Dense phase judgment method and system for supercritical carbon dioxide, storage medium and terminal
By calculating the critical parameters and density of CO2, building three-dimensional space and using clustering algorithms, the problem of inaccurate division of CO2 density and supercritical phases is solved, and simple and accurate phase state judgment is achieved.
Patent Information
- Application Number
- CN202311595015.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-11-27
- Publication Date
- 2025-05-27
AI Technical Summary
In the prior art, the classification criteria for CO2 dense phase and supercritical phase are inaccurate, especially in the ranges above the critical temperature and critical pressure, resulting in large deviations in the selection of equipment and facilities.
通过获得超临界二氧化碳的组分信息、实际压力和实际温度，计算其临界压力、临界温度以及密度，构建三维空间，并使用k-means算法进行聚类，得到拟合函数以判断CO2的密相或超临界相状态。
The process of judging CO2 dense phase and supercritical phase is simplified, complex parameter calculations are avoided, the results are close to the results of Aspen HYSYS chemical process simulation software, and it is convenient to simply and conveniently judge the phase state of CO2.
Smart Images
Figure CN120045962A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the fields of supercritical carbon dioxide development, pipeline transportation and metering, and particularly relates to a method and system for determining the dense phase of supercritical carbon dioxide, a storage medium, and a terminal. Background Art
[0002] With the development and advancement of global CCUS (Carbon Capture, Utilization and Storage) projects, research on the properties of CO 2 and related technologies is also undergoing changes. Different from natural gas, CO 2 has a relatively high critical temperature and critical pressure (the critical temperature of pure CO 2 is 31.1 °C, and the critical pressure is 7.38 MPa), and it is prone to CO 2 phase transition under conventional working conditions. At the same time, due to the special physical properties of CO 2 itself, in addition to the solid phase, liquid phase, and gas phase, there are relatively special dense phases and supercritical phases, as shown in Figure 1 . The dense phase and supercritical phase are mainly defined according to the critical temperature and critical pressure. The state above the critical pressure and below the critical temperature is called the dense phase, and the state above the critical pressure and above the critical temperature is called the supercritical phase. CO 2 in these two phase states are highly compressible fluids with the properties of both gas and liquid, but the difference is that the properties of dense-phase CO 2 are closer to those of liquids, and pump equipment is required for pressurization, while the properties of supercritical CO 2 are closer to those of gases, and compressor equipment is required for pressurization.
[0003] According to the operating experience of domestic and foreign CCUS projects, it has better economic advantages for CO 2 to complete storage, transportation, injection and other links in the dense phase. However, at present, the criteria for dividing the dense phase and supercritical phase of CO 2 are relatively inaccurate, especially in the range above the critical temperature and critical pressure. Due to the high-pressure conditions, CO 2 even within a certain range above the critical temperature, its properties are still closer to those of liquids, and strictly speaking, it should belong to the dense phase rather than the supercritical phase. If the supercritical phase is used for process design such as CO 2 transportation, storage, injection, etc., there will be relatively large deviations in the selection of equipment and facilities.
[0004] Currently, Aspen HYSYS chemical process simulation software is used to judge the dense phase and supercritical phase of CO 2 , the advantage is accurate judgment, and the disadvantage is that the calculation process is complex and requires a large number of parameter calculations. Summary of the Invention
[0005] One of the purposes of this application is to provide a method for determining the dense phase of supercritical carbon dioxide, so as to solve the problem of complex calculation process and numerous parameter calculations for judging the dense phase and supercritical phase of CO 2 by using Aspen HYSYS chemical process simulation software.
[0006] This application provides a method for determining the dense phase of supercritical carbon dioxide, including the following steps:
[0007] Step 1, obtain the component information, actual pressure and actual temperature of supercritical carbon dioxide;
[0008] Step 2, calculate the critical pressure, critical temperature and density of supercritical carbon dioxide according to the component information;
[0009] Step 3, calculate the reduced pressure of supercritical carbon dioxide according to the actual pressure and critical pressure, and calculate the reduced temperature of supercritical carbon dioxide according to the actual temperature and critical temperature;
[0010] Step 4, map the reduced pressure, reduced temperature and density of supercritical carbon dioxide into three-dimensional space, and use the k-means algorithm in three-dimensional space to obtain three-dimensional clusters;
[0011] Step 5, project the three-dimensional clusters onto the P r -T r plane to be transformed into two-dimensional clusters, where P r represents the reduced pressure and T r represents the reduced temperature;
[0012] Step 6, fit the boundaries of two adjacent clusters in the two-dimensional clusters to obtain the fitting function f = P r -8.536T r +7.29; substitute the values of the reduced pressure P r and the reduced temperature T r into the fitting function f. If f is greater than or equal to 0, it means that supercritical carbon dioxide is in the dense phase. If f is less than 0, supercritical carbon dioxide is in the supercritical phase.
