An orc power generation system working medium optimization method and system

Abstract

The present application belongs to the field of ORC power generation system working medium selection, and specifically relates to an ORC power generation system working medium optimization method and system, aiming to solve the problem of high production cost and poor applicability of the working medium selected by the existing cycle working medium selection method. The present application comprises: according to the waste heat resource parameters of the ORC power generation system, a plurality of organic working media are initially selected; a hierarchical structure model comprising a target layer, a criterion layer and a scheme layer is established; a judgment matrix between each two indexes in the criterion layer is constructed, and the subjective weight vector is calculated after consistency test; the objective weight vector of each index is calculated by the entropy weight method; and the comprehensive weight matrix is calculated; the weighted Euclidean distance of each working medium and the positive / negative ideal solution is calculated to obtain the closeness degree varying with time; and the working medium is selected according to the closeness degree sorting result of the plurality of organic working media. The present application improves the long-term stability, environmental protection and thermal efficiency of the working medium, reduces the material cost, and realizes precise, efficient and low-cost system operation.

Description

Technical Field

[0001] This invention belongs to the field of working fluid selection for ORC power generation systems, and specifically relates to a method and system for optimizing the working fluid in ORC power generation systems. Background Technology

[0002] Organic Rankine Cycle (ORC) power generation technology is a geothermal energy conversion technology that uses low-boiling-point organic working fluids as the working medium. Compared with traditional steam Rankine cycles, ORC systems have advantages such as wide heat source adaptability (applicable temperature range of 60–300℃), high modularity, and low operation and maintenance costs. They are particularly suitable for medium and low temperature waste heat recovery (such as industrial waste heat and geothermal wells) and distributed energy systems (installed capacity covering 1kW–10MW).

[0003] As the core carrier of the ORC system, the working fluid's physicochemical properties (such as critical temperature, boiling point, thermal stability, and environmental friendliness) directly affect the system's efficiency and economy. Currently, commercially used working fluids mainly include alkanes (n-butane, isobutane), fluorinated hydrocarbons, and siloxanes, but they generally suffer from the following technical bottlenecks:

[0004] Insufficient environmental friendliness: Fluorinated working fluids have high global warming potential, and some chlorinated hydrocarbons (such as chloroethane) do not meet the ozone depletion potential (ODP) standards, failing to comply with the Montreal Protocol and EU F-Gas (fluorinated gas) regulations;

[0005] Limited thermal efficiency: Traditional working fluids have poor temperature drop matching under subcritical cycle conditions, resulting in significant loss of thermal efficiency at the evaporator terminals (approximately 15-20% thermal efficiency reduction).

[0006] Poor material compatibility: Highly corrosive working fluids (such as ammonia) require special alloy pipes and sealing materials, which increases the system construction cost (accounting for about 30% of the total investment).

[0007] The following documents are technical background information related to this invention:

[0008] CN108491579A (Publication Date: 2018-09-04, Applicant: Tianjin University) discloses "A method for screening mixed working fluids through multi-objective cycle performance optimization". By giving the operating conditions (evaporation temperature, condensation temperature) of refrigeration, heat pump, and organic Rankine cycles and the composition of the mixed working fluid, the method uses the coefficient of performance (COP) or thermal efficiency (ηt) and the heating / cooling capacity or output work as multi-objective functions, and determines the exhaust pressure, compression ratio, exhaust temperature, etc. as constraints to obtain the optimal ratio of the working fluid and the objective function value. This method is applicable to the screening of mixed working fluids in various thermodynamic cycles such as refrigeration, heat pump, and organic Rankine.

[0009] CN111816264A (Publication Date: 2020-10-23, Applicant: Central South University) discloses "A Machine Learning-Based Organic Rankine Cyclic Working Fluid Design Method." To improve the efficiency of working fluid screening and develop new, environmentally friendly, and efficient working fluids, machine learning is applied to predict cycle performance and working fluid properties. A third-generation working fluid design method is proposed, optimizing cycle conditions and molecular groups based on working fluid properties. Based on a learning library of cycling parameters and a basic property database, machine learning models for cycle performance and working fluid properties are established to achieve large-scale molecular design of working fluids under different cold and heat source conditions and cycle configurations.

