CN115705445A - A method of managing the thermal efficiency of a supercritical carbon dioxide cycle unit - Google Patents

Abstract

The 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 SCO cycle machine, wherein x is an integer greater than or equal to 2; using principal component analysis to select m variables from the x variables, wherein m is less than n Integer; using the m variables to establish a thermodynamic model of the thermal efficiency of the SCO2 cycle machine; using the thermodynamic model to manage the thermal efficiency of the SCO2 cycle machine.

Description

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

technical field

The embodiment of the present application relates to the field of information processing, especially a method for managing the thermal efficiency of a supercritical carbon dioxide cycle unit.

Background technique

Since the beginning of the 21st century, the world's energy structure has continued to develop and transform towards a clean, low-carbon, high-efficiency, and diversified direction, but coal still plays an important role 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 coal demand has grown for two consecutive years. Most coal is used to generate electricity to meet growing electricity demand. The traditional coal-fired power generation system uses the Rankine cycle with steam as the working fluid, but due to the cycle characteristics and material limitations, it is difficult to further improve the efficiency of the power plant. The supercritical carbon dioxide power cycle is efficient and compact, and is expected to replace the traditional steam power cycle.

The main advantage of supercritical carbon dioxide Brayton cycle coal-fired power generation technology is that on the one hand, it adopts a high-efficiency Brayton cycle, and the power generation efficiency is higher than that of conventional thermal power units with the same parameters; on the other hand, the volume of turbines, compressors and other components is greatly reduced. , no steam extraction design, reduced pipeline complexity, is a potential solution to further improve the efficiency of coal-fired power generation.

Due to the physical properties of supercritical carbon dioxide closed-type Brayton cycle working fluid, in order to improve the efficiency of the whole cycle, intermediate regeneration is often used in the cycle to make full use of the high-temperature exhaust gas of the turbine to preheat the working medium at the outlet of the compressor ( heat recovery process), thereby reducing the loss of the cold end, and the cycle can also use multi-stage compression intercooling technology to further improve efficiency. Due to the many factors affecting the efficiency and the high coupling between the factors, 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.

Contents of the invention

In order to solve any of the above technical problems, an embodiment of the present application provides a method for managing the thermal efficiency of a supercritical carbon dioxide cycle unit.

In order to achieve the purpose of the embodiment of this application, the embodiment of this application provides a method for managing the thermal efficiency of a supercritical carbon dioxide cycle unit, including:

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;

selecting m variables from said x variables using principal component analysis, wherein m is an integer less than n;

Utilize described m variables to establish the thermodynamic model of SCO cycle machine thermal efficiency;

The thermal efficiency of the SCO2 cycler is managed using the thermodynamic model.

A storage medium, in which a computer program is stored, wherein the computer program is configured to execute the method described above when running.

An electronic device includes 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 perform the method described above.

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

By determining the x variables that affect the thermal efficiency of the SCO2 cycle machine, using the principal component analysis method to select m variables from the x variables, using the m variables to establish a thermodynamic model of the thermal efficiency of the SCO2 cycle machine, using the thermodynamic model to SCO2 The thermal efficiency of the cycle machine is managed, and the difficulty of modeling and the amount of calculation for modeling are greatly reduced by simplifying the number of variables.

Other features and advantages of the embodiments of the present application will be set forth in the following description, and partly become apparent from the description, or can be understood by implementing the embodiments of the present application. The objectives and other advantages of the embodiments of the present application can be realized and obtained through the structures particularly pointed out in the specification, claims and drawings.

Description of drawings

The accompanying 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, together with the embodiments of the embodiments of the present application, they are used to explain the technical solutions of the embodiments of the present application, and do not constitute a technical solution to the embodiments of the present application limits.

Fig. 1 is the flow chart of the method for managing the thermal efficiency of SCO cycle unit provided by the embodiment of the present application;

Fig. 2 is the flow chart of the SCO cycle machine thermal efficiency optimization method provided by the embodiment of the application;

Fig. 3 is a flow chart of the processing method of the principal component analysis method provided by the embodiment of the present application.

Fig. 4 is a schematic diagram of building a thermodynamic model provided in the embodiment of the present application.

FIG. 5 is a flow chart of a genetic algorithm processing method provided by an embodiment of the present application.

FIG. 6 is a flow chart of the optimized genetic algorithm processing method provided by the embodiment of the present application.

Fig. 7 is a schematic diagram of the influence of the inlet temperature and pressure of the high-pressure turbine on the thermal efficiency of the cycle provided by the embodiment of the present application.

