Physical property mixing solving method and device applied to digital twinning, equipment and medium

Abstract

The invention relates to the technical field of digital twinning in the chemical process, and discloses a physical property hybrid solving method, device and equipment applied to digital twinning and a medium, and the method comprises the steps: obtaining physical property data of a digital twinning system; establishing a state equation and a state equation coefficient corresponding to the digital twin system according to the mixing rule and the physical property data; calculating by using the state equation coefficient to obtain a discriminant, and determining that the state equation has a three-real-number solution or a single-real-number solution by using the discriminant; when the state equation has three real number solutions, calculating to obtain a compression factor of a target phase state by using an analytical method; when the state equation has a single real number solution, calculating a compression factor of a target phase state by using an iterative method; and simulating and optimizing the digital twin system by using the compression factor to obtain an optimized target digital twin system. According to the method, on the basis of meeting the robustness requirement of solution of the state equation, the solution speed of the state equation is increased, and the overall performance of the calculation speed of the digital twin system in the chemical process is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of digital twins of chemical processes, and in particular to a mixed physical property solution method, device, equipment and medium applied to digital twins. Background Art

[0002] The cubic equation of state (CEoS) solution is an important part of the digital twin system of chemical processes and is the basis for the calculation of phase equilibrium related modules such as flash tanks and towers. In the digital twin system of chemical processes, accurate calculation of the physical properties of substances is crucial to predicting the behavior and performance of substances. CEoS is a classic physical property calculation tool that is widely used in process simulation. It is mainly used to solve the compression factor to calculate thermodynamic properties such as molar volume, enthalpy, entropy and fugacity. At the same time, the solution of CEoS is the lowest-level and most frequently called calculation in the digital twin system of chemical processes. The speed and robustness of the calculation are very important.

[0003] In the related art, there are two main methods for solving CEoS: one is the analytical method represented by the Cardano formula, which is characterized by flexible use, simple form, and easy code program implementation, but it involves the calculation of trigonometric functions and cube roots, and the speed is relatively slow. The second is the numerical method (iterative method). The numerical method first uses the Newtonian iterative method to find a root, and then substitute this root into the original equation to find other roots. The numerical method is fast and can largely avoid rounding errors, but there are certain requirements for the selection of the initial value of the iteration. If a good initial value cannot be given, it will lead to slow convergence or even no convergence. The number of real roots of CEoS may be three or one. For the case of three roots, the smallest one corresponds to the liquid phase, the largest one corresponds to the gas phase, and the middle one has no physical meaning. In the traditional method, all three roots need to be found and then sorted to ensure that the roots are assigned to the correct phase. The iterative method is faster but cannot guarantee that it is better than the analytical method in any case. It even requires additional programs to handle calculation failures, which affects the performance of the entire system to a certain extent. Summary of the invention

[0004] In view of this, the present invention provides a mixed solution method, device, equipment and medium for physical properties applied to digital twins, so as to solve the problem of time-consuming physical property calculation in existing chemical process digital twin systems.

[0005] In a first aspect, the present invention provides a physical property hybrid solution method applied to digital twins, the method comprising:

[0006] Obtain the physical property data of the digital twin system, including critical temperature, critical pressure, eccentricity factor and binary interaction parameters;

[0007] According to the mixing rules and physical property data, the state equation and state equation coefficient corresponding to the digital twin system are established;

[0008] The discriminant is obtained by calculating the coefficients of the state equation, and the discriminant is used to determine whether the state equation has three real solutions or a single real solution;

[0009] When the state equation has three real solutions, the compression factor of the target phase is calculated using the analytical method;

[0010] When the state equation has a single real number solution, the compression factor of the target phase is calculated using an iterative method;

[0011] The compression factor is used to simulate and optimize the digital twin system to obtain the optimized target digital twin system.

[0012] In the present invention, the physical property data of the digital twin system is obtained, the state equation is established, and the solution of the state equation is determined by using the discriminant. In the case of three real roots, the discriminant is directly used to solve the equation, thereby avoiding unnecessary root solving and sorting. In the case of a single real root, an initial value can be given by a simple method and the solution can be obtained by flexibly switching between the iterative method and the analytical method, so that the method has both computing speed and robustness. On the basis of meeting the robustness requirements for solving the cubic state equation, the solution speed of the cubic state equation can be greatly improved. As the most frequent calculation in the digital twin of a chemical process, the improvement of the solution method of the cubic state equation can significantly improve the performance of the entire chemical process digital twin system, thereby significantly improving the overall performance of the computing speed of the chemical process digital twin system.

[0013] In an optional implementation, a state equation corresponding to the digital twin system is established according to the hybrid rule and physical property data, including:

[0014] Establish the physical property database, unit calculation module and simulation flow chart of the digital twin system. The unit calculation module includes chemical unit equipment and the corresponding mathematical models of each chemical unit equipment;

[0015] Based on the chemical unit equipment corresponding to the digital twin system, obtain the physical property data and mathematical model of the chemical unit equipment;

[0016] According to the hybrid rule, the state equation coefficients corresponding to the digital twin system are calculated;

[0017] Based on the state equation coefficients, the state equation is transformed to obtain the standard form of the state equation.

