Physical property prediction method, system and equipment of hexafluoropropylene refrigerant, medium and product

Abstract

The invention discloses a physical property prediction method, system and equipment of a hexafluoropropylene refrigerant, a medium and a product, and relates to the field of chemical thermodynamics, and the method comprises the following steps: obtaining basic physical property data of hexafluoropropylene; screening the basic physical property data by using a thermodynamic consistency principle to obtain screened basic physical property data; constructing an initial multi-parameter state equation according to the screened basic physical property data; constructing a target function according to the screened basic physical property data and the initial multi-parameter state equation; according to the target function, based on a least square method, obtaining a multi-parameter state equation; utilizing the multi-parameter state equation to predict the physical property of the hexafluoropropylene; according to the method, the physical property of the hexafluoropropylene can be accurately predicted, and reliable thermodynamic support is provided for evaluating the safety and usability of the refrigerant under different conditions.

Description

Technical Field

[0001] This application relates to the field of chemical thermodynamics, and in particular to a method, system, equipment, medium, and product for predicting the physical properties of hexafluoropropylene refrigerant. Background Technology

[0002] In the refrigeration and air conditioning field, the energy efficiency optimization of air conditioning systems highly depends on the accurate description and efficient utilization of the thermophysical properties of refrigerants. This goal is often achieved by establishing dedicated multi-parameter equations of state. Hydrofluoroolefin (HFO) refrigerants, due to their low Global Warming Potential (GWP) and zero Ozone Depletion Potential (ODP), have become an important development direction in the industry. Hexafluoropropylene (CAS No. 116-15-4), also known as R1216 (chemical formula C3F6, molar mass 150.023), is a prime example. Hydrofluoroolefins (HFO) are one of the most important representative refrigerants, widely used in various refrigeration and air conditioning equipment. The accuracy of their thermophysical property data directly affects the design and operational efficiency of air conditioning systems.

[0003] Currently, dedicated multi-parameter equations of state have been established for HFO refrigerants such as 2,3,3,3-tetrafluoro-1-propene (R1234yf), trans-1,1,1,3-tetrafluoropropene (R1234ze(E)), 1,1,2-trifluoroethylene (R1123), 3,3,3-trifluoropropene (R1243zf), and cis-1,1,1,4,4,4-hexafluoro-2-butene (R1336mzz(Z)), enabling accurate prediction of their physical properties. However, a similar multi-parameter equation of state specifically for hexafluoropropene (R1216) is still lacking. This deficiency significantly restricts the accurate calculation and simulation of the thermodynamic properties of this refrigerant over a wide temperature and pressure range, and also limits the theoretical basis for system optimization and the design of new equipment, becoming a significant technical gap in the current field of refrigerant property research.

[0004] Therefore, based on the above problems, there is an urgent need to provide a method, system, equipment, medium, and product for predicting the physical properties of hexafluoropropylene refrigerant, which can accurately predict the physical properties of hexafluoropropylene and provide reliable thermodynamic support for assessing the safety and availability of refrigerant under different conditions. Summary of the Invention

[0005] The purpose of this application is to provide a method, system, device, medium, and product for predicting the physical properties of hexafluoropropylene refrigerant, which can accurately predict the physical properties of hexafluoropropylene and provide reliable thermodynamic support for assessing the safety and availability of refrigerant under different conditions.

[0006] To achieve the above objectives, this application provides the following solution: In a first aspect, this application provides a method for predicting the physical properties of hexafluoropropylene refrigerant, including: Obtain the basic physical property data of hexafluoropropylene; the basic physical property data includes: Data, vapor velocity, saturated vapor pressure, saturated vapor-liquid density, and ideal gas heat capacity; The data includes: pressure, density, and temperature; The basic physical property data are filtered using the principle of thermodynamic consistency to obtain the filtered basic physical property data; Based on the filtered basic physical property data, an initial multi-parameter equation of state is constructed. Based on the screened basic physical property data and the initial multi-parameter equation of state, an objective function is constructed; the objective function is the sum of the physical property calculation deviation objective function and the thermodynamic constraint objective function; the physical property calculation deviation objective function is the sum of the relative squared differences between the estimated physical property values ​​and the actual physical property values; Based on the objective function, the initial multi-parameter state equation is fitted using the least squares method to obtain the multi-parameter state equation. The physical properties of hexafluoropropylene are predicted using a multi-parameter equation of state.

