Multi-level comprehensive screening method for organic Rankine cycle working medium

Abstract

The invention belongs to the technical field of energy conversion, and mainly relates to an organic Rankine cycle working medium multi-level comprehensive screening method. The method comprises the steps that S1, according to the availability of working media, a candidate working medium set I is obtained through preliminary screening; s2, obtaining a candidate working medium set II according to the physical property of the working medium; s3, obtaining a candidate working medium set III according to the thermal property of the working medium; s4, obtaining a candidate working medium set IV according to the safety of the working medium; s5, obtaining a final working medium set V according to the environmental factors of the working medium; s6, performing multi-objective optimization on the working medium of the final working medium set V, and determining a Pareto optimal solution of each working medium based on a TOPSIS decision method; and S7, carrying out normalization processing on the data, and carrying out comprehensive evaluation by adopting grey correlation analysis so as to determine the optimal working medium. By adopting the method, the index criterion of working medium screening is perfected, the limitation of one-sided conventional screening indexes is broken through, and a new thought and guidance are provided for reasonably, scientifically and efficiently determining the optimal working medium.

Description

Technical Field

[0001] The invention belongs to the technical field of energy conversion, and mainly relates to a multi-level comprehensive screening method for organic Rankine cycle working fluids. Background Art

[0002] In the field of energy utilization, the Organic Rankine Cycle (ORC) technology is an effective low-temperature heat energy conversion method. Due to its compact structure, simple operation and high efficiency, it has been widely used in the recovery and utilization of low-grade heat energy such as solar energy, geothermal energy, biomass energy and industrial waste heat energy. However, the performance of the ORC system depends to a large extent on the characteristics of the selected working fluid. The choice of working fluid not only affects the thermodynamic performance of the system, but is also directly related to the economy of the system, and even has an important impact on the environment and safety.

[0003] At present, research on organic working fluid screening methods has made some progress, but there are still many shortcomings; for example, in the prior art, a screening method based on the structural characteristics of working fluids is proposed. This method mainly screens working fluids from the perspective of microscopic mechanisms, focusing on the analysis of the quantum chemical properties of working fluids, but ignoring the thermodynamic properties, economic performance and environmental performance of working fluids in practical applications; in addition, this method only uses the optimal thermal efficiency as the performance indicator during optimization, and the objective function selection is somewhat one-sided. Based on this, another technology proposes a multi-objective optimization method for organic working fluid screening based on neural networks. Although this method focuses on parameter optimization and analyzes the environmental performance of the system, it only simply considers CO2 emissions and lacks comprehensive consideration of factors such as working fluid applicability, physical properties, and safety, and lacks a multi-level comprehensive screening process of "checking at every level".

[0004] In summary, the existing working fluid screening methods have the following main problems: First, the screening indicators are not comprehensive, and often only focus on one-sided performance of the working fluid, such as basic physical properties or thermodynamic properties, resulting in the phenomenon of "losing sight of one thing while focusing on another" in the performance of the screened working fluid; Second, there are certain limitations in the considerations of multi-objective optimization, and the environmental performance of the system is usually ignored, especially the comprehensive environmental analysis based on the full life cycle theory, and how to balance the conflicts between various character indicators remains to be resolved; These problems limit the rationality and accuracy of ORC working fluid screening.

[0005] Therefore, developing a multi-level comprehensive screening method that comprehensively considers the physical properties, thermodynamic properties, safety, environmental factors and thermodynamic-economic-environmental performance of the working fluid is of great significance for rationally, scientifically and efficiently determining the optimal working fluid; the present invention aims to address the deficiencies in the prior art and provide a comprehensive and scientific method for screening organic Rankine cycle working fluids, providing strong support for the optimal design and efficient operation of the ORC system. Summary of the invention

[0006] In view of the problem of "one-sided indicators and different focuses" in the existing screening methods, the present invention proposes a multi-level comprehensive screening method for organic working fluids to provide a comprehensive and scientific screening benchmark for selecting a reasonable ORC working fluid. The method of the present invention comprises the following steps:

[0007] S1 obtains candidate working fluid set I based on the availability of working fluids through preliminary screening based on database and software;

[0008] S2: screening the candidate working fluid set I based on the physical properties of the working fluid, the critical temperature and the boiling point temperature, to obtain the candidate working fluid set II;

