the binary interaction parameters are estimated using the binary interaction estimation model.

[0039] S6: Based on the condition that the binary interaction parameters obtained from the regression are equal to the binary interaction parameters obtained from the estimation model, determine whether the second iteration termination condition is met; if yes, end the iteration and output the binary interaction estimation model parameters; if no, combine the nonlinear least squares method to determine the second value of the binary interaction estimation model parameters, use the second value of the estimation model parameters as input, use the binary interaction estimation model to calculate the binary interaction parameters, and return to the step of "determining whether the binary interaction parameters obtained from the regression are equal to the binary interaction parameters obtained from the estimation model, and whether the second iteration termination condition is met".

[0040] In S1, applied in the field of chemical separation, the composition of the target substance is the extractant and asphaltenes, and the molecular weight and specific gravity of the asphaltenes are input.

[0041] In S2, based on the molecular weight and specific gravity input in S1, the critical temperature, critical pressure, and eccentricity factor of the asphaltene family components are estimated. The estimation model for the above basic properties is as follows: The prediction of the average boiling point of the group components adopts the model proposed by Xu Zhiming, as shown in Equation (1).

[0042] (1) in, T b Boiling point (K) d Density (g / m³) 3 ), MW Molecular weight (g / mol).

[0043] The critical temperature and critical pressure of the group components were predicted using the Sim modified Winn model, as shown in equations (2) and (3).

[0044] (2) (3) in P c The critical pressure is denoted as Pa. T c The critical temperature (K) is given. d Density (g / m³) 3 ), MW Molecular weight (g / mol) SG Specific gravity.

[0045] ω The Lee-Kesler model was used for estimation, as shown in equations (4) and (5).

[0046] (4) (5) in, K The characteristic factor is calculated using the following formula: (6) T br For temperature comparison, P br To compare the pressure, the calculation method is as follows: (7) (8) In the formula, P The standard atmospheric pressure is 101325 Pa.

[0047] In S2, the binary interaction parameter between the target substance extractant and the asphaltenes group components is set to the first value.

[0048] In S2, the first value is used as input, and the first liquid phase fugacity and the second liquid phase fugacity corresponding to the first value are calculated using the SRK or PR equation of state.

[0049] SRK state equation model: (9) Gravitational term parameters a ( T Temperature is related to critical properties, and its calculation formula is as follows: (10) in, a c These are the gravitational parameters under critical conditions, and their calculation formula is: (11) b This is the repulsion term parameter, and its calculation formula is: (12) In the formula, T c , P c These are the critical temperature and critical pressure, respectively. R is the gas constant.

[0050] α ( T () is the temperature correction function, used to describe the change of the gravitational term parameter with temperature, and its calculation formula is: (13) in, ω i Components i The eccentricity factor, T r For comparison temperature: (14) The fugacity coefficient of component i in the first or second liquid phase mixture system is calculated using the following formula: (15) In the formula, Z m The compressibility factor of the mixture, V m The value represents the molar volume of the mixture.

[0051] When used for mixtures, the gravitational term parameter a m and repulsion term parameters b m The calculation is performed using the mixed rules of equations (8)-(10).

[0052] (16) (17) In the formula k ij These are the parameters for binary interaction.

[0053] (18) First liquid phase fugacity Second liquid phase fugacity for (19) (20) Model of PR state equation: (twenty one) Gravitational term parameters a ( T Temperature is related to critical properties, and its calculation formula is as follows: (twenty two) in, a c These are the gravitational parameters under critical conditions, and their calculation formula is: (twenty three) b This is the repulsion term parameter, and its calculation formula is: (twenty four) In the formula, T c , P c These are the critical temperature and critical pressure, respectively. R is the gas constant.

[0054] α ( T () is the temperature correction function, used to describe the change of the gravitational term parameter with temperature, and its calculation formula is: (25) in, ω i Components i The eccentricity factor, T r For comparison temperature: (26) The fugacity coefficient of component i in the first or second liquid phase mixture system is calculated using the following formula: (27) In the formula, Z m The compressibility factor of the mixture, V m The value represents the molar volume of the mixture.

