Formula design optimization method of solar photothermal conversion composite material

Abstract

The invention discloses a formula design optimization method of a solar photothermal conversion composite material. The method comprises the following steps that a single-factor control variable is determined, the single-factor control variable can enable the solar photothermal conversion composite material to have a target index extreme value, and the selected single-factor control variable and a target index are designed into a formula data set; obtaining a single-factor optimal combination; obtaining an m-factor k-level orthogonal experiment table; expanding the m-factor k-level orthogonal experiment table, and finally obtaining a single-factor k-level k-group experiment orthogonal experiment table; and calculating a target index average value of k groups of experiments corresponding to each single-factor k level, selecting each single-factor optimal level, taking out the single-factor optimal levels, and combining the single-factor optimal levels into an optimal design formula for selecting the target performance for the composite material. According to the method, the formula with the optimal target performance can be efficiently and scientifically selected from all possible formulas, the experiment frequency is reduced, and the formula design efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of formulation design of solar photothermal conversion composite materials, and specifically refers to an optimization method for the formulation design of solar photothermal conversion composite materials. Background Art

[0002] Solar thermal power generation technology is developing towards higher operating parameters. However, higher operating temperatures require the development and research of new high-temperature-resistant photothermal conversion materials. At the same time, considering practical factors during operation, it is also required that the photothermal conversion composite materials have high spectral absorptivity, high thermal conductivity, and good seismic and antioxidant capabilities. Composite materials play an important role in industrial production, medical devices, building materials, aerospace, new energy and other fields due to their unique and diverse properties (excellent mechanical strength, good corrosion resistance, excellent optical performance, high thermal conductivity, high specific heat capacity).

[0003] The key to achieving the above-mentioned numerous performance manifestations lies in the formulation design of composite materials. The formulation design of composite materials is to regulate the physical, chemical, and mechanical properties of composite materials by accurately selecting the base materials of composite materials, optimizing the embryo-making process and calcination conditions, and performing sufficient permutations and combinations according to the performance requirements of the target product. An excellent composite material formulation can make up for the strengths and weaknesses of the base materials and achieve the maximum economic benefits and production value. Therefore, formulation design is very important when developing composite materials.

[0004] In the prior art, limited by the current development level of the research on action mechanisms, the formulation design of new materials mainly relies on full permutations and combinations, extensive characterization experiments and performance tests, and then in-depth analysis of a large amount of data to try to discover laws, or even directly select the formulation with the optimal performance from a large amount of sample data.

[0005] However, the above-mentioned formulation design of new materials has the following problems:

[0006] First, a large number of experiments and characterization tests are required, and then a large number of comparisons, searches and other operations are needed, which is time-consuming and laborious;

[0007] Second, it is often competent for simple scenarios, but in the face of complex formulation systems with multiple factors and multiple levels, there is no efficient and scientific design method, and it is impossible to successfully complete scientific and effective experimental design while considering the base materials, embryo-making process and calcination conditions. Summary of the Invention

[0008] Aiming at the deficiencies of the prior art, the present invention proposes an optimization method for the formulation design of solar photothermal conversion composite materials, which can not only efficiently and scientifically select the formulation with the optimal target performance from all possible formulations, but also reduce the number of experiments and improve the efficiency of formulation design.

[0009] To achieve the above object, a method for optimizing the formulation design of a solar thermal conversion composite material designed by the present invention is characterized in that it includes the following steps:

[0010] S1) Determine the base materials, embryo-making process, and calcination conditions to be used for the solar thermal conversion composite material. Select a single component in the base materials, the ratio between components, a single process in the embryo-making process, and a single condition in the calcination conditions as single-factor control variables, determine the target index, and design the selected m single-factor control variables and the selected one target index into a formulation data set;

[0011] S2) Designate the single-factor data group corresponding to each single-factor control variable in the formulation data set such that the data of this single-factor variable is variable and the single-factor data corresponding to the remaining single-factor control variables remains unchanged. Conduct experiments and performance tests. The change in the designated single-factor variable causes a change in the target index, and the selected target index shows the maximum or minimum performance value;

[0012] Take out and combine the single-factor optimal points corresponding to the maximum or minimum performance value of the target index in each group of single-factor data to obtain the single-factor optimal combination;

[0013] S3) Supplement k - 1 level points around each of the m single-factor optimal points in the single-factor optimal combination and fill them into the orthogonal experimental design table to obtain an m-factor k-level orthogonal experimental table;

[0014] S4) Expand the m-factor k-level orthogonal experimental table. The expansion rule is as follows: Under the condition that a certain level of each single factor remains unchanged, perform permutations and combinations of the different k levels of the other m - 1 factors to obtain k groups of experiments corresponding to a certain level of this single factor, and finally obtain the orthogonal experimental table of k groups of experiments for this single factor at k levels;

[0015] S5) Calculate the average value of the target index for each of the k groups of experiments corresponding to each single factor at k levels, and select the optimal level for each single factor. Take out the optimal levels of each single factor and combine them into the optimal design formulation for the composite material to select this target performance.