Detailed ways

In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the embodiments of the embodiments of the present application will be described in detail below with reference to the accompanying drawings. It should be noted that, in the case of no conflict, the embodiments in the embodiments of the present application and the features in the embodiments can be combined arbitrarily with each other.

Fig. 1 is a flowchart of a method for managing the thermal efficiency of an SCO2 cycle unit provided by an embodiment of the present application. As shown in Figure 1, including:

Step 101, determining x variables that affect the thermal efficiency of the SCO2 cycler, where x is an integer greater than or equal to 2;

Step 102, using principal component analysis to select m variables from the x variables, wherein m is an integer smaller than n;

Step 103, using the m variables to establish a thermodynamic model of the thermal efficiency of the SCO2 cycler;

Step 104, using the thermodynamic model to manage the thermal efficiency of the SCO2 cycle machine.

In the method provided in the embodiment of the present application, by determining x variables that affect the thermal efficiency of the SCO cycle machine, using the principal component analysis method to select m variables from the x variables, and using the m variables to establish a thermodynamic model of the thermal efficiency of the SCO cycle machine , using the thermodynamic model to manage the thermal efficiency of the SCO2 cycle machine, and greatly reducing the difficulty of modeling and the calculation amount of modeling by simplifying the number of variables.

The method provided by the embodiment of the present application is described below:

Fig. 2 is a flow chart of the method for optimizing the thermal efficiency of the SCO2 cycle machine provided by the embodiment of the present application. As shown in Figure 2, the operation mechanism of the supercritical carbon dioxide cycle unit is first analyzed to find the factors affecting the thermal efficiency of the SCO2 cycle, and the influencing factors are screened for subsequent research. Through reasonable and effective mechanism analysis, a thermodynamic model was established. Afterwards, with the thermal efficiency of the SCO2 cycle as the optimization target, the multi-parameter optimization of the previously established thermodynamic model was carried out through the improved genetic algorithm. The optimized results were compared with the literature, and the accuracy and accuracy of the model of the present invention were judged to meet the requirements by comparison. Finally, the SCO2 cycle thermal efficiency is calculated by taking the influencing factors of the SCO2 cycle thermal efficiency as the model input.

The method shown in Figure 2 includes four stages, which are key variable screening stage, model building stage, model verification stage and model testing stage. Each stage is described below:

1. Key variable screening stage

Analyze the operating mechanism of the supercritical carbon dioxide cycle unit, and find out the influencing factors of the thermal efficiency of the SCO2 cycle. Determine the 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 out the variables with a high contribution rate. According to the calculation results, the contribution The m variables with higher rate are used to replace the initial x variables for subsequent analysis and research.

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

HP turbine inlet pressure, HP turbine inlet temperature, LP turbine inlet pressure, LP turbine inlet temperature, main compressor inlet temperature, main compressor pressure loss, re-compressor inlet temperature, re-compressor pressure loss and compressor split ratio.

In an exemplary embodiment, said using principal component analysis to select m variables from said x variables comprises:

The x variables are screened by principal component analysis 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 in the x-1 variables, m-1 variables are selected, and the variable with the highest contribution rate is combined with the m-1 variables as the final variable.

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

In an exemplary embodiment, the following calculation expressions are used to calculate the correlation coefficient, including:

Among them, X and Y represent two different variables, the covariance of X and Y, and the mean value of the sample.

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

The correlation strength of variables is judged by the following range of values:

The absolute value of the correlation coefficient

1. Very strong correlation: 0.8-1.0

2. Strong correlation: 0.6-0.8

3. Moderate correlation: 0.4-0.6

4. Weak correlation: 0.2-0.4

5. Very weak or no correlation: 0.0-0.2

Fig. 3 is a flow chart of the processing method of the principal component analysis method provided by the embodiment of the present application. As shown in Figure 3, the processing method includes the following steps:

1. Standardize and preprocess the original data, and then organize the data into a matrix X with n rows and m columns;

2. Zero meanization. That is, each feature minus the average value of the respective row;

3. Calculate the covariance matrix;

4. Calculate the eigenvalues and eigenvectors of the covariance matrix;

5. Arrange the eigenvalues from small to large;

6. Transform the data into a new space constructed by eigenvectors.

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

Contribution rate:

newrate＝0.7578 0.1571 0.0544 0.0250 0.0029 0.0017 0.0011

Number of Principal Components: 2

Principal component loadings:

It can be seen from the above results that a total of 2 pivots are needed to achieve a cumulative contribution rate of 80%, and the contribution rate of the first pivot has reached 75%, indicating that the first pivot has a great influence, and in the first Among the principal elements, variable 1 (high pressure turbine inlet pressure), variable 4 (high pressure turbine inlet temperature), variable 5 (pressure loss), and variable 6 (main compressor inlet temperature) have relatively large loads and the highest score, passing the above Based on the calculated and analyzed results, these four variables are selected as the key variables.

