CN115705445A - Method for managing thermal efficiency of supercritical carbon dioxide circulating unit - Google Patents
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Abstract
The embodiment of the application discloses a method for managing the thermal efficiency of a supercritical carbon dioxide circulating unit. The method comprises the following steps: determining x variables influencing the thermal efficiency of the supercritical carbon dioxide SCO2 circulating machine, 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 cycle machine by utilizing the thermodynamic model.
Description
Technical Field
The embodiment of the application relates to the field of information processing, in particular to a method for managing the thermal efficiency of a supercritical carbon dioxide circulating unit.
Background
Since the 21 st century, the world energy structure is continuously developed and transformed to a clean, low-carbon, high-efficiency and multi-element direction, but coal still occupies an important position in the world energy supply. According to statistics, coal consumption accounts for 27.2% of world primary energy supply in 2018, and the coal demand continuously increases for 2 years. Most coals are used for power generation to meet increasing power demands. The traditional coal-fired power generation system adopts Rankine cycle with steam as a working medium, but the efficiency of a power plant is difficult to further improve due to the limitation of cycle characteristics and materials. The supercritical carbon dioxide power cycle is efficient and compact, and is expected to replace the traditional steam power cycle.
The supercritical carbon dioxide Brayton cycle coal-fired power generation technology has the main advantages that on one hand, the high-efficiency Brayton cycle is adopted, and the power generation efficiency is higher than that of a conventional thermal power unit with the same level of parameters; on the other hand, the size of parts such as a turbine, a compressor and the like is greatly reduced, no steam extraction design is adopted, the complexity of pipelines is reduced, and the potential scheme for further improving the coal-fired power generation efficiency is provided.
Due to the physical property characteristics of the supercritical carbon dioxide closed Brayton cycle working medium, in order to improve the whole cycle efficiency, an intermediate heat regeneration mode is usually adopted in the cycle, and the high-temperature exhaust of a turbine is fully utilized to preheat the working medium at the outlet of the compressor (heat regeneration process), so that the cold end loss is reduced, and the cycle can further improve the efficiency by adopting a multi-stage compression intermediate cooling technology. The efficiency is affected by a plurality of factors, and the factors are highly coupled, so that the improvement of the parameter is difficult. 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.
Disclosure of Invention
In order to solve any one of the above technical problems, embodiments of the present application provide a method for managing thermal efficiency of a supercritical carbon dioxide cycle unit.
To achieve the purpose of the embodiments of the present application, the embodiments of the present application provide a method for managing thermal efficiency of a supercritical carbon dioxide cycle unit, including:
determining x variables influencing the thermal efficiency of the supercritical carbon dioxide SCO2 circulating machine, 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.
A storage medium having a computer program stored therein, wherein the computer program is arranged to perform the method as described above when executed.
An electronic apparatus comprising a memory having a computer program stored therein and a processor arranged to execute the computer program to perform the method as described above.
One of the above technical solutions has the following advantages or beneficial effects:
the method comprises the steps of determining x variables influencing the thermal efficiency of the SCO2 cycle machine, selecting m variables from the x variables by using a principal component analysis method, establishing a thermodynamic model of the thermal efficiency of the SCO2 cycle machine by using the m variables, managing the thermal efficiency of the SCO2 cycle machine by using the thermodynamic model, and greatly reducing the modeling difficulty and the modeling calculated amount by simplifying the number of the variables.
Additional features and advantages of the embodiments of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the application. The objectives and other advantages of the embodiments of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings are included to provide a further understanding of the embodiments of the present application and are incorporated in and constitute a part of this specification, illustrate embodiments of the present application and together with the examples of the embodiments of the present application do not constitute a limitation of the embodiments of the present application.
FIG. 1 is a flow chart of a method for managing thermal efficiency of an SCO2 cycle unit according to an embodiment of the present application;
FIG. 2 is a flow chart of a thermal efficiency optimization method for an SCO2 cycle machine according to an embodiment of the present application;
fig. 3 is a flowchart of a processing method of a principal component analysis method according to an 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 flowchart of a processing method of a genetic algorithm provided in an embodiment of the present application.
Fig. 6 is a flowchart of a processing method of an optimized genetic algorithm provided in an embodiment of the present application.