[0010] Both of the above patents address the issues of insufficient environmental friendliness, limited thermal efficiency, and poor material compatibility of existing commercially available working fluids by designing circulating working fluids. However, the technical problem of high cost of specialized materials for circulating working fluids remains. Summary of the Invention

[0011] To address the aforementioned problems in existing technologies, namely the three major drawbacks of commercially available working fluids used in existing ORC systems—insufficient environmental friendliness, limited thermal efficiency, and poor material compatibility—and the fact that existing improvement schemes (such as mixed working fluid optimization and machine learning-aided design) have failed to effectively solve the problem of excessively high costs for specialized materials, this invention provides a method and system for optimizing the working fluid of an ORC power generation system.

[0012] In a first aspect, the present invention proposes a method for optimizing the working fluid of an ORC power generation system, comprising: initially selecting multiple organic working fluids based on the waste heat resource parameters of the ORC power generation system; establishing a hierarchical model including a target layer, a criterion layer, and a scheme layer, and adding a time dimension evaluation index to the criterion layer, wherein the time dimension evaluation index includes the working fluid aging coefficient and the performance degradation factor;

[0013] The target layer is used to screen the optimal working fluid, the scheme layer is used to initially select multiple organic working fluids, and the criteria layer includes at least thermophysical parameters, stability parameters, environmental parameters, and time-dimensional evaluation indicators.

[0014] Construct the judgment matrix between each pair of indicators in the criterion layer, and calculate the subjective weight vector after performing a consistency check;

[0015] Collect the temperature and pressure parameters of the working fluid during actual operation, combine them with the thermophysical data of the initially selected organic working fluid, and calculate the objective weight vector of each index using the entropy weight method.

[0016] The subjective weight vector and the objective weight vector are multiplied together and then normalized to obtain the comprehensive weight matrix.

[0017] The weights of the indicators in the comprehensive weight matrix are adjusted according to the geothermal resource composition and temperature gradient characteristics of the region, and the weighted Euclidean distance between each working fluid and the positive / negative ideal solution is calculated to obtain the closeness over time.

[0018] The working medium is selected based on the proximity ranking of multiple organic working media.

[0019] Furthermore, the waste heat resource parameters include at least temperature, instantaneous quantity, and heating stability.

[0020] Furthermore, the operating temperature and pressure parameters of the working fluid are input into the dynamic TOPSIS evaluation model. Based on the time-dimensional evaluation index, the thermophysical parameters and performance degradation factor of the working fluid are dynamically updated, and the updated pairwise judgment matrix of the parameters is constructed.

[0021] Furthermore, the method for constructing the judgment matrix is ​​as follows: pairwise importance comparisons are performed on the thermal property parameters, stability parameters, environmental protection parameters, and time dimension evaluation indicators of the criterion layer, and the judgment matrix is ​​generated based on the preset scaling rules.

[0022] Furthermore, the weights of the indicators in the comprehensive weight matrix are adjusted according to the composition of regional geothermal resources and temperature gradient characteristics, specifically as follows:

[0023] Based on the content of corrosive components in geothermal water, the weight ratio of stability parameters is increased according to a set proportion;

[0024] Based on the temperature gradient distribution characteristics of geothermal fields, the weight of boiling point in thermal property parameters is reduced according to a set ratio.

[0025] Furthermore, the method for dynamically updating the thermophysical parameters of the working fluid is as follows:

[0026] Based on the real-time collected working fluid pressure and temperature, combined with the working fluid property database, the specific enthalpy and specific entropy under the current state are interpolated and the thermal property parameters in the TOPSIS evaluation model are updated.

[0027] Furthermore, the working fluid is selected based on the proximity ranking results of multiple organic working fluids, specifically as follows:

[0028] The proximity of multiple organic working fluids is sorted in descending order, and the working fluid is selected based on the descending order result.

[0029] Furthermore, the thermophysical parameters include at least the critical temperature and boiling point, and the stability parameters include at least thermal stability.

[0030] In a second aspect, the present invention proposes an ORC power generation system working fluid selection system, based on an ORC power generation system working fluid selection method, the system comprising:

[0031] The initial selection module is configured to initially select multiple organic working fluids based on the waste heat resource parameters of the ORC power generation system; establish a hierarchical model including a target layer, a criterion layer, and a scheme layer; and add time-dimensional evaluation indicators to the criterion layer, including the working fluid aging coefficient and performance degradation factor.