[0007] Optionally, constructing an initial multi-parameter equation of state based on the filtered basic physical property data specifically includes: Using formula Constructing the initial multi-parameter state equations ; in, For the ideal term, , For the remaining terms, , For density comparison, A constant related to the entropy value under the reference state. A constant related to enthalpy under reference conditions. Inverse contrast temperature, This is the critical temperature of hexafluoropropylene. To characterize the fundamental components of the heat capacity of an ideal gas, excluding vibrational contributions, These are parameters used to adjust the contribution of different vibration modes to the heat capacity. The coefficient representing the influence of different vibration modes on heat capacity. The coefficient of the remaining term. For the remaining term, temperature index, The density index of the residual terms. Adjusting parameters for density dependence, The coefficient that controls the width of the Gaussian term in the density direction, The coefficient is used to control the center position of the Gaussian term in the density direction. The coefficient used to control the width of the Gaussian term in the temperature direction, The coefficient used to control the center position of the Gaussian term in the temperature direction.

[0008] Optionally, the step of constructing the objective function based on the filtered basic physical property data and the initial multi-parameter equation of state specifically includes: Based on the screened basic physical property data and the initial multi-parameter equation of state, a physical property estimation model is constructed; the physical property estimation model includes: pressure physical property estimation model, compressibility factor physical property estimation model, gas phase sound velocity physical property estimation model, isochoric heat capacity physical property estimation model, entropy physical property estimation model and Gibbs free energy physical property estimation model. Based on the aforementioned property estimation model, determine the estimated values ​​of the basic property data; Based on the estimated physical properties, determine the weighted physical property deviation objective function; Based on the weighted property deviation objective function and the thermodynamic constraint objective function, an objective function is constructed.

[0009] Optionally, the step of constructing a property estimation model based on the filtered basic physical property data and the initial multi-parameter equation of state specifically includes: Using formula Construct a physical property estimation model for pressure; whereby, These are estimates of pressure properties. For density, The first-order partial derivative of the initial multi-parameter equation of state with respect to density. The molar gas constant, For temperature, For density comparison, For the remaining terms The first-order partial derivative; Using formula Construct a physical property estimation model for the compressibility factor; where, This is an estimate of the compressibility factor physical property. Using formula A physical property estimation model for gas phase sound velocity is constructed; among which, This is an estimate of the gas phase sound velocity properties. The molar molecular weight of hexafluoropropylene is... The first partial derivative of pressure with respect to density. For the remaining terms Second-order partial derivatives, Inverse contrast temperature, For the remaining terms and The mixed second-order partial derivatives, For ideal terms The second-order partial derivative, For the remaining terms Second-order partial derivative; Using formula Determine the physical property estimation model for isochoric heat capacity; where, This is an estimate of the isochoric heat capacity property. For ideal terms The second-order partial derivative, For the remaining terms Second-order partial derivatives; Using formula Determine the physical property estimation model for the Gibbs free energy; whereby, This is an estimate of the Gibbs free energy property. For the ideal term, , For the remaining terms, ; Using formula Determine the physical property estimation model for entropy; where, This is an estimate of entropy properties. For ideal terms The first-order partial derivative, It is a pair of remaining terms The first-order partial derivative.

[0010] Optionally, determining the estimated physical property values ​​of the basic physical property data based on the physical property estimation model specifically includes: Based on the aforementioned property estimation model, the corresponding estimated values ​​for pressure property, compressibility factor property, gas phase sound velocity property, isochoric heat capacity property, entropy property, and Gibbs free energy property are determined respectively. Based on the principle that the estimated values ​​of pressure properties and Gibbs free energy are equal, the estimated values ​​of saturated vapor pressure and saturated gas-liquid density properties are determined. Based on the gas phase sound velocity estimate, determine the ideal gas heat capacity property estimate.

[0011] Optionally, the step of constructing the objective function based on the weighted property deviation objective function and the thermodynamic constraint objective function specifically includes: Using formula Construct the objective function; in, Let be the objective function. The objective function is the weighted property deviation. , Let the objective function be a thermodynamic constraint. , The scaling factor is the weighting factor of the objective function for weighted property deviation. is the scaling factor for the thermodynamically constrained objective function. For the first Weighting factors for each stress data point For the first Weighting factors for density data, For the first Weighting factors for gas phase sound velocity data For the first A weighting factor for the heat capacity of an ideal gas. For the first The relative deviation of each pressure data point For the first The relative deviation of each density data point For the first The relative deviation of the gas phase sound velocity data For the first The relative deviation of the ideal gas heat capacity data For the first A thermodynamic constraint.