FIG. 7 is a schematic illustration of the effect of high pressure turbine inlet temperature and pressure on cycle thermal efficiency provided by an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application more apparent, 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 embodiments and features of the embodiments of the present application may be arbitrarily combined with each other without conflict.
FIG. 1 is a flow chart of a method for managing thermal efficiency of an SCO2 cycle unit according to an embodiment of the present application. As shown in fig. 1, includes:
102, selecting m variables from the x variables by using a principal component analysis method, wherein m is an integer less than n;
103, establishing a thermodynamic model of the heat efficiency of the SCO2 cycle machine by using the m variables;
and step 104, managing the thermal efficiency of the SCO2 cycle machine by utilizing the thermodynamic model.
According to the method, x variables influencing the thermal efficiency of the SCO2 cycle machine are determined, m variables are selected from the x variables by using a principal component analysis method, a thermodynamic model of the thermal efficiency of the SCO2 cycle machine is established by using the m variables, the thermodynamic model is used for managing the thermal efficiency of the SCO2 cycle machine, and the modeling difficulty and the modeling calculated amount are greatly reduced by simplifying the number of the variables.
The following describes a method provided in an embodiment of the present application:
FIG. 2 is a flowchart of a thermal efficiency optimization method for an SCO2 cycle machine according to an embodiment of the present disclosure. As shown in fig. 2, the operation mechanism of the supercritical carbon dioxide cycle unit is firstly analyzed, the influence factors of the SCO2 cycle thermal efficiency are searched, and the influence factors are screened for subsequent research. A thermodynamic model is established through reasonable and effective mechanism analysis. And then, carrying out multi-parameter optimization on the previously established thermodynamic model by taking the SCO2 cycle thermal efficiency as an optimization target through an improved genetic algorithm, comparing the optimized result with a document, and judging that the accuracy and the precision of the model are in accordance with requirements through comparison. And finally, calculating the SCO2 cycle thermal efficiency by taking the influence factors of the SCO2 cycle thermal efficiency as model input.
The method shown in fig. 2 includes 4 stages, which are a key variable screening stage, a model building stage, a model verifying stage and a model testing stage. Each stage is described below:
1. critical variable screening stage
Analyzing the operation mechanism of the supercritical carbon dioxide circulating unit and searching the influence factors of the SCO2 circulating heat efficiency. Determining x variables which have large influence on the cycle thermal efficiency, analyzing and screening the x variables by using a principal component analysis method, finding out the variables with high contribution rate by calculating the contribution rate and the correlation coefficient, and replacing the m variables with high contribution rate with the initial x variables according to the calculation result for subsequent analysis and research.
In one exemplary embodiment, the x variables include 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.
In an exemplary embodiment, the selecting m variables from the x variables using pivot analysis includes:
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.
Analyzing the operation mechanism of the supercritical carbon dioxide circulating unit, and searching the influence factors of the SCO2 circulating heat efficiency. The variables that were determined to have a greater impact on the cycle thermal efficiency were: 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 and pressure loss of a main compressor, the inlet temperature and pressure loss of a recompressor and the split ratio of the compressor are 9 variables; and then analyzing and screening the 9 variables by using a principal component analysis method, finding out the variables with higher contribution rates by calculating the contribution rates and the correlation coefficients, and using 4 key variables for subsequent analysis and research according to the calculation results.
In one exemplary embodiment, the correlation coefficient is calculated using the following computational expression, including:
where X and Y represent two different variables, the covariance of X and Y, and the mean of the samples.
The calculated correlation coefficient range is (-1, 1), and when the absolute value of the correlation coefficient is larger, the stronger the correlation is shown: the stronger the correlation, the closer the correlation coefficient is to 1 or-1; when the correlation coefficient is closer to 0, the correlation is weaker.
The correlation strength of the variables is judged by the following value ranges:
absolute value of correlation coefficient
1. The strong correlation is as follows: 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 weakly correlated or uncorrelated: 0.0-0.2
Fig. 3 is a flowchart of a processing method of a principal component analysis method according to an embodiment of the present application. As shown in fig. 3, the processing method includes the following steps:
1. carrying out standardization pretreatment on original data, and then forming a matrix X with n rows and m columns by the data according to columns;
2. and (4) zero equalization. That is, the average value of each bit feature minus the respective line;
3. calculating a covariance matrix;
4. calculating an eigenvalue and an eigenvector of the covariance matrix;
5. arranging the characteristic values from small to large;
6. and converting the data into a new space constructed by the feature vectors.