[0013] In an example, the component information includes the number of components of supercritical carbon dioxide, the molar fraction of each component in supercritical carbon dioxide, the critical pressure and critical temperature of each component;
[0014] The calculation methods of the critical pressure and critical temperature in Step 2 specifically include:
[0015] Calculate the critical pressure of supercritical carbon dioxide according to the component information, and according to calculate the critical pressure, where y irepresents the mole fraction of a single component in supercritical carbon dioxide, n represents the number of components in supercritical carbon dioxide; P c represents the critical pressure, P ci represents the critical pressure of component i;
[0016] According to calculate the critical temperature, where y i represents the mole fraction of a single component in supercritical carbon dioxide, n represents the number of components in supercritical carbon dioxide; T c represents the critical temperature, T ci represents the critical temperature of component i.
[0017] In one example, the calculation methods of the reduced pressure and reduced temperature in step 3 specifically include:
[0018] According to P r = P / P c calculate the reduced pressure, where P r represents the reduced pressure, P represents the actual pressure, P c represents the critical pressure;
[0019] According to T r = T / T c calculate the reduced temperature, where T r represents the reduced temperature, T represents the actual temperature, T c represents the critical temperature.
[0020] In one example, the clustering algorithm is the k-means algorithm.
[0021] In one example, the specific method of clustering by the k-means algorithm includes:
[0022] Step a, initialize the cluster centers, randomly select K sample points from N sample points x as the initial cluster centers C i , i = 1, 2,..., K;
[0023] Step b, assign samples to the cluster centers, calculate the Euclidean distance between each sample point x and each cluster center C i The Euclidean distance calculation formula and assign each sample point x to the cluster center C that is closest to them in Euclidean distance i to form K clusters; d i is the distance from the x sample point to the i-th cluster center C i ; m is the dimension of the data object, x j , C ij is the j-th dimensional attribute value of x and C i , where, x 1 , C i1respectively represent the sample point x and the cluster center C i of the reduced temperature T r , x 2 , C i2 respectively represent the sample point x and the cluster center C i of the reduced pressure P r , x 3 , C i3 respectively represent the sample point x and the cluster center C i at the corresponding reduced temperature T r , reduced pressure P r the density ρ;
[0024] Step c, adjust the cluster center, and recalculate the center of each cluster as the new cluster center;
[0025] Step d, repeat Step b and Step c until the termination condition is met: the change in the distance between the new cluster center and the previous cluster center is less than or equal to the set threshold, and stop moving the cluster center.
[0026] In one example, the number of clusters K is 2.
[0027] This application also provides a dense phase determination system for supercritical carbon dioxide, including an acquisition module, a first calculation module, a second calculation module, and a determination module; the acquisition module is used to acquire the component information, actual pressure, and actual temperature of supercritical carbon dioxide; the first calculation module is used to calculate the critical pressure and critical temperature of supercritical carbon dioxide according to the component information; the second calculation module is used to calculate the reduced pressure of supercritical carbon dioxide according to the actual pressure and critical pressure, and calculate the reduced temperature of supercritical carbon dioxide according to the actual temperature and critical temperature; the determination module is used to execute Steps 4 to 6 in the above-mentioned dense phase determination method for supercritical carbon dioxide.
[0028] This application also provides a storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the above-mentioned dense phase determination method for supercritical carbon dioxide.
[0029] This application also provides a dense phase determination terminal for supercritical carbon dioxide, including: a processor and a memory: the memory is used to store a computer program; the processor is used to execute the computer program stored in the memory so that the dense phase determination terminal for supercritical carbon dioxide executes the above-mentioned dense phase determination method for supercritical carbon dioxide.
