A design method and components of an organic Rankine cycle system

Abstract

The present application discloses a design method and component of an organic Rankine cycle system. The method disclosed in the present application includes: determining the operating conditions of the organic Rankine cycle system; constructing a database based on the relationship between heat exchanger parameters and working fluid physical parameters; the database includes: a heat exchanger parameter set and a working fluid physical parameter set; using the database to train a neural network so that the neural network determines a function with heat exchanger parameters and working fluid physical parameters as independent variables; determining the optimal value of the function under preset independent variables, and determining the optimal heat exchanger and the optimal working fluid according to the optimal value. The present application uses a neural network to determine a function with heat exchanger parameters and working fluid physical parameters as independent variables; calculating the optimal value of the function under preset independent variables, and based on the optimal value, the optimal working fluid and the optimal heat exchanger under the current situation can be designed to maximize the system performance. A design component of an organic Rankine cycle system provided in the present application also has the above-mentioned technical effects.

Description

Technical Field

[0001] The present application relates to the technical field of organic Rankine cycle, and in particular to a design method and components of an organic Rankine cycle system. Background Art

[0002] The organic Rankine cycle system generally includes: working fluid pump, heat exchanger and expander. The loss accounts for the total system The heat exchanger accounts for 70%-90% of the loss, while the working fluid pump and expander have relatively little impact on the system. Therefore, the optimization of the heat exchanger can enable the system to achieve better performance.

[0003] At present, the design of heat exchangers is mostly determined based on empirical values, and the heat exchanger parameters and the working fluid physical parameters will affect each other. In the actual system design process, the working fluid design and the heat exchanger design are carried out independently, and the two do not interfere with each other, resulting in limited system improvement.

[0004] Therefore, how to design an organic Rankine cycle system with better performance is a problem that needs to be solved by those skilled in the art. Summary of the invention

[0005] In view of this, the purpose of this application is to provide a design method and components of an organic Rankine cycle system, so as to design an organic Rankine cycle system with better performance. The specific scheme is as follows:

[0006] In a first aspect, the present application provides a design method for an organic Rankine cycle system, comprising:

[0007] Determine the operating conditions of the organic Rankine cycle system;

[0008] Constructing a database based on the relationship between heat exchanger parameters and working fluid physical property parameters; the database includes: a heat exchanger parameter set and a working fluid physical property parameter set;

[0009] Using the database to train a neural network so that the neural network determines a function with the heat exchanger parameters and the working fluid physical property parameters as independent variables;

[0010] The optimal value of the function under the preset independent variable is determined, and the optimal heat exchanger and the optimal working fluid are determined according to the optimal value.

[0011] Preferably, the step of constructing a database based on the relationship between heat exchanger parameters and working fluid physical parameters includes:

[0012] According to KA = n (μl / v) a (μCp / λ) b (λ / d) construct the database;

[0013] Wherein, K is the heat transfer coefficient of the heat exchanger, A is the heat transfer area of ​​the heat exchanger, n, a, b are the heat transfer parameters during the flow of the working fluid, μ, v, Cp are the physical parameters of the working fluid, and l, λ, d are the parameters of the heat exchanger.

[0014] Preferably, the neural network is a BP neural network.

[0015] Preferably, determining the optimal value of the function under a preset independent variable, and determining the optimal heat exchanger and the optimal working fluid according to the optimal value, comprises:

[0016] Substituting the preset independent variables into the function to obtain a plurality of system performance values; the preset independent variables are the value range corresponding to the heat exchanger parameter and the value range corresponding to the working fluid physical property parameter;

[0017] Determining a maximum value among a plurality of system performance values ​​as the optimal value;

[0018] The optimal heat exchanger is designed according to the heat exchanger parameters corresponding to the optimal values, and the optimal working fluid is designed according to the working fluid physical property parameters corresponding to the optimal values.