Through the above processing, the purpose of fully mining the hidden information in multiple variables can be realized, thereby improving the efficiency of the SCO2 cycle, and it also has the advantages of fast operation speed and strong generalization ability.

2. Model building stage

Fig. 4 is a schematic diagram of building a thermodynamic model provided in the embodiment of the present application. As shown in Figure 4, the internal mechanism of the carbon dioxide cycle is analyzed, using the lumped parameter method, ignoring the distribution of system parameters along the space, and only considering the time derivative term; assuming that the flue gas and air are ideal gases, which satisfy the law of ideal gas state; The system satisfies basic physical and thermodynamic laws, such as mass conservation, energy conservation, momentum conservation, heat transfer equation, thermodynamic state parameter equation, etc.

According to its thermodynamic laws, a thermodynamic model of supercritical carbon dioxide cycle is established. The thermodynamic cycle system includes two types of equipment: one is impeller machinery, such as compressor, turbine; the other is heat exchange equipment, such as regenerator, cooling device etc. Thermodynamic cycle analysis generally establishes thermodynamic models for different equipment, and then forms a closed loop through the connection relationship between them, and finally solves the state parameters of each point.

After the key variables are determined by the principal component analysis method, the internal mechanism of the carbon dioxide cycle is analyzed, and the lumped parameter method is used to ignore the distribution of the system parameters along the space, and only consider the time derivative term; assuming that the flue gas and air are ideal gases, satisfying the ideal gas Laws of state: Each system satisfies basic laws of physics and thermodynamics, such as mass conservation, energy conservation, momentum conservation, heat transfer equations, thermodynamic state parameter equations, etc.

According to its thermodynamic laws, a thermodynamic model of supercritical carbon dioxide cycle is established; the thermodynamic cycle system includes two types of equipment: one is impeller machinery, such as compressor, turbine; the other is heat exchange equipment, such as regenerator, cooling device etc.

Thermodynamic cycle analysis generally establishes thermodynamic models for different equipment, and then forms a closed loop through the connection relationship between them, and finally solves the state parameters of each point.

For rotating equipment such as compressors and turbines, the isentropic compression and isentropic expansion models considering the isentropic efficiency of the equipment are adopted.

For compressors:

P _co =P _ci ε _c

s _co =s(P _ci ,T _ci )

h _co ＝h(P _co ,s _co )

Δh _c =(h _co -h _ci )/α _c

T _co ＝T[P _co ,(h _ci +Δh _c )]

For turbines:

P _to = P _ti /ε _t

s _to ＝s(P _ti ,T _ti )

h _to ＝h(P _to ,s _to )

Δh _t = (h _to -h _ti )/α _t

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

For heat exchange equipment, printed circuit board heat exchangers that can withstand high temperature and high pressure are generally used, which can achieve lower end temperature difference and higher efficiency under the condition of controlling the volume of heat exchange equipment.

According to the conservation of energy, it can be known that the heat exchange equipment:

m ₁ (h _1i -h ₁₀ )=m ₂ (h _2o -h _2i )

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

Turbine output power and compressor power consumption are respectively:

W _t = m _t Δh _t

W _c =m _c Δh _c

In the formula, W is the power, the unit is MW; m is the flow rate, the unit is kg/s; Δh is the enthalpy rise, the unit is kJ/kg.

Then the thermal efficiency of the system is:

In the formula, η is the thermal efficiency of the system; Q is the heat source power, the unit is MW.

3. Model verification stage

In order to further improve the established thermodynamic model, the relevant main parameter values of the supercritical carbon dioxide cycle were determined by consulting and comparing relevant domestic and foreign literature. As shown in the table below:

Device Status Parameters unit value main compressor efficiency % 60 turbine efficiency % 81 recompressor efficiency % 50 combustion chamber efficiency % 80

The improved genetic algorithm is used to optimize the model with cycle thermal efficiency as the optimization goal.