[0009] S3 screens the candidate working fluid set II according to the thermodynamic properties of the working fluid based on dryness, wetness and isentropic characteristics to obtain a candidate working fluid set III;

[0010] S4 screening the candidate working fluid set III according to the safety of the working fluid based on flammability (FH), toxicity (HH), and chemical stability (RH) to obtain a candidate working fluid set IV;

[0011] S5: screening the candidate working fluid set IV according to the environmental factors of the working fluid, based on ozone depletion (ODP) and global warming potential (GWP), to obtain a final working fluid set V;

[0012] S6 uses a genetic algorithm to perform multi-objective optimization on the working fluids of the final working fluid set V based on the three indicators of system thermodynamic performance, economic performance and environmental performance, obtains a Pareto optimal solution set of each working fluid, and determines the Pareto optimal solution of each working fluid based on the TOPSIS decision method;

[0013] S7 combines the system thermodynamics, economic and environmental performance and the safety and environmental standards of the working fluid, normalizes the data, uses grey correlation analysis, and conducts a comprehensive evaluation to determine the best working fluid.

[0014] As a further solution of the present invention: the S1 database is ASHRAE, and the software is REFPROP9.0.

[0015] As a further solution of the present invention: S2 selects candidate organic working fluid II that meets the heat source temperature based on the relationship between the heat source temperature and the critical temperature and boiling point temperature of the candidate organic working fluid, wherein the organic working fluid critical temperature close to the heat source temperature is beneficial to improving the system efficiency, and the organic working fluid critical temperature lower than half of the heat source temperature cannot be used. The specific expression for screening is as follows:

[0016] 0.50T s <T c <1.30T s (1)

[0017] Where, Tc is the critical temperature of the working fluid, T s In addition, the boiling point of the organic working fluid should be low enough to promote vaporization, and high enough to ensure the lowest possible temperature during the phase change. Loss, the specific screening expression is as follows:

[0018] 0.35T s <T b <0.90T s (2)

[0019] Where, T b is the boiling point temperature of the working fluid.

[0020] As a further solution of the present invention: S3 selects candidate working fluid set III that meets thermodynamic characteristics based on the dryness, wetness and isentropic properties of the working fluid. The working fluid type is judged according to the slope ε value of the saturated steam side of the working fluid temperature-entropy (TS) curve, where the dry working fluid has an ε greater than 0.5; the isentropic working fluid has an ε greater than -0.5 and less than 0.5; the wet working fluid has an ε less than -0.5; in order to avoid the formation of liquid in the ORC system during the turbine expansion process, a dry working fluid or an isentropic working fluid is selected and the wet working fluid is eliminated. The specific screening expression is as follows:

[0021] -0.5<ε (3)

[0022] As a further solution of the present invention: S4 screens candidate working fluid set IV that meets safety requirements based on working fluid flammability (FH), toxicity (HH), and chemical stability (RH). The working fluid safety parameters FH, HH, and RH range from 0 to 4, with a total of five levels. Among them, the recommended level range of working fluid FH is 0 to 4; toxic liquids with a working fluid HH level exceeding 2 are not considered; unstable fluids with a working fluid RH level exceeding 2 should be avoided. The specific screening expression is as follows:

[0023] 0≤FH≤4 (4)

[0024] 0≤HH≤2 (5)

[0025] 0≤RH≤2 (6)

[0026] As a further solution of the present invention: S5 screens the final working fluid set V that meets environmental requirements based on ODP and GWP. Considering the damage to ozone, organic working fluids with an ODP greater than 0.5 should be avoided; considering the impact on global warming, organic working fluids with a GWP greater than 1000 should be avoided. The specific screening expression is as follows:

[0027] ODP<0.5 (7)

[0028] GWP<1000 (8)

[0029] As a further solution of the present invention: S6 analyzes the useful energy of the system thermodynamic performance index based on the laws of thermodynamics, reveals the irreversible loss of each component, and reflects the effective utilization of energy. Efficiency (η ex ) as the evaluation index; for the system economic performance index, the factors such as component cost, system construction investment, and system operation and maintenance cost are considered, and the investment payback period (PBP) is used as the evaluation index; for the system environmental performance index, based on the full life cycle evaluation theory, the environmental impact caused by the emissions generated in the production and processing stage, the operation stage, and the decommissioning stage is analyzed, and the environmental impact load (EIL) is used as the evaluation index. The specific expression is as follows:

[0030]

[0031] EIL=∑WP(j) (11)

[0032] Where W net is the net output work of the system, Ex D,tot For the system loss; k is the depreciation rate, Cost is the actual investment cost of the year, E p is the selling price of the electricity generated by the system, h full-load is the annual operating time of the system, f k is the cost factor for system operation and maintenance; WP is the impact weight of various environmental impact potentials.