[0019] In a second aspect, the present application provides a design device for an organic Rankine cycle system, comprising:

[0020] A determination module, used for determining the operating conditions of the organic Rankine cycle system;

[0021] A construction module is used to construct a database according to the relationship between the heat exchanger parameters and the working fluid physical property parameters; the database includes: a heat exchanger parameter set and a working fluid physical property parameter set;

[0022] A training module, used for training a neural network using the database, so that the neural network determines a function with the heat exchanger parameters and the working fluid physical property parameters as independent variables;

[0023] The design module is used to determine the optimal value of the function under the preset independent variable, and determine the optimal heat exchanger and the optimal working fluid according to the optimal value.

[0024] Preferably, the building blocks are specifically used for:

[0025] According to KA = n (μl / v) a (μCp / λ) b (λ / d) construct the database;

[0026] Wherein, K is the heat transfer coefficient of the heat exchanger, A is the heat transfer area of ​​the heat exchanger, n, a, b are the forced convection parameters in the tube, μ, v, Cp are the physical parameters of the working fluid, and l, λ, d are the parameters of the heat exchanger.

[0027] Preferably, the neural network is a BP neural network.

[0028] Preferably, the design module includes:

[0029] A calculation unit, used for substituting the preset independent variables into the function to obtain a plurality of system performance values; the preset independent variables are the value range corresponding to the heat exchanger parameter and the value range corresponding to the working fluid physical property parameter;

[0030] a determining unit, configured to determine a maximum value among a plurality of system performance values ​​as the optimal value;

[0031] The design unit is used to design the optimal heat exchanger according to the heat exchanger parameters corresponding to the optimal value, and to design the optimal working fluid according to the working fluid physical property parameters corresponding to the optimal value.

[0032] In a third aspect, the present application provides a design device for an organic Rankine cycle system, comprising:

[0033] Memory for storing computer programs;

[0034] A processor is used to execute the computer program to implement the aforementioned disclosed method for designing an organic Rankine cycle system.

[0035] In a fourth aspect, the present application provides a readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the aforementioned disclosed method for designing an organic Rankine cycle system.

[0036] It can be seen from the above scheme that the present application provides a design method for an organic Rankine cycle system, including: determining the operating conditions of the organic Rankine cycle system; constructing a database based on the relationship between heat exchanger parameters and working fluid physical property parameters; the database includes: a heat exchanger parameter set and a working fluid physical property parameter set; using the database to train a neural network so that the neural network determines a function with the heat exchanger parameters and the working fluid physical property parameters as independent variables; determining the optimal value of the function under preset independent variables, and determining the optimal heat exchanger and the optimal working fluid based on the optimal value.

[0037] It can be seen that the present application determines the working conditions of the organic Rankine cycle system, and after constructing a database based on the relationship between the heat exchanger parameters and the working fluid physical parameters, a neural network is used to determine a function with the heat exchanger parameters and the working fluid physical parameters as independent variables; after the function is determined, the optimal value of the function under the preset independent variables can be calculated, and this optimal value is the maximum heat exchange of the system under the preset independent variables. At this time, the heat exchanger parameters and working fluid physical parameters corresponding to the maximum heat exchange of the system are the optimal values ​​under the current situation, so the optimal working fluid and the optimal heat exchanger under the current situation can be designed based on this, so that the performance of the designed system is maximized as much as possible.

[0038] Correspondingly, the design components of an organic Rankine cycle system provided in the present application (ie, a device, an apparatus and a readable storage medium) also have the above-mentioned technical effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0040] Figure 1 A flow chart of a design method of an organic Rankine cycle system disclosed in this application;

[0041] Figure 2 This is an energy schematic diagram of an ORC system disclosed in this application;

[0042] Figure 3 A training diagram of a neural network disclosed in this application;

[0043] Figure 4 A schematic diagram of a design device of an organic Rankine cycle system disclosed in this application;

[0044] Figure 5 This is a schematic diagram of the design equipment of an organic Rankine cycle system disclosed in this application. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0046] At present, the design of heat exchangers is mostly determined based on empirical values, and the heat exchanger parameters and the working fluid physical parameters will affect each other. In the actual design process of the system, the design of the working fluid and the design of the heat exchanger are carried out independently, and the two do not interfere with each other, resulting in limited system improvement. To this end, the present application provides a design scheme for an organic Rankine cycle system, which can be designed to achieve an organic Rankine cycle system with better performance.