[0018] In this method, a digital twin system of the chemical process consisting of a physical property database, a unit calculation module, and a simulation flow chart is established to obtain physical property data, calculate the state equation coefficients according to the mixing rules, and convert them into a standard form represented by a compression factor to facilitate solving the state equation.

[0019] In an optional implementation, the discriminant is obtained by calculating the state equation coefficients, and the discriminant is used to determine whether the state equation has three real number solutions or a single real number solution, including:

[0020] Using the state equation coefficients, a first intermediate variable and a second intermediate variable are calculated;

[0021] Using the first intermediate variable and the second intermediate variable, a discriminant is calculated;

[0022] Determine whether the discriminant is greater than 0;

[0023] When the discriminant is greater than 0, it is determined that the state equation has a single real solution;

[0024] When the discriminant is less than or equal to 0, it is determined that the state equation has three real solutions.

[0025] In this method, by judging whether the discriminant is greater than 0, when the discriminant is greater than 0, it is determined that the state equation has a single real solution, and when the discriminant is less than or equal to 0, it is determined that the state equation has three real solutions. There is no need to find all three solutions and then sort them to ensure that the solutions are assigned to the correct phase, which greatly reduces the time spent on solving the problem and improves the solving speed.

[0026] In an optional implementation, the first intermediate variable and the second intermediate variable are calculated by the following formula:

[0027]

[0028] Among them, α, β and γ are different state equation coefficients of the state equation, R is the first intermediate variable, and Q is the second intermediate variable;

[0029] The discriminant is calculated by the following formula:

[0030] D=Q 3 +R 2

[0031] Among them, D is the discriminant.

[0032] In this method, according to the theory of the Cardano formula, the number of real solutions to the state equation can be determined by whether the discriminant D is greater than 0, which improves the speed of solving the state equation and also ensures the accuracy of solving the state equation.

[0033] In an optional embodiment, the compression factor of the target phase is calculated using an analytical method, including:

[0034] Using the first intermediate variable and the second intermediate variable, the third intermediate variable is calculated by the following formula:

[0035]

[0036] Among them, R is the first intermediate variable, Q is the second intermediate variable, and θ is the third intermediate variable;

[0037] The compression factor of the target phase is calculated using the first intermediate variable and the third intermediate variable.

[0038] In this method, according to the physical meaning of the state equation, in the case of three real roots, the largest root corresponds to the gas phase, and the smallest root corresponds to the liquid phase. The three roots of the Cardano formula are in order. Therefore, when using the Cardano formula, there is no need to find the three roots and then sort them. The roots of the specified phase can be directly solved, which reduces the specimen consumption and improves the speed of solving the state equation.

[0039] In an optional implementation, the compression factor of the target phase is calculated using an iterative method, including:

[0040] Set an upper limit on the number of iterations;

[0041] The compression factor of the next iteration is calculated by using the state equation coefficient and the compression factor of the current iteration number;

[0042] Determine whether the current number of iterations is greater than the upper limit of the number of iterations;

[0043] When the current number of iterations is less than or equal to the upper limit of the number of iterations, the compression factor of the target phase is calculated using the Newton iteration formula, the first intermediate variable and the state equation coefficient;

[0044] When the current number of iterations is greater than the upper limit of the number of iterations, the Cardano formula is used to solve the compression factor of the target phase.

[0045] In this method, in the case of a single real root, since the physical meaning of the compressibility factor is the correction of the real fluid to the ideal gas, the molar volume of the liquid phase is much smaller than the molar volume of the gas phase, the compressibility factor is close to 0, while the compression factor corresponding to the ideal gas is 1. Therefore, the Newton iteration method is used to solve the state equation, which greatly improves the speed of solving the cubic state equation CEoS, thereby effectively improving the overall performance of the chemical process digital twin system.

[0046] In a second aspect, the present invention provides a physical property hybrid solution device applied to digital twins, the device comprising:

[0047] The data acquisition module is used to obtain the physical property data of the digital twin system, including critical temperature, critical pressure, eccentricity factor and binary interaction parameters;

[0048] The equation building module is used to build the state equation and state equation coefficients corresponding to the digital twin system according to the mixing rules and physical property data;

[0049] An equation solution determination module is used to calculate a discriminant using the state equation coefficients, and to determine whether the state equation has three real number solutions or a single real number solution using the discriminant;

[0050] A three-real solution solving module, used to calculate the compression factor of the target phase state by using an analytical method when the state equation has three real solutions;

[0051] A single real number solution solving module is used to calculate the compression factor of the target phase state by using an iterative method when the state equation has a single real number solution;

[0052] The simulation calculation module is used to simulate and optimize the digital twin system using the compression factor to obtain the optimized target digital twin system.

[0053] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the mixed physical property solution method applied to digital twins according to the above-mentioned first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0054] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the mixed physical property solution method applied to digital twins according to the above-mentioned first aspect or any corresponding embodiment thereof.

[0055] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions, which are used to enable a computer to execute the mixed solution method for physical properties applied to digital twins according to the above-mentioned first aspect or any corresponding embodiment. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0057] Figure 1 It is a flowchart of a mixed solution method for physical properties applied to digital twins according to an embodiment of the present invention.

[0058] Figure 2 It is a flowchart of a CEoS hybrid solution method for digital twins according to an embodiment of the present invention.

[0059] Figure 3It is a schematic diagram of an iterative structure of a sequential module method according to an embodiment of the present invention.