[0012] Secondly, this application provides a property prediction system for hexafluoropropylene refrigerant, comprising: The basic physical property data acquisition module is used to acquire the basic physical property data of hexafluoropropylene; the basic physical property data includes: Data, vapor velocity, saturated vapor pressure, saturated vapor-liquid density, and ideal gas heat capacity; The data includes: pressure, density, and temperature; The filtering module is used to filter the basic physical property data using the principle of thermodynamic consistency, and obtain the filtered basic physical property data. An initial multi-parameter equation of state construction module is used to construct an initial multi-parameter equation of state based on the filtered basic physical property data. The objective function construction module is used to construct an objective function based on the filtered basic physical property data and the initial multi-parameter equation of state; the objective function is the sum of the physical property calculation deviation objective function and the thermodynamic constraint objective function; the physical property calculation deviation objective function is the sum of the relative squared differences between the estimated physical property values ​​and the actual physical property values; A multi-parameter state equation construction module is used to fit the initial multi-parameter state equation based on the objective function using the least squares method to obtain the multi-parameter state equation. The prediction module is used to predict the physical properties of hexafluoropropylene using a multi-parameter equation of state.

[0013] Thirdly, 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 perform the above-described steps for predicting the physical properties of a hexafluoropropylene refrigerant.

[0014] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps described above for predicting the physical properties of a hexafluoropropylene refrigerant.

[0015] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, performs the steps described above for predicting the physical properties of a hexafluoropropylene refrigerant.

[0016] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a method, system, device, medium, and product for predicting the physical properties of hexafluoropropylene refrigerant. It constructs an initial multi-parameter equation of state using basic physical property data of hexafluoropropylene and fits this equation using the least squares method; further, it predicts the physical properties of hexafluoropropylene. This application can accurately predict the physical properties of hexafluoropropylene, filling the gap in experimental data for this substance through theoretical calculations. It provides reliable thermodynamic support for evaluating and optimizing the energy efficiency of refrigeration systems and assessing the safety and usability of refrigerants under different conditions. Attached Figure Description

[0017] 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.

[0018] Figure 1 This is a flowchart illustrating a method for predicting the physical properties of a hexafluoropropylene refrigerant in one embodiment of this application. Figure 2 This is a schematic diagram of the pressure prediction results of hexafluoropropylene in one embodiment of this application; Figure 3 This is a schematic diagram of the predicted density of hexafluoropropylene in one embodiment of this application; Figure 4 This is a schematic diagram of the predicted saturated vapor pressure of hexafluoropropylene in one embodiment of this application; Figure 5 This is a schematic diagram of the predicted density of the saturated gas-liquid phase of hexafluoropropylene in one embodiment of this application; Figure 6This is a schematic diagram of the predicted heat capacity of hexafluoropropylene as an ideal gas in one embodiment of this application; Figure 7 This is a schematic diagram of the gas phase sound velocity prediction results of hexafluoropropylene in one embodiment of this application. Detailed Implementation

[0019] 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.

[0020] To make the above-mentioned 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.

[0021] In one exemplary embodiment, such as Figure 1 As shown, a method for predicting the physical properties of hexafluoropropylene refrigerant is provided, comprising the following S1 to S6. Wherein: S1: Obtain basic physical property data for hexafluoropropylene.

[0022] Specifically, the basic physical property data includes: Data, vapor velocity, saturated vapor pressure, saturated vapor-liquid density, and ideal gas heat capacity; The data includes: pressure, density, and temperature.

[0023] S2: The basic physical property data are screened using the principle of thermodynamic consistency to obtain the screened basic physical property data.

[0024] Based on the principle of thermodynamic consistency, unreasonable experimental data points in the basic physical property data of hexafluoropropylene were screened and eliminated to ensure the accuracy of the fitting process.

[0025] Specifically, the Clausius-Clapeyron equation was used to test the linearity of the saturated vapor pressure data; through... The data relationships were used to verify the smoothness and critical behavior of the saturated vapor-liquid density curves; and outliers were eliminated based on the smoothness of the gas phase sound velocity and the ideal gas heat capacity and temperature, finally obtaining reliable basic physical property data after screening.

[0026] S3: Based on the screened basic physical property data, construct the initial multi-parameter state equation.

[0027] Initial multi-parameter state equations From ideal terms and the remaining terms It consists of two parts, with the remaining term being... Including polynomial terms exponent term Gaussian bell-shaped term The initial multi-parameter state equations are calculated using the following formulas: .