[0047] See also Figure 1 As shown, the embodiment of the present application discloses a design method of an organic Rankine cycle system, comprising:

[0048] S101. Determine the operating conditions of the organic Rankine cycle system.

[0049] Among them, the operating conditions generally include the temperature and flow of the heat source, the temperature and flow of the cold source, and other corresponding parameters.

[0050] Under given working conditions, if two working fluids are screened by temperature, there are many working fluids suitable for ORC (Organic Rankine Cycle) systems. There are nearly 115 pure working fluids with mature applications. Different working fluids have large differences in system performance under the same working conditions. After the working fluid is determined, the heat exchanger needs to be designed. The current heat exchanger design usually assumes the geometric parameters of the heat exchanger after selecting the heat exchanger material, but does not fully utilize the heat of the heat source, and there is a large pinch point temperature difference.

[0051] See also Figure 2 , Figure 2 The area covered by the vertical line indicates that the energy is not utilized. This part is determined by the working fluid and the heat exchange equipment. Minimizing the area covered by the vertical line as much as possible can improve the system performance. The energy source of the ORC system is the heat source, so the degree of utilization of the heat source will directly determine the efficiency and performance of the ORC system. The area of ​​the vertical line in the figure is the unutilized heat source part of the ORC system. Therefore, the smaller the area of ​​the vertical line part, the more conducive to obtaining better ORC system performance. As can be seen from the figure, the energy of the heat source part is fixed, so improving the pure working fluid line can improve the performance of the ORC system. However, the pure working fluid line is not determined only by the heat exchanger parameters, but by the working fluid physical parameters and the heat exchanger parameters. The non-azeotropic working fluid is a mixture of two working fluids in a certain proportion. As can be seen from the figure, just replacing the pure working fluid with a non-azeotropic working fluid can reduce the area of ​​the vertical line. Therefore, synchronous design of the working fluid and the heat exchanger can ensure better ORC system performance.

[0052] S102: construct a database based on the relationship between heat exchanger parameters and working fluid physical parameters.

[0053] The database includes: a heat exchanger parameter set and a working fluid physical property parameter set. In a specific implementation, the database is constructed based on the relationship between the heat exchanger parameters and the working fluid physical property parameters, including: according to KA = n (μl / v) a (μCp / λ) b (λ / d) to build a database; where K is the heat transfer coefficient of the heat exchanger, A is the heat transfer area of ​​the heat exchanger, n, a, b are the heat transfer parameters during the flow of the working fluid, μ, v, Cp are the physical parameters of the working fluid, and l, λ, d are the parameters of the heat exchanger. Specifically, when μ=1, d=1..., one KA can be calculated; when μ=2, d=2, another KA can be calculated, and so on, the corresponding database can be built.

[0054] It should be noted that the heat transfer parameters of the working fluid flowing in different places are different, that is, n, a, and b can take different values ​​according to the actual situation. For example, when the medium flows quickly in the tube (i.e. forced convection), n is 0.23, a is 0.3, and b is 0.4. If the medium flows smoothly, n, a, and b can take other values ​​according to the actual situation, and the details can be referred to the relevant existing technology.