[0060] Figure 4 It is a flowchart of another mixed physical property solution method applied to digital twins according to an embodiment of the present invention.

[0061] Figure 5 It is a flow chart of another mixed physical property solution method applied to digital twins according to an embodiment of the present invention.

[0062] Figure 6 It is a structural block diagram of a physical property hybrid solution device applied to digital twins according to an embodiment of the present invention.

[0063] Figure 7 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0065] In the related art, there are two main methods for solving CEoS: one is the analytical method represented by the Cardano formula, which is characterized by flexible use, simple form, and easy code program implementation, but it involves the calculation of trigonometric functions and cube roots, and the speed is relatively slow. The second is the numerical method (iterative method). The numerical method first uses the Newtonian iterative method to find a root, and then substitute this root into the original equation to find other roots. The numerical method is fast and can largely avoid rounding errors, but there are certain requirements for the selection of the initial value of the iteration. If a good initial value cannot be given, it will lead to slow convergence or even no convergence. The number of real roots of CEoS may be three or one. For the case of three roots, the smallest one corresponds to the liquid phase, the largest one corresponds to the gas phase, and the middle one has no physical meaning. In the traditional method, all three roots need to be found and then sorted to ensure that the roots are assigned to the correct phase. The iterative method is faster but cannot guarantee that it is better than the analytical method in any case. It even requires additional programs to handle calculation failures, which affects the performance of the entire system to a certain extent.

[0066] To solve the above problems, a method for solving mixed physical properties applied to digital twins is provided in an embodiment of the present invention, which is used in a computer device. It should be noted that its execution subject can be a mixed physical property solving device applied to digital twins, which can be implemented as part or all of a computer device through software, hardware, or a combination of software and hardware. The computer device can be a terminal, a client, or a server. The server can be a single server or a server cluster composed of multiple servers. The terminal in the embodiment of the present application can be a smart phone, a personal computer, a tablet computer, or other intelligent hardware devices. In the following method embodiments, the execution subject is a computer device as an example for explanation.

[0067] The computer device in this embodiment is suitable for solving cubic state equations in the process of calculating phase equilibrium related modules such as flash tanks and towers in a chemical process digital twin system. The present invention provides a physical property mixed solution method applied to digital twins. By acquiring the physical property data of the digital twin system, a state equation is established, and the solution of the state equation is determined by using a discriminant. In the case of three real roots, the discriminant is directly used to solve, avoiding unnecessary root solutions and sorting; in the case of a single real root, an initial value can be given by a simple method and solved by flexibly switching between iterative methods and analytical methods, so that the method has both calculation speed and robustness. On the basis of meeting the robustness requirements of solving cubic state equations, the solution speed of cubic state equations can be greatly improved. The cubic state equation solution is the most frequent calculation in the chemical process digital twin. The improvement of its solution method can significantly improve the performance of the entire chemical process digital twin system, thereby significantly improving the overall performance of the calculation speed of the chemical process digital twin system.

[0068] According to an embodiment of the present invention, an embodiment of a hybrid solution method for physical properties applied to digital twins is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0069] In this embodiment, a mixed physical property solution method for digital twins is provided, which can be used for the above-mentioned computer device. Figure 1 is a flow chart of a mixed physical property solution method applied to digital twins according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0070] Step S101, obtaining physical property data of the digital twin system.

[0071] In the embodiment of the present invention, the physical property data includes critical temperature, critical pressure, eccentricity factor and binary interaction parameter.

[0072] In one example, by establishing the system hardware and software environment, a chemical process digital twin system consisting of a physical property database, a unit calculation module, a simulation flow chart, etc. is established, and a physical property calculation interface is provided. Physical property data is obtained based on physical property methods such as SRK and PR.

[0073] Specifically, the physical property database mainly includes basic physical property data such as critical parameters, eccentricity factors, binary interaction parameters, 25-degree ideal gas formation enthalpy, and temperature-related parameters such as saturated vapor pressure and ideal gas enthalpy. The physical property data at least include the critical temperature, critical pressure, eccentricity factor and binary interaction parameters of each component required to construct the cubic equation of state (CEoS).

[0074] The unit calculation module is a common chemical unit equipment such as flash tank, distillation tower, heat exchanger and its corresponding mathematical model, which includes the unit model and solution program. Taking the flash tank as an example, the mathematical model of flash evaporation can usually be expressed as: component material balance equation z i F = x i L+y i V; Phase equilibrium equation y i =K i (T,P,Z L (T,P,x i ),Z V (T,P,y i ))x i ; Material balance equation F = L + V; Energy balance equation H F F+Q=H L (T,P,Z L (T,P,x i ))L+H V (T,P,Z V (T,P,y i ))V; component constraint equation

[0075] Among them, z i is the feed composition, F is the feed flow rate, y i and x i are the gas phase and liquid composition, V and L are the gas phase and liquid flow rates, H F is the feed enthalpy, Q is the heat load, H V and H L are the gas phase and liquid phase enthalpy, respectively, both of which are functions of temperature T, pressure P and compressibility factor. V and Z L K is the gas phase and liquid phase compressibility factor, which is a function of temperature, pressure and composition.i The phase equilibrium constant is also a function of temperature, pressure and composition.