[0028] The formula for calculating the ideal term is as follows: ; in, For density comparison, , For density, The critical density of hexafluoropropylene is 3.85g in this application. , A constant related to the entropy value under the reference state. A constant related to enthalpy under reference conditions. Inverse contrast temperature, , For temperature, To characterize the fundamental component of the heat capacity of an ideal gas excluding vibrational contributions, its theoretical value is always 4 for nonlinear polyatomic molecules. The parameter used to adjust the contribution of different vibration modes to the heat capacity determines the magnitude of this term's contribution to the initial multi-parameter state equation. The coefficient representing the influence of different vibration modes on heat capacity. The critical temperature of hexafluoropropylene is 358.9 K in this application.

[0029] The formula for calculating the remaining term is as follows: ; in, The coefficient of the remaining term. This is the temperature index of the remaining term, used to control the temperature dependence of this term. This is the residue density index, used to control for the residue's dependence on density. This is a density dependence adjustment parameter used to adjust the density dependence of the exponential decay term. The coefficient that controls the width of the Gaussian term in the density direction, The coefficient is used to control the center position of the Gaussian term in the density direction. The coefficient used to control the width of the Gaussian term in the temperature direction, The coefficient used to control the center position of the Gaussian term in the temperature direction.

[0030] Ideal gas heat capacity The calculation formula is as follows: ; in, is the molar gas constant.

[0031] S4: Construct the objective function based on the selected basic physical property data and the initial multi-parameter state equation.

[0032] S41: Construct a property estimation model based on the screened basic physical property data and the initial multi-parameter state equation.

[0033] The property estimation models include: pressure property estimation model, compressibility factor property estimation model, gas phase sound velocity property estimation model, isochoric heat capacity property estimation model, entropy property estimation model, and Gibbs free energy property estimation model. Specifically, based on the initial multi-parameter equation of state, the pressure property estimation model, compressibility factor property estimation model, entropy property estimation model, and Gibbs free energy property estimation model are derived through first-order partial derivatives, and the isochoric heat capacity property estimation model and gas phase sound velocity property estimation model are derived through second-order partial derivatives.

[0034] The formula for calculating the physical property estimation model of pressure is as follows: ; in, These are estimates of pressure properties. For density, The first-order partial derivative of the initial multi-parameter equation of state with respect to density. is the molar gas constant, with a value of 8.314462618. , For temperature, For density comparison, For the remaining terms The first-order partial derivative.

[0035] The formula for calculating the compressibility factor using the physical property estimation model is as follows: ; in, This is an estimate of the compressibility factor.

[0036] The formula for calculating entropy using a physical property estimation model is as follows: ; in, This is an estimate of entropy properties. For ideal terms The first-order partial derivative, For the remaining terms The first-order partial derivative.

[0037] The formula for calculating the Gibbs free energy using the physical property estimation model is as follows: ; in, This is an estimate of the Gibbs free energy property. For the ideal term, , For the remaining terms, .

[0038] The formula for calculating the physical property estimation model of gas phase sound velocity is as follows: ; in, This is an estimate of the gas phase sound velocity properties. This represents the molar molecular weight of hexafluoropropylene, with a value of 150.023. , Let be the partial derivative of pressure with respect to density. For the remaining terms Second-order partial derivatives, For the remaining terms and The mixed second-order partial derivatives, For ideal terms The second-order partial derivative, For the remaining terms Second-order partial derivatives.

[0039] The formula for estimating isochoric heat capacity using a physical property model is as follows: ; in, This is an estimate of the isochoric heat capacity property. For ideal terms The second-order partial derivative, For the remaining terms Second-order partial derivatives.

[0040] S42: Determine the estimated values ​​of the basic physical property data based on the physical property estimation model.

[0041] S42 specifically includes: S421: Based on the property estimation model, determine the corresponding estimated values ​​for pressure property, compressibility factor property, gas phase sound velocity property, isochoric heat capacity property, entropy property, and Gibbs free energy property, respectively.

[0042] S422: Determine the estimated values ​​of saturated vapor pressure and saturated gas-liquid density based on the principle that the estimated values ​​of pressure properties and Gibbs free energy are equal.

[0043] Specifically, based on the Maxwell criterion, namely the criterion that the vapor-liquid phase pressure and Gibbs free energy are equal, an iterative algorithm for solving saturated vapor pressure and saturated vapor-liquid phase density is established to obtain estimated values ​​of saturated vapor pressure and saturated vapor-liquid phase density.

