A method for managing the thermal efficiency of a supercritical carbon dioxide cycle unit

Abstract

An embodiment of the present application discloses a method for managing the thermal efficiency of a supercritical carbon dioxide cycle unit. The method includes: determining x variables that affect the thermal efficiency of a supercritical carbon dioxide (SCO2) cycle machine, where x is an integer greater than or equal to 2; using the principal component analysis method to select m variables from the x variables, where m is an integer less than n; establishing a thermodynamic model of the thermal efficiency of the SCO2 cycle machine using the m variables; and managing the thermal efficiency of the SCO2 cycle machine using the thermodynamic model.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of information processing, and particularly to a method for managing the thermal efficiency of a supercritical carbon dioxide cycle unit. Background Art

[0002] Since the 21st century, the world's energy structure has been continuously developing and transforming towards clean, low-carbon, efficient, and diversified directions. However, coal still occupies an important position in the world's energy supply. According to statistics, coal consumption accounted for 27.2% of the world's primary energy supply in 2018, and the demand for coal has been increasing for two consecutive years. Most of the coal is used for power generation to meet the growing electricity demand. The traditional coal-fired power generation system uses the Rankine cycle with steam as the working fluid. However, due to the cycle characteristics and material limitations, it is difficult to further improve the power plant efficiency. The supercritical carbon dioxide power cycle is efficient and compact, and is expected to replace the traditional steam power cycle.

[0003] The supercritical carbon dioxide Brayton cycle coal-fired power generation technology has the main advantages that on the one hand, it uses an efficient Brayton cycle, and the power generation efficiency is higher than that of conventional thermal power units with the same grade of parameters; on the other hand, the volumes of components such as turbines and compressors are greatly reduced, there is no extraction steam design, and the pipeline complexity is reduced, which is a potential solution for further improving the coal-fired power generation efficiency.

[0004] Due to the physical property characteristics of the working fluid in the supercritical carbon dioxide closed Brayton cycle, in order to improve the overall cycle efficiency, the intermediate regeneration method is often adopted in the cycle to fully utilize the high-temperature exhaust gas of the turbine to preheat the working fluid at the outlet of the compressor (regeneration process), thereby reducing the cold-end loss. The cycle can also adopt the multi-stage compression intermediate cooling technology to further improve the efficiency. Since there are many factors affecting this efficiency and the factors are highly coupled with each other, it is difficult to improve this parameter. Therefore, how to determine the key parameters for the optimal thermal efficiency of the supercritical carbon dioxide cycle is an urgent problem to be solved. Summary of the Invention

[0005] In order to solve any of the above technical problems, the embodiments of the present application provide a method for managing the thermal efficiency of a supercritical carbon dioxide cycle unit.

[0006] To achieve the purpose of the embodiments of the present application, the embodiments of the present application provide a method for managing the thermal efficiency of a supercritical carbon dioxide cycle unit, including:

[0007] Determine x variables that affect the thermal efficiency of the supercritical carbon dioxide SCO2 cycle machine, where x is an integer greater than or equal to 2;

[0008] Use the principal component analysis method to select m variables from the x variables, where m is an integer less than n;

[0009] Establish a thermodynamic model of the thermal efficiency of the SCO2 cycle machine using the m variables;

[0010] Manage the thermal efficiency of the SCO2 cycle machine using the thermodynamic model.

[0011] A storage medium stores a computer program, where the computer program is configured to execute the method described above when running.

[0012] An electronic device includes a memory and a processor. The memory stores a computer program, and the processor is configured to run the computer program to execute the method described above.

[0013] One of the above technical solutions has the following advantages or beneficial effects:

[0014] By determining x variables that affect the thermal efficiency of the SCO2 cycle machine, selecting m variables from the x variables using the principal component analysis method, establishing a thermodynamic model of the thermal efficiency of the SCO2 cycle machine using the m variables, and managing the thermal efficiency of the SCO2 cycle machine using the thermodynamic model, the difficulty of modeling and the computational amount of modeling are greatly reduced by streamlining the number of variables.