[0030] This application has at least the following beneficial effects:
[0031] Compared with the prior art of using Aspen HYSYS chemical process simulation software to judge CO 2Compared with the dense phase and supercritical phase, whose calculation process is complex and requires a large number of parameter calculations, the method for determining the dense phase of supercritical carbon dioxide in this application only needs to calculate three parameters, namely the reduced pressure, reduced temperature and density of supercritical carbon dioxide by using component information. A three-dimensional space is constructed through these three parameters, and a fitting function is obtained through clustering and fitting. The fitting function is used to judge the state of the dense phase or supercritical phase of supercritical carbon dioxide. The result is close to that of the Aspen HYSYS chemical process simulation software, and can simply and conveniently judge the state of the dense phase or supercritical phase of supercritical carbon dioxide. Brief Description of the Drawings
[0032] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other relevant drawings can also be obtained based on these drawings.
[0033] Figure 1 For the pure CO of the prior art 2 phase diagram.
[0034] Figure 2 It shows mapping the reduced pressure, reduced temperature and density of supercritical carbon dioxide into a three-dimensional space, and obtaining a three-dimensional cluster by using a clustering algorithm in the three-dimensional space according to the embodiments of this application.
[0035] Figure 3 It shows projecting the three-dimensional cluster onto the P r -T r plane to convert it into a two-dimensional cluster, where P r represents the reduced pressure and T r represents the reduced temperature. Detailed Embodiments
[0036] The following specific embodiments illustrate the implementation manners of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or operated through other different specific implementation manners. Various details in this application can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application.
[0037] Referring to Figure 2 and Figure 3 , this application provides a method for determining the dense phase of supercritical carbon dioxide, including the following steps:
[0038] Step 1: Obtain the component information, actual pressure and actual temperature of supercritical carbon dioxide;
[0039] Step 2: Calculate the critical pressure, critical temperature, and density of supercritical carbon dioxide according to the component information;
[0040] Step 3: Calculate the reduced pressure of supercritical carbon dioxide according to the actual pressure and critical pressure, and calculate the reduced temperature of supercritical carbon dioxide according to the actual temperature and critical temperature;
[0041] Step 4: Map the reduced pressure, reduced temperature, and density of supercritical carbon dioxide to three-dimensional space, and use a clustering algorithm in three-dimensional space to obtain three-dimensional clusters; where the density is calculated by the professional chemical analysis software Aspen HYSYS for pure CO 2 Density data in the range of 31.1 °C to 60 °C and 7.38 MPa to 15 MPa, with a temperature step of 5 °C and a pressure step of 0.5 MPa.
[0042] Step 5: Project the three-dimensional clusters onto the P r -T r plane to be transformed into two-dimensional clusters, where P r represents the reduced pressure and T r represents the reduced temperature;
[0043] Step 6: Fit the boundaries of two adjacent clusters in the two-dimensional clusters to obtain the fitting function f = P r -8.536T r +7.29; Substitute the values of the reduced pressure P r and the reduced temperature T r into the fitting function f. If f is greater than or equal to 0, it indicates that the supercritical carbon dioxide is in the dense phase. If f is less than 0, the supercritical carbon dioxide is in the supercritical phase.
[0044] Compared with the prior art of judging the dense phase and supercritical phase of CO 2 by using the Aspen HYSYS chemical process simulation software, the calculation process is complex and requires a large number of parameter calculations. The method for judging the dense phase of supercritical carbon dioxide in this application only needs to calculate three parameters, namely the reduced pressure, reduced temperature, and density of supercritical carbon dioxide by using the component information. A three-dimensional space is constructed through these three parameters, and a fitting function is obtained through clustering and fitting. The fitting function is used to judge the state of the dense phase or supercritical phase of supercritical carbon dioxide. The result is close to the result of the Aspen HYSYS chemical process simulation software, and it can simply and conveniently judge the state of the dense phase or supercritical phase of supercritical carbon dioxide.
[0045] In an example, the component information includes the number of components of supercritical carbon dioxide, the mole fraction of a single component in supercritical carbon dioxide, the critical pressure and critical temperature of the single component.
[0046] The calculation methods for the critical pressure and critical temperature in Step 2 specifically include:
[0047] Calculate the critical pressure of supercritical carbon dioxide according to the component information. According to Calculate the critical pressure, where y i represents the mole fraction of a single component in supercritical carbon dioxide, and n represents the number of components in supercritical carbon dioxide; P c represents the critical pressure, and P ci represents the critical pressure of component i.
[0048] According to Calculate the critical temperature, where y i represents the mole fraction of a single component in supercritical carbon dioxide, and n represents the number of components in supercritical carbon dioxide; T c represents the critical temperature, and T ci represents the critical temperature of component i.