[0044] S423: Determine the estimated values ​​of ideal gas heat capacity properties based on the estimated values ​​of gas phase sound velocity.

[0045] S43: Determine the weighted property deviation objective function based on the estimated property values.

[0046] The objective function for the deviation in property calculation is the sum of the relative squared differences between the estimated and actual property values. The formula for calculating the weighted property deviation objective function is as follows: ; in, For weighted property deviation, For the first Weighting factors for each stress data point For the first Weighting factors for density data, For the first Weighting factors for gas phase sound velocity data For the first A weighting factor for the heat capacity of an ideal gas. For the first The relative deviation of each pressure data point For the first The relative deviation of each density data point For the first The relative deviation of the gas phase sound velocity data For the first The relative deviation of the ideal gas heat capacity data.

[0047] relative deviation of pressure The calculation formula is as follows: ; in, These are the actual values ​​of the pressure properties, i.e., experimental data. These are the estimated values ​​of pressure properties, i.e., the estimated values ​​of properties determined by the property estimation model.

[0048] Relative deviation of density The calculation formula is as follows: ; in, These are the actual values ​​of the density property, i.e., experimental data. This is the density property estimate, that is, the property estimate determined by the property estimation model. It is the first-order partial derivative of density with respect to pressure.

[0049] Relative deviation of gas phase sound velocity The calculation formula is as follows: ; in, These are actual values ​​of the gas phase sound velocity properties, i.e., experimental data. This is the estimated value of the gas phase sound velocity, that is, the estimated value of the physical property determined by the physical property estimation model.

[0050] Ideal gas heat capacity The formula for calculating the relative deviation is as follows: ; in, These are the actual values ​​of the heat capacity of an ideal gas, i.e., experimental data. These are the estimated values ​​of the heat capacity of an ideal gas, i.e., the estimated values ​​of the properties determined by the property estimation model.

[0051] S44: Construct the objective function based on the weighted property deviation objective function and the thermodynamic constraint objective function.

[0052] Based on fundamental thermodynamic relations, the objective function of thermodynamic constraints is determined. The fundamental thermodynamic constraints include: the first partial derivative of pressure with respect to density is monotonically decreasing below the critical point, monotonically increasing above the critical point, and equal to zero at the critical point; the second partial derivative of pressure with respect to density is always monotonically increasing and equal to zero at the critical point; the heat capacity of an ideal gas reaches 4R at low temperatures and approaches (4+21R) at extremely high temperatures; the critical point refers to the highest temperature and pressure at which the gas and liquid phases can coexist; beyond this point, there is no macroscopic difference between the gas and liquid phases, and gas-liquid phase transitions no longer occur.

[0053] The objective function is the sum of the objective function for the deviation from the property calculation and the objective function for the thermodynamic constraints. The formula for calculating the objective function is as follows: ; in, Let be the objective function. Let the objective function be a thermodynamic constraint. , The scaling factor is the weighting factor of the objective function for weighted property deviation. is the scaling factor for the thermodynamically constrained objective function. For the first A thermodynamic constraint.

[0054] S5: Based on the objective function, the initial multi-parameter state equation is fitted using the least squares method to obtain the multi-parameter state equation.

[0055] S6: Predict the physical properties of hexafluoropropylene using a multi-parameter equation of state.

[0056] In one exemplary embodiment, the acquired basic physical property data includes 560 sets. Data, 105 sets of gas phase sound velocity, 160 sets of saturated vapor pressure, 15 sets of saturated gas-liquid phase density, and 15 sets of ideal gas heat capacity data.

[0057] Based on the 560 groups after screening data, The experimental data ranged in temperature from 263.41 to 362.9 K and in density from 0.0078 to 9.83 mol / dm³. 3 The experimental values ​​for pressure ranged from 0.0024 to 10.1 MPa; for 105 sets of gas phase sound velocities, the temperature range for gas phase sound velocity experiments was 293.149–358.15 K, and the pressure range was 0.13–1.1 MPa; for 160 sets of saturated vapor pressures, the temperature range for saturated vapor pressure experiments was 243.06–358.76 K, and the experimental values ​​for saturated vapor pressure ranged from 0.07 to 3.5 MPa; for 15 sets of saturated gas-liquid phase densities, the temperature range for saturated gas-liquid phase density experiments was 263.4–358.2 K, and the experimental values ​​for saturated gas phase density ranged from 0.1 to 2.7 mol / m³. 3 The experimental values ​​for the density of the saturated liquid phase range from 4.5 to 9.8 mol / m³. 3 15 sets of ideal gas heat capacity data. The temperature range of the ideal gas heat capacity experimental data is 293.149-363.15K, and the experimental values ​​of ideal gas heat capacity are between 119-133J / mol / K.