[0015] Other features and advantages of the embodiments of the present application will be described in the subsequent description, and some of them will be obvious from the description, or understood by implementing the embodiments of the present application. The objectives and other advantages of the embodiments of the present application can be achieved and obtained through the structures specifically pointed out in the description, claims, and drawings. Description of the Drawings

[0016] The drawings are used to provide a further understanding of the technical solutions of the embodiments of the present application, and constitute a part of the description. They are used together with the embodiments of the present application to explain the technical solutions of the embodiments of the present application, and do not constitute a limitation to the technical solutions of the embodiments of the present application.

[0017] Figure 1 It is a flowchart of the method for managing the thermal efficiency of the SCO2 cycle unit provided by the embodiments of the present application;

[0018] Figure 2 It is a flowchart of the method for optimizing the thermal efficiency of the SCO2 cycle machine provided by the embodiments of the present application;

[0019] Figure 3 It is a flowchart of the processing method of the principal component analysis method provided by the embodiments of the present application.

[0020] Figure 4 It is a schematic diagram of the construction of the thermodynamic model provided by the embodiments of the present application.

[0021] Figure 5Flowchart of the processing method of the genetic algorithm provided by the embodiments of the present application.

[0022] Figure 6 Flowchart of the processing method of the optimized genetic algorithm provided by the embodiments of the present application.

[0023] Figure 7 Schematic diagram of the influence of the high-pressure turbine inlet temperature and pressure on the cycle thermal efficiency provided by the embodiments of the present application. Detailed implementation manners

[0024] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer and more understandable, the embodiments of the present application will be described in detail below with reference to the accompanying drawings. It should be noted that, without conflict, the embodiments and features in the embodiments of the present application can be combined with each other arbitrarily.

[0025] Figure 1 Flowchart of the method for managing the thermal efficiency of the SCO2 cycle unit provided by the embodiments of the present application. As Figure 1 shown, it includes:

[0026] Step 101: Determine x variables that affect the thermal efficiency of the SCO2 cycle machine, where x is an integer greater than or equal to 2;

[0027] Step 102: Use the principal component analysis method to select m variables from the x variables, where m is an integer less than n;

[0028] Step 103: Establish a thermodynamic model of the thermal efficiency of the SCO2 cycle machine using the m variables;

[0029] Step 104: Manage the thermal efficiency of the SCO2 cycle machine using the thermodynamic model.

[0030] The method provided by the embodiments of the present application determines x variables that affect the thermal efficiency of the SCO2 cycle machine, selects m variables from the x variables using the principal component analysis method, establishes a thermodynamic model of the thermal efficiency of the SCO2 cycle machine using the m variables, and manages the thermal efficiency of the SCO2 cycle machine using the thermodynamic model. By streamlining the number of variables, the difficulty of modeling is greatly reduced and the computational amount of modeling is reduced.

[0031] The method provided by the embodiments of the present application will be described below:

[0032] Figure 2 Flowchart of the SCO2 cycle machine thermal efficiency optimization method provided by the embodiments of the present application. As Figure 2As shown in the figure, first, analyze the operating mechanism of the supercritical carbon dioxide cycle unit, find the influencing factors of its SCO2 cycle thermal efficiency, and screen the influencing factors for subsequent research. Through reasonable and effective mechanism analysis, establish a thermodynamic model. Then, taking the SCO2 cycle thermal efficiency as the optimization goal, use the improved genetic algorithm to perform multi-parameter optimization on the previously established thermodynamic model. Compare the optimized results with the literature to judge whether the accuracy and accuracy of the model of the present invention meet the requirements. Finally, taking the influencing factors of the SCO2 cycle thermal efficiency as the model input, calculate its SCO2 cycle thermal efficiency.