In an exemplary embodiment, after using the m variables to establish a thermodynamic model of the thermal efficiency of the SCO cycle machine, the method further includes:

Determine the configuration values of the m variables from the pre-recorded thermodynamic model of the thermal efficiency of the SCO2 cycle machine established by using a variable, where a is an integer greater than m;

In the process of running the thermodynamic model with the configuration parameters of m variables, with the goal of optimizing thermal efficiency, a genetic algorithm is used to adjust the values of the variables in the thermodynamic model.

Since the number of variables used in the determined values of the m variables is different from the number of variables used in the present application, by adjusting the values of the obtained m variables, the adaptation to the model used in the application can be realized and the improvement can be improved. Accuracy of model processing.

In an exemplary embodiment, a genetic algorithm is used to adjust the values of the variables of the thermodynamic model, including:

Identify variables that need to be optimized;

Use the variables that need to be optimized to establish the first generation group;

Calculate the fitness of the first generation population;

Genetic algorithm is used to generate the next generation population;

Calculating whether the fitness of the next generation population meets a preset fitness condition, wherein the fitness condition is set according to the fitness of the first generation;

If the fitness of the next-generation population satisfies the fitness condition, the encoded value of the optimized variable; otherwise, continue to generate the next-generation population until the fitness of the obtained next-generation population satisfies the fitness condition or the number of iterations until the preset threshold is reached.

FIG. 5 is a flow chart of a genetic algorithm processing method provided by an embodiment of the present application. As shown in Figure 5, the processing method includes the following steps:

A) Optimization variable confirmation:

X＝[X ₁ X ₂ ... X _n ]

Where n is the number of variables to be optimized;

B) Encode the above required variables:

B=from10to2(X)

The purpose of the above is to convert decimal variables into binary codes;

C) Through conversion, generate the first generation population:

B ₀ =random(n,range)

Where range is the variable range, the purpose of the above is to randomly determine the first generation group according to the variable number n and the variable range range;

D) Calculate population fitness:

q ₀ =Q(B ₀ )

In the formula: q ₀ is the fitness of the first generation group; Q is the fitness function;

E) Three operations to generate the next generation population:

1) Select the operation:

Select the first-generation individuals with the best fitness:

[b ₀ i]=max(q ₀ )

In the formula, i depends on the variable n to be optimized, and the range is [1,2,…,n];

Keep the first-generation individuals with the best fitness to the next generation:

B ₁ (i) = B ₀ (i)

2) Cross operation:

Since the individual is coded with a real number, the crossover operation method adopts the real number crossover method, the kth chromosome a _k and the lth chromosome a _j , the crossover operation method at the j position is as follows:

In the formula, b is a random number between [0, 1].

3) Mutation operation:

Select the j-th gene a _ij of the i-th individual for mutation, and the mutation operation method is as follows:

In the formula, a _max is the upper bound of gene a _ij ; a _min is the lower bound of gene a _ij ; f(g)=r ₂ (1-g/G _max ) ² ; r ₂ is a random number; g is the current iteration times; G _max is the maximum evolution times; r is a random number between [0,1].

F) Judging whether the fitness meets the requirements:

[q _best i]=max(q ₀ )

G) The fitness does not meet the requirements, the next generation group is used as the first generation group, continue to calculate the fitness and generate the next generation B ₀ = B ₁ and use the next generation of individuals as the first generation;

H) The fitness meets the requirements, and the coded value of the optimized variable is obtained:

[q _best i]=max(q ₀ )

b _best = B ₀ (i)

b _best is what you want;

1) The obtained value is reverse-coded to obtain the final value:

X _best ＝from2to10(b _best )

In the formula: X _best is the optimized variable value;

In an exemplary embodiment, the generation of the next-generation population using a genetic algorithm includes:

Calculate the fitness value of each individual in the next generation population;

According to the fitness value of each individual, determine the optimal solution and the worst solution in the next generation crowd;

If the fitness value of the optimal solution of the previous generation is greater than the fitness value of the current optimal solution, replace the current optimal solution with the optimal solution of the previous generation;

If the optimal solution function value of the previous generation is smaller than the fitness value of the current optimal solution, replace the current worst solution with the optimal solution of the previous generation.

Although the above-mentioned genetic algorithm has a good global search ability and can quickly search for all solutions in the solution space, the local search ability of the traditional genetic algorithm is poor, which leads to a time-consuming simple genetic algorithm and low search efficiency in the later stage of evolution. . Therefore, in order to solve this problem, the present invention improves on the traditional genetic algorithm.