[0055] Under given working conditions, the maximum available heat or cooling capacity can be calculated. The heat balance of the evaporator and condenser in the heat exchanger is the same. Taking the evaporator as an example, Q (evaporator) = KAΔt, where Δt is the temperature change of the working fluid in the heat exchanger, which is affected by the two parameters K and A. Q (evaporator) means: the heat transfer capacity of the heat exchanger, the larger it is, the better the system performance. Among them, K = Nuλ / d, λ / d is the parameter of the heat exchanger, d is the inner diameter of the heat exchanger; Nu = nRe a Pr b (For example, Nu = 0.023Re 0.8 Pr 0.4 ); Re = μl / v, μ and v are working fluid physical parameters, l is heat exchanger parameter; Pr = μCp / λ, μ and Cp are working fluid physical parameters, λ is heat exchanger parameter. Therefore, KA = (Nuλ / d) × A = (nRe a Pr b λ / d)A=n(μl / v) a (μCp / λ) b (λ / d). KA is a function of the working fluid physical parameters and heat exchanger parameters. It is not difficult to see from the functional relationship that the working fluid physical parameters are an important factor affecting the system performance, and the design of the heat exchanger needs to match the optimal heat exchanger parameters according to different working fluids, so it is best to design the two synchronously.

[0056] Generally, Q = cm (t inlet - t outlet). Under a given heat source state, the specific heat capacity c, mass flow rate m and inlet temperature t of the heat source entering the heat exchanger can be known, so Q = cm (t inlet - t outlet) = KAΔt.

[0057] S103, using the database to train the neural network so that the neural network determines a function with heat exchanger parameters and working fluid physical property parameters as independent variables.

[0058] The purpose of the neural network is to use a function to represent the relationship between the heat exchanger parameters and the working fluid physical parameters, and the heat exchanger parameters and the working fluid physical parameters are the independent variables of the function. The output of the neural network is KA=Y.

[0059] See also Figure 3 , W I,1 , W I,2 …W I,n , B I,1 , WI,2 …W I,i and b0 is the weight of the neural network. The neural network can be regarded as a black box model, and the input and output need to be determined by yourself. For example: x=1, y=2, x=2, y=4, x=3, y=9, if this is input into the neural network, a black box is obtained that can calculate when x=1, y=2, and so on. The neural network finds the function relationship based on the deviation between the output result and the expected result. Using the above example, x=1, y=2, x=2, y=4, x=3, y=9 are used as neural network training samples. If the training results are x=1, y=1, x=2, y=2, x=3, y=3, the training is considered to have failed. If the training results are x=1, y=2.01, x=2, y=4.02, x=3, y=9.03, it is considered to be successful.

[0060] In a specific implementation, the neural network is a BP neural network.

[0061] S104, determining the optimal value of the function under the preset independent variable, and determining the optimal heat exchanger and the optimal working fluid according to the optimal value.

[0062] In a specific embodiment, the optimal value of a function under preset independent variables is determined, and the optimal heat exchanger and the optimal working fluid are determined based on the optimal values, including: substituting the preset independent variables into the function to obtain multiple system performance values; the preset independent variables are the value ranges corresponding to the heat exchanger parameters and the value ranges corresponding to the working fluid physical property parameters; the maximum value among the multiple system performance values ​​is determined as the optimal value; the optimal heat exchanger is designed based on the heat exchanger parameters corresponding to the optimal values, and the optimal working fluid is designed based on the working fluid physical property parameters corresponding to the optimal values.

[0063] Assuming that the optimal density of the working fluid is 0.88kg / cm3, and there is no corresponding working fluid at this temperature, it is necessary to mix the working fluids, such as working fluid a is 1kg / cm3, and working fluid b is 0.6kg / cm3. By mixing working fluids a and b in a certain ratio, such as a and b in a ratio of 0.78:0.22, a working fluid of 0.88kg / cm3 can be obtained. Based on this, the working fluid can be reversely designed.

[0064] It can be seen that the embodiment of the present application determines the working conditions of the organic Rankine cycle system, and after constructing a database based on the relationship between the heat exchanger parameters and the working fluid physical parameters, a neural network is used to determine a function with the heat exchanger parameters and the working fluid physical parameters as independent variables; after the function is determined, the optimal value of the function under the preset independent variables can be calculated, and this optimal value is the maximum heat exchange of the system under the preset independent variables. At this time, the heat exchanger parameters and working fluid physical parameters corresponding to the maximum heat exchange of the system are the optimal values ​​under the current situation, so the optimal working fluid and the optimal heat exchanger under the current situation can be designed based on this, so that the performance of the designed system is maximized as much as possible.