[0033] Figure 2 The method shown includes 4 stages, namely the key variable screening stage, the model establishment stage, the model verification stage, and the model testing stage. The following is a description of each stage separately:

[0034] 1. Key variable screening stage

[0035] Analyze the operating mechanism of the supercritical carbon dioxide cycle unit, and find the influencing factors of its SCO2 cycle thermal efficiency. Determine x variables that have a greater impact on its cycle thermal efficiency, and then use the principal component analysis method to analyze and screen the above x variables. By calculating the contribution rate and correlation coefficient, find the variables with a higher contribution rate. According to the calculation results, replace the initial x variables with m variables with a higher contribution rate for subsequent analysis and research.

[0036] In an exemplary embodiment, the x variables include at least one of the following:

[0037] The inlet pressure of the high-pressure turbine, the inlet temperature of the high-pressure turbine, the inlet pressure of the low-pressure turbine, the inlet temperature of the low-pressure turbine, the inlet temperature of the main compressor, the pressure loss of the main compressor, the inlet temperature of the recompressor, the pressure loss of the recompressor, and the compressor split ratio.

[0038] In an exemplary embodiment, the use of the principal component analysis method to select m variables from the x variables includes:

[0039] Use the principal component analysis method to screen the x variables to obtain the variable with the highest contribution rate;

[0040] Calculate the correlation coefficient between the variable with the highest contribution rate and the remaining x - 1 variables;

[0041] According to the correlation coefficient of each variable in the x - 1 variables, select m - 1 variables, and combine the variable with the highest contribution rate and the m - 1 variables as the final variables.

[0042] Analyze the operating mechanism of the supercritical carbon dioxide cycle unit to find the influencing factors of its SCO2 cycle thermal efficiency. The variables that have a greater impact on its cycle thermal efficiency are determined to be: the inlet pressure of the high-pressure turbine, the inlet temperature of the high-pressure turbine, the inlet pressure of the low-pressure turbine, the inlet temperature of the low-pressure turbine, the inlet temperature and pressure loss of the main compressor, the inlet temperature and pressure loss of the recompressor, and the compressor split ratio, a total of 9 variables; then use the principal component analysis method to analyze and screen the above 9 variables, and find the variables with a higher contribution rate by calculating the contribution rate and the correlation coefficient. According to the calculation results, 4 key variables are used for subsequent analysis and research.

[0043] In an exemplary embodiment, the following calculation expression is used to calculate the correlation coefficient, including:

[0044]

[0045] Among them, X and Y represent two different variables, is the covariance of X and Y, and and represent the average values of the samples.

[0046] The calculated range of the correlation coefficient is (-1, 1). When the absolute value of the correlation coefficient is larger, it indicates a stronger correlation: when the correlation coefficient is closer to 1 or -1, the correlation is stronger; when the correlation coefficient is closer to 0, the correlation is weaker.

[0047] The correlation strength of the variable is judged by the following value range:

[0048] The absolute value of the correlation coefficient

[0049] 1. Very strong correlation: 0.8 - 1.0

[0050] 2. Strong correlation: 0.6 - 0.8

[0051] 3. Moderate correlation: 0.4 - 0.6

[0052] 4. Weak correlation: 0.2 - 0.4

[0053] 5. Very weak correlation or no correlation: 0.0 - 0.2

[0054] Figure 3 It is a flowchart of the processing method of the principal component analysis method provided by the embodiment of the present application. As Figure 3 shown, the processing method includes the following steps:

[0055] 1. Perform standardized preprocessing on the original data, and then form the data into a matrix X of n rows and m columns by columns;

[0056] 2. Zero mean. That is, subtract the average value of each row from each feature;

[0057] 3. Calculate the covariance matrix;

[0058] 4. Calculate the eigenvalues and eigenvectors of the covariance matrix;

[0059] 5. Arrange the eigenvalues in ascending order;

[0060] 6. Transform the data into the new space constructed by the eigenvectors.