[0032] The target layer is used to screen the optimal working fluid, the scheme layer is used to initially select multiple organic working fluids, and the criteria layer includes at least thermophysical parameters, stability parameters, environmental parameters, and time-dimensional evaluation indicators.

[0033] The subjective vector construction module is configured to construct the judgment matrix between each pair of indicators in the criterion layer, and calculate the subjective weight vector after performing a consistency check.

[0034] The objective vector construction module is configured to collect the temperature and pressure parameters of the working fluid in actual operation, combine them with the thermophysical property data of the initially selected organic working fluid, and calculate the objective weight vector of each index using the entropy weight method.

[0035] The fusion module is configured to multiply and synthesize the subjective weight vector and the objective weight vector, and then perform normalization processing to obtain a comprehensive weight matrix.

[0036] The proximity calculation module is configured to adjust the index weights in the comprehensive weight matrix according to the geothermal resource composition and temperature gradient characteristics of the region, and calculate the weighted Euclidean distance between each working fluid and the positive / negative ideal solution to obtain the proximity degree as it changes over time.

[0037] The filtering module is configured to select the working medium based on the proximity ranking results of multiple organic working media.

[0038] The beneficial effects of this invention are:

[0039] Enhance the ability to assess the long-term stability of working fluids: By introducing time-dimensional indicators (working fluid aging coefficient and performance degradation factor) into the evaluation criteria layer, it is possible to dynamically quantify the performance degradation trend of working fluids in long-term operation, effectively screen out working fluids that are resistant to aging and have low degradation, and reduce system operation and maintenance costs.

[0040] Balancing environmental friendliness and thermal efficiency optimization: By comprehensively calculating the weights of multiple criteria such as thermophysical parameters and environmental parameters, the evaluation results are avoided from being dominated by a single indicator, ensuring that the selected working fluid simultaneously meets the requirements of low environmental hazard and high thermodynamic performance.

[0041] Integrating subjective and objective weights improves the scientific nature of decision-making: By combining the subjective experience weights of the analytic hierarchy process (AHP) with the objective data weights of the entropy weight method, the differences between expert experience and actual operational data can be effectively balanced, avoiding evaluation bias caused by a single weighting method.

[0042] Enhanced regional adaptability: By dynamically adjusting the weight matrix based on the composition and temperature gradient characteristics of geothermal resources, the working fluid optimization results can be accurately matched with the waste heat characteristics of different regions, thereby improving the synergistic efficiency between the ORC system and the heat source.

[0043] Reduce material compatibility costs: Based on a comprehensive evaluation of stability parameters and time dimensions, select low-corrosion, long-life working fluids to reduce dependence on special materials, thereby reducing initial system investment and long-term maintenance costs.

[0044] Dynamic proximity ranking optimization selection: By calculating the dynamic proximity between the working fluid and the ideal solution through weighted Euclidean distance, the performance degradation of the working fluid over time can be predicted and optimized, providing a basis for decision-making in the whole life cycle management of the system. Attached Figure Description

[0045] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0046] Figure 1 This is a flowchart illustrating a method for selecting the working fluid in an ORC power generation system according to the present invention.

[0047] Figure 2 This is a schematic diagram illustrating the calculation process of subjective weights in an ORC (Organic Response Code) power generation system working fluid selection method.

[0048] Figure 3 This is a schematic diagram illustrating the calculation process of objective weights in an ORC (Organic Response Code) power generation system working fluid selection method.

[0049] Figure 4 This is a flowchart of a working medium selection method for ORC power generation systems, which selects the optimal working medium based on subjective and objective weights. Detailed Implementation

[0050] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0051] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0052] The first embodiment of the present invention proposes a method for selecting the working fluid in an ORC power generation system, characterized in that it includes:

[0053] Based on the waste heat resource parameters of the ORC power generation system, several organic working fluids were initially selected; a hierarchical model including a target layer, a criterion layer, and a scheme layer was established, and a time dimension evaluation index was added to the criterion layer, which included the working fluid aging coefficient and performance degradation factor.

[0054] The target layer is used to screen the optimal working fluid, the scheme layer is used to initially select multiple organic working fluids, and the criteria layer includes at least thermophysical parameters, stability parameters, environmental parameters, and time-dimensional evaluation indicators.

[0055] Construct the judgment matrix between each pair of indicators in the criterion layer, and calculate the subjective weight vector after performing a consistency check;

[0056] Collect the temperature and pressure parameters of the working fluid during actual operation, combine them with the thermophysical data of the initially selected organic working fluid, and calculate the objective weight vector of each index using the entropy weight method.

