Prediction method and prediction model for physical and chemical parameters of multi-element fused salt heat storage material system

Abstract

The invention discloses a prediction method and a prediction model for physical and chemical parameters of a multi-element fused salt heat storage material system, and establishes a theoretical prediction model for accurately and quantitatively predicting the density of the multi-element fused salt heat storage material system directly according to the mass fraction and thermodynamic property data of each component of each fused salt heat storage material system without fitting. The prediction error of the theoretical prediction model is within an allowable range, and the theoretical prediction model is suitable for predicting the density of a binary fused salt heat storage material system and even a multi-element fused salt heat storage material system.

Description

Technical Field

[0001] The present invention relates to the technical field of molten salt heat storage materials, and particularly to a method and a prediction model for predicting the physical and chemical parameters of a multi-component molten salt heat storage material system. Background Art

[0002] Solar thermal power generation technology is an emerging energy technology. Heat storage materials are the core of solar thermal power generation, and molten salts are the preferred heat storage materials for solar thermal power generation. Molten salt heat storage technology is also widely used in the field of high-temperature heating. Compared with electric heating, molten salt heating has precise temperature control, high safety and reliability, and is a near-atmospheric pressure system. Molten salt heat storage materials mainly include nitrates, fluorides, chlorides, carbonates, sulfates, etc., among which nitrates have prominent performance advantages.

[0003] Binary or multi-component molten salt heat storage materials have also been successfully applied in commercial cases of solar thermal power plants. Their common disadvantage is that the melting point is relatively too high, so they are prone to solidify and block pipelines. Therefore, it is necessary to increase the heat insulation device, which not only increases the heat storage cost but also causes additional heat consumption.

[0004] Molten salt heat storage materials are both heat storage media and heat transfer working fluids, and their density is directly related to the energy storage density and heat transfer efficiency of the heat storage system. Molten salts with higher density can store more heat in a smaller volume, thereby improving the energy storage capacity of the system and reducing the floor area and cost of heat storage equipment.

[0005] Therefore, improving the performance of molten salt heat storage materials has become an international research hotspot. The research focus is to obtain ideal performance parameter values such as density, freezing point temperature, specific heat capacity, thermal conductivity, and hygroscopicity by regulating the chemical composition of multi-component mixed molten salt heat storage materials. For example, adding one or several other molten salts that can dissolve in the matrix molten salt to the matrix molten salt to form a composite molten salt can increase the specific heat capacity of the matrix molten salt.

[0006] In order to establish an ideal molten salt heat storage material, it is necessary to simultaneously optimize the physical property parameter values such as density, thermal stability, freezing point temperature, specific heat capacity, and thermal conductivity of the molten salt heat storage material. However, due to the large variety of molten salt heat storage materials, it is impossible to conduct experimental screening based on the measurement of the physical and chemical parameters of all heat storage materials one by one. Instead, it is necessary to establish a theoretical prediction model. According to the physical and chemical parameter data of pure components or binary molten salts widely reported in the literature, accurately predict the physical and chemical parameter data of multi-component molten salt heat storage materials, and then comprehensively analyze and compare to determine the molten salt heat storage materials with excellent physical property parameters. However, so far, only theoretical models for predicting the phase diagrams of molten salt heat storage materials have been reported in the literature, and theoretical models that can accurately predict physical property data such as the specific heat capacity and thermal conductivity of multi-component molten salt heat storage materials are blank.

[0007] Therefore, it is necessary to propose a method for establishing a model that can predict the physical and chemical parameters of a multi-component molten salt heat storage material. Summary of the Invention

[0008] The object of the present invention is to provide a method for predicting the physical and chemical parameters of a multi-component molten salt heat storage material system in view of the technical defects existing in the prior art.

[0009] Another object of the present invention is to provide a prediction model based on the above prediction method.

[0010] The technical solution adopted to achieve the object of the present invention is as follows:

[0011] A method for predicting the physical and chemical parameters of a multi-component molten salt heat storage material system includes the following steps:

[0012] Step 1, obtain the physical and chemical parameters of the multi-component molten salt heat storage material system, the physical and chemical parameters of each component in the multi-component molten salt heat storage material system, and the composition of each component, where the composition is the mole fraction, mass fraction, or volume fraction of each component;

[0013] Step 2, establish multiple theoretical prediction models for the physical and chemical parameters of the multi-component molten salt heat storage material system, and the theoretical prediction models include any two or more of the following models:

[0014] Logarithmic form model: Log(L m.cal ) = ∑(y i *Log(L i ));