[0061] After the analysis is completed, the obtained contribution rate information is as follows:

[0062] Contribution rate:

[0063] newrate = 0.7578 0.1571 0.0544 0.0250 0.0029 0.0017 0.0011

[0064] Number of principal components: 2

[0065] Principal component loadings:

[0066]

[0067] It can be seen from the above results that a total of 2 principal components are required to reach the cumulative contribution rate of 80%, and the contribution rate of the first principal component reaches 75%, indicating that the influence of the first principal component is very large. Among the first principal component, the loadings of variable 1 (inlet pressure of high-pressure turbine), variable 4 (inlet temperature of high-pressure turbine), variable 5 (pressure loss), and variable 6 (inlet temperature of main compressor) are relatively large and the scores are the highest. Through the above calculation and analysis, these 4 variables are selected as the key variables.

[0068] Through the above processing, the purpose of fully mining the implicit information in multiple variables can be achieved, thereby improving the SCO2 cycle efficiency. At the same time, it also has the advantages of fast operation speed and strong generalization ability.

[0069] 2. Model establishment stage

[0070] Figure 4 This is a schematic diagram of the construction of the thermodynamic model provided by the embodiment of the present application. As Figure 4 shown, analyze the internal mechanism of the carbon dioxide cycle, adopt the lumped parameter method, ignore the spatial distribution of system parameters, and only consider the time derivative term; assume that the flue gas and air are ideal gases and satisfy the ideal gas state law; each system satisfies the basic physical and thermodynamic laws, such as mass conservation, energy conservation, momentum conservation, heat transfer equation, thermodynamic state parameter equation, etc.

[0071] According to its thermodynamic laws, a thermodynamic model of the supercritical carbon dioxide cycle is established. The thermodynamic cycle system includes two major categories of equipment: one is turbomachinery, such as compressors and turbines; the other is heat exchange equipment, such as regenerators and coolers. The thermodynamic cycle analysis generally establishes a thermodynamic model for different equipment, and then forms a closed cycle through the connection relationship between them and finally solves the state parameters at each point.

[0072] After determining the key variables through the principal component analysis method, analyze the internal mechanism of the carbon dioxide cycle. Adopt the lumped parameter method, ignoring the spatial distribution of system parameters, and only considering the time derivative term; assume that flue gas and air are ideal gases, satisfying the ideal gas state law; each system satisfies the basic physical and thermodynamic laws, such as mass conservation, energy conservation, momentum conservation, heat transfer equation, thermodynamic state parameter equation, etc.

[0073] According to its thermodynamic laws, a thermodynamic model of the supercritical carbon dioxide cycle is established; the thermodynamic cycle system includes two major categories of equipment: one is turbomachinery, such as compressors and turbines; the other is heat exchange equipment, such as regenerators and coolers.

[0074] The thermodynamic cycle analysis generally establishes a thermodynamic model for different equipment, and then forms a closed cycle through the connection relationship between them and finally solves the state parameters at each point.

[0075] For rotating equipment such as compressors and turbines, an isentropic compression and isentropic expansion model considering the isentropic efficiency of the equipment is adopted.

[0076] For the compressor:

[0077] P co = P ci ε c

[0078] s co = s(P ci , T ci )

[0079] h co = h(P co , s co )

[0080] Δh c = (h co - h ci ) / α c

[0081] T co = T[P co , (h ci + Δh c )]

[0082] For the turbine:

[0083] P to = P ti / ε t

[0084] s to = s(P ti , T ti )

[0085] h to = h(P to , s to )

[0086] Δh t = (h to - h ti ) / α t

[0087] In the formulas, P is the pressure with the unit of MPa; s is the entropy with the unit of kJ / kg·°C; h is the enthalpy with the unit of kJ / kg; T is the temperature with the unit of °C; Δh is the enthalpy rise with the unit of kJ / kg; ε is the pressure ratio; α is the isentropic efficiency of the rotating equipment. The subscripts c and t represent the compressor and the turbine respectively; i and o represent the inlet and the outlet respectively.