[0057] The subjective weight vector and the objective weight vector are multiplied together and then normalized to obtain the comprehensive weight matrix.

[0058] The weights of the indicators in the comprehensive weight matrix are adjusted according to the geothermal resource composition and temperature gradient characteristics of the region, and the weighted Euclidean distance between each working fluid and the positive / negative ideal solution is calculated to obtain the closeness over time.

[0059] The working medium is selected based on the proximity ranking of multiple organic working media.

[0060] To more clearly explain the method for selecting the working fluid in an ORC power generation system according to the present invention, the following is in conjunction with... Figure 1 The steps in the embodiments of the present invention are described in detail below:

[0061] Based on the waste heat resource parameters of the ORC power generation system, several organic working fluids were initially selected; a hierarchical model including a target layer, a criterion layer, and a scheme layer was established, and a time dimension evaluation index was added to the criterion layer, which included the working fluid aging coefficient and performance degradation factor.

[0062] The target layer is used to screen the optimal working fluid, the scheme layer is used to initially select multiple organic working fluids, and the criteria layer includes at least thermophysical parameters, stability parameters, environmental parameters, and time-dimensional evaluation indicators.

[0063] The waste heat resource parameters include at least temperature, instantaneous quantity, and heating stability.

[0064] The initial selection criteria in this embodiment are: screening for working fluids whose critical temperature is at least 20°C higher than the highest waste heat temperature, for example:

[0065] When the waste heat temperature is 150℃, select a working fluid with a critical temperature ≥170℃ (such as R245fa, R1234ze, n-pentane).

[0066] Exclude working fluids with ODP>0 or GWP>1500 (e.g., R134a is excluded).

[0067] In this embodiment, the target layer is to select the optimal ORC working fluid.

[0068] In this embodiment, the specific criteria layer indicators are:

[0069] Thermophysical properties: critical temperature, boiling point, latent heat of vaporization, specific heat ratio;

[0070] Stability parameters: thermal decomposition temperature, material compatibility level;

[0071] Environmental parameters: ODP value, GWP value, atmospheric lifetime;

[0072] Time-based evaluation indicators: working fluid aging coefficient α(t), performance degradation factor β(t).

[0073] The scheme layer consists of a preliminary selection of organic working fluids (such as R245fa, R1234ze, etc.).

[0074] like Figure 2 As shown, a judgment matrix is ​​constructed between each pair of indicators in the criterion layer, and the subjective weight vector is calculated after a consistency check. Specifically:

[0075] The importance of the thermophysical parameters, stability parameters, environmental parameters, and time-dimensional evaluation indicators of the criterion layer is compared pairwise, and a judgment matrix is ​​generated based on the preset scaling rules.

[0076] The preset scaling rules can adopt a technical parameter-driven 1-9 scaling method, for example:

[0077] If the correlation coefficient between indicator A and power generation efficiency is more than 30% higher than that between indicator B, then the importance scale of A relative to B is 5.

[0078] Example matrix fragment:

[0079] thermal property stability

[0080] Thermal properties 1 3

[0081] Stability 1 / 3 1

[0082] Consistency check: Calculate the consistency ratio (CR), which should be less than 0.1. If the CR exceeds the limit, automatically adjust the correlation coefficient weights and regenerate the matrix.

[0083] Another way to calculate the judgment matrix in this embodiment includes:

[0084] The working fluid's operating temperature and pressure parameters are input into the dynamic TOPSIS evaluation model. Based on the time-dimensional evaluation index, the thermophysical parameters and performance degradation factor of the working fluid are dynamically updated, and the updated pairwise judgment matrix of the parameters is constructed.

[0085] like Figure 3 As shown, the temperature and pressure parameters of the working fluid during actual operation are collected, and combined with the thermophysical property data of the initially selected organic working fluid, the objective weight vector of each index is calculated using the entropy weight method. Specifically:

[0086] Real-time acquisition of working fluid operating temperature T (±0.5℃ accuracy) and pressure P (±1kPa accuracy);

[0087] Retrieve the thermophysical parameters from the working fluid property database (such as NIST REFPROP data).