[0058] Under the premise of satisfying thermodynamic constraints, the objective function is to minimize the sum of the objective function of the property calculation deviation and the objective function of the thermodynamic constraints. The initial multi-parameter state equation is fitted by the least squares method to obtain the parameters of the initial multi-parameter state equation. The specific values ​​obtained by fitting are shown in Table 1 and Table 2.

[0059] Table 1. Schematic diagram of ideal term coefficients

[0060] Table 2. Residual Term Coefficients Schematic Table

[0061] In one exemplary embodiment, the pressure and density property estimation model derived from the initial multi-parameter equation of state is used to estimate the properties of hexafluoropropylene. The experimental values ​​of the data verify the accuracy of the multi-parameter state equation. The experimental data ranged from 263.41 to 362.9 K in temperature and from 0.0078 to 9.83 mol / dm³ in density. 3 The experimental pressure values ​​ranged from 0.04 to 7.4 MPa. The relative deviation between the calculated pressure obtained from the property estimation model derived from the multi-parameter equation of state and the experimental pressure obtained was 0.29%. Figure 2 As shown; the relative deviation between the calculated density obtained from the property estimation model derived from the multi-parameter equation of state and the experimental density obtained from experiments is 0.023%, and as... Figure 3 As shown.

[0062] In one exemplary embodiment, the accuracy of the saturated property estimation model derived from the multi-parameter equation of state is verified using experimental data on the saturated vapor pressure and saturated gas-liquid density of hexafluoropropylene. The temperature range for the experimental data on saturated vapor pressure and saturated gas-liquid density is 243.06-358.76 K, the experimental values ​​for saturated vapor pressure are between 0.07-3.5 MPa, and the experimental values ​​for saturated gas density are between 0.1-2.7 mol / m³. 3 The experimental values ​​for the density of the saturated liquid phase range from 4.5 to 9.8 mol / m³. 3 The relative deviation between the calculated saturated vapor pressure (calculated pressure) obtained from the property estimation model derived from the multi-parameter equation of state and the experimentally obtained saturated vapor pressure (experimental pressure) is 1.28%, and as shown in the figure... Figure 4 As shown, the relative deviations between the calculated saturated gas-liquid phase density (calculated density) obtained from the property estimation model derived from the multi-parameter equation of state and the experimental saturated gas-liquid phase density (experimental density) are 2.19% and 0.89%, respectively. Figure 5 As shown.

[0063] In an exemplary embodiment, an ideal gas heat capacity property estimation model derived from a multi-parameter equation of state is used to assess the accuracy of the multi-parameter equation of state based on experimental data of the ideal gas heat capacity of hexafluoropropylene. The temperature test range of the ideal gas heat capacity experimental data is 293.149-363.15 K, and the experimental values ​​of the ideal gas heat capacity are between 119-133 J / mol / K. The relative deviation between the calculated value of the ideal gas heat capacity obtained from the property estimation model derived from the multi-parameter equation of state and the experimental value of the ideal gas heat capacity is 0.34%. Figure 6 As shown.

[0064] In an exemplary embodiment, the accuracy of the multi-parameter equation of state is verified using experimental data on the gas-phase sound velocity of hexafluoropropylene, based on a gas-phase sound velocity estimation model derived from the multi-parameter equation of state. The experimental data for the gas-phase sound velocity were measured at temperatures ranging from 293.149 to 358.15 K and pressures from 0.13 to 0.863 MPa. The experimental values ​​of the gas-phase sound velocity ranged from 128 to 142 m / s. The relative deviation between the calculated gas-phase sound velocity value (calculated sound velocity value) obtained from the property estimation model derived from the multi-parameter equation of state and the experimentally obtained gas-phase sound velocity value (experimental sound velocity value) was 0.34%. Figure 7 As shown.

[0065] Based on the same inventive concept, this application also provides a system for predicting the physical properties of hexafluoropropylene refrigerant to implement the above-described method. The solution provided by this system is similar to the solution described in the above-described method. Therefore, the specific limitations of one or more embodiments of the hexafluoropropylene refrigerant property prediction system provided below can be found in the limitations of the hexafluoropropylene refrigerant property prediction method described above, and will not be repeated here.