[0088] For the heat exchange equipment, a printed circuit board heat exchanger that can withstand high temperature and high pressure is generally used, which can achieve a lower end temperature difference and higher efficiency under the condition of controlling the volume of the heat exchange equipment.

[0089] According to the law of conservation of energy, it can be known for the heat exchange equipment that:

[0090] m1(h 1i - h 10 ) = m2(h 2o - h 2i )

[0091] In the formulas, m is the flow rate with the unit of Kg / s; h is the enthalpy with the unit of KJ / Kg. The subscripts i and o represent the inlet and the outlet respectively, and 1 and 2 represent the hot side and the cold side of the heat exchanger respectively.

[0092] The work output of the turbine and the work consumption of the compressor are respectively:

[0093] W t = m t Δh t

[0094] W c = m c Δh c

[0095] Wherein, W is the power with the unit of MW; m is the flow rate with the unit of kg / s; Δh is the enthalpy rise with the unit of kJ / kg.

[0096] Then the system thermal efficiency is:

[0097]

[0098] Wherein, η is the system thermal efficiency; Q is the heat source power with the unit of MW.

[0099] 3. Model verification stage

[0100] In order to further improve the established thermodynamic model, by referring to and comparing relevant domestic and foreign literatures, the relevant main parameter values of the supercritical carbon dioxide cycle are determined as shown in the following table:

[0101] Equipment status parameter Unit Value Main compressor efficiency ％ 60 Turbine efficiency ％ 81 Re-compressor efficiency ％ 50 Combustion chamber efficiency ％ 80

[0102] The improved genetic algorithm is adopted to optimize this model with the cycle thermal efficiency as the optimization objective.

[0103] In an exemplary embodiment, after establishing the thermodynamic model of the SCO2 cycle machine using the m variables, the method further includes:

[0104] Determining the configuration values of the m variables from the thermodynamic models of the SCO2 cycle machine thermal efficiency established using a variables, where a is an integer greater than m;

[0105] During the process of running the thermodynamic model with the configuration parameters of the m variables, with the optimization of the thermal efficiency as the goal, the genetic algorithm is used to adjust the values of the variables in the thermodynamic model.

[0106] Since the number of variables used for the determined values of the m variables is different from the number of variables used in this application, by adjusting the obtained values of the m variables, the adaptation to the model used in this application is realized, and the accuracy of model processing is improved.

[0107] In an exemplary embodiment, using the genetic algorithm to adjust the values of the variables in the thermodynamic model includes:

[0108] Determining the variables to be optimized;

[0109] Using the variables to be optimized to establish an initial population;

[0110] Calculating the fitness of the initial population;

[0111] Generating the next generation population using the genetic algorithm;

[0112] Calculate whether the fitness of the next generation population meets the preset fitness condition, where the fitness condition is set according to the initial fitness;

[0113] If the fitness of the next generation population meets the fitness condition, then encode the value of the optimized variable; otherwise, continue to generate the next generation population until the fitness of the obtained next generation population meets the fitness condition or the number of iterations reaches the preset number threshold.

[0114] Figure 5 It is the flowchart of the processing method of the genetic algorithm provided by the embodiments of this application. As Figure 5 shown, the processing method includes the following steps:

[0115] A) Optimization variable confirmation:

[0116] X = [X1 X2 … X n

[0117] where n is the number of variables to be optimized;

[0118] B) Encode the above required variables:

[0119] B = from10to2(X)

[0120] The above purpose is to convert the decimal variables into binary codes;

[0121] C) Generate the initial population through conversion:

[0122] B0 = random(n,range)

[0123] where range is the variable range, and the above purpose is to randomly determine the initial population according to the number of variables n and the variable range range;

[0124] D) Calculate the population fitness:

[0125] q0 = Q(B0)

[0126] In the formula: q0 is the fitness of the initial population; Q is the fitness function;