[0088] Entropy calculation: Normalize the index values ​​of each working fluid and calculate the information entropy E. j :

[0089]

[0090] Where m represents the number of working fluids participating in the evaluation, i.e., the total number of samples, and p ij This represents the normalized value of the i-th working fluid on the j-th index, which must satisfy...

[0091] Normalization: For each indicator j, the original data x ij Standardization (eliminating the influence of dimensions) is commonly achieved through the following methods:

[0092]

[0093] E j ∈[0,1], the smaller the value, the greater the difference in the data of the indicator, and the more information it provides. When all working substances have the same value for a certain indicator, Ej=1, and the weight of the indicator is zero (no distinguishing power).

[0094] Calculating objective weights based on information entropy

[0095] Among them, the difference coefficient D j =1-E j .

[0096] Example calculation:

[0097] Suppose we are evaluating the stability parameters (index j = 2) of a certain ORC system, and initially selecting three working fluids m = 3 (R245fa, R1234ze, n-pentane). The normalized stability scores are as follows: working medium <![CDATA[p i2 ]]> R245fa 0.5 R1234ze 0.3 n-Pentane 0.2

[0098] Information entropy

[0099] The coefficient of difference D² = 1 - 0.87 = 0.13. If the sum of the coefficients of difference for other indicators is 0.40, then the objective weight of the stability parameter is:

[0100]

[0101] like Figure 4 As shown, the index weights in the comprehensive weight matrix are adjusted according to the geothermal resource composition and temperature gradient characteristics of the region, specifically as follows:

[0102] Based on the content of corrosive components in geothermal water, the weight ratio of stability parameters is increased according to a set proportion;

[0103] Based on the temperature gradient distribution characteristics of geothermal fields, the weight of boiling point in thermal property parameters is reduced according to a set ratio.

[0104] In this embodiment, if the H2S content in the geothermal water is >200ppm, the weight of the stability parameter will be increased by 15%.

[0105] If the temperature gradient of a geothermal field is greater than 40℃ / km, the boiling point weight in the thermal property parameters will be reduced by 10%.

[0106] The subjective weight vector and the objective weight vector are multiplied together and then normalized to obtain a comprehensive weight matrix. Specifically:

[0107] The subjective weight vector, which is calculated using the AHP method, is denoted as...

[0108] satisfy

[0109] The objective weight vector, which is calculated using the entropy weight method, is denoted as...

[0110] satisfy

[0111] The number of indicators n is 4, including the criteria layer containing thermal properties, stability, environmental friendliness, and time dimension.

[0112] The calculation of the overall weight consists of two steps: multiplicative composition and normalization.

[0113] (1) Multiplicative composition

[0114] Multiply the subjective and objective weights item by item according to the indicators:

[0115]

[0116] Multiplication composition results Normalization is performed to ensure that the sum of the overall weights is 1:

[0117]

[0118] The final comprehensive weight vector is obtained as follows:

[0119]

[0120] Suppose that a certain ORC system has four indicators at the criteria level, with the following subjective and objective weights: index Subjective weight Objective weight Thermal property parameters 0.4 0.3 Stability parameters 0.3 0.25 Environmental parameters 0.2 0.35 Time dimension indicators 0.1 0.1

[0121] Multiplication composition:

[0122]

[0123]

[0124]

[0125]

[0126] The synthesis result is: 0.12 + 0.075 + 0.07 + 0.01 = 0.275.

[0127] Normalization process:

[0128]

[0129]

[0130]

[0131]

[0132] Final comprehensive weight matrix: [0.436, 0.273, 0.255, 0.036].

[0133] The comprehensive weight matrix is ​​adjusted based on the composition of regional geothermal resources and temperature gradient characteristics. The specific operation is as follows:

[0134] Example of adjustment rules:

[0135] If the content of corrosive components (such as H2S) in the geothermal water is >200ppm, the weight of the stability parameter will be increased by Δw (e.g., );

[0136] After adjustment, re-normalization is required.

[0137] Calculate the weighted Euclidean distance between each working fluid and the positive / negative ideal solution to obtain the closeness over time, specifically:

[0138] Constructing a standardized decision matrix: The range standardization process is applied to various indicators of the initially selected working fluid (thermal properties, stability, environmental friendliness, aging coefficient, performance degradation factor, etc.).

[0139] Benefit-related indicators (the higher the better):

[0140] Cost-related metrics (the smaller the better):

[0141] Among them, X ij Let i be the original value of index j for working fluid i. and For all working fluids, the maximum and minimum values ​​of index j are given.