FIG. 6 is a flow chart of the optimized genetic algorithm processing method provided by the embodiment of the present application. As shown in Figure 6, the optimized genetic algorithm is improved in Step E of the genetic algorithm, including:

Using the optimal preservation strategy, first calculate the fitness function value of each individual, and then sort to find the optimal solution and the worst solution; then if the function value of the optimal solution of the previous generation is greater than the function value of the current optimal solution, Then replace the current optimal solution with the optimal solution of the previous generation; if the optimal solution function value of the previous generation is small, replace the current worst solution with the optimal solution of the previous generation.

In an exemplary embodiment, after the values of the variables of the thermodynamic model are adjusted using a genetic algorithm, the method further includes:

Obtain the thermal cycle efficiency of the thermal model after adjusting the value of the variable;

The thermal cycle efficiency is compared with the reference thermal cycle efficiency of the thermodynamic model of the SCO cycle machine thermal efficiency established by a variable, and the comparison result is obtained;

If the comparison result shows that the error between the thermal cycle efficiency and the reference thermal cycle efficiency satisfies a preset error condition, the verification of the thermal model after determining the value of the adjustment variable is passed.

The optimized results are compared with the collected literature content, and the precision and accuracy of the model of the present invention are judged to meet the requirements through comparison.

The optimized results are compared with the collected literature content, as shown in the table below, the error between the optimized thermal efficiency calculated by the model and the literature results is only 0.026%, and the precision and accuracy meet the requirements.

parameter Literature results Calculation results error Cycle efficiency/% 56.079 56.053 0.026

4. Thermal efficiency detection stage

Control the inlet temperature of the high-pressure turbine at a certain temperature and start to rise, and control the inlet temperature of the main compressor at a certain temperature. The model input selects the two variables of the inlet temperature and pressure of the high-pressure turbine. Other parameters are cycled The thermal efficiency is the optimization goal, and the single-objective optimization is carried out through the genetic algorithm, so as to judge the influence of variables on the optimal thermal efficiency of the SCO2 cycle.

The inlet temperature of the high-pressure turbine is controlled to rise from 500°C, the inlet temperature of the main compressor is controlled at 32°C, and the model input selects the two variables of the inlet temperature and pressure of the high-pressure turbine, and other parameters are based on The thermal efficiency of the cycle is the optimization target, and the genetic algorithm is used for single-objective optimization to determine the impact of variables on the optimal thermal efficiency of the SCO2 cycle.

Fig. 7 is a schematic diagram of the influence of the inlet temperature and pressure of the high-pressure turbine on the thermal efficiency of the cycle provided by the embodiment of the present application. It can be seen from Figure 7 that the optimal thermal efficiency of the cycle increases in a parabolic shape with the increase of the abscissa, and then decreases, that is, the optimal thermal efficiency of the cycle has a quadratic function relationship with the inlet pressure of the high-pressure turbine. This clearly shows that when the inlet temperature of the high-pressure turbine starts from 500°C to 700°C, the thermal efficiency of the cycle is not a simple linear function relationship with the inlet pressure of the high-pressure turbine. Instead, as the inlet pressure of the high-pressure turbine gradually increases between 20 and 37 MPa, appropriately increasing the inlet pressure of the high-pressure turbine in the cycle will help improve the thermal efficiency of the cycle, but it will be counterproductive if the temperature is too high. The optimum temperature is roughly 31 ℃ or so. In addition, it can also be seen from Figure 7 that the change of 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 the improvement of the thermal efficiency of the cycle. The above simulation results show that the present invention has the ability to optimize the cycle thermal efficiency of the supercritical carbon dioxide cycle unit.

The method provided in the embodiment of the present application has the following advantages, including:

Using the principal component analysis method to determine the key variables can greatly reduce the difficulty of modeling and reduce the amount of calculation of modeling;

The establishment process of the model is relatively simple and requires less information, so the present invention is more conducive to implementation and has stronger practicability than other methods;

The invention adopts the improved genetic algorithm, which can overcome the defects of poor local search ability and time-consuming calculation of the traditional genetic algorithm;

The model established based on the thermodynamic mechanism of the 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.

An embodiment of the present application provides a storage medium, and a computer program is stored in the storage medium, wherein the computer program is configured to execute any one of the methods described above when running.

An embodiment of the present application provides an electronic device, including a memory and a processor, where a computer program is stored in the memory, and the processor is configured to run the computer program to perform any of the methods described above.