[0055] The PR equation of state and the SRK equation of state use the same mixing rules, as shown in equations (16)-(18). In S2, the first liquid phase fugacity and the second liquid phase fugacity are calculated using the SRK or PR equation of state.

[0056] In S3, based on the condition that the first liquid phase fugacity and the second liquid phase fugacity are equal, it is determined whether the first iteration termination condition is met. The first iteration termination condition (also known as the convergence condition) is that the absolute value of the difference between the first liquid phase fugacity and the second liquid phase fugacity is less than the convergence tolerance, that is: in, The first liquid phase fugacity, This represents the fugacity of the second liquid phase.

[0057] The first iteration termination condition is determined based on whether the first liquid phase fugacity and the second liquid phase fugacity are equal. This means determining whether the absolute difference between the first liquid phase fugacity and the second liquid phase fugacity meets the convergence condition. Specifically, if the absolute value of the difference between the first liquid phase fugacity and the second liquid phase fugacity is less than the convergence tolerance, then the first iteration termination condition is met, and the calculation is stopped.

[0058] In S3, if the first iteration termination condition is met, the iteration ends, and the first initial value is used as the binary interaction parameter corresponding to the target substance.

[0059] In S3, if not, i.e. the first iteration termination condition is not met, then the second value of the binary interaction parameter is determined by combining the nonlinear least squares method. Using the second value as input, the fugacity of the first liquid phase and the second liquid phase is calculated using the SRK or PR state equations. Then, the step of "determining whether the first iteration termination condition is met based on the condition that the fugacity of the first liquid phase and the fugacity of the second liquid phase are equal" is returned.

[0060] In S4, based on the binary interaction parameters obtained from regression, the initial values ​​of the binary interaction estimation model parameters for the target material system are set.

[0061] In S5, a binary interaction estimation model for the SRK or PR state equations is proposed. The specific form of the binary interaction estimation model is as follows: (28) (29) in, k ij This represents the binary interaction parameter between the extractant and the asphaltenes group components; T For temperature; Symbolic representation for estimating model parameters of binary interactions. MW Molecular weight SG denoted by ρ, S represents solvent, VR represents asphaltene, and a, b, c, d, e, f, g, h represent estimated model parameters.

[0062] In S6, the first value of the estimation model parameter is used as input, and the binary interaction parameter is calculated using the binary interaction estimation model.

[0063] In S6, the condition of whether the binary interaction parameters obtained from the regression are equal to the binary interaction parameters obtained from the estimation model is used to determine whether the termination condition of the second iteration is met.

[0064] The second iteration termination condition (also known as the convergence condition) is that the absolute value of the difference between the regression binary interaction parameters and the estimated binary interaction parameters is less than the convergence tolerance, i.e.: , y j Let be the binary interaction parameter between the j-th extractant and the asphaltenes group components, res be the regression value, and est be the estimated value.

[0065] The second iteration termination condition is determined by whether the binary interaction parameters obtained from the regression and the estimated model are equal. Specifically, it is determined whether the absolute difference between the binary interaction parameters obtained from the regression and the estimated model meets the convergence condition. If the absolute value of the difference between the binary interaction parameters obtained from the regression and the estimated model is less than the convergence tolerance, then the second iteration termination condition is met, and the calculation is stopped.

[0066] In S6, if the second iteration termination condition is met, the iteration ends, and the first value of the estimated model parameters is used as the parameters of the binary interaction estimation model.

[0067] In S6, if not, i.e. the second iteration termination condition is not met, then the second value of the parameter of the binary interaction estimation model is determined by combining the nonlinear least squares method. The second value of the parameter of the estimation model is used as input, and the binary interaction parameter is calculated by the binary interaction estimation model. Then, the step of "whether the binary interaction parameter based on regression is equal to the binary interaction parameter obtained by the estimation model, and whether the second iteration termination condition is met" is returned.

[0068] The fitting results of the binary interaction parameter estimation model based on the SRK state equation are shown in equations (29) and (30).

[0069] (29) (30) The fitting yields a binary interaction parameter estimation model based on the PR state equation, as shown in equations (29) and (31).