[0076] The simulation flow chart includes the connection relationship between each unit module and the order of operations, and is usually represented by a directed graph structure.

[0077] The physical property calculation interface requires the unit module to be able to obtain the physical property calculation results, such as the flash module needs to obtain K i (T,P,Z L (T,P,x i ),Z V (T,P,y i ))、H L (T,P,Z L (T,P,x i ))、H V (T,P,Z V (T,P,y i )) and other thermodynamic data.

[0078] Step S102: Establish the state equation and state equation coefficient corresponding to the digital twin system according to the mixing rules and physical property data.

[0079] In one example, the CEoS state equation coefficients are calculated according to the mixing rule and converted into a standard form represented by a compression factor. Data is obtained from the physical property database established in the above steps, and the CEoS state equation coefficients are calculated.

[0080] Step S103, using the state equation coefficients to calculate a discriminant, and using the discriminant to determine whether the state equation has three real solutions or a single real solution.

[0081] In one example, according to the theory of the Cardano formula, the number of real solutions to the state equation can be determined by whether the discriminant D is greater than 0. If D is less than or equal to 0, there are three real roots, and if D is greater than 0, there is only one real root, where D is the discriminant of the Cardano formula.

[0082] Step S104, when the state equation has three real number solutions, the compression factor of the target phase is calculated using an analytical method.

[0083] In one example, according to the physical meaning of the state equation, in the case of three real roots, the largest root corresponds to the gas phase, and the smallest root corresponds to the liquid phase. The three roots of the Cardano formula are in order. When using the Cardano formula, there is no need to find the three roots and then sort them, and the roots of the specified phase can be directly found.

[0084] Step S105, when the state equation has a single real number solution, the compression factor of the target phase is calculated using an iterative method.

[0085] In one example, the Newton iteration method is used to solve the single real root case. The physical meaning of the compressibility factor is the correction of the real fluid to the ideal gas. The molar volume of the liquid phase is much smaller than the molar volume of the gas phase, and the compressibility factor is close to 0, while the compressibility factor corresponding to the ideal gas is 1. Therefore, the initial value is set to 0.01 for the liquid phase root and 1 for the gas phase root. In addition, an upper limit of the number of iterations is set (6-8 times can be taken). If the upper limit is exceeded, the Cardano formula is switched to solve.

[0086] Step S106, using the compression factor to simulate and optimize the digital twin system to obtain an optimized target digital twin system.

[0087] In one implementation scenario, Figure 2 is a flow chart of a CEoS hybrid solution method for digital twins according to an embodiment of the present invention, such as Figure 2 As shown, the CEoS hybrid solution method for digital twins includes the following steps: Obtain physical property data. Establish a state equation based on the hybrid rules and physical property data, calculate the CEoS state equation coefficients, and convert them into a standard form represented by the compression factor. The number of real solutions to the state equation is determined by whether the discriminant is greater than 0. If D is less than or equal to 0, there are three real roots, and if D is greater than 0, there is only one real root. When D>0, try to solve the state equation using the iteration method; determine whether the iteration converges within the specified number of times, and when the iteration converges within the specified number of times, output the compression factor; when the iteration does not converge within the specified number of times, use the Cardano formula to calculate the compression factor; when D is not greater than 0, use the improved Cardano formula to calculate the compression factor of the specified phase. Figure 3 is a schematic diagram of an iterative structure of a sequential module method according to an embodiment of the present invention, such as Figure 3 As shown in the figure, CEoS solves the physical property calculation in the digital twin of the chemical process. The physical property calculation provides support for the unit module calculation, the unit module calculation provides support for the process calculation, the process calculation provides support for the design specification calculation, and the design specification calculation provides support for the optimization calculation. Together, the iterative structure of the sequential module method is realized, and the thermodynamic physical properties such as density, enthalpy, entropy, and phase equilibrium are calculated based on the obtained compression factor. According to the needs, the compression factor solved by CEoS is used to complete the unit module calculation, process calculation, design specification calculation, and optimization calculation.

[0088] In order to evaluate the computational speed and stability of the CEoS hybrid solution method for digital twins, the method was implemented in the chemical process digital twin system, and the performance of the algorithm was tested using actual industrial process simulation cases, as shown below: In one process example, the process mainly separates the liquefied gas from the catalytic unit into high-purity propylene, propane, and alkylate by distillation. The physical property method selected is SRK. In another process example, the process is used to separate and purify benzene, toluene, and paraxylene in aromatics, including four units with a total of ten towers. The physical property method selected is PR.

[0089] The calculation time of CEoS was measured using two process instances. The CEoS hybrid solution method, Halley method (the fastest known numerical solution method) and Cardano method were used as CEoS solution methods respectively. The relative average time was statistically calculated. The results are shown in Table 1. Table 1 shows the relative average CEoS solution time of the three methods: CEoS hybrid solution method, Halley method and Cardano method.

[0090] Table 1

[0091]

[0092]

[0093] The calculation time of process simulation was measured. Four process simulation examples including case1 and case2 were used. The CEoS hybrid solution method and the Cardano method were used as CEOS solution methods respectively. The average time of process simulation was statistically calculated. The results are shown in Table 2. Table 2 shows the simulation calculation speed of the CEoS hybrid solution method and the Cardano method.