[0049] In one example, the calculation methods for the reduced pressure and reduced temperature in Step 3 specifically include:
[0050] According to P r = P / P c Calculate the reduced pressure, where P r represents the reduced pressure, P represents the actual pressure, and P c represents the critical pressure;
[0051] According to T r = T / T c Calculate the reduced temperature, where T r represents the reduced temperature, T represents the actual temperature, and T c represents the critical temperature.
[0052] In one example, the clustering algorithm is the k-means algorithm. In another example, the clustering algorithm is the DBSCAN algorithm. In other examples, the clustering algorithm can also adopt any other algorithm that obtains clusters based on the distance between points or density.
[0053] In one example, the specific method for clustering using the k-means algorithm includes:
[0054] Step a, initialize the cluster centers. Randomly select K sample points from N sample points x as the initial cluster centers C i , i = 1, 2,..., K;
[0055] Step b, assign samples to the cluster centers. Calculate the Euclidean distance between each sample point x and each cluster center C i The Euclidean distance calculation formula And assign each sample point x to the cluster center C that is closest to them in terms of Euclidean distancei Form K clusters; d i is the distance from the x sample point to the i-th cluster center C i ; m is the dimension of the data object, x j , C ij are the j-th dimensional attribute values of x and C i , where x 1 , C i1 represent the sample point x and the cluster center C i 's contrast temperature T r , x 2 , C i2 represent the sample point x and the cluster center C i 's contrast pressure P r , x 3 , C i3 represent the sample point x and the cluster center C i at the corresponding contrast temperature T r , contrast pressure P r under the density ρ.
[0056] Step c, adjust the cluster center and recalculate the center of each cluster as the new cluster center.
[0057] Step d, repeat Step b and Step c until the termination condition is met: the change in the distance between the new cluster center and the previous cluster center is less than or equal to the set threshold, and stop moving the cluster center. In one example, the threshold is 0.
[0058] In one example, the number of clusters K is 2, and two three-dimensional clusters can be obtained.
[0059] Specifically, when the number of clusters K is 2, there are two cluster centers. The density of one cluster center is closer to 500 kg / m 3 than that of the other cluster center. Starting from the material density attribute, its physical state tends to be the liquid phase. Therefore, this cluster is identified as the dense phase, which means that all sample points in the cluster centered on this cluster center are in the dense phase.
[0060] Specifically, when the number of clusters K is 2, there are two cluster centers. The density of one cluster center is closer to 200 kg / m 3 than that of the other cluster center. Starting from the material density attribute, its physical state tends to be the gas phase. Therefore, this cluster is identified as the supercritical phase, which means that all sample points in the cluster centered on this cluster center are in the supercritical phase.
[0061] The method for determining the dense phase of supercritical carbon dioxide of the present application will be further elaborated through specific embodiments. It should be noted that the measured values of supercritical carbon dioxide involved in the following embodiments are taken from laboratory data or calculated by the PR model of Aspen HYSYS software.
[0062] Example 1
[0063] For the high-concentration CO in Example 1 2 The component data adopts the components in the American Cortez pipeline with 98.5% CO 2 +0.14% CH 4 +1.3% N 2 +0.06% H 2 O component data. The high-concentration CO 2 density is calculated by the PR model of Aspen HYSYS software.
[0064] The calculation formulas for the critical pressure and critical temperature are respectively and where y i is the mole fraction of the single component in Table 1, n is the number of components in the high-concentration CO 2 , and 4 is taken in the calculation example. The critical pressure P 2 of the high-concentration CO c logistics is 7.33 MPa, and the critical temperature T c is 301.8 K respectively.
[0065] The reduced temperature, reduced pressure, and dense phase determination results of this high-concentration CO 2 at different actual temperatures and actual pressures, according to the Figure 1 partitioning method, and the results given by Aspen HYSYS calculation are shown in Table 1. It should be noted that in Aspen HYSYS, when the actual temperature and actual pressure of the logistics exceed the critical temperature and critical pressure, the liquid phase is used to characterize the dense phase, and the gas phase is used to characterize the supercritical phase.
[0066] Table 1 Determination Results of Example 1
[0067]
[0068]
[0069]
[0070]
[0071] It can be seen from Table 1 that by using Figure 1The method of dividing the dense phase and supercritical phase states according to the critical temperature identifies that the number of samples in the dense phase is 0. That is to say, no samples in the supercritical phase can be identified from these 112 groups of test samples. The HYSYS software can analyze and obtain 42 groups of samples in the supercritical phase. However, the HYSYS software requires other parameters in addition to the reduced pressure and reduced temperature as the judgment basis, and the amount of calculation is large. Although the dense phase determination method of supercritical carbon dioxide in this application is not as accurate as the data obtained by the HYSYS software, it can simply and conveniently obtain the judgment result of the samples in the supercritical phase by virtue of the reduced pressure, reduced temperature and density, and is close to the HYSYS calculation result. Compared with Figure 1 the determination method is more accurate.