[0070] (31) The following example demonstrates the effectiveness of the binary interaction parameter solution method established for the extractant-asphalt mixture system based on the SRK and PR equations of state.

[0071] Example 1 The implementation process specifically includes: (1) Select propane-Athabasca asphalt and CO2-Athabasca asphalt mixture systems to obtain bubble point pressure experimental data.

[0072] (2) Divide Athabasca asphalt into four groups: saturated hydrocarbons, aromatic hydrocarbons, resins and asphaltenes. Input the molecular weight and specific gravity of each group.

[0073] (3) Estimate the average boiling point, critical temperature, critical pressure, and eccentricity factor of the group components based on the molecular weight and specific gravity of the group components and the basic property prediction model.

[0074] (4) The binary interaction parameters were estimated using the established binary interaction estimation model between the extractant and the asphaltenes.

[0075] (5) Using the estimated binary interaction parameters as input, phase equilibrium calculations are performed on the extractant-asphalt mixture system based on the SRK or PR equation of state, respectively, to predict the bubble point pressure of the mixture system, which is recorded as the prediction result of the modified SRK or PR model.

[0076] (6) Perform phase equilibrium calculations on the extractant-asphalt mixture system using the original SRK or PR equation of state, predict the bubble point pressure of the mixture system, and record the prediction results of the original SRK or PR model.

[0077] Taking the propane-Athabasca asphalt mixture as an example, the average relative errors between the predicted and experimental values ​​of the bubble point pressure of the propane-asphalt mixture by the original SRK and PR models were 18.45% and 16.65%, respectively; the average relative deviations between the predicted and experimental values ​​of the bubble point pressure by the modified SRK and PR equations of state were 5.76% and 5.68%, respectively. The average relative deviation results are shown in Table 1.

[0078] Table 1. Average relative error of different models in predicting bubble point pressure of propane-asphalt mixture system.

[0079] Taking the CO2-Athabasca asphalt mixture as an example, the average relative errors between the predicted and experimental values ​​of the bubble point pressure of the CO2-asphalt mixture by the original SRK and PR models were 39.46% and 40.80%, respectively; the average relative deviations between the predicted and experimental values ​​of the bubble point pressure by the modified SRK or PR equation of state were 6.67% and 6.71%, respectively. The average relative deviation results are shown in Table 2.

[0080] Table 2 shows the average relative error of different models in predicting the bubble point pressure of the CO2-asphalt mixture system.

[0081] The binary interaction estimation model developed based on the SRK or PR equation of state can be used for phase equilibrium calculation of the extractant-asphaltene mixture. By predicting the binary interaction parameters between the extractant and the group components in the mixture, the SRK or PR equation of state improves the calculation accuracy of the phase equilibrium of the extractant-asphaltene mixture.

[0082] Example 2 The developed binary interaction estimation model was validated using an ethane-asphaltene mixture. The bubble point pressure of the ethane-asphaltene mixture was predicted using the modified SRK or PR equation of state, which incorporates the developed binary interaction estimation model. The average relative deviations between the model's predicted and experimental bubble point pressures were 4.66% and 3.98%, respectively. The model prediction results are shown in […]. Figure 2 As shown.

[0083] Example 3 The developed binary interaction parameter estimation model was validated using a propane-CO2-asphaltene ternary mixture system. The bubble point pressure of the propane-CO2-asphaltene mixture system was calculated using the modified SRK or PR equation of state incorporating the developed binary interaction estimation model. The average relative deviations between the model's predicted and experimental bubble point pressures were 2.13% and 3.29%, respectively. The model prediction results are shown in […]. Figure 3 As shown.

[0084] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments. The computer device can be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (I / O), and a communication interface. The processor, memory, and I / O interface are connected via a system bus, and the communication interface is connected to the system bus via the I / O interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device stores data to be processed. The I / O interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communicating with an external terminal via a network connection. When the computer program is executed by the processor, it implements the above-described methods.