Those of ordinary skill in the art can understand that all or some of the steps in the methods disclosed above, the functional modules/units in the system, and the device can be implemented as software, firmware, hardware, and an appropriate combination thereof. In a hardware implementation, the division between functional modules/units mentioned in the above description 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 composed of several physical components. Components cooperate to execute. Some or all of the components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). As known to those of ordinary skill in the art, the term computer storage media includes both volatile and nonvolatile media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. permanent, removable and non-removable media. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cartridges, tape, magnetic disk storage or other magnetic storage devices, or can Any other medium used to store desired information and which can be accessed by a computer. In addition, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media .

Claims (10)

1. A method of managing thermal efficiency of a supercritical carbon dioxide cycle unit, comprising:

determining x variables influencing the thermal efficiency of the supercritical carbon dioxide SCO2 circulator, wherein x is an integer greater than or equal to 2;

selecting m variables from the x variables using principal component analysis, wherein m is an integer less than n;

establishing a thermodynamic model of the heat efficiency of the SCO2 cycle machine by utilizing the m variables;

and managing the thermal efficiency of the SCO2 circulating machine by utilizing the thermodynamic model.

2. The method of claim 1, wherein the x variables comprise at least one of:

high pressure turbine inlet pressure, high pressure turbine inlet temperature, low pressure turbine inlet pressure, low pressure turbine inlet temperature, inlet temperature of the main compressor, pressure loss of the main compressor, inlet temperature of the recompressor, pressure loss of the recompressor, and compressor split ratio.

3. The method of claim 1, wherein said selecting m variables from said x variables using principal component analysis comprises:

screening the x variables by adopting a principal component analysis method to obtain a variable with the highest contribution rate;

calculating correlation coefficients between the variable with the highest contribution rate and the rest x-1 variables;

and selecting m-1 variables according to the correlation coefficient of each variable in the x-1 variables, and combining the variable with the highest contribution rate and the m-1 variables to serve as a final variable.

4. The method of claim 3, wherein calculating the correlation coefficient using the following computational expression comprises:

wherein X and Y represent two different variables, cov (X, Y) is covariance of X and Y, σ _X And σ _Y The average of the samples is represented.

5. The method of claim 1, wherein after said using said m variables to create a thermodynamic model of thermal efficiency of the SCO2 cycle machine, said method further comprises:

determining the configuration values of m variables from a pre-recorded thermodynamic model of the SCO2 cycle machine thermal efficiency established by adopting a variables, wherein a is an integer greater than m;

in the process of operating the thermodynamic model by adopting configuration parameters of m variables, the values of the variables in the thermodynamic model are adjusted by adopting a genetic algorithm with the aim of optimizing the thermal efficiency.

6. The method of claim 1, wherein adjusting the values of the variables of the thermodynamic model using a genetic algorithm comprises:

determining variables needing to be optimized;

establishing a primary generation group by using variables needing to be optimized;

calculating the fitness of the primary population;

generating a next generation population by adopting a genetic algorithm;

calculating whether the fitness of the next generation population meets a preset fitness condition, wherein the fitness condition is set according to the initial fitness;

if the fitness of the next generation population meets the fitness condition, the encoded value of the optimized variable; otherwise, continuing to generate the next generation group until the fitness of the obtained next generation group meets the fitness condition or the iteration number reaches a preset number threshold.

7. The method of claim 6, wherein generating the next generation population using a genetic algorithm comprises:

calculating a fitness value of each individual in the next generation population;

determining an optimal solution and a worst solution in the next generation of crowd according to the fitness value of each individual;

if the fitness value of the previous generation optimal solution is larger than that of the current optimal solution, replacing the current optimal solution with the previous generation optimal solution;

and if the function value of the previous generation of the optimal solution is smaller than the fitness value of the current optimal solution, replacing the current worst solution with the previous generation of the optimal solution.

8. The method according to claim 6 or 7, wherein after the adjusting the values of the variables of the thermodynamic model using a genetic algorithm, the method further comprises:

acquiring the thermal cycle efficiency of the thermal model after the value of the adjusting variable is obtained;

comparing the thermal cycle efficiency with a reference thermal cycle efficiency of a thermodynamic model of the SCO2 cycle machine thermal efficiency established by adopting a variables to obtain a comparison result;

and if the comparison result shows that the error between the thermal cycle efficiency and the reference thermal cycle efficiency meets a preset error condition, the verification of the thermal model after the value of the adjusting variable is determined to be passed.

9. A storage medium, in which a computer program is stored, wherein the computer program is arranged to perform the method of any of claims 1 to 8 when executed.

10. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and wherein the processor is arranged to execute the computer program to perform the method of any of claims 1 to 8.

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