[0033] Among them, for multi-objective optimization, the objective function is the maximum η ex and minimum PBP and EIL, the optimization variable is the evaporation temperature (T eva ), condensation temperature (T con ), superheat (ΔT sup ), evaporator narrow point temperature difference (ΔT pp,eva ) and the condenser narrow point temperature difference (ΔT pp,con ), its mathematical model can be expressed as follows:

[0034]

[0035] Among them, the TOPSIS decision method is a method for finding the optimal solution in the Pareto optimal solution set, which is selected by comprehensively evaluating the points that are close to the "ideal point" and the points that are far away from the "non-ideal point". The decision process can be expressed as follows:

[0036]

[0037] Where, d i+ and d i-is the Euclidean distance of each solution to the ideal point and non-ideal point. C i is the proximity coefficient, which is the TOPSIS evaluation index.

[0038] As a further solution of the present invention: the grey correlation analysis method is used in S7 to perform comprehensive evaluation to obtain the best working fluid. The main steps of the grey correlation analysis method are as follows:

[0039] First, normalization is performed. In grey relational analysis, all data are normalized between 0 and 1. When the larger the index, the better the performance, equation (16) is used for normalization; when the smaller the index, the better the performance, equation (17) is used for normalization.

[0040]

[0041] Furthermore, the grey correlation coefficient is calculated. The grey correlation coefficient represents the degree of relationship between the comparison series and the reference series, and its calculation is as follows:

[0042]

[0043] Where ζ is the resolution coefficient, which ranges from 0 to 1 and is usually 0.5; Δ 0i(k) represents the absolute difference at factor k; Δ min and Δ max Respectively represent the minimum and maximum absolute differences of all reference series and comparison series at each factor.

[0044] Δ 0i(k) =|x0(k)-x j (k)| (19)

[0045] Δ min =min j min k |x0(k)-x j (k)| (20)

[0046] Δ max =max j max k |x0(k)-x j (k)| (21)

[0047] In the formula, x0(k) refers to the reference sequence generated after data processing, x j (k) refers to the comparison sequence.

[0048] Finally, the grey correlation degree is calculated. The grey correlation degree is calculated by the weighted average of the grey correlation coefficients, and its formula is as follows:

[0049]

[0050] In the formula, w i (k) is the weight.

[0051] Compared with the prior art, the beneficial effects of the present invention are: a multi-level comprehensive screening method for organic Rankine cycle working fluids not only improves the index criteria for working fluid screening, improves the scientificity and rationality of screening, but also optimizes working fluid selection and enhances the flexibility and adaptability of the screening method, providing strong support for the optimal design and efficient operation of organic Rankine cycle technology. Compared with the prior art, the beneficial effects of the present invention are mainly reflected in the following aspects:

[0052] (I) Improved the index criteria for working fluid screening: The multi-level comprehensive screening method for organic Rankine cycle working fluids proposed in this invention breaks through the one-sidedness of conventional index screening, comprehensively considers the physical properties, thermal properties, safety, environmental factors and thermal-economic-environmental performance of the working fluid, and screens the working fluids step by step. The method has the advantages of being scientific, efficient and intuitive.

[0053] (II) Balancing the conflict of indicators and taking into account the performance of various working fluids: The TOPSIS decision method is used to determine the Pareto optimal solution of each working fluid after multi-objective optimization. The method of the present invention overcomes the conflict between the optimization performance indicators and can take into account the thermal The method is helpful to improve the rationality and accuracy of job screening by considering multiple performance indicators such as efficiency, economic investment payback period and environmental impact load.

[0054] (III) Enhanced flexibility and adaptability of the screening method: In the grey correlation analysis stage, the method of the present invention allows the weight of each indicator to be flexibly determined according to the actual application situation, so that the screening method can adapt to different application scenarios and needs, thereby enhancing its flexibility and adaptability.