[0094] Table 2

[0095]

[0096] The results show that the CEoS hybrid solution method for digital twins can greatly improve the calculation speed of CEoS and significantly improve the overall performance of the chemical process digital twin system.

[0097] The physical property hybrid solution method applied to digital twins provided in this embodiment obtains the physical property data of the digital twin system, establishes the state equation, and uses the discriminant to determine the solution of the state equation. In the case of three real roots, the discriminant is directly used to solve the equation, avoiding unnecessary root solving and sorting. In the case of a single real root, an initial value can be given by a simple method and the solution can be performed by flexibly switching between the iterative method and the analytical method, so that the method has both computing speed and robustness. On the basis of meeting the robustness requirements for solving the cubic state equation, the solution speed of the cubic state equation can be greatly improved. The cubic state equation solution is the most frequent calculation in the digital twin of the chemical process. The improvement of its solution method can significantly improve the performance of the entire chemical process digital twin system, thereby significantly improving the overall performance of the computing speed of the chemical process digital twin system.

[0098] In this embodiment, a mixed physical property solution method for digital twins is provided, which can be used for the above-mentioned computer device. Figure 4 is a flow chart of another physical property hybrid solution method applied to digital twins according to an embodiment of the present invention. Figure 4 As shown, the process includes the following steps:

[0099] Step S401: Obtain physical property data of the digital twin system. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0100] Step S402: Establish the state equation and state equation coefficient corresponding to the digital twin system according to the mixing rules and physical property data.

[0101] Specifically, the above step S402 includes:

[0102] Step S4021, establish a physical property database, unit calculation module and simulation flow chart of the digital twin system, the unit calculation module includes chemical unit equipment and the mathematical model corresponding to each chemical unit equipment.

[0103] Step S4022: Based on the chemical unit equipment corresponding to the digital twin system, obtain the physical property data and mathematical model of the chemical unit equipment.

[0104] Step S4023: Calculate the state equation coefficients corresponding to the digital twin system according to the hybrid rule.

[0105] Step S4024, based on the state equation coefficients, the state equation is converted to obtain a standard form of the state equation.

[0106] In one example, by establishing the system hardware and software environment, a chemical process digital twin system consisting of a physical property database, a unit calculation module, a simulation flow chart, etc. is established, and a physical property calculation interface is provided. Physical property data is obtained based on physical property methods such as SRK and PR.

[0107] Based on physical property methods such as SRK and PR, physical property data are obtained, CEoS parameters a and b are calculated according to the mixing rule, and converted into a standard form expressed as a compression factor.

[0108] Obtain data from the physical property database established in the above steps and calculate CEoS parameters a and b.

[0109] For the gas phase:

[0110] a=f(y i ,Tc,Pc,ω,K ij )

[0111] b = f(y i ,Tc,Pc,ω,K ij )

[0112] For the liquid phase:

[0113] a=f(x i ,Tc,Pc,ω,K ij )

[0114] b = f(x i ,Tc,Pc,ω,K ij )

[0115] Among them, Tc, Pc, ω, K ij are the critical temperature, critical pressure, eccentricity factor and binary interaction left and right parameters respectively.

[0116] The cubic state equation CEoS coefficients α, β, and γ are calculated according to the form in Table 3 to obtain the standard form of CEoS.

[0117] Table 3

[0118]

[0119] In Table 3, A and B are intermediate variables with no practical significance, and R is a constant usually taken as 8.314.

[0120] f(Z)=Z 3 +αZ 2 +βZ+γ=0

[0121] Where Z is the compressibility factor of the gas or liquid phase.

[0122] In this method, a digital twin system of the chemical process consisting of a physical property database, a unit calculation module, and a simulation flow chart is established to obtain physical property data, calculate the state equation coefficients according to the mixing rules, and convert them into a standard form represented by a compression factor to facilitate solving the state equation.

[0123] Step S403, using the state equation coefficients to calculate a discriminant, using the discriminant, determine whether the state equation has three real solutions or a single real solution. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0124] Step S404: When the state equation has three real number solutions, the compression factor of the target phase is calculated using an analytical method. Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0125] Step S405: When the state equation has a single real number solution, the compression factor of the target phase is calculated using an iterative method. Figure 1 Step S105 of the illustrated embodiment will not be described in detail here.

[0126] Step S406: Use the compression factor to simulate and optimize the digital twin system to obtain an optimized target digital twin system. Figure 1 Step S106 of the illustrated embodiment will not be described in detail here.

[0127] The physical property hybrid solution method applied to digital twins provided in this embodiment obtains physical property data by establishing a chemical process digital twin system consisting of a physical property database, a unit calculation module, and a simulation flow chart, calculates the state equation coefficients according to the hybrid rules, and converts them into a standard form represented by a compression factor, so as to facilitate the solution of the state equation.

[0128] In this embodiment, a mixed physical property solution method for digital twins is provided, which can be used for the above-mentioned computer device. Figure 5 is a flowchart of another physical property hybrid solution method applied to digital twins according to an embodiment of the present invention, such as Figure 5 As shown, the process includes the following steps:

[0129] Step S501, obtain the physical property data of the digital twin system. Figure 4 Step S401 of the illustrated embodiment will not be described in detail here.