[0015] Additive form model: L m.cal = ∑(y i *L i );

[0016] Fractional form model:

[0017] Power exponent form model:

[0018] Wherein, L m.cal represents the predicted physical and chemical parameters of the multi-component molten salt heat storage material system, L i represents the actual physical and chemical parameters of the single-component molten salt heat storage material, and y i refers to the composition of each component in the multi-component heat storage material system, where the composition is the mole fraction, mass fraction, or volume fraction of each component;

[0019] Step 3, use the theoretical prediction model established in Step 2 to predict the physical and chemical parameters of the molten salt heat storage material system, and compare them with the physical and chemical parameters of the multi-component molten salt heat storage material system obtained in Step 1, and select a theoretical prediction model with the smallest average error as the ideal prediction model;

[0020] Step 4: Modify the composition y of each component in the multi-component heat storage material system in the ideal prediction model obtained in Step 3 to obtain a modified theoretical prediction model. i to obtain a modified theoretical prediction model;

[0021] Step 5: Use the physical and chemical parameters of the multi-component molten salt heat storage material system obtained in Step 1 to verify that the average error of the physical and chemical parameters predicted by the modified theoretical prediction model in Step 4 is within the allowable error range.

[0022] Step 6: Input the corresponding physical and chemical parameters of each component and the composition of each component in the multi-component heat storage material system to be predicted, and obtain the physical and chemical parameters of the multi-component molten salt heat storage material system to be predicted through the modified theoretical prediction model obtained in Step 4.

[0023] In the above technical solution, in Step 1, the physical and chemical parameters are obtained by absorbing the measured literature experimental data and the newly measured experimental data.

[0024] In the above technical solution, in Step 1, the physical and chemical parameters include thermodynamic property parameters and / or transport property parameters.

[0025] In the above technical solution, in Step 1, the molten salt heat storage material is nitrate, fluoride, chloride, carbonate, and / or sulfate.

[0026] In the above technical solution, in Step 1, the thermodynamic property parameters are specific heat capacity, melting point, freezing point, decomposition temperature, density or density.

[0027] In the above technical solution, in Step 1, the transport property parameters are thermal conductivity and viscosity data.

[0028] In the above technical solution, in Steps 3 and 5, the average error of the N multi-component molten salt heat storage material systems is calculated by the following formula for error judgment:

[0029]

[0030] where L m.exp is the physical and chemical parameter of the multi-component molten salt heat storage material system obtained in Step 1, and L m.cal is the physical and chemical parameter of the multi-component molten salt heat storage material system predicted by the theoretical prediction model obtained in Step 4.

[0031] Another aspect of the present invention also includes a theoretical prediction model for the physical and chemical parameters of a multi-component molten salt heat storage material system, and the theoretical prediction model is:

[0032]

[0033] Among them, ρ m is the density calculated by the theoretical prediction model, and p i is the actual density of component i in the multi-component molten salt heat storage material system;

[0034] ω i is the mass fraction of component i in the multi-component molten salt heat storage material system, and ω i,min is the mass fraction of the component with the lowest density in the multi-component molten salt heat storage material system, and r ion,max is the maximum radius of the cations in the multi-component molten salt heat storage material system, and r ion,min is the minimum radius of the cations in the multi-component molten salt heat storage material system.

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0036] The present invention establishes a theoretical prediction model that can accurately and quantitatively predict the density of a multi-component molten salt heat storage material system directly based on the mass fractions and thermodynamic property data of each component of each molten salt heat storage material system without fitting. The prediction error of this theoretical prediction model is within the allowable range, and it is suitable for predicting the density of binary molten salt heat storage material systems and even multi-component molten salt heat storage material systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a schematic flow chart of the present invention;

[0038] Figure 2 is a schematic diagram of the relative errors of the predicted values of four theoretical prediction models in the present invention;

[0039] Figure 3 is a comparison chart of the average error and total error of four theoretical prediction models in the present invention;

[0040] Figure 4 is a comparison chart of the predicted values of the corrected theoretical prediction model and the experimental values in the present invention.

[0041] Figure 5 is a comparison chart of the error broken lines of the predicted values before and after the correction of Model III in the present invention, where Revised is after correction and Unrecised is before correction;

[0042] Figure 6 is a comparison chart of the error bars of the predicted values before and after the correction of Model III in the present invention, where Revised is after correction and Unrecised is before correction. DETAILED DESCRIPTION OF THE INVENTION

[0043] The following further describes the present invention in detail with reference to specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0044] Example 1

[0045] As Figure 1 shown, in order to establish a theoretical prediction model for predicting the physical and chemical parameters of a multi-component molten salt heat storage material system, the following experiments were carried out.