[0055] (IV) Providing new ideas and guidance: The present invention expands the evaluation indicators for the screening of organic Rankine cycle working fluids. Through multi-angle and multi-level comprehensive screening, it provides a new idea and guidance for the rational, scientific and efficient determination of the optimal working fluid, which is helpful to promote the further development and application of organic Rankine cycle technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention 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 some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0057] Figure 1It is a flow chart of the multi-level comprehensive screening method for working fluids in the ORC system.

[0058] Figure 2 It is a comprehensive evaluation chart for ORC working fluid screening.

[0059] Figure 3 This is a schematic diagram of a simple ORC system.

[0060] Figure 4 It is the optimization flow chart of the thermal economic environment analysis of the ORC system.

[0061] Figure 5 It is a three-dimensional diagram of the Pareto optimal boundary of the final working fluid set multi-objective optimization.

[0062] Figure 6 is the η corresponding to the Pareto optimal solution of the final working fluid set ex , PBP and EIL diagrams.

[0063] Figure 7 It is the grey relational ranking diagram of the final working fluid set. DETAILED DESCRIPTION

[0064] The technical solution of the present invention will be further described in detail below in conjunction with the accompanying drawings in which the present invention is implemented.

[0065] The multi-level comprehensive screening process of the ORC system working fluid provided by the present invention is shown in the attached Figure 1 For a comprehensive evaluation, see the attached Figure 2 The simple ORC system is used as an example to introduce the working fluid screening process. The working fluid screening of other thermodynamic cycles such as the regenerative ORC system and the two-stage ORC system can also refer to this process. The main components of the simple ORC system include the evaporator, condenser, turbine and working fluid pump. The schematic diagram is as follows: Figure 3 shown.

[0066] The flue gas at the tail of cement kiln is used as the heat source, and the flue gas inlet temperature is T fg is 713K, and the flow rate is m fg is 48kg / s; cooling water inlet temperature T cw is 293K; the ambient temperature T0 is 293K. The ORC system evaporation temperature, condensation temperature, superheat, evaporator narrow point temperature difference and condenser narrow point temperature difference are recorded as T eva 、T con , ΔT sup , ΔT pp,eva and ΔT pp,con .

[0067] Using the ASHRAE database and software REFPROP9.0, a total of 28 potential available hydrocarbons, perfluorocarbons, chlorofluorocarbons, hydrofluorocarbons and hydrochlorofluorocarbons were preliminarily selected to obtain candidate working fluid set I, whose basic physical properties are shown in Table 1.

[0068] The candidate working fluid set I is screened according to the physical properties of the working fluid. Considering the relationship between the heat source temperature and the critical temperature of the candidate working fluid, based on expression (1), T c / T s The working fluids with a ratio less than or equal to 0.5 or greater than or equal to 1.3 are not considered, so a total of 5 working fluids, including R-32, R-115, R-125, R-143a and R-218, are eliminated; considering the relationship between the heat source temperature and the boiling point temperature, based on expression (2), T b / T s The working fluids with a ratio less than or equal to 0.35 or greater than or equal to 0.9 are not considered, so R-12, R-22, R-134a and R-152a are eliminated. Based on the critical temperature and boiling point temperature of the working fluid, the selected candidate working fluid set II is obtained, as shown in Table 2.

[0069] The above candidate working fluid set II is screened according to the thermodynamic properties of the working fluid. Considering the problem of liquid formation in the turbine expansion process of the ORC system in practice, dry working fluid or isentropic working fluid is preferred. Based on expression (3), wet working fluids with ε not exceeding -0.5 are not considered, so R-21 and R-142b are eliminated. Based on the dry and wet characteristics of the working fluid, the screened candidate working fluid set III is obtained, as shown in Table 3.

[0070] The candidate working fluid set III is screened according to the safety requirements of the working fluid. Based on the toxicity and chemical stability requirements, according to expressions (5) and (6), toxic liquids and unstable fluids with a grade code greater than 2 should be avoided. Therefore, R-245ca is eliminated, and the screened candidate working fluid set IV is obtained, as shown in Table 4.