[0085] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0086] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0087] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for predicting bubble point pressure of a solvent deasphalting system, characterized by, The method comprises the following steps: Simulating a separation process of the target substances based on initial values of binary interaction parameters of the target substances, and calculating a first liquid fugacity and a second liquid fugacity; The target substances comprise a component of an extractant and a group component of asphaltene; and the binary interaction parameters are binary interaction parameters between the extractant and the group component of asphaltene; The first liquid fugacity and the second liquid fugacity are calculated by using an SRK state equation or a PR state equation; Regression of the binary interaction parameters is performed according to the first liquid fugacity and the second liquid fugacity; Fitting of binary interaction estimation model parameters is performed according to the regression of the binary interaction parameters; Construction of a binary interaction estimation model is performed according to the fitting of the binary interaction estimation model parameters; the binary interaction estimation model is applicable to the SRK state equation or the PR state equation; Phase equilibrium calculation is performed according to the binary interaction estimation model, and a bubble point pressure of a solvent deasphalting system is predicted.

2. The solvent deasphalting system bubble point pressure prediction method of claim 1, wherein, The SRK state equation is as follows: wherein, P is standard atmospheric pressure; ɑ is a temperature; T is a gravitational term parameter; R is a gas constant; T is a temperature; V is a volume; b is a repulsive term parameter.

3. The solvent deasphalting system bubble point pressure prediction method of claim 2, wherein, first liquid fugacity of the i-th component : Second liquid phase fugacity of the ith component is: wherein, P P is the pressure, and xi and x2are the mole fractions of the components of the first and second liquid phases, respectively, and ai and a2are the fugacity coefficients of the first and second liquid phases, respectively. derived from the SRK equation of state and The calculation formula is the same, both are: wherein is or ; b i is the repulsive term parameter for each component in the first liquid phase L1 or the second liquid phase L2; b m is the repulsive term parameter for the first liquid phase L1 mixture or the second liquid phase L2 mixture; Z m is the compressibility factor for the first liquid phase L1 mixture or the second liquid phase L2 mixture; a ij is the attractive term parameter for each component in the first liquid phase L1 or the second liquid phase L2; a m is the attractive term parameter for the first liquid phase L1 mixture or the second liquid phase L2 mixture; V m is the molar volume for the first liquid phase L1 mixture or the second liquid phase L2 mixture; x j is the composition of each component in the first liquid phase L1 or the second liquid phase L2; j is the component number in the first liquid phase L1 or the second liquid phase L2; n is the total number of components in the first liquid phase L1 or the second liquid phase L2.

4. The solvent deasphalting system bubble point pressure prediction method of claim 1, wherein, The PR state equation is as follows: wherein, P is the standard atmospheric pressure; ɑ is the temperature; T is the gravitational term parameter; R is the gas constant; T is the temperature; V is the volume; b is the repulsive term parameter.

5. The solvent deasphalting system bubble point pressure prediction method of claim 4, wherein, first liquid fugacity of the i-th component : Second liquid phase fugacity of the ith component is: wherein, P P is the pressure, and Xi and X2are the mole fractions of the components of the first and second liquid phases, respectively, and and φi and φ2are the fugacity coefficients of the first and second liquid phases, respectively. derived from the PR state equation and the same calculation formula, both are: wherein is or ; b i is the repulsive term parameter for each component in the first liquid phase L1 or the second liquid phase L2; b m is the repulsive term parameter for the first liquid phase L1 mixture or the second liquid phase L2 mixture; Z m is the compressibility factor for the first liquid phase L1 mixture or the second liquid phase L2 mixture; a ij is the attractive term parameter for each component in the first liquid phase L1 or the second liquid phase L2; a m is the attractive term parameter for the first liquid phase L1 mixture or the second liquid phase L2 mixture; V m is the molar volume for the first liquid phase L1 mixture or the second liquid phase L2 mixture; x j is the composition for each component in the first liquid phase L1 or the second liquid phase L2; j is the component number in the first liquid phase L1 or the second liquid phase L2; n is the total number of components in the first liquid phase L1 or the second liquid phase L2.