After the analysis is completed, the contribution rate information obtained is as follows:
contribution rate:
newrate＝0.7578 0.1571 0.0544 0.0250 0.0029 0.0017 0.0011
the number of main components: 2
Loading main components:
as can be seen from the above results, a total of 2 principal elements are required to achieve an accumulated contribution rate of 80%, and the contribution rate of the first principal element reaches 75%, which indicates that the influence of the first principal element is large, and in the first principal element, the load ratios of the variable 1 (high-pressure turbine inlet pressure), the variable 4 (high-pressure turbine inlet temperature), the variable 5 (pressure loss), and the variable 6 (main compressor inlet temperature) are relatively large, and the score is the highest, and the 4 variables are selected as key variables through the above calculation and analysis.
Through the processing, the purpose of fully mining the implicit information in a plurality of variables can be achieved, so that the SCO2 circulation efficiency is improved, and the method has the advantages of high operation speed, strong generalization capability and the like.
2. Stage of model building
Fig. 4 is a schematic diagram of building a thermodynamic model provided in an embodiment of the present application. As shown in fig. 4, the internal mechanism of carbon dioxide cycle is analyzed, a lumped parameter method is adopted, the distribution of system parameters along the space is ignored, and only the time derivative term is considered; the flue gas and the air are assumed to be ideal gases, and the ideal gas state law is satisfied; each system satisfies basic laws of physics and thermodynamics, such as conservation of mass, conservation of energy, conservation of momentum, heat transfer equation, thermodynamic state parameter equation, and the like.
A thermodynamic model of supercritical carbon dioxide circulation is established according to the thermodynamic law, and a thermodynamic circulation system comprises two types of equipment: one is turbomachinery, such as compressors, turbines; another type is heat exchange equipment such as regenerators, coolers, etc. Thermodynamic cycle analysis generally includes establishing thermodynamic models for different devices, forming a closed cycle loop through mutual connection relation, and finally solving state parameters of each point.
After key variables are determined through a principal component analysis method, the internal mechanism of carbon dioxide circulation is analyzed, a lumped parameter method is adopted, the distribution condition of system parameters along the space is ignored, and only a time derivative term is considered; the flue gas and the air are assumed to be ideal gases, and the ideal gas state law is satisfied; each system satisfies basic laws of physics and thermodynamics, such as conservation of mass, conservation of energy, conservation of momentum, heat transfer equation, thermodynamic state parameter equation, and the like.
Establishing a thermodynamic model of supercritical carbon dioxide circulation according to the thermodynamic law; thermodynamic cycle systems comprise two broad classes of devices: one is turbomachinery, such as compressors, turbines; another type is heat exchange equipment such as regenerators, coolers, etc.
Thermodynamic cycle analysis generally includes establishing thermodynamic models for different devices, forming a closed cycle loop through mutual connection relations, and finally solving state parameters of each point.
For rotating equipment such as a gas compressor and a turbine, an isentropic compression model and an isentropic expansion model considering the isentropic efficiency of the equipment are adopted.
For the compressor:
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 )]
aiming at a turbine:
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
wherein P is pressure in MPa; s is entropy with the unit kJ/kg DEG C; h is enthalpy, and the unit is kJ/kg; t is temperature in units of; delta h is enthalpy rise and has a unit of kJ/kg; ε is the pressure ratio; α is the isentropic efficiency of the rotating device. The lower corner marks c and t represent the compressor and the turbine, respectively; i and o denote the inlet and outlet, respectively.
Aiming at heat exchange equipment, a printed circuit board type heat exchanger capable of resisting high temperature and high pressure is generally adopted, and lower end temperature difference and higher efficiency can be realized under the condition of controlling the volume of the heat exchange equipment.
According to the conservation of energy, the heat exchange equipment is known as follows:
m 1 (h 1i -h 10 )＝m 2 (h 2o -h 2i )
in the formula, m is flow, and the unit is Kg/s; h is enthalpy, in KJ/Kg. The lower corner marks i and o denote the inlet and outlet, respectively, and 1 and 2 denote the hot and cold sides of the heat exchanger, respectively.