[0130] Step S502: Establish the state equation and state equation coefficient corresponding to the digital twin system according to the hybrid rule and physical property data. Figure 4 Step S402 of the illustrated embodiment will not be described in detail here.

[0131] Step S503, using the state equation coefficients to calculate a discriminant, and using the discriminant to determine whether the state equation has three real number solutions or a single real number solution.

[0132] Specifically, the above step S503 includes:

[0133] Step S5031, using the state equation coefficients, calculate and obtain the first intermediate variable and the second intermediate variable.

[0134] In some optional implementations, the above step S5031 includes:

[0135] Step a1, calculate the first intermediate variable and the second intermediate variable by the following formula:

[0136]

[0137] Among them, α, β and γ are different state equation coefficients of the state equation, R is the first intermediate variable, and Q is the second intermediate variable.

[0138] Step S5032: Calculate and obtain a discriminant using the first intermediate variable and the second intermediate variable.

[0139] In some optional implementations, the above step S5032 includes:

[0140] Step b1, calculate the discriminant using the following formula:

[0141] D=Q 3 +R 2

[0142] Among them, D is the discriminant.

[0143] Step S5033, determine whether the discriminant is greater than 0.

[0144] Step S5034: when the discriminant is greater than 0, determine that the state equation has a single real solution.

[0145] Step S5035: when the discriminant is less than or equal to 0, determine that the state equation has three real number solutions.

[0146] In one example, according to the theory of the Cardano formula, the number of real solutions to the state equation can be determined by judging whether the discriminant D is greater than 0. If D is less than or equal to 0, there are three real roots, and if D is greater than 0, there is only one real root.

[0147]

[0148] D=Q 3 +R 2

[0149] Among them, D is the discriminant of the Cardano formula, R is the first intermediate variable, and Q is the second intermediate variable.

[0150] In this method, by judging whether the discriminant is greater than 0, when the discriminant is greater than 0, it is determined that the state equation has a single real number solution, and when the discriminant is less than or equal to 0, it is determined that the state equation has three real number solutions. There is no need to find all three solutions and then sort them to ensure that the solutions are assigned to the correct phase state, which greatly reduces the time consumption of solving and improves the solving speed. According to the theory of Cardano formula, the number of real number solutions of the state equation can be judged by whether the discriminant D is greater than 0, which improves the solving speed of the state equation and also ensures the accuracy of solving the state equation.

[0151] Step S504: When the state equation has three real number solutions, the compression factor of the target phase is calculated using an analytical method.

[0152] In an optional implementation, the compression factor of the target phase is calculated using the discriminant, including:

[0153] Using the first intermediate variable and the second intermediate variable, the third intermediate variable is calculated by the following formula:

[0154]

[0155] Among them, R is the first intermediate variable, Q is the second intermediate variable, and θ is the third intermediate variable;

[0156] The compression factor of the target phase is calculated using the first intermediate variable and the third intermediate variable.

[0157] In one example, according to the physical meaning of the state equation, in the case of three real roots, the largest root corresponds to the gas phase, and the smallest root corresponds to the liquid phase. The three roots of the Cardano formula are in order. When using the Cardano formula, there is no need to find the three roots and then sort them, and the roots of the specified phase can be directly found.

[0158]

[0159] Among them, θ third is an intermediate variable.

[0160] In this method, according to the physical meaning of the state equation, in the case of three real roots, the largest root corresponds to the gas phase, and the smallest root corresponds to the liquid phase. The three roots of the Cardano formula are in order. Therefore, when using the Cardano formula, there is no need to find the three roots and then sort them. The roots of the specified phase can be directly solved, which reduces the specimen consumption and improves the speed of solving the state equation.

[0161] Step S505: When the state equation has a single real number solution, the compression factor of the target phase is calculated using an iterative method.

[0162] Specifically, the above step S505 includes:

[0163] Step S5051, setting an upper limit on the number of iterations.

[0164] In an example, this step is explained in detail.

[0165] Step S5052: Calculate the compression factor of the next iteration number using the state equation coefficient and the compression factor of the current iteration number.

[0166] Step S5053, determine whether the current number of iterations is greater than the upper limit of the number of iterations.

[0167] Step S5054: When the current number of iterations is less than or equal to the upper limit of the number of iterations, the compression factor of the target phase is calculated using the Newton iteration formula, the first intermediate variable and the state equation coefficient.

[0168] Step S5055: When the current number of iterations is greater than the upper limit of the number of iterations, the compression factor of the target phase is obtained by solving the Cardano formula.

[0169] In one example, the Newton iteration method is used to solve the single real root case. The physical meaning of the compressibility factor is the correction of the real fluid to the ideal gas. The molar volume of the liquid phase is much smaller than the molar volume of the gas phase, and the compressibility factor is close to 0, while the compressibility factor corresponding to the ideal gas is 1. Therefore, the initial value is set to 0.01 for the liquid phase root and 1 for the gas phase root. In addition, an upper limit of the number of iterations is set (6-8 times can be taken, and there is no limit in the present invention). If the upper limit is exceeded, the Cardano formula is switched to solve.

[0170]

[0171] Among them, x n and x n+1 are the current compression factor and the compression factor of the next iteration, Z * is the compression factor in the case of a single real root.