[0046] Step 1, data preparation:

[0047] In order to improve the current research on the density of the molten salt heat storage material system, the density of the binary molten salt heat storage material system, the density of each component in the binary molten salt heat storage material system, and the composition of each component, where the composition is the mass fraction of each component, are obtained as shown in Table 1:

[0048] Table 1

[0049]

[0050]

[0051] As can be seen from Table 1, the densities of different types of binary molten salt heat storage material systems are different, and the density of the same type of binary molten salt heat storage material system is also affected by its composition. At the same time, the influence of the mass ratio of the component composition on the density of the binary molten salt heat storage material system is not significant.

[0052] Step 2, model construction:

[0053] Assume that the composition of the multi-component molten salt heat storage material system is expressed as mass fraction, mole fraction, and volume fraction. Based on the composition of the multi-component molten salt heat storage material system, the following composition fraction relationship is initially established:

[0054] 0 ≤ m i ≤ 1

[0055] where m i represents the mass, mole, or volume fraction of each individual component in the multi-component molten salt heat storage material system. Currently, the theoretical prediction model is mainly studied based on the assumption model and then continuously improved until the accuracy of the model prediction is achieved.

[0056] The main theoretical prediction models selected in this example are shown in Table 2:

[0057] Table 2

[0058]

[0059] a The exponent in the form of the power exponential equation needs to depend on the situation.

[0060] where L in Table 2 m.cal represents the density predicted by the theoretical prediction model of the multi-component molten salt heat storage material system, Li represents the density of each component in the multi-component molten salt heat storage material system, y i refers to the composition of each component in the multi-component molten salt heat storage material system, expressed as mass fraction.

[0061] Step 3: Substitute the density of each component and the composition of each component in the binary molten salt heat storage material system obtained in Step 1 into the above four theoretical prediction models to calculate the predicted density of the binary molten salt heat storage material system, and then compare it with the density of the binary molten salt heat storage material system obtained in Step 1 to calculate the error. The specific results are shown in Table 3 as follows:

[0062] Table 3

[0063]

[0064]

[0065] As can be seen from Table 3, by comparing the predicted density of the initially established theoretical prediction model with the density of the system obtained in Step 1, the four theoretical prediction models have different prediction abilities for the density of three types of binary molten salt heat storage material systems.

[0066] Since there is a large amount of data in Table 3, in order to better display the error d in Table 3 and more intuitively see the advantages and disadvantages of the four theoretical prediction models, a visualization graph is drawn, as Figures 2-3 shown. From Figure 2 and Figure 3 it can be seen that the predicted density of Model II and Model IV deviates greatly from the density of the binary molten salt heat storage material system obtained in Step 1 (the density in Table 1). At the same time, in the nitrate molten salt system and the carbonate molten salt system, the error predicted by Model II is higher than that of Model IV. This shows that compared with Model IV, the prediction ability of Model II is weaker. From Model II, it can be seen that the density of the multi-component molten salt heat storage material system cannot be determined by simply adding the densities of individual components.

[0067] Meanwhile, the four theoretical prediction models have good prediction performance for chlorides and carbonates, but poor prediction performance for fluorides and nitrates, with the prediction ability being only about 18%, which far exceeds the allowable error range of the prediction. All four theoretical prediction models are not suitable for predicting the density of the multi-component molten salt heat storage material system. For the fractional form Model III, it is the model with the smallest error among the theoretical prediction models of the density of three types of binary molten salt heat storage material systems. However, in the prediction error of Model III, there are still many systems that exceed the allowable error range. At the same time, the prediction effect of this model for nitrate molten salts and carbonate molten salts is better than that for chlorate molten salts, indicating that there is a fractional non-linear relationship between the density of the binary molten salt heat storage material system and the densities of the components in the binary molten salt heat storage material system.

[0068] Therefore, none of the four established theoretical prediction models is suitable for predicting the density of the binary molten salt heat storage material system; to improve the accuracy of the theoretical prediction model, more complex models or alternative methods are needed, which need to consider other factors, such as the temperature, pressure, and other physical and chemical properties of the molten salt heat storage material system.

[0069] After analysis, the predicted system density results are compared with the system density obtained in Step 1. Within the entire data composition range of the binary molten salt heat storage material system, the mean absolute error between the predicted density and the obtained density can be defined by the following formula (i.e., the mean error in Table 3 is calculated by the following formula):

[0070]

[0071] where ρ m.exp is the density of the binary molten salt heat storage material system obtained in Step 1, and ρ m.cal is the density of the binary molten salt heat storage material system predicted by the theoretical prediction model.