The turbine output power and the compressor power consumption are respectively as follows:
W t ＝m t Δh t
W c ＝m c Δh c
wherein W is power, and the unit is MW; m is flow rate, and the unit is kg/s; Δ h is the enthalpy rise in kJ/kg.
The system thermal efficiency is:
in the formula, eta is the system thermal efficiency; q is heat source power in MW.
3. Model verification phase
In order to further improve the established thermodynamic model, relevant main parameter values of the supercritical carbon dioxide cycle are determined by referring and comparing relevant documents at home and abroad. As shown in the following table:
| device state parameter | Unit | Numerical value | |
| Efficiency of main compressor | ％ | 60 | |
| Efficiency of turbine | ％ | 81 | |
| | ％ | 50 | |
| Efficiency of combustion chamber | ％ | 80 |
And optimizing the model by adopting an improved genetic algorithm and taking the circulating heat efficiency as an optimization target.
In an exemplary embodiment, after said using said m variables to build 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 thermal efficiency.
Because the number of the variables used by the determined values of the m variables is different from the number of the variables used in the application, the obtained values of the m variables are adjusted to realize the adaptation of the model used in the application and improve the accuracy of model processing.
In an exemplary embodiment, adjusting the values of the variables of the thermodynamic model using a genetic algorithm includes:
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 population until the obtained fitness of the next generation population meets the fitness condition or the iteration times reach a preset time threshold.
Fig. 5 is a flowchart of a processing method of a genetic algorithm provided in an embodiment of the present application. As shown in fig. 5, the processing method includes the following steps:
a) And (3) confirming optimized variables:
X＝[X 1 X 2 … X n ]
wherein n is the number of variables to be optimized;
b) Encoding the above required variables:
B＝from10to2(X)
the above purpose is to convert decimal variables into binary codes;
c) Through conversion, a primary population is generated:
B 0 ＝random(n,range)
wherein the range is a variable range, and the purpose is to randomly determine the initial population according to the variable number n and the variable range;
d) Calculating population fitness:
q 0 ＝Q(B 0 )
in the formula: q. q.s 0 The fitness of the primary population is obtained; q is a fitness function;
e) Three operations operate to generate the next generation population:
1) Selecting and operating:
selecting the initial generation individuals with the best fitness:
[b 0 i]＝max(q 0 )
wherein i is dependent on the variable n to be optimized and ranges from [1,2, \8230;, n ];
and (3) reserving the initial generation individuals with the best fitness to the next generation:
B 1 (i)＝B 0 (i)
2) And (3) cross operation:
since individuals adopt real number coding, the cross operation method adopts a real number cross method, the kth chromosome a k And the l-th chromosome a j The method of interleaving at j bits is as follows:
wherein b is a random number between [0,1 ].
3) And (3) mutation operation:
selecting the jth gene a of the ith individual ij Carrying out mutation by the following operation method:
in the formula, a max Is gene a ij An upper bound of (c); a is min Is gene a ij The lower bound of (c); f (g) = r 2 (1-g/G max ) 2 ；r 2 Is a random number; g is the current iteration number; g max The maximum number of evolutions; r is [0,1]]A random number in between.
F) Judging whether the fitness meets the requirements:
[q best i]＝max(q 0 )
g) The fitness is not satisfactory, the next generation group is taken as the initial generation group, the fitness is continuously calculated, and a next generation B is generated 0 ＝B 1 Will be downTaking a generation individual as a primary generation;
h) The fitness meets the requirements, and the coded values of the optimized variables are obtained:
[q best i]＝max(q 0 )
b best ＝B 0 (i)
b best the result is obtained;
i) Performing inverse coding on the obtained value to obtain a final value:
X best ＝from2to10(b best )
in the formula: x best I.e. the optimized variable value;
in an exemplary embodiment, the 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.
Although the genetic algorithm has good global search capability and can quickly search out the whole solution in the solution space, the traditional genetic algorithm has poor local search capability, so that the simple genetic algorithm is time-consuming and has low search efficiency in the later evolution stage. Therefore, in order to solve this problem, the present invention improves on the conventional genetic algorithm.