[0127] E) Three arithmetic operations to generate the next generation population:

[0128] 1) Selection operation:

[0129] Select the initial individual with the best fitness:

[0130] [b0 i] = max(q0)

[0131] In the formula, i depends on the number of variables n to be optimized, and the range is [1, 2, …, n]; ​

[0132] Keep the initial individual with the best fitness for the next generation:

[0133] B1(i) = B0(i)

[0134] 2) Crossover operation:

[0135] Since the individuals use real number coding, the real number crossover method is adopted for the crossover operation. For the k-th chromosome a k and the l-th chromosome a j , the crossover operation method at the j-th position is as follows:

[0136]

[0137] where b is a random number in the range [0, 1].

[0138] 3) Mutation operation:

[0139] Select the j-th gene a ij of the i-th individual for mutation. The mutation operation method is as follows:

[0140]

[0141] where a max is the upper bound of gene a ij ; a min is the lower bound of gene a ij ; f(g) = r2(1 - g / G max ); 2 r2 is a random number; g is the current iteration number; G max is the maximum number of evolutions; r is a random number in the range [0, 1].

[0142] F) Determine whether the fitness meets the requirements:

[0143] [q best i] = max(q0)

[0144] G) If the fitness does not meet the requirements, use the next generation population as the initial population, continue to calculate the fitness and generate the next generation B0 = B1, and use the next generation individuals as the initial ones;

[0145] H) If the fitness meets the requirements, obtain the encoded value of the optimized variable:

[0146] [q best i] = max(q0)

[0147] b best = B0(i)

[0148] b best is what is required;

[0149] I) Decode the obtained value to get the final value:

[0150] X best = from2to10(b best )

[0151] Where: X best That is, the optimized variable value;

[0152] In an exemplary embodiment, the generating the next generation population by using the genetic algorithm includes:

[0153] Calculating the fitness value of each individual in the next generation population;

[0154] Determining the optimal solution and the worst solution in the next generation population according to the fitness value of each individual;

[0155] If the fitness value of the optimal solution in the previous generation is greater than the fitness value of the current optimal solution, then replace the current optimal solution with the optimal solution in the previous generation;

[0156] If the function value of the optimal solution in the previous generation is smaller than the fitness value of the current optimal solution, then replace the current worst solution with the optimal solution in the previous generation.

[0157] Although the above genetic algorithm has good global search ability and can quickly search out all solutions in the solution space, the local search ability of the traditional genetic algorithm is poor, resulting in the pure genetic algorithm being time-consuming and having low search efficiency in the later stage of evolution. Therefore, to solve this problem, the present invention improves on the traditional genetic algorithm.

[0158] Figure 6 It is a flowchart of the processing method of the optimized genetic algorithm provided by the embodiments of the present application. As Figure 6 shown, the optimized genetic algorithm is improved in step E of the genetic algorithm, including:

[0159] Adopting the optimal preservation strategy, first calculating the fitness function value of each individual, then sorting, and finding the optimal solution and the worst solution; then if the function value of the optimal solution in the previous generation is greater than the function value of the current optimal solution, then replace the current optimal solution with the optimal solution in the previous generation; if the function value of the optimal solution in the previous generation is smaller, then replace the current worst solution with the optimal solution in the previous generation.

[0160] In an exemplary embodiment, after adjusting the value of the variable of the thermodynamic model by using the genetic algorithm, the method further includes:

[0161] Obtaining the thermal cycle efficiency of the calorimetric model after adjusting the value of the variable;

[0162] Compare the thermal cycle efficiency with the benchmark thermal cycle efficiency of the thermodynamic model of the SCO2 cycle machine established with a variables to obtain a comparison result;

[0163] If the comparison result shows that the error between the thermal cycle efficiency and the benchmark thermal cycle efficiency meets the preset error condition, it is determined that the calorimetric model after determining the value of the adjustment variable passes the verification.