[0071] Considering the impact on the environment, the candidate working fluid set IV is screened. Fluids with an ozone depletion potential (ODP) greater than 0.5 should be avoided. Based on expression (7), three working fluids, R-11, R-113 and R-114, are eliminated. Based on the impact on global warming, organic working fluids with a GWP greater than 1000 should be avoided. According to expression (8), three working fluids, R-227ea, R-236fa and RC-318, are eliminated. The final working fluid set V is obtained after screening, as shown in Table 5.

[0072] The working fluid is further screened based on the system thermodynamic, economic and environmental performance indicators. Based on the first and second laws of thermodynamics, a thermodynamic model of the system is established. Efficiency (η ex ) as the thermodynamic performance evaluation index; based on modular cost analysis, the economic model of the system is established, and the payback period (PBP) is used as the economic performance evaluation index; based on the life cycle theory, the environmental model of the system is established, and the environmental impact load (EIL) is used as the environmental performance evaluation index.

[0073] The thermodynamic model of the ORC system is constructed as follows:

[0074] The work done by the turbine during the isentropic expansion process is expressed as follows:

[0075] W turb =m wf (h1-h2)η me,turb (twenty three)

[0076] The power consumption of the working fluid pump during the isentropic compression process is expressed as follows:

[0077] W p =m wf (h4-h3)η me,p (twenty four)

[0078] The heat absorbed by the evaporator during the isobaric heating process can be expressed as follows:

[0079] Q eva =m wf (h1-h4) (25)

[0080] The heat released by the condenser during the isobaric condensation process can be expressed as follows:

[0081] Q con =m wf (h3-h2) (26)

[0082] 1. Efficiency evaluation model

[0083] The net output work of the system can be expressed as follows:

[0084] W net =W turb -W p (27)

[0085] The energy state point calculation equation can be expressed as follows:

[0086] Ex=(h-h0)-T0(s-s0) (28)

[0087] System components The loss can be calculated as follows:

[0088] Ex D=∑Ex in -∑Ex out -W (29)

[0089] System Total The loss can be calculated as follows:

[0090] Ex D.tot =Ex D,turb +Ex D,p +Ex D,eva +Ex D,con (30)

[0091] system The efficiency can be expressed as follows:

[0092]

[0093] In the formula, m represents mass, h represents enthalpy, η me represents mechanical efficiency, Q represents heat, and Ex represents Yes,Ex D express Subscripts 1, 2, 3, and 4 represent state points, 0 represents the environmental state, wf represents the working fluid, tot represents the total, turbo represents the turbine, p represents the working fluid pump, eva represents the evaporator, and con represents the condenser.

[0094] 2. Investment payback period evaluation model

[0095] The actual investment amount for each component of the system is calculated as follows:

[0096] C bm =C p F bm (32)

[0097] The initial investment cost of the system in 2001 is calculated as follows:

[0098] Cost2001=C bm,turb +C bm,p +C bm,eva +C bm,cou (33)

[0099] The system investment cost in 2022 is calculated as follows:

[0100] Cost2022=Cost2001·CEPCI2022 / CEPCI2001 (34)

[0101] The system investment payback period is calculated as follows:

[0102]

[0103] In the formula, Cp Indicates the base investment amount of the component, F bm represents the actual investment factor of the component, CEPCI represents the chemical equipment cost index, k represents the discount rate, E p represents the electricity price sold, h full-load represents the annual operating time, f k Represents the operation and maintenance cost coefficient.

[0104] 3. Environmental impact load assessment model

[0105] The environmental impact potential can be calculated as follows:

[0106] EP=EP GWP + AP +EP EP +EP HTP +EP SWP +EP SAP (36)

[0107] The standardized impact potential can be calculated as follows:

[0108] NEP=EP / EP 90 (37)

[0109] The weighted assessment of environmental impact can be calculated as follows:

[0110] WP=NEP·WF (38)

[0111] The environmental impact load can be calculated as follows:

[0112] EIP=WP GWP +WP AP +WP EP +WP HTP +WP SWP +WP SAP (39)

[0113] In the above formula, ER 90 It represents the total environmental impact potential of China in 1990. WF is the weight factor of environmental impact. The subscript GWP represents the greenhouse effect, AP represents the acidification effect, EP represents the eutrophication effect, HTP represents the human toxicity effect, SWP represents solid waste, and SAP represents smoke and dust.