[0172] Result correction: Under low temperature conditions, the liquid phase compression factor is small. Due to the limitation of computer effective figures, rounding errors will occur in the calculation process. Therefore, the result from the Cardano formula is subjected to another Newton iteration (the same formula as the above step) to obtain a higher precision result.

[0173] Step S506: Use the compression factor to simulate and optimize the digital twin system to obtain an optimized target digital twin system. Figure 4 Step S406 of the illustrated embodiment will not be described in detail here.

[0174] The mixed solution method for physical properties applied to digital twins provided in this embodiment determines whether the discriminant is greater than 0. When the discriminant is greater than 0, it is determined that the state equation has a single real number solution. When the discriminant is less than or equal to 0, it is determined that the state equation has three real number solutions. There is no need to find all three solutions and then sort them to ensure that the solutions are assigned to the correct phase state, which greatly reduces the time consumption of solving and improves the solving speed. According to the theory of the Cardano formula, the number of real number solutions of the state equation can be judged by whether the discriminant D is greater than 0, which improves the solving speed of the state equation and also ensures the accuracy of solving the state equation. According to the physical meaning of the state equation, the largest root corresponds to the gas phase in the case of three real roots, and the smallest root corresponds to the liquid phase. The three roots of the Cardano formula are ordered. Therefore, when using the Cardano formula, there is no need to find the three roots and then sort them. The roots of the specified phase state can be directly solved, which reduces the specimen consumption and improves the solving speed of the state equation.

[0175] In this embodiment, a physical property hybrid solution device applied to digital twins is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and will not be repeated here. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0176] This embodiment provides a physical property mixed solution device applied to digital twins, such as Figure 6 As shown, including:

[0177] The data acquisition module 601 is used to obtain the physical property data of the digital twin system, including critical temperature, critical pressure, eccentricity factor and binary interaction parameters. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0178] The equation building module 602 is used to build the state equation and state equation coefficient corresponding to the digital twin system according to the mixing rules and physical property data. Figure 1 Step S102 of the illustrated embodiment will not be described in detail here.

[0179] The equation solution determination module 603 is used to calculate the discriminant using the state equation coefficients, and use the discriminant to determine whether the state equation has three real number solutions or a single real number solution. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0180] The three real number solution solving module 604 is used to calculate the compression factor of the target phase state by using an analytical method when the state equation has three real number solutions. Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0181] The single real number solution solving module 605 is used to calculate the compression factor of the target phase state by using an iterative method when the state equation has a single real number solution. Figure 1 Step S105 of the illustrated embodiment will not be described in detail here.

[0182] The simulation calculation module 606 is used to simulate and optimize the digital twin system using the compression factor to obtain the optimized target digital twin system. Figure 1 Step S106 of the illustrated embodiment will not be described in detail here.

[0183] In some optional implementations, the equation building module 602 includes:

[0184] The digital twin system establishment unit is used to establish the physical property database, unit calculation module and simulation flow chart of the digital twin system. The unit calculation module includes chemical unit equipment and the corresponding mathematical models of each chemical unit equipment.

[0185] Data acquisition is based on the chemical unit equipment corresponding to the digital twin system to obtain the physical property data and mathematical model of the chemical unit equipment.

[0186] The state equation coefficient calculation unit is used to calculate the state equation coefficients corresponding to the digital twin system according to the hybrid rule.

[0187] The state equation conversion unit is used to convert the state equation based on the state equation coefficient to obtain the state equation in a standard form.

[0188] In some optional implementations, the equation solution determination module 603 includes:

[0189] The intermediate variable calculation unit is used to calculate the first intermediate variable and the second intermediate variable using the state equation coefficient.

[0190] The discriminant calculation unit is used to calculate the discriminant using the first intermediate variable and the second intermediate variable.

[0191] The discriminant judgment unit is used to judge whether the discriminant is greater than 0.

[0192] The single real number solution determination unit is used to determine that the state equation has a single real number solution when the discriminant is greater than 0.

[0193] The three real number solution determination unit is used to determine that the state equation has three real number solutions when the discriminant is less than or equal to 0.

[0194] In some optional implementations, the first intermediate variable and the second intermediate variable are calculated by the following formula:

[0195]

[0196] Among them, α, β and γ are different state equation coefficients of the state equation, R is the first intermediate variable, and Q is the second intermediate variable;

[0197] The discriminant is calculated by the following formula:

[0198] D=Q 3 +R 2

[0199] Among them, D is the discriminant.

[0200] In some optional implementations, the three real number solution solving module 604 includes:

[0201] The third intermediate variable calculation unit is used to calculate the third intermediate variable by using the first intermediate variable and the second intermediate variable through the following formula:

[0202]

[0203] Among them, R is the first intermediate variable, Q is the second intermediate variable, and θ is the third intermediate variable.

[0204] The first compression factor calculation unit is used to calculate the compression factor of the target phase using the first intermediate variable and the third intermediate variable.

[0205] In some optional implementations, the single real number solution solving module 605 includes:

[0206] The iteration number upper limit setting unit is used to set the iteration number upper limit.

[0207] The next iteration compression factor calculation unit is used to calculate the compression factor of the next iteration number by using the state equation coefficient and the compression factor of the current iteration number.

[0208] The iteration number upper limit judgment unit is used to judge whether the current iteration number is greater than the iteration number upper limit.