[0142] Calculate the positive / negative ideal solution:

[0143] Positive Ideal Solution (PIS): The set of optimal values ​​for each indicator, i.e.:

[0144] PIS = {maxX' 1j ,maxX′ 2j ,maxX′ 3j ...maxX′ nj For benefit-type indicators, the maximum value is taken; for cost-type indicators, the minimum value is taken.

[0145] Negative Ideal Values ​​(NIS): The worst-case set for each indicator, i.e.:

[0146] NIS={minX′ 1j ,minX′ 2j ,minX′ 3j ...minX′ nj For benefit-type indicators, the minimum value is taken; for cost-type indicators, the maximum value is taken.

[0147] Calculate the weighted Euclidean distance:

[0148] Distance to the ideal solution:

[0149]

[0150] Distance to the negative ideal solution:

[0151]

[0152] Calculate the closeness C over time. i :

[0153]

[0154] Among them, C i ∈[0,1], the larger the value, the closer the working fluid is to the positive ideal solution and the further away from the negative ideal solution.

[0155] This invention selects the optimal working fluid based on proximity ranking results and outputs early warning parameters for working fluid performance degradation over time. Specifically:

[0156] The working fluid performance degradation early warning parameters include the performance degradation factor, the working fluid aging coefficient, and the dynamic proximity change rate; among them, the performance degradation factor and the working fluid aging coefficient are compared with preset thresholds.

[0157] For the dynamic proximity change rate, the slope of the proximity within adjacent time windows (such as 24 hours) is calculated to reflect the rate of deterioration of the working fluid performance.

[0158] Although the steps in the above embodiments are described in the above order, those skilled in the art will understand that in order to achieve the effect of this embodiment, different steps do not need to be executed in such an order. They can be executed simultaneously (in parallel) or in a reverse order. These simple variations are all within the protection scope of this invention.

[0159] The system based on the ORC power generation system working fluid selection method according to the second embodiment of the present invention includes:

[0160] The scoring module is configured to calculate the scores of multiple indicators for each type of organic working medium, as well as the proportion of the construction target of the construction unit; and to construct the scoring dataset of the corresponding organic working medium by combining the multiple scores and the proportion of the construction target.

[0161] The comprehensive scoring module is configured to assign weights to the data in the scoring dataset to obtain a comprehensive evaluation value for each organic working fluid.

[0162] The organic working medium selection module is configured to sort the comprehensive evaluation values ​​in descending order and select the organic working medium based on the sorting results.

[0163] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the system described above can be found in the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0164] It should be noted that the system based on the ORC power generation system working fluid selection method provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be further decomposed or combined. For example, the modules in the above embodiments can be merged into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the various modules or steps and are not considered as an improper limitation of the present invention.

[0165] An electronic device according to a third embodiment of the present invention includes:

[0166] At least one processor; and

[0167] A memory that is communicatively connected to at least one processor; wherein,

[0168] The memory stores instructions that can be executed by the processor to implement the above-described ORC power generation system working fluid selection method.

[0169] A computer-readable storage medium according to a fourth embodiment of the present invention stores computer instructions, which are executed by a computer to implement the above-described ORC power generation system working fluid selection method.

[0170] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the storage device and processing device described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0171] Those skilled in the art will recognize that the modules and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. The programs corresponding to the software modules and method steps can be placed in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the art. To clearly illustrate the interchangeability of electronic hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the invention.

[0172] The terms “first”, “second”, etc., are used to distinguish similar objects, not to describe or indicate a specific order or sequence.

[0173] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent in such process, method, article, or apparatus / device.

[0174] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for selecting the optimal working fluid for an ORC power generation system, characterized in that, include: Based on the waste heat resource parameters of the ORC power generation system, several organic working fluids were initially selected; a hierarchical model including a target layer, a criterion layer, and a scheme layer was established, and a time dimension evaluation index was added to the criterion layer, which included the working fluid aging coefficient and performance degradation factor. The target layer is used to screen the optimal working fluid, the scheme layer is used to initially select multiple organic working fluids, and the criteria layer includes at least thermophysical parameters, stability parameters, environmental parameters, and time-dimensional evaluation indicators. Construct the judgment matrix between each pair of indicators in the criterion layer, and calculate the subjective weight vector after performing a consistency check; Collect the temperature and pressure parameters of the working fluid during actual operation, combine them with the thermophysical data of the initially selected organic working fluid, and calculate the objective weight vector of each index using the entropy weight method. The subjective weight vector and the objective weight vector are multiplied together and then normalized to obtain the comprehensive weight matrix. The weights of the indicators in the comprehensive weight matrix are adjusted according to the geothermal resource composition and temperature gradient characteristics of the region, and the weighted Euclidean distance between each working fluid and the positive / negative ideal solution is calculated to obtain the closeness over time. The working medium is selected based on the proximity ranking of multiple organic working media.