[0066] In one exemplary embodiment, a property prediction system for hexafluoropropylene refrigerant is provided, comprising: The basic physical property data acquisition module is used to acquire the basic physical property data of hexafluoropropylene; the basic physical property data includes: Data, vapor velocity, saturated vapor pressure, saturated vapor-liquid density, and ideal gas heat capacity; The data includes: pressure, density, and temperature.

[0067] The filtering module is used to filter the basic physical property data using the principle of thermodynamic consistency, and obtain the filtered basic physical property data.

[0068] The initial multi-parameter state equation construction module is used to construct the initial multi-parameter state equation based on the filtered basic physical property data.

[0069] The objective function construction module is used to construct an objective function based on the filtered basic physical property data and the initial multi-parameter equation of state; the objective function is the sum of the physical property calculation deviation objective function and the thermodynamic constraint objective function; the physical property calculation deviation objective function is the sum of the relative squared differences between the estimated physical property values ​​and the actual physical property values.

[0070] The multi-parameter state equation construction module is used to fit the initial multi-parameter state equation based on the objective function using the least squares method to obtain the multi-parameter state equation.

[0071] The prediction module is used to predict the physical properties of hexafluoropropylene using a multi-parameter equation of state.

[0072] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is connected to the system bus via the I / O interfaces. 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, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the computer device stores property prediction data for hexafluoropropylene refrigerant. The I / O interfaces of the computer device are used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communicating with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for predicting the properties of hexafluoropropylene refrigerant.

[0073] 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.

[0074] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0075] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0076] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0077] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0078] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units, etc., and are not limited to these.

[0079] 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.

[0080] 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 the physical properties of hexafluoropropylene refrigerant, characterized in that, The method for predicting the physical properties of the hexafluoropropylene refrigerant includes: Obtain the basic physical property data of hexafluoropropylene; the basic physical property data includes: Data, vapor velocity, saturated vapor pressure, saturated vapor-liquid density, and ideal gas heat capacity; The data includes: pressure, density, and temperature; The basic physical property data are filtered using the principle of thermodynamic consistency to obtain the filtered basic physical property data; Based on the filtered basic physical property data, an initial multi-parameter equation of state is constructed. Based on the screened basic physical property data and the initial multi-parameter equation of state, an objective function is constructed; the objective function is the sum of the physical property calculation deviation objective function and the thermodynamic constraint objective function; the physical property calculation deviation objective function is the sum of the relative squared differences between the estimated physical property values ​​and the actual physical property values; Based on the objective function, the initial multi-parameter state equation is fitted using the least squares method to obtain the multi-parameter state equation. The physical properties of hexafluoropropylene are predicted using a multi-parameter equation of state.

2. The method for predicting the physical properties of hexafluoropropylene refrigerant according to claim 1, characterized in that, The step of constructing an initial multi-parameter equation of state based on the screened basic physical property data specifically includes: Using formula Constructing the initial multi-parameter state equations ; in, For the ideal term, , For the remaining terms, , For density comparison, A constant related to the entropy value under the reference state. A constant related to enthalpy under reference conditions. Inverse contrast temperature, This is the critical temperature of hexafluoropropylene. To characterize the fundamental components of the heat capacity of an ideal gas, excluding vibrational contributions, These are parameters used to adjust the contribution of different vibration modes to the heat capacity. The coefficient representing the influence of different vibration modes on heat capacity. The coefficient of the remaining term. For the remaining term, temperature index, The density index of the residual terms. Adjusting parameters for density dependence, The coefficient that controls the width of the Gaussian term in the density direction, The coefficient is used to control the center position of the Gaussian term in the density direction. The coefficient used to control the width of the Gaussian term in the temperature direction, The coefficient used to control the center position of the Gaussian term in the temperature direction.

3. The method for predicting the physical properties of hexafluoropropylene refrigerant according to claim 1, characterized in that, The step of constructing the objective function based on the filtered basic physical property data and the initial multi-parameter equation of state specifically includes: Based on the screened basic physical property data and the initial multi-parameter equation of state, a physical property estimation model is constructed; the physical property estimation model includes: pressure physical property estimation model, compressibility factor physical property estimation model, gas phase sound velocity physical property estimation model, isochoric heat capacity physical property estimation model, entropy physical property estimation model and Gibbs free energy physical property estimation model. Based on the aforementioned property estimation model, determine the estimated values ​​of the basic property data; Based on the estimated physical properties, determine the weighted physical property deviation objective function; Based on the weighted property deviation objective function and the thermodynamic constraint objective function, an objective function is constructed.