[0072] Example 2
[0073] The high-concentration CO 2 component data in Example 2 adopts the component data of 97.5% CO 2 +0.35% CH 4 +2.1% N 2 +0.05% H 2 O in a domestic CCUS project. The density of the high-concentration CO 2 is calculated by the PR model of the Aspen HYSYS software.
[0074] The calculation formulas for the critical pressure and critical temperature are respectively and where y i is the mole fraction of the single component in Table 1, and n is the number of components in the high-concentration CO 2 , and 4 is taken in the calculation example. The critical pressure P 2 of the high-concentration CO c logistics calculated thereby is 7.29 MPa, and the critical temperature T c is 300.1 K respectively.
[0075] The reduced temperature, reduced pressure and dense phase determination results of the high-concentration CO 2 at different actual temperatures and actual pressures, according to the Figure 1 division method in, and the results given by the Aspen HYSYS calculation are shown in Table 2. It should be noted that in Aspen HYSYS, when the actual temperature and actual pressure of the logistics exceed the critical temperature and critical pressure, the liquid phase is used to represent the dense phase, and the gas phase is used to represent the supercritical phase.
[0076] Table 2 Determination Results of Example 2
[0077]
[0078]
[0079]
[0080] As can be seen from Table 2, using the method of Figure 1 dividing the dense phase and supercritical phase states according to the critical temperature, the number of samples identified as being in the dense phase is 0. That is to say, from these 112 groups of test samples, no samples in the supercritical phase can be identified. The HYSYS software can analyze and obtain 36 groups of samples in the supercritical phase. However, the HYSYS software requires other parameters in addition to the reduced pressure and reduced temperature as the judgment basis, and the computational workload is relatively large. Although the method for determining the dense phase of supercritical carbon dioxide in this application is not as accurate as the data obtained by the HYSYS software, it can simply and conveniently obtain the judgment result of the samples in the supercritical phase by virtue of the reduced pressure and reduced temperature, and Figure 1 is closer to the determination method.
[0081] In one example, a system for determining the dense phase of supercritical carbon dioxide includes an acquisition module, a first calculation module, a second calculation module, and a determination module; the acquisition module is used to acquire the component information, actual pressure, and actual temperature of supercritical carbon dioxide; the first calculation module is used to calculate the critical pressure and critical temperature of supercritical carbon dioxide according to the component information; the second calculation module is used to calculate the reduced pressure of supercritical carbon dioxide according to the actual pressure and the critical pressure, and calculate the reduced temperature of supercritical carbon dioxide according to the actual temperature and the critical temperature; the determination module is used to execute steps 4 to 6 of the method for determining the dense phase of supercritical carbon dioxide described above.
[0082] In one example, a storage medium stores a computer program, and when the program is executed by a processor, the method for determining the dense phase of supercritical carbon dioxide described above is implemented.
[0083] In one example, a terminal for determining the dense phase of supercritical carbon dioxide includes: a processor and a memory: the memory is used to store a computer program; the processor is used to execute the computer program stored in the memory so that the terminal for determining the dense phase of supercritical carbon dioxide executes the method for determining the dense phase of supercritical carbon dioxide described above.
[0084] The above are only the preferred embodiments of the present application. It should be noted that for those of ordinary skill in the art in this technical field, without departing from the technical principle of the present application, several improvements and substitutions can be made, and these improvements and substitutions should also be regarded as the protection scope of the present application.
Claims
1. A method for determining the dense phase of supercritical carbon dioxide, characterized in that, it includes the following steps: Step 1, obtain the component information, actual pressure and actual temperature of supercritical carbon dioxide; Step 2, calculate the critical pressure, critical temperature and density of supercritical carbon dioxide according to the component information; Step 3, calculate the reduced pressure of supercritical carbon dioxide according to the actual pressure and critical pressure, and calculate the reduced temperature of supercritical carbon dioxide according to the actual temperature and critical temperature; Step 4, map the reduced pressure, reduced temperature and density of supercritical carbon dioxide into three-dimensional space, and use a clustering algorithm in the three-dimensional space to obtain three-dimensional clusters; Step 5, project the three-dimensional cluster onto the P r -T r plane to convert it into a two-dimensional cluster, where P r represents the reduced pressure and T r represents the reduced temperature; Step 6: Fit the boundaries of two adjacent clusters in the two-dimensional cluster to obtain the fitting function f = P r - 8.536T r + 7.29; Substitute the values of the reduced pressure P r and the reduced temperature T r into the fitting function f. If f is greater than or equal to 0, it indicates that the supercritical carbon dioxide is in the dense phase. If f is less than 0, the supercritical carbon dioxide is in the supercritical phase and the dense phase.