2. The method for selecting the working fluid for an ORC power generation system according to claim 1, characterized in that, The waste heat resource parameters include at least temperature, instantaneous quantity, and heating stability.

3. The method for selecting the working fluid in an ORC power generation system according to claim 1, characterized in that, The working fluid's operating temperature and pressure parameters are input into the dynamic TOPSIS evaluation model. Based on the time-dimensional evaluation index, the thermophysical parameters and performance degradation factor of the working fluid are dynamically updated, and the updated pairwise judgment matrix of the parameters is constructed.

4. The method for selecting the working fluid for an ORC power generation system according to claim 3, characterized in that, The method for constructing the judgment matrix is ​​as follows: the importance of the thermophysical parameters, stability parameters, environmental parameters and time dimension evaluation indicators of the criterion layer is compared pairwise, and the judgment matrix is ​​generated based on the preset scaling rules.

5. The method for selecting the working fluid for an ORC power generation system according to claim 1, characterized in that, The weights of the indicators in the comprehensive weight matrix are adjusted based on the composition of regional geothermal resources and temperature gradient characteristics, specifically as follows: Based on the content of corrosive components in geothermal water, the weight ratio of stability parameters is increased according to a set proportion; Based on the temperature gradient distribution characteristics of geothermal fields, the weight of boiling point in thermal property parameters is reduced according to a set ratio.

6. The method for selecting the working fluid in an ORC power generation system according to claim 3, characterized in that, The method for dynamically updating the thermophysical parameters of the working fluid is as follows: Based on the real-time collected working fluid pressure and temperature, combined with the working fluid property database, the specific enthalpy and specific entropy under the current state are interpolated and the thermal property parameters in the TOPSIS evaluation model are updated.

7. The method for selecting the working fluid for an ORC power generation system according to claim 3, characterized in that, The working fluid is selected based on the proximity ranking of multiple organic working fluids, specifically as follows: The proximity of multiple organic working fluids is sorted in descending order, and the working fluid is selected based on the descending order result.

8. The method for selecting the working fluid for an ORC power generation system according to claim 1, characterized in that, The thermophysical parameters include at least the critical temperature and boiling point, and the stability parameters include at least thermal stability.

9. A working fluid selection system for an ORC power generation system, based on the working fluid selection method for an ORC power generation system according to any one of claims 1-8, characterized in that, The system includes: The initial selection module is configured to initially select multiple organic working fluids based on the waste heat resource parameters of the ORC power generation system; establish a hierarchical model including a target layer, a criterion layer, and a scheme layer; and add time-dimensional evaluation indicators to the criterion layer, including the working fluid aging coefficient and performance degradation factor. The target layer is used to screen the optimal working fluid, the scheme layer is used to initially select multiple organic working fluids, and the criteria layer includes at least thermophysical parameters, stability parameters, environmental parameters, and time-dimensional evaluation indicators. The subjective vector construction module is configured to construct the judgment matrix between each pair of indicators in the criterion layer, and calculate the subjective weight vector after performing a consistency check. The objective vector construction module is configured to collect the temperature and pressure parameters of the working fluid in actual operation, combine them with the thermophysical property data of the initially selected organic working fluid, and calculate the objective weight vector of each index using the entropy weight method. The fusion module is configured to multiply and synthesize the subjective weight vector and the objective weight vector, and then perform normalization processing to obtain a comprehensive weight matrix. The proximity calculation module is configured to adjust the index weights in the comprehensive weight matrix according to the geothermal resource composition and temperature gradient characteristics of the region, and calculate the weighted Euclidean distance between each working fluid and the positive / negative ideal solution to obtain the proximity degree as it changes over time. The filtering module is configured to select the working medium based on the proximity ranking results of multiple organic working media.

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