4. The method for predicting the physical properties of hexafluoropropylene refrigerant according to claim 3, characterized in that, The step of constructing a property estimation model based on the screened basic physical property data and the initial multi-parameter equation of state specifically includes: Using formula Construct a physical property estimation model for pressure; whereby, These are estimates of pressure properties. For density, The first-order partial derivative of the initial multi-parameter equation of state with respect to density. The molar gas constant, For temperature, For density comparison, For the remaining terms The first-order partial derivative; Using formula Construct a physical property estimation model for the compressibility factor; where, This is an estimate of the compressibility factor physical property. Using formula A physical property estimation model for gas phase sound velocity is constructed; among which, This is an estimate of the gas phase sound velocity properties. The molar molecular weight of hexafluoropropylene is... The first partial derivative of pressure with respect to density. For the remaining terms Second-order partial derivatives, Inverse contrast temperature, For the remaining terms and The mixed second-order partial derivatives, For ideal terms The second-order partial derivative, For the remaining terms Second-order partial derivatives; Using formula Determine the physical property estimation model for isochoric heat capacity; where, This is an estimate of the isochoric heat capacity property. For ideal terms The second-order partial derivative, For the remaining terms Second-order partial derivatives; Using formula Determine the physical property estimation model for the Gibbs free energy; whereby, This is an estimate of the Gibbs free energy property. For the ideal term, , For the remaining terms, ; Using formula Determine the physical property estimation model for entropy; where, This is an estimate of entropy properties. For ideal terms The first-order partial derivative, It is a pair of remaining terms The first-order partial derivative.

5. The method for predicting the physical properties of hexafluoropropylene refrigerant according to claim 3, characterized in that, The step of determining the estimated physical property values ​​of the basic physical property data based on the physical property estimation model specifically includes: Based on the aforementioned property estimation model, the corresponding estimated values ​​for pressure property, compressibility factor property, gas phase sound velocity property, isochoric heat capacity property, entropy property, and Gibbs free energy property are determined respectively. Based on the principle that the estimated values ​​of pressure properties and Gibbs free energy are equal, the estimated values ​​of saturated vapor pressure and saturated gas-liquid density properties are determined. Based on the gas phase sound velocity estimate, determine the ideal gas heat capacity property estimate.

6. The method for predicting the physical properties of hexafluoropropylene refrigerant according to claim 3, characterized in that, The construction of the objective function based on the weighted property deviation objective function and the thermodynamic constraint objective function specifically includes: Using formula Construct the objective function; in, Let be the objective function. The objective function is the weighted property deviation. , Let the objective function be a thermodynamic constraint. , The scaling factor is the weighting factor of the objective function for weighted property deviation. is the scaling factor for the thermodynamically constrained objective function. For the first Weighting factors for each stress data point For the first Weighting factors for density data, For the first Weighting factors for gas phase sound velocity data For the first A weighting factor for the heat capacity of an ideal gas. For the first The relative deviation of each pressure data point For the first The relative deviation of each density data point For the first The relative deviation of the gas phase sound velocity data For the first The relative deviation of the ideal gas heat capacity data For the first A thermodynamic constraint.

7. A property prediction system for hexafluoropropylene refrigerant, characterized in that, The property prediction system for the hexafluoropropylene refrigerant includes: The basic physical property data acquisition module is used to acquire the basic physical property data of hexafluoropropylene; the basic physical property data includes: Data, vapor velocity, saturated vapor pressure, saturated vapor-liquid density, and ideal gas heat capacity; The data includes: pressure, density, and temperature; The filtering module is used to filter the basic physical property data using the principle of thermodynamic consistency, and obtain the filtered basic physical property data. An initial multi-parameter equation of state construction module is used to construct an initial multi-parameter equation of state based on the filtered basic physical property data. The objective function construction module is used to construct an objective function based on the filtered basic physical property data and the initial multi-parameter equation of state; the objective function is the sum of the physical property calculation deviation objective function and the thermodynamic constraint objective function; the physical property calculation deviation objective function is the sum of the relative squared differences between the estimated physical property values ​​and the actual physical property values; A multi-parameter state equation construction module is used to fit the initial multi-parameter state equation based on the objective function using the least squares method to obtain the multi-parameter state equation. The prediction module is used to predict the physical properties of hexafluoropropylene using a multi-parameter equation of state.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method for predicting the physical properties of hexafluoropropylene refrigerant according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method for predicting the physical properties of hexafluoropropylene refrigerant as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method for predicting the physical properties of hexafluoropropylene refrigerant as described in any one of claims 1-6.

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