[0164] Compare the optimized result with the content of the collected literature to judge whether the accuracy and accuracy of the model of the present invention meet the requirements through the comparison.

[0165] Compare the optimized result with the content of the collected literature. As shown in the following table, it is found through the comparison that the error between the optimized thermal efficiency calculated by the model and the literature result is only 0.026%, and the accuracy and accuracy meet the requirements.

[0166] Parameter Literature result Calculation result Error Cycle efficiency / % 56.079 56.053 0.026

[0167] 4. Thermal efficiency detection stage

[0168] Control the high-pressure turbine inlet temperature to start rising at a certain temperature, and control the main compressor inlet temperature to remain constant at a certain temperature. Select the high-pressure turbine inlet temperature and pressure as two variables for the model input. Other parameters except these are optimized by genetic algorithm with the cycle thermal efficiency as the optimization goal to judge the influence of the variables on the best thermal efficiency of the SCO2 cycle.

[0169] Control the high-pressure turbine inlet temperature to start rising from 500 °C, and control the main compressor inlet temperature to remain constant at 32 °C. Select the high-pressure turbine inlet temperature and pressure as two variables for the model input. Other parameters except these are optimized by genetic algorithm with the cycle thermal efficiency as the optimization goal to judge the influence of the variables on the best thermal efficiency of the SCO2 cycle.

[0170] Figure 7 It is a schematic diagram showing the influence of the high-pressure turbine inlet temperature and pressure on the cycle thermal efficiency provided by the embodiment of the present application. From Figure 7 It can be seen that the best thermal efficiency of the cycle first increases and then decreases in a parabolic shape as the abscissa increases, that is, the best thermal efficiency of the cycle has a quadratic function relationship with the high-pressure turbine inlet pressure. This clearly shows that when the high-pressure turbine inlet temperature ranges from 500 °C to 700 °C, the thermal efficiency of the cycle and the high-pressure turbine inlet pressure do not have a simple linear function relationship. Instead, as the high-pressure turbine inlet pressure gradually increases between 20 and 37 MPa, appropriately increasing the high-pressure turbine inlet pressure of the cycle is beneficial to improving the thermal efficiency of the cycle, but too high a temperature will have the opposite effect, and its optimum temperature is approximately around 31 °C. In addition, from Figure 7It can also be seen that the change in the inlet temperature of the high-pressure turbine will affect the optimal thermal efficiency of the cycle. This shows that increasing the inlet temperature of the high-pressure turbine is also beneficial to improving the cycle thermal efficiency. The above simulation results indicate that the present invention has the ability to optimize the cycle thermal efficiency of a supercritical carbon dioxide cycle unit.

[0171] The method provided by the embodiment of the present application has the following advantages, including:

[0172] Using the principal component analysis method to determine the key variables can greatly reduce the difficulty of modeling and the computational amount of modeling;

[0173] The process of establishing the model is relatively simple and requires less information. Therefore, the present invention is more conducive to implementation than other methods and has stronger practicability;

[0174] The present invention uses an improved genetic algorithm, which can overcome the defects of the traditional genetic algorithm, such as poor local search ability and time-consuming calculation;

[0175] The model established based on the thermodynamic mechanism of an actual supercritical carbon dioxide cycle unit can adapt to the calculation of cycle thermal efficiency under different working conditions and has better practicability and generalization ability.

[0176] The embodiment of the present application provides a storage medium, in which a computer program is stored. Wherein, the computer program is set to execute the method described in any one of the above when running.

[0177] The embodiment of the present application provides an electronic device, including a memory and a processor. A computer program is stored in the memory, and the processor is set to run the computer program to execute the method described in any one of the above.