[0209] The second compression factor calculation unit is used to calculate the compression factor of the target phase state by using the Newton iteration formula, the first intermediate variable and the state equation coefficient when the current iteration number is less than or equal to the iteration number upper limit.

[0210] The third compression factor calculation unit is used to use the Cardano formula to solve and obtain the compression factor of the target phase when the current number of iterations is greater than the upper limit of the number of iterations.

[0211] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0212] The physical property hybrid solution device applied to digital twins in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above-mentioned functions.

[0213] The embodiment of the present invention also provides a computer device having the above Figure 6 The hybrid physical property solving device applied to digital twins is shown.

[0214] See also Figure 7 , Figure 7 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 7 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 7 A processor 10 is taken as an example.

[0215] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0216] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0217] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0218] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0219] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 7 The example of connecting through bus is taken in the following.

[0220] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device can be a touch screen.

[0221] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0222] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0223] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A physical property hybrid solution method applied to digital twins, characterized in that: The method comprises: Acquiring physical property data of the digital twin system, wherein the physical property data includes critical temperature, critical pressure, eccentricity factor, and binary interaction parameter; According to the mixing rule and the physical property data, a state equation and a state equation coefficient corresponding to the digital twin system are established; Using the state equation coefficients to calculate a discriminant, and using the discriminant to determine whether the state equation has three real number solutions or a single real number solution; When the state equation has three real number solutions, the compression factor of the target phase is calculated using an analytical method; When the state equation has a single real number solution, the compression factor of the target phase is calculated by an iterative method; The digital twin system is simulated and optimized using the compression factor to obtain an optimized target digital twin system.

2. The method according to claim 1, characterized in that The establishing of the state equation corresponding to the digital twin system according to the mixing rule and the physical property data includes: Establishing a physical property database, a unit calculation module and a simulation flow chart of the digital twin system, wherein the unit calculation module includes chemical unit equipment and mathematical models corresponding to each of the chemical unit equipment; Based on the chemical unit equipment corresponding to the digital twin system, obtaining the physical property data and mathematical model of the chemical unit equipment; According to the hybrid rule, the state equation coefficients corresponding to the digital twin system are calculated; Based on the state equation coefficients, the state equation is transformed to obtain a standard form of the state equation.

3. The method according to claim 2, characterized in that The step of calculating a discriminant using the state equation coefficients and determining that the state equation has three real number solutions or a single real number solution using the discriminant comprises: Using the state equation coefficients, calculating a first intermediate variable and a second intermediate variable; Using the first intermediate variable and the second intermediate variable, calculating and obtaining the discriminant; Determine whether the discriminant is greater than 0; When the discriminant is greater than 0, determining that the state equation has a single real number solution; When the discriminant is less than or equal to 0, it is determined that the state equation has three real number solutions.

4. The method according to claim 3, characterized in that The first intermediate variable and the second intermediate variable are calculated by the following formula: Wherein, α, β and γ are different state equation coefficients of the state equation respectively, R is the first intermediate variable, and Q is the second intermediate variable; The discriminant is calculated by the following formula: D=Q 3 +R 2 Wherein, D is the discriminant.

5. The method according to claim 3, characterized in that: The method of calculating the compression factor of the target phase using the analytical method includes: Using the first intermediate variable and the second intermediate variable, the third intermediate variable is calculated by the following formula: Wherein, R is the first intermediate variable, Q is the second intermediate variable, and θ is the third intermediate variable; The compression factor of the target phase is calculated using the first intermediate variable and the third intermediate variable.

6. The method according to claim 3, characterized in that The method of calculating the compression factor of the target phase using an iterative method includes: Set an upper limit on the number of iterations; Calculating the compression factor of the next iteration number using the state equation coefficient and the compression factor of the current iteration number; Determine whether the current number of iterations is greater than the upper limit of the number of iterations; When the current number of iterations is less than or equal to the upper limit of the number of iterations, the compression factor of the target phase is calculated using the Newton iteration formula, the first intermediate variable and the state equation coefficient; When the current number of iterations is greater than the upper limit of the number of iterations, the compression factor of the target phase is obtained by solving the Cardano formula.

7. A physical property hybrid solution device applied to digital twins, characterized in that: The device comprises: A data acquisition module, used to acquire physical property data of the digital twin system, wherein the physical property data includes critical temperature, critical pressure, eccentricity factor and binary interaction parameter; An equation building module, used to build a state equation and state equation coefficients corresponding to the digital twin system according to the mixing rule and the physical property data; An equation solution determination module, used to calculate a discriminant using the state equation coefficients, and use the discriminant to determine whether the state equation has three real number solutions or a single real number solution; A three-real-number solution solving module, used for calculating the compression factor of the target phase state by using an analytical method when the state equation has three real-number solutions; A single real number solution solving module, used for calculating the compression factor of the target phase state by using an iterative method when the state equation has a single real number solution; A simulation calculation module is used to simulate and optimize the digital twin system using the compression factor to obtain an optimized target digital twin system.

8. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the mixed physical property solution method applied to digital twins as described in any one of claims 1 to 6 by executing the computer instructions.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the mixed physical property solution method applied to digital twins according to any one of claims 1 to 6.

10. A computer program product, characterized in that It includes computer instructions, which are used to enable a computer to execute the mixed physical property solution method applied to digital twins as described in any one of claims 1 to 6.

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