[0114] Taking into account the thermodynamic, economic and environmental performance, the maximum η ex With the minimum PBP and EIL as the optimization objectives, a multi-objective optimization model of the system is constructed, which is expressed as follows:

[0115]

[0116] The genetic algorithm II in Matlab is used to perform multi-objective optimization on the system. The optimization process is as follows: Figure 4 After optimization, the Pareto optimal boundary of each working fluid in the final working fluid set V is determined, as shown in Figure 5 As shown. And further based on the TOPSIS decision method, the Pareto optimal solution of each working fluid is determined, and the η corresponding to the optimal solution ex , PBP and EIL Figure 6 shown.

[0117] The thermal-economic-environmental performance indicators are combined with safety and environmental impact for comprehensive evaluation to determine the best working fluid. The comprehensive evaluation process is carried out using grey correlation analysis. First, all data are normalized according to expressions (16) and (17). Then, the normalized data is used to calculate the grey correlation coefficient of each working fluid. Among them, when the indicator is 1, the performance is optimal, so the elements of the reference set are set to 1. Finally, the grey correlation degree is calculated by the weighted average of the grey correlation coefficients. In the present invention, the weight value of each indicator is set equal. However, the weights of all indicators can be flexibly determined according to the specific application. For example: if the weights of safety and environmental impact are relatively increased, the screened working fluid will be safer and more environmentally friendly. After each working fluid in working fluid set V is analyzed by a comprehensive evaluation strategy, its grey correlation degree is as follows Figure 7 As shown. Figure 7 It can be seen that the optimal working fluid of the ORC waste heat recovery system is determined to be R-601a, and its grey correlation degree is 0.884. The grey correlation degrees of other candidate working fluids correspond to R-365mfc, R-601, R-600, R-236ea, R-600a, R-123, R-141b, R-245fa and R-124 from high to low, and their values ​​are 0.880, 0.827, 0.788, 0.781, 0.721, 0.683, 0.673, 0.669 and 0.666 respectively.

[0118] The present invention is described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned implementation modes. The above-mentioned specific implementation modes are merely illustrative and not restrictive. Any modifications and improvements based on the ideas of the present invention are deemed to be within the protection scope of the present invention.

[0119] Table 1 Candidate working fluid set I

[0120]

[0121] Table 2 Candidate working fluid set II

[0122]

[0123] Table 3 Candidate working fluid set III

[0124]

[0125] Table 4 Candidate working fluid set IV

[0126]

[0127] Table 5 Final working fluid set V

[0128]

Claims

1. A multi-level comprehensive screening method for organic Rankine cycle working fluids, characterized in that: The following steps are involved: S1 obtains candidate working fluid set I based on the availability of working fluids through preliminary screening based on database and software; S2: screening the candidate working fluid set I based on the physical properties of the working fluid, the critical temperature and the boiling point temperature, to obtain the candidate working fluid set II; S3 screens the candidate working fluid set II according to the thermodynamic properties of the working fluid based on dryness, wetness and isentropic characteristics to obtain a candidate working fluid set III; S4 screening the candidate working fluid set III according to the safety of the working fluid based on flammability (FH), toxicity (HH), and chemical stability (RH) to obtain a candidate working fluid set IV; S5: screening the candidate working fluid set IV according to the environmental factors of the working fluid, based on ozone depletion (ODP) and global warming potential (GWP), to obtain a final working fluid set V; S6 uses a genetic algorithm to perform multi-objective optimization on the working fluids of the final working fluid set V based on the three indicators of system thermodynamic performance, economic performance and environmental performance, obtains a Pareto optimal solution set of each working fluid, and determines the Pareto optimal solution of each working fluid based on the TOPSIS decision method; S7 combines the system's thermodynamic performance, economic performance and environmental performance as well as the safety and environmental standards of the working fluid, normalizes the data, uses grey correlation analysis, and conducts a comprehensive evaluation to determine the best working fluid.

2. The multi-level comprehensive screening method for organic Rankine cycle working fluids according to claim 1, characterized in that: The database in S1 is ASHRAE, and the software is REFPROP9.

0.