[0065] A design device for an organic Rankine cycle system provided in an embodiment of the present application is introduced below. The design device for an organic Rankine cycle system described below and the design method for an organic Rankine cycle system described above can be referenced to each other.

[0066] See also Figure 4 As shown, the embodiment of the present application discloses a design device for an organic Rankine cycle system, comprising:

[0067] A determination module 401 is used to determine the operating conditions of the organic Rankine cycle system;

[0068] A construction module 402 is used to construct a database according to the relationship between the heat exchanger parameters and the working fluid physical property parameters; the database includes: a heat exchanger parameter set and a working fluid physical property parameter set;

[0069] A training module 403 is used to train the neural network using the database so that the neural network determines a function with heat exchanger parameters and working fluid physical property parameters as independent variables;

[0070] The design module 404 is used to determine the optimal value of the function under the preset independent variable, and determine the optimal heat exchanger and the optimal working fluid according to the optimal value.

[0071] In a specific embodiment, the building block is specifically used to:

[0072] According to KA = n (μl / v) a (μCp / λ) b (λ / d) build database;

[0073] Among them, K is the heat transfer coefficient of the heat exchanger, A is the heat transfer area of ​​the heat exchanger, n, a, b are the forced convection parameters in the tube, μ, v, Cp are the physical parameters of the working fluid, and l, λ, d are the parameters of the heat exchanger.

[0074] In a specific implementation, the neural network is a BP neural network.

[0075] In a specific embodiment, the design module includes:

[0076] A calculation unit, used for substituting preset independent variables into the function to obtain multiple system performance values; the preset independent variables are the value range corresponding to the heat exchanger parameters and the value range corresponding to the working fluid physical property parameters;

[0077] a determination unit, configured to determine a maximum value among a plurality of system performance values ​​as an optimal value;

[0078] The design unit is used to design an optimal heat exchanger according to the heat exchanger parameters corresponding to the optimal values, and to design an optimal working fluid according to the working fluid physical property parameters corresponding to the optimal values.

[0079] Among them, for more specific working processes of each module and unit in this embodiment, reference can be made to the corresponding contents disclosed in the aforementioned embodiments, which will not be repeated here.

[0080] It can be seen that this embodiment provides a design device for an organic Rankine cycle system. After determining the working conditions of the organic Rankine cycle system and building a database based on the relationship between the heat exchanger parameters and the working fluid physical parameters, the device uses a neural network to determine a function with the heat exchanger parameters and the working fluid physical parameters as independent variables; after the function is determined, the optimal value of the function under the preset independent variables can be calculated, and this optimal value is the maximum heat exchange of the system under the preset independent variables. At this time, the heat exchanger parameters and working fluid physical parameters corresponding to the maximum heat exchange of the system are the optimal values ​​under the current situation, so the optimal working fluid and the optimal heat exchanger under the current situation can be designed based on this, so that the performance of the designed system is maximized as much as possible.

[0081] A design device for an organic Rankine cycle system provided in an embodiment of the present application is introduced below. The design device for an organic Rankine cycle system described below and the design method and device for an organic Rankine cycle system described above can be referenced to each other.

[0082] See also Figure 5 As shown, the embodiment of the present application discloses a design device for an organic Rankine cycle system, comprising:

[0083] Memory 501, used for storing computer programs;

[0084] The processor 502 is used to execute the computer program to implement the method disclosed in any of the above embodiments.

[0085] A readable storage medium provided in an embodiment of the present application is introduced below. The readable storage medium described below and the design method, device and apparatus of an organic Rankine cycle system described above can be referenced to each other.

[0086] A readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the design method of the organic Rankine cycle system disclosed in the above embodiment. The specific steps of the method can refer to the corresponding contents disclosed in the above embodiment, and will not be repeated here.