[0072] Step 4, model correction: Correct Model III. The formula of Model III is as follows:

[0073]

[0074] where ρ m.cal is the density of the binary molten salt heat storage material system calculated by the model, ρ i is the density value of each component in the binary molten salt heat storage material system, and y i is the corresponding composition of each component, and this composition is the mass fraction.

[0075] In the formula of Model III, the error in a single binary molten salt heat storage material system may be attributed to the invalidity of the previous assumption. Specifically, the properties and composition of each component in the binary molten salt heat storage material system may not simply follow the simple superposition of the components in the binary molten salt heat storage material system because of the interaction between the molecules in the system, which affects the composition of each component in the binary molten salt heat storage material system.

[0076] Initially, the composition of the binary molten salt heat storage material system is represented by the mass fraction of each component. Due to the interaction between the molecules in the system, the error caused by the previous assumption is now reduced by considering the radius of each ion in each molecule. Now, assuming that the molecular composition configuration is spherical, the volume of each ion in the system can be calculated using the ionic radius, thereby determining the composition of each molecule in the system.

[0077] The finally corrected theoretical prediction model is:

[0078]

[0079] Among them, ρ m is the density calculated by the theoretical prediction model, and p i is the actual density of component i in the multi-component molten salt heat storage material system;

[0080] ω i is the mass fraction of component i in the multi-component molten salt heat storage material system, and ω i,min is the mass fraction of the component with the lowest density in the multi-component molten salt heat storage material system, and r ion,max is the maximum radius of the cations in the multi-component molten salt heat storage material system, and r ion,min is the minimum radius of the cations in the multi-component molten salt heat storage material system.

[0081] Step 5: Introduce the finally corrected expression into the density and the composition of each component obtained in Step 1, and verify its prediction error until it is within the allowable error range, as shown in Table 4:

[0082] Table 4

[0083]

[0084] Since there is a large amount of data in Table 4, in order to visually display the data in Table 4 in the form of a data graph, as Figure 4 shown, and compare the predicted values and experimental values of the theoretical prediction model. It can be seen from Figure 4 that the predicted values are close to the experimental values.

[0085] In order to more intuitively display, a comparison graph of the predicted values before and after the correction of Model III is drawn, as Figures 5-6 shown. It can be seen from Figures 5-6 that after the correction of Model III, the prediction deviation decreases and remains within the error range of 0.5%. Therefore, the hypothesis of model improvement can be proven by the results, and the corrected model can be well adapted, and for molten salts composed of large molecules such as nitrate molten salts and carbonate molten salts, that is, molten salts affected by the volume between molecules, the prediction effect is better.

[0086] Step 6: Model and generalization:

[0087] Through the establishment and modification of the model, a new theoretical prediction model is obtained This theoretical prediction model can predict the density of various binary molten salt heat storage material systems, and through the comparison results, the prediction effect is good. Next, the established theoretical prediction model will be extended to the multi-component molten salt heat storage material system, and the accuracy of the model will be verified by referring to the literature.

[0088] To ensure the accuracy and adaptability of the newly established theoretical prediction model, relevant experimental values of density were collected by referring to the literature, and a training dataset for testing the theoretical prediction model was established. The specific data are shown in Table 5:

[0089] Table 5

[0090]

[0091] The data in Table 5 and the densities of each component in the system were substituted into the theoretical prediction model for prediction, and the predicted density values of each system were obtained. The results are shown in Table 6:

[0092] Table 6

[0093]

[0094]

[0095] It can be seen from Table 6 that the trend of the density values of the binary and multi-component molten salt heat storage material systems in the dataset is similar to the trend calculated by the theoretical prediction model. In addition, the relative errors of the 21 molten salt systems are all below 0.5.

[0096] In summary, in this embodiment, a theoretical prediction model that can predict the density of multi-component molten salt heat storage material systems is proposed and established. Four initially hypothesized and established prediction models were compared for prediction. The advantages and disadvantages of the four hypothesized models were visually demonstrated in the form of data tables and comparison charts, and it was found that Model III had a better prediction effect; however, the relative error of this model was still relatively large for some systems, and Model III needed to be corrected. Through theoretical assumptions, a new mass fraction representing the composition was proposed and applied to Model III. By predicting the systems in the above-mentioned proposed table and comparing the errors, it can be concluded that the prediction effect of the theoretical prediction model of the molten salt system density has been improved compared with that before correction and the error is lower.