Fig. 6 is a flowchart of a processing method of an optimized genetic algorithm according to an embodiment of the present application. As shown in fig. 6, the optimized genetic algorithm is improved in step E of the genetic algorithm, and includes:
adopting an optimal storage strategy, firstly calculating a fitness function value of each individual, then sequencing, and finding out an optimal solution and a worst solution; then, if the function 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 optimal solution of the previous generation is small, replacing the current worst solution with the optimal solution of the previous generation.
In an exemplary embodiment, after 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 thermal efficiency of the SCO2 cycle machine 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.
And comparing the optimized result with the collected document content, and judging that the accuracy and the precision of the model are satisfactory through comparison.
The optimized result is compared with the collected literature content, as shown in the following table, the error between the optimized thermal efficiency obtained by model calculation and the literature result is only 0.026% through comparison, and the precision and the accuracy meet the requirements.
| Parameter(s) | Results of the literature | Calculation results | Error of |
| Efficiency of the cycle/%) | 56.079 | 56.053 | 0.026 |
4. Thermal efficiency detection stage
The method comprises the steps of controlling the inlet temperature of a high-pressure turbine to start rising at a certain temperature, controlling the inlet temperature of a main compressor to be constant at a certain temperature, inputting and selecting two variables of the inlet temperature and the inlet pressure of the high-pressure turbine by a model, and performing single-target optimization on other parameters by taking the cycle thermal efficiency as an optimization target through a genetic algorithm so as to judge the influence of the variables on the optimal thermal efficiency of the SCO2 cycle.
The inlet temperature of the high-pressure turbine is controlled to rise from 500 ℃, the inlet temperature of the main compressor is controlled to be constant at 32 ℃, two variables of the inlet temperature and the inlet pressure of the high-pressure turbine are input and selected by the model, other parameters except the inlet temperature and the inlet pressure of the main compressor are optimized by taking the circulating thermal efficiency as an optimization target through a genetic algorithm, and therefore the influence of the variables on the optimal thermal efficiency of the SCO2 circulation is judged.
FIG. 7 is a schematic illustration of the effect of high pressure turbine inlet temperature and pressure on cycle thermal efficiency provided by an embodiment of the present application. As can be seen from fig. 7, the optimum thermal efficiency of the cycle increases and then decreases in a parabolic shape as the abscissa increases, i.e., the optimum thermal efficiency of the cycle is a quadratic function of the high pressure turbine inlet pressure. This clearly shows that the thermal efficiency of the cycle is not a simple linear function of the high pressure turbine inlet pressure as the high pressure turbine inlet temperature goes from 500 c to 700 c. Instead, as the inlet pressure of the high-pressure turbine is gradually increased between 20 and 37MPa, the proper increase of the inlet pressure of the high-pressure turbine of the cycle is beneficial to improving the thermal efficiency of the cycle, but the adverse effect is caused by the excessively high temperature, and the optimal temperature is about 31 ℃. In addition, as can be seen in FIG. 7, changes in the high pressure turbine inlet temperature have an effect on the optimum thermal efficiency of the cycle. This indicates that increasing the high pressure turbine inlet temperature also contributes to the increase in cycle thermal efficiency. The simulation results show that the method has the capability of optimizing the circulating heat efficiency of the supercritical carbon dioxide circulating unit.
The method provided by the embodiment of the application has the following advantages that:
the key variables are determined by adopting a principal component analysis method, so that the modeling difficulty can be greatly reduced, and the modeling calculation amount can be reduced;
the establishment process of the model is simple, and the required information amount is less, so that the method is more beneficial to implementation compared with other methods, and has stronger practicability;
the improved genetic algorithm is adopted, so that the defects of poor local searching capability and time-consuming calculation of the traditional genetic algorithm can be overcome;
the model established based on the thermodynamic mechanism of the actual supercritical carbon dioxide circulating unit can adapt to the calculation of the circulating heat efficiency under different working conditions, and has better practicability and generalization capability.
An embodiment of the present application provides a storage medium, in which a computer program is stored, wherein the computer program is configured to perform the method described in any one of the above when the computer program runs.
An embodiment of the application provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to perform the method described in any one of the above.
It will be understood by those of ordinary skill in the art that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations 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 performed by several physical components in cooperation. 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). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, 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 includes any information delivery media as known to those skilled in the art.
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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