[0087] The "first", "second", "third", "fourth", etc. (if any) referred to in this application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods or devices.

[0088] It should be noted that the descriptions involving "first", "second", etc. in this application are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in this field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0089] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0090] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of readable storage medium known in the art.

[0091] Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A design method for an organic Rankine cycle system, characterized in that: include: Determine the operating conditions of the organic Rankine cycle system; Construct a database based on the relationship between heat exchanger parameters and working fluid physical parameters; The database includes: a heat exchanger parameter set and a working fluid physical property parameter set; Using the database to train a neural network so that the neural network determines a function with the heat exchanger parameters and the working fluid physical property parameters as independent variables; Determine the optimal value of the function under the preset independent variable, and determine the optimal heat exchanger and the optimal working fluid according to the optimal value; Wherein, the step of constructing a database based on the relationship between heat exchanger parameters and working fluid physical parameters includes: According to K=n( ul / v ) a ( μC p / λ ) b ( λ / d) constructing the database; Among them, K is the heat transfer coefficient of the heat exchanger, n, a, b are the heat transfer parameters during the flow of the working fluid, u , μ , v , C p is the physical property parameter of the working fluid, l , λ , d are the parameters of the heat exchanger; Wherein, determining the optimal value of the function under the preset independent variable, and determining the optimal heat exchanger and the optimal working fluid according to the optimal value, includes: Substituting the preset independent variables into the function to obtain a plurality of system performance values; the preset independent variables are the value range corresponding to the heat exchanger parameter and the value range corresponding to the working fluid physical property parameter; Determining a maximum value among a plurality of system performance values ​​as the optimal value; The optimal heat exchanger is designed according to the heat exchanger parameters corresponding to the optimal values, and the optimal working fluid is designed according to the working fluid physical property parameters corresponding to the optimal values.

2. The method for designing an organic Rankine cycle system according to claim 1, characterized in that: The neural network is a BP neural network.

3. A design device for an organic Rankine cycle system, characterized in that: include: A determination module, used for determining the operating conditions of the organic Rankine cycle system; A construction module is used to construct a database according to the relationship between the heat exchanger parameters and the working fluid physical property parameters; the database includes: a heat exchanger parameter set and a working fluid physical property parameter set; A training module, used for training a neural network using the database, so that the neural network determines a function with the heat exchanger parameters and the working fluid physical property parameters as independent variables; A design module, used to determine the optimal value of the function under a preset independent variable, and determine the optimal heat exchanger and the optimal working fluid according to the optimal value; Wherein, the building blocks are specifically used for: According to K=n( ul / v ) a ( μC p / λ ) b ( λ / d) constructing the database; Among them, K is the heat transfer coefficient of the heat exchanger, n, a, b are the forced convection parameters in the tube, u 、 μ , v , C p is the physical property parameter of the working fluid, l , λ , d are the parameters of the heat exchanger; Wherein, the design module includes: A calculation unit, used for substituting the preset independent variables into the function to obtain a plurality of system performance values; the preset independent variables are the value range corresponding to the heat exchanger parameter and the value range corresponding to the working fluid physical property parameter; a determining unit, configured to determine a maximum value among a plurality of system performance values ​​as the optimal value; The design unit is used to design the optimal heat exchanger according to the heat exchanger parameters corresponding to the optimal value, and to design the optimal working fluid according to the working fluid physical property parameters corresponding to the optimal value.

4. The design device of the organic Rankine cycle system according to claim 3, characterized in that: The neural network is a BP neural network.

5. A design device for an organic Rankine cycle system, characterized in that: include: Memory for storing computer programs; A processor, configured to execute the computer program to implement the method for designing an organic Rankine cycle system according to any one of claims 1 to 2.

6. A readable storage medium, characterized in that: Used to store a computer program, wherein when the computer program is executed by a processor, the method for designing an organic Rankine cycle system according to any one of claims 1 to 2 is implemented.

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