[0178] Those of ordinary skill in the art will understand that all or some of the steps in the methods disclosed above, and the functional modules / units in systems and devices, can be implemented as software, firmware, hardware, and appropriate combinations thereof. In a hardware implementation, the division of functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be executed by several physical components in cooperation. Some or all components may be implemented as software executed by a processor, such as a digital signal processor or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include a computer storage medium (or non-transitory medium) and a communication medium (or transitory medium). As is well known to those of ordinary skill in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data. Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those of ordinary skill in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transmission mechanism, and can include any information delivery medium.

Claims

1. A method for managing the thermal efficiency of a supercritical carbon dioxide cycle unit, comprising: Determine x variables that affect the thermal efficiency of a supercritical carbon dioxide (SCO2) cycle machine, where x is an integer greater than or equal to 2; Use principal component analysis to select m variables from the x variables, where m is an integer less than n; Establish a thermodynamic model of the SCO2 cycle machine's thermal efficiency using the m variables; Manage the thermal efficiency of the SCO2 cycle machine using the thermodynamic model; Wherein, after establishing the thermodynamic model of the SCO2 cycle machine's thermal efficiency using the m variables, the method further includes: Determine the configuration values of the m variables from the thermodynamic models of the SCO2 cycle machine's thermal efficiency established using a variables pre-recorded, where a is an integer greater than m; During the process of running the thermodynamic model with the configuration parameters of the m variables, with the goal of optimizing the thermal efficiency, use a genetic algorithm to adjust the values of the variables in the thermodynamic model; Wherein, when using a genetic algorithm to adjust the values of the variables in the thermodynamic model, perform selection operation, crossover operation, and mutation operation using the genetic algorithm to generate the next generation population; Wherein, performing the selection operation, crossover operation, and mutation operation using the genetic algorithm to generate the next generation population includes: Calculate the fitness value of each individual in the next generation population; Determine the optimal solution and the worst solution in the next generation population according to the fitness value of each individual; If the fitness value of the previous generation's optimal solution is greater than the fitness value of the current optimal solution, replace the current optimal solution with the previous generation's optimal solution; If the function value of the previous generation's optimal solution is less than the fitness value of the current optimal solution, replace the current worst solution with the previous generation's optimal solution.

2. The method according to claim 1, wherein The x variables include at least one of the following: High-pressure turbine inlet pressure, high-pressure turbine inlet temperature, low-pressure turbine inlet pressure, low-pressure turbine inlet temperature, main compressor inlet temperature, main compressor pressure loss, recompressor inlet temperature, recompressor pressure loss, and compressor split ratio.

3. The method according to claim 1, wherein The using principal component analysis to select m variables from the x variables includes: Use principal component analysis to screen the x variables to obtain the variable with the highest contribution rate; Calculate the correlation coefficient between the variable with the highest contribution rate and the remaining x - 1 variables; According to the correlation coefficient of each variable among the x - 1 variables, select m - 1 variables, and combine the variable with the highest contribution rate and the m - 1 variables as the final variables.

4. The method according to claim 3, wherein Use the following calculation expression to calculate the correlation coefficient, including: Among them, X and Y represent two different variables, cov(X, Y) is the covariance of X and Y, σ X and σ Y represent the mean values of the samples.

5. The method according to claim 1, wherein After using a genetic algorithm to adjust the values of the variables in the thermodynamic model, the method further includes: Obtain the thermal cycle efficiency of the calorimetric model after adjusting the values of the variables; Compare the thermal cycle efficiency with the benchmark thermal cycle efficiency of the thermodynamic model of the SCO2 cycle machine's thermal efficiency established using a variables to obtain a comparison result; If the comparison result shows that the error between the thermal cycle efficiency and the benchmark thermal cycle efficiency meets the preset error condition, determine that the calorimetric model after adjusting the values of the variables is verified.

6. A storage medium, wherein A computer program is stored in the storage medium, wherein the computer program is configured to execute the method described in any one of claims 1 to 5 when running.

7. An electronic device, comprising a memory and a processor, wherein A computer program is stored in the memory, and the processor is configured to run the computer program to execute the method described in any one of claims 1 to 5.

Images