[0097] Finally, to verify the applicability of the theoretical prediction model, the model equation was extended to multi-component molten salt heat storage material systems. From the data of various multi-component molten salt heat storage material systems collected, the density was predicted by the theoretical prediction model and the numerical values and the errors between the two were visually compared. It can be seen that the prediction effect is good for multi-component molten salt heat storage material systems, especially for nitrate molten salt and carbonate molten salt systems.

[0098] It should be noted that the solution of this embodiment is also applicable to the prediction of the enthalpy of fusion.

[0099] The above are only the preferred embodiments of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for predicting the physicochemical parameters of a multi-component molten salt heat storage material system, characterized in that: The following steps are involved: Step 1, obtaining the physicochemical parameters of the multi-component molten salt heat storage material system, the physicochemical parameters of each component in the multi-component molten salt heat storage material system and the composition of each component, wherein the composition is the mole fraction, mass fraction or volume fraction of each component; Step 2, establishing a theoretical prediction model for the physicochemical parameters of multiple multi-component molten salt heat storage material systems; Step 3, using the theoretical prediction model established in step 2 to predict the physicochemical parameters of the molten salt heat storage material system, and comparing them with the physicochemical parameters of the multi-component molten salt heat storage material system obtained in step 1, and selecting a theoretical prediction model with the smallest average error as the ideal prediction model; Step 4: the composition y of each component in the multi-component heat storage material system in the ideal prediction model obtained in step 3 i Make corrections to obtain a corrected theoretical prediction model; Step 5, using the physicochemical parameters of the multi-component molten salt heat storage material system obtained in step 1, verifying that the average error of the physicochemical parameters predicted by the revised theoretical prediction model in step 4 is within the allowable error range; Step 6, input the corresponding physicochemical parameters and composition of each component in the multi-component heat storage material system to be predicted, and obtain the physicochemical parameters of the multi-component molten salt heat storage material system to be predicted through the revised theoretical prediction model obtained in step 4.

2. The prediction method according to claim 1, characterized in that: In the step 1, the physicochemical parameters are obtained by absorbing the experimental data measured in the literature and the newly measured experimental data.

3. The prediction method according to claim 1, characterized in that: In step 1, the physicochemical parameters include thermodynamic property parameters and / or transport property parameters.

4. The prediction method according to claim 1, characterized in that: In step 1, the molten salt heat storage material is nitrate, fluoride, chloride, carbonate and / or sulfate.

5. The prediction method according to claim 1, characterized in that: In step 1, the thermodynamic property parameter is specific heat capacity, melting point, freezing point, decomposition temperature, melting enthalpy or density.

6. The prediction method according to claim 1, characterized in that: In the step 1, the transfer property parameters are thermal conductivity and viscosity data.

7. The prediction method according to claim 1, characterized in that: In step 2, the theoretical prediction model includes any two or more of the following models: Logarithmic form model: Log(L m.cal )=∑(y i *Log(L i )); Additive form model: L m.cal =∑(y i *L i ); Fractional form model: Power exponential form model: Among them, L m.cal represents the predicted physicochemical parameters of the multi-component molten salt thermal storage material system, L i represents the actual physicochemical parameters of the single-component molten salt thermal storage material, y i It refers to the composition of each component in the multi-component heat storage material system, and the composition is the mole fraction, mass fraction or volume fraction of each component.

8. The prediction method according to claim 1, characterized in that: In step 3 and step 5, the average error of N multi-component molten salt heat storage material systems is calculated by the following formula to perform error judgment: Among them, L m.exp is the physicochemical parameters of the multi-component molten salt heat storage material system obtained in step 1, L m.cal The physicochemical parameters of the multi-component molten salt heat storage material system predicted by the theoretical prediction model obtained in step 4.

9. A theoretical prediction model for the physicochemical parameters of a multi-component molten salt heat storage material system, characterized in that: The theoretical prediction model is: Among them, L m is the physicochemical parameter calculated by the theoretical prediction model, L i is the actual physicochemical parameter of component i in the multi-component molten salt heat storage material system; ω i is the mass fraction of component i in the multi-component molten salt heat storage material system, ω i,min is the mass fraction of the lowest density component in the multi-component molten salt heat storage material system, r ion,max is the maximum radius of cations in the multi-component molten salt thermal storage material system, r ion,min It is the minimum radius of cations in the multi-component molten salt heat storage material system.

10. The theoretical prediction model according to claim 9, characterized in that: The physicochemical parameter is density or melting enthalpy.

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