[0016] Further, in S1), the base materials include simple substances, oxides, salts, and natural mixtures that make up the solar thermal conversion composite material.

[0017] Furthermore, in S1), the embryo-making process includes the selection of the material mixing method, the forming method of the green embryo, and the drying method before calcination.

[0018] Further, in S1), the calcination conditions include calcination duration, calcination temperature, type of calcination furnace used, position of the green body in the furnace, heating rate, and cooling rate.

[0019] Further, in S1), the target indicators include the spectral absorptivity, thermal conductivity, specific heat capacity, hardness, and oxidation resistance of the solar thermal conversion composite material.

[0020] Further, in S1), the single-factor control variables selected can make the target indicators of the solar thermal conversion composite material reach definite maximum or minimum values.

[0021] Further, in S3), the k - 1 level point data supplemented around the m single-factor optimal points in the single-factor optimal combination are selected from the historical database of solar conversion materials or obtained directly from experiments according to the design situation.

[0022] Further, in S4), when expanding the m - factor k - level orthogonal experiment table, under the condition that one level of each single factor remains unchanged, different k levels of the other m - 1 factors need to be fully permuted and combined.

[0023] The advantages of the present invention are as follows:

[0024] 1. The present invention takes the candidate base materials, green body manufacturing process, and proposed calcination conditions of the solar thermal conversion composite material as basic elements, designs a formula dataset using the single-factor control variable method, specifies that the data of the single factor variable corresponding to each single-factor control variable in the formula dataset is variable, and makes the target indicators of the solar thermal conversion composite material reach maximum or minimum values while the single-factor data corresponding to the remaining single-factor control variables remain unchanged;

[0025] 2. The present invention takes out and combines the single-factor optimal points corresponding to the maximum values of the target indicator performance in each group of single-factor data to construct a single-factor optimal combination, and then supplements level point data around each single-factor optimal point to construct a multi-factor multi-level orthogonal experiment table; the supplemented level point data can be selected from the historical database of solar conversion materials or obtained directly from experiments according to the design situation;

[0026] 3. For the constructed multi-factor multi-level orthogonal experiment table, if the traditional method is used for permutation and combination, a large number of experiments and characterization tests are required, and then a large number of comparisons, searches, etc. are also needed, which is time-consuming and laborious; considering the repeatability and comparability of experimental data and the excessive experimental difficulty, the present invention expands the multi-factor multi-level orthogonal experiment table, designs orthogonal experiments for independent single-factor experimental groups, and while considering the mutual influence between other different single factors, reduces the required number of experiments;

[0027] 4. The present invention conducts target index tests on the orthogonal experimental distribution of the independent single-factor experimental group design, calculates the average target index of the independent single-factor experimental group at each level obtained by permuting and combining other factors and levels when the fixed factors are at fixed levels, and then selects the optimal level of each single factor as the optimal design formula for the composite material to select the target performance.

[0028] The formula design optimization method of the solar thermal conversion composite material of the present invention can not only efficiently and scientifically select the formula with the optimal target performance from all possible formulas, but also reduce the number of experiments and improve the formula design efficiency, which has great significance in the field of solar thermal composite materials. Brief Description of the Drawings

[0029] Figure 1 is the flow chart of the present invention;

[0030] Figure 2 is the graph of the thermal conductivity change of the samples at different calcination temperatures at 25°C in this embodiment;

[0031] Figure 3 is the graph of the thermal conductivity change of the samples with different heat preservation durations at 25°C in this embodiment;

[0032] Figure 4 is the graph of the thermal conductivity change of the samples with different SiC contents at 25°C in this embodiment

[0033] Figure 5 is the graph of the thermal conductivity change of the samples with different ratios of Al2O3+TiO2 to Fe2O3+MnO2 at 25°C in this embodiment;

[0034] Figure 6 is the graph of the thermal conductivity change of the samples with different ratios of MnO2 and Fe2O3 at 25°C in this embodiment;

[0035] Figure 7 is the graph of the thermal conductivity change of the samples with different ratios of Al2O3 and TiO2 at 25°C in this embodiment. Detailed Description of the Specific Embodiment

[0036] The following further describes the present invention in detail with reference to the drawings and specific embodiments.