2. The dense phase determination method according to claim 1, characterized in that, the component information includes the number of components of supercritical carbon dioxide, the mole fraction of each single component in supercritical carbon dioxide, the critical pressure and critical temperature of each single component; The specific calculation methods of the critical pressure and critical temperature in Step 2 specifically include: Calculate the critical pressure of supercritical carbon dioxide according to the component information. According to Calculate the critical pressure, where y i represents the mole fraction of a single component in supercritical carbon dioxide, and n represents the number of components in supercritical carbon dioxide; P c represents the critical pressure, and P ci represents the critical pressure of component i; According to calculate the critical temperature, where y i represents the mole fraction of a single component in supercritical carbon dioxide, n represents the number of components in supercritical carbon dioxide; T c represents the critical temperature, T ci represents the critical temperature of component i.
3. The dense phase determination method according to claim 1, characterized in that, The specific calculation methods of the reduced pressure and reduced temperature in Step 3 specifically include: According to P r = P / P c Calculate the reduced pressure, where P r represents the reduced pressure, P represents the actual pressure, and P c represents the critical pressure; According to T r = T / T c Calculate the reference temperature, where T r represents the reference temperature, T represents the actual temperature, and T c represents the critical temperature.
4. The dense phase determination method according to claim 1, characterized in that, the clustering algorithm is the k-means algorithm.
5. The dense phase determination method according to claim 4, characterized in that, The specific method of clustering by the k-means algorithm specifically includes: Step a, initialize the cluster centers. Randomly select K sample points from N sample points x as the initial cluster centers C, where i = 1, 2, …, K; i , i = 1, 2, …, K; Step b: Assign samples to the cluster centers and calculate the Euclidean distance between each sample point x and each cluster center C i The Euclidean distance calculation formula is and assign each sample point x to the cluster center C with the closest Euclidean distance to them i to form K clusters; d i is the distance from the x sample point to the i-th cluster center C i ; m is the dimension of the data object, x j , C ij are the j-th dimensional attribute values of x and C i , where x 1 , C i1 represent the sample point x and the cluster center C i 's comparison temperature T r , x 2 , C i2 represent the sample point x and the cluster center C i 's comparison pressure P r , x 3 , C i3 represent the sample point x and the cluster center C i at the corresponding comparison temperature T r , comparison pressure P r under the density ρ; Step c, adjust the clustering center, and recalculate the center of each cluster as the new clustering center; Step d, repeat Step b and Step c until the termination condition is met: the change amount of the distance between the new clustering center and the previous clustering center is less than or equal to the set threshold, and stop moving the clustering center.
6. The dense phase determination method according to claim 5, characterized in that, the number of clusters K is 2.
7. A dense phase determination system for supercritical carbon dioxide, characterized in that, it includes an acquisition module, a first calculation module, a second calculation module and a determination module; the acquisition module is used to acquire the component information, actual pressure and actual temperature of supercritical carbon dioxide; the first calculation module is used to calculate the critical pressure and critical temperature of supercritical carbon dioxide according to the component information; the second calculation module is used to calculate the reduced pressure of supercritical carbon dioxide according to the actual pressure and critical pressure, and calculate the reduced temperature of supercritical carbon dioxide according to the actual temperature and critical temperature; The determination module is used to execute Steps 4 to 6 in the dense phase determination method for supercritical carbon dioxide according to any one of claims 1 to 6.
8. A storage medium, on which a computer program is stored, characterized in that, when the program is executed by a processor, it implements the dense phase determination method for supercritical carbon dioxide according to any one of claims 1 to 6.
9. A dense phase determination terminal for supercritical carbon dioxide, characterized in that, it includes: a processor and a memory: the memory is used to store a computer program; the processor is used to execute the computer program stored in the memory, so that the dense phase determination terminal for supercritical carbon dioxide executes the dense phase determination method according to any one of claims 1 to 6.