3. The multi-level comprehensive screening method for organic Rankine cycle working fluids according to claim 1, characterized in that: The specific expression based on critical temperature screening in S2 is: 0.50T s <T c <1.30T s (1) Where, T c is the critical temperature of the working fluid, T s is the temperature of the heat source; The specific expression based on boiling point temperature screening is: <h2 style=";text-align:left;direction:ltr">0.35T<h2 style=";text-align:left;direction:ltr"> s <h2 style=";text-align:left;direction:ltr"><T <h2 style=";text-align:left;direction:ltr"> b <h2 style=";text-align:left;direction:ltr"> <0.90T<h2 style=";text-align:left;direction:ltr"> s <h2 style=";text-align:left;direction:ltr"> (2) Where, T b is the boiling point temperature of the working fluid.

4. The multi-level comprehensive screening method for organic Rankine cycle working fluids according to claim 1, characterized in that: The specific expression of S3 selected according to the slope ε value of the saturated steam side of the working fluid temperature entropy (TS) curve is: -0.5<ε (3) 5. The multi-level comprehensive screening method for organic Rankine cycle working fluids according to claim 1, characterized in that: The FH, HH and RH levels in S4 are divided into five levels from 0 to 4, and the specific screening expression is: 0≤FH≤4 (4) 0≤HH≤2 (5) 0≤RH≤2 (6).

6. The multi-level comprehensive screening method for organic Rankine cycle working fluids according to claim 1, characterized in that: The specific expressions for ODP and GWP screening in S5 are: ODP<0.5 (7) GWP<1000 (8).

7. The multi-level comprehensive screening method for organic Rankine cycle working fluids according to claim 1, characterized in that: The thermodynamic performance index of the system in S6 is specifically based on the laws of thermodynamics, analyzing the useful energy of energy, revealing the irreversible loss of each component, and reflecting the effective utilization of energy. Efficiency (η ex ) as the evaluation index; its system economic performance index specifically considers factors such as component cost, system construction investment, and system operation and maintenance costs, and uses the investment payback period (PBP) as the evaluation index; its system environmental performance index specifically analyzes the environmental impact caused by emissions generated in the production and processing stage, operation stage, and decommissioning stage based on the full life cycle evaluation theory, and uses the environmental impact load (EIL) as the evaluation index. The specific expression is: EIL=∑WP(j) (11) Where W net is the net output work of the system, Ex D,tot For the system loss; k is the depreciation rate, Cost is the actual investment cost of the year, E p is the selling price of the electricity generated by the system, h full-load is the annual operating time of the system, f k is the cost factor for system operation and maintenance; WP is the impact weight of various environmental impact potentials.

8. The multi-level comprehensive screening method for organic Rankine cycle working fluids according to claim 7, characterized in that: In S6, a genetic algorithm is used to perform multi-objective optimization on the working fluids of the final working fluid set V, and its objective function is to maximize η ex and minimum PBP and EIL, the optimization variable is the evaporation temperature (T eva ), condensation temperature (T con ), superheat (ΔT sup ), evaporator narrow point temperature difference (ΔT pp,eva ) and the condenser narrow point temperature difference (ΔT pp,con ), its mathematical model can be expressed as:

9. The multi-level comprehensive screening method for organic Rankine cycle working fluids according to claim 7, characterized in that: The decision-making process of the TOPSIS decision-making method in S6 is expressed as: Where, d i+ and d i- is the Euclidean distance of each solution to the ideal point and the non-ideal point; C i is the proximity coefficient, which is the TOPSIS evaluation index.

10. The multi-level comprehensive screening method for organic Rankine cycle working fluids according to claim 1, characterized in that: The specific steps of the grey correlation analysis method in S7 are: First, all data are normalized between 0 and 1. When the index is larger, the performance is better. Then, equation (16) is used for processing. When the index is smaller, the performance is better, and equation (17) is used for processing; Further, the grey relational coefficient is calculated as follows: Where ζ is the resolution coefficient, which ranges from 0 to 1 and is usually 0.5; Δ 0i(k) represents the absolute difference at factor k; Δ min and Δ max Respectively represent the minimum and maximum absolute differences of all reference series and comparison series at each factor. The specific expressions are as follows: Δ 0i(k) =|x0(k)-x j (k)| (19) D min =min j minutes k |x0(k)-x j (k)| (20) Δ max =max j max k |x0(k)-x j (k)| (21) In the formula, x0(k) refers to the reference sequence generated after data processing, x j (k) refers to the comparison sequence; Finally, the grey relational degree is calculated by the weighted average of the grey relational coefficients, and its formula is expressed as: In the formula, w i (k) is the weight.

Images