[0037] As Figure 1 shown, a formula design optimization method for a solar thermal conversion composite material includes the following steps:

[0038] S1) Determine the base materials to be selected for the solar thermal conversion composite material, the embryo-making process, and the calcination conditions to be adopted. Select a single component in the base materials, the ratio between components, a single process in the embryo-making process, and a single condition in the calcination conditions as single-factor control variables. Moreover, the single-factor control variables can enable the solar thermal conversion composite material to exhibit an extreme value of a target index. Design the selected m single-factor control variables and the selected target index into a formulation data set.

[0039] In the present invention, the base materials to be selected for the solar thermal conversion composite material, the embryo-making process, and the calcination conditions to be adopted are used as basic elements, and a formulation data set is designed by the single-factor control variable method. The single-factor control variables in the formulation data set enable the solar thermal conversion composite material to exhibit an extreme value of the target index.

[0040] Specifically, the base materials include simple substances, oxides, salts, and natural mixtures that make up the solar thermal conversion composite material.

[0041] Specifically, the embryo-making process includes the selection of the material mixing method, the forming method of the green embryo, and the drying method before calcination.

[0042] Specifically, the calcination conditions include the calcination duration, the calcination temperature, the type of calcination furnace used, the position of the green embryo in the furnace, the heating rate, and the cooling rate.

[0043] Specifically, the selected single-factor control variables can enable the solar thermal conversion composite material to exhibit a definite extreme value of the target index.

[0044] In this embodiment, in step S1), according to historical data information or reference documents, SiC, Al2O3, TiO2, MnO2, and Fe2O3 are selected as the base materials; four single-factor control variables are designed, namely the SiC ratio, the (Al2O3 + TiO2) / (Al2O3 + TiO2 + Fe2O3 + MnO2) ratio, the MnO2 / (Fe2O3 + MnO2) ratio, and the Al2O3 / (Al2O3 + TiO2) ratio.

[0045] Meanwhile, according to historical data information or reference documents, the calcination duration and the calcination temperature in the calcination conditions are selected as two single-factor control variables.

[0046] Specifically, the target index includes the spectral absorptivity, thermal conductivity, specific heat capacity, hardness, and oxidation resistance of the solar thermal conversion composite material.

[0047] In this embodiment, the selected target index is the thermal conductivity of the solar thermal composite material.

[0048] The above six single-factor control variables and one target index constitute a formulation data set. There are six single-factor data groups in the formulation data set, corresponding to the six single-factor control variables respectively.

[0049] S2) For each single-factor data group corresponding to a single-factor control variable in the formulation data set, specify that the data of this single-factor variable is variable, and the single-factor data corresponding to the remaining single-factor control variables remains unchanged. Conduct experiments and performance tests. The change of the specified single-factor variable causes the change of the target index, and the performance maximum or minimum value of the selected target index appears.

[0050] Take out and combine the single-factor optimal points corresponding to the performance maximum or minimum value of the target index in each group of single-factor data to obtain the single-factor optimal combination.

[0051] As Figures 2 to 7 shown, it is the thermal conductivity change diagram at 25 °C of samples with different calcination temperatures, different heat preservation durations, different SiC contents, different ratios of Al2O3+TiO2 to Fe2O3+MnO2, different ratios of MnO2 to Fe2O3, and different ratios of Al2O3 to TiO2 in this embodiment.

[0052] Take out and combine the single-factor optimal points corresponding to the maximum thermal conductivity value in each group of single-factor data to obtain: calcination temperature 1250 °C, calcination time 10 h, SiC ratio 75%, (Al2O3+TiO2) / (Al2O3+TiO2+Fe2O3+MnO2) ratio 50%, MnO2 / (Fe2O3+MnO2) ratio 50%, Al2O3 / (Al2O3+TiO2) ratio 50%.

[0053] S3) Supplement k - 1 level points around each of the m single-factor optimal points in the single-factor optimal combination, and fill them into the orthogonal experimental design table to obtain an m-factor k-level orthogonal experimental table.

[0054] In this embodiment, supplement four level points around each of the above six single-factor optimal points, and fill them into the orthogonal experimental design table to obtain a six-factor five-level orthogonal experimental table, as shown in Table 1.