6. The solvent deasphalting system bubble point pressure prediction method of claim 1, wherein, The regression of the binary interaction parameters according to the first liquid fugacity and the second liquid fugacity specifically comprises the following steps: A first judgment result is obtained by judging whether the first liquid fugacity and the second liquid fugacity are equal; If the first judgment result is yes, the initial values of the binary interaction parameters are taken as the regression of the binary interaction parameters; If the first judgment result is no, the initial values of the binary interaction parameters are updated by using a first nonlinear least square formula, and the step of simulating the separation process of the target substances based on the initial values of the binary interaction parameters of the target substances and calculating the first liquid fugacity and the second liquid fugacity is returned; a second judgment result is obtained by judging whether the first liquid fugacity and the second liquid fugacity satisfy a first iteration termination condition; If the second judgment result is yes, the initial values of the binary interaction parameters are taken as the regression of the binary interaction parameters. If the second determination result is no, the initial value of the binary interaction parameter is updated in combination with a first nonlinear least square formula, and the process of simulating the separation process of the substance to be separated based on the initial value of the binary interaction parameter of the target substance, calculating the first liquid phase fugacity and the second liquid phase fugacity is returned until the first liquid phase fugacity and the second liquid phase fugacity meet a first iteration termination condition, and the last updated regression binary interaction parameter is taken as the regression binary interaction parameter; the first nonlinear least square formula is: ; wherein, is the first liquid phase fugacity of the i th component; is the second liquid phase fugacity of the i th component; L1 is the first liquid phase; L2 is the second liquid phase; i is the component serial number; N is the total number of components; and the first iteration termination condition is: .

7. The solvent deasphalting system bubble point pressure prediction method of claim 6, wherein, The regression of the binary interaction parameters is as follows: wherein, k ij is the binary interaction parameter between the extractant and the asphaltenes group component; is the binary interaction estimation model parameter 1 ; is the binary interaction estimation model parameter 2; is the binary interaction estimation model parameter 3; T is the temperature.

8. The solvent deasphalting system bubble point pressure prediction method of claim 1, wherein, The fitting of the binary interaction estimation model parameters according to the regression of the binary interaction parameters specifically comprises the following steps: Initial values of binary interaction estimation model parameters of the target substances are determined based on the regression of the binary interaction parameters, and the initial values of the binary interaction estimation model parameters are input into a binary interaction estimation model to calculate estimated binary interaction parameters; A third judgment result is obtained by judging whether the regression of the binary interaction parameters and the estimated binary interaction parameters are equal; If the third judgment result is yes, the initial values of the binary interaction estimation model parameters are taken as the binary interaction estimation model parameters; If the third judgment result is no, the initial values of the binary interaction estimation model parameters are updated by using a second nonlinear least square formula, and the step of inputting the initial values of the binary interaction estimation model parameters into the binary interaction estimation model to calculate the estimated binary interaction parameters is returned; a fourth judgment result is obtained by judging whether the regression of the binary interaction parameters and the estimated binary interaction parameters satisfy a second iteration termination condition; If the fourth determination result is yes, the binary interaction estimation model parameter initial value is taken as a binary interaction estimation model parameter; If the fourth determination result is no, the initial value of the binary interaction estimation model parameter is updated in combination with a second nonlinear least square formula, and the process returns to "inputting the initial value of the binary interaction estimation model parameter into a binary interaction estimation model to calculate an estimated binary interaction parameter" until the regression binary interaction parameter and the estimated binary interaction parameter satisfy a second iteration termination condition, and the binary interaction estimation model parameter after the last update is taken as the binary interaction estimation model parameter; the second nonlinear least square formula is: ; wherein, is a regression value of a binary interaction parameter between the jth extractant and the asphaltene group component; is an estimated value of a binary interaction parameter between the jth extractant and the asphaltene group component; j is a component pair serial number; M is the total number of component pairs; and the second iteration termination condition is: .

9. The solvent deasphalting system bubble point pressure prediction method of claim 1, wherein, The binary interaction estimation model is: wherein, k ij is the binary interaction parameter between the solvent and the asphaltenes; T is the temperature; is the symbolic representation of the binary interaction estimation model parameters, MW is the molecular weight, SG is the specific gravity, subscript S is the solvent, subscript VR is the asphaltenes, a, b, c, d, e, f, g, h are the binary interaction estimation model parameters.

10. A computer device comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that the processor executes the computer program to implement the solvent deasphalting system bubble point pressure prediction method of any one of claims 1-9.

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