[0055] Table 1 Six-factor five-level orthogonal experimental table

[0056]

[0057]

[0058] As can be seen from Table 1, the k3 level points are the combination of single-factor optimal points, and the k1, k2, k4, and k5 level points are the four supplemented level points.

[0059] Specifically, the data of the k-1 level points supplemented around the m single-factor optimal points in the single-factor optimal combination are selected from the historical database of solar energy conversion materials or obtained directly through experiments according to the design situation.

[0060] For the above-mentioned six-factor and five-level experiment, if arranged and combined by traditional methods, 5 6 experiments are required. Considering the repeatability and comparability of experimental data, the experimental difficulty is too great. Therefore, the extension of the orthogonal experiment table is introduced.

[0061] S4) Expand the m-factor k-level orthogonal experiment table according to the following rules. Under the condition that a certain level of each single factor remains unchanged, arrange and combine the different k levels of the other m-1 factors to obtain k groups of experiments corresponding to a certain level of this single factor, and finally obtain the orthogonal experiment table of k groups of experiments at k levels of this single factor.

[0062] Specifically, when expanding the m-factor k-level orthogonal experiment table, under the condition that a certain level of each single factor remains unchanged, the different k levels of the other m-1 factors need to be fully arranged and combined.

[0063] In this embodiment, expand the six-factor and five-level orthogonal experiment table. Under the condition that a certain level of each single factor remains unchanged, arrange and combine the different k levels of the other five factors to obtain 5 groups of experiments corresponding to a certain level of this single factor.

[0064] For example, when the calcination temperature of 1150 °C remains unchanged, arrange and combine the different k levels of the calcination time, SiC ratio, (Al2O3 + TiO2) / (Al2O3 + TiO2 + Fe2O3 + MnO2) ratio, MnO2 / (Fe2O3 + MnO2) ratio, and Al2O3 / (Al2O3 + TiO2) ratio to obtain 1-5 groups of experiments; when the calcination temperature of 1200 °C remains unchanged, arrange and combine the different k levels of the calcination time, SiC ratio, (Al2O3 + TiO2) / (Al2O3 + TiO2 + Fe2O3 + MnO2) ratio, MnO2 / (Fe2O3 + MnO2) ratio, and Al2O3 / (Al2O3 + TiO2) ratio to obtain 6-10 groups of experiments. Specifically, as shown in Table 2.

[0065] Table 2 Orthogonal experiment table after expansion

[0066]

[0067]

[0068] As shown in Table 2, for the single factor of calcination temperature, while considering the mutual influence among other different factors, only 25 experiments are needed, reducing the amount of experiments required.

[0069] S5) Calculate the average value of the target indicators for the k groups of experiments corresponding to each single factor at the k level, and select the optimal level of each single factor. Take out the optimal levels of each single factor and combine them to form the optimal design formula for the composite material to select the target performance.

[0070] In this embodiment, calculate the average value of the thermal conductivity of the 5 groups of experiments corresponding to each single factor at the k level. Calculate the average value of the thermal conductivity of each group of experiments obtained by arranging and combining other factors and levels when the fixed factors are at the fixed level, and obtain the orthogonal experiment analysis results, as shown in Table 3.

[0071] Table 3 Orthogonal experiment analysis results

[0072]

[0073] As can be seen from Table 3, taking the calcination temperature k1 as an example, five groups of experiments were carried out under the condition that the temperature level remained unchanged and their average value was A1. The different levels of the other five factors were combined relatively fully, which ensured that the true thermal conductivity at this temperature level was 3.02 W / (m·K) when the other factors were constantly changing. Similarly, when the different levels of the other five factors were fully combined, A2 was the true thermal conductivity of the temperature level k2, which was 2.67 W / (m·K). Therefore, when the different levels of the other five factors were fully combined, the optimal level among the five levels of temperature could be obtained as k5, and the best thermal conductivity performance was 4.455 W / (m·K). By analogy, the optimal levels of the six factors can be obtained.

[0074] In this embodiment, the optimal design formula obtained by taking out the optimal levels of each factor is A5B4C3D3E1F1, that is, the formula is 75% SiC, 10% Al2O3, 2.5% TiO2, 2.5% Fe2O3, 10% MnO2, and keep warm at 1350 °C for 11 h; at the same time, considering that the k3 and k5 levels of SiC / total formula are basically equivalent, take A5B4C5D3E1F1 (hereinafter referred to as C5) as the best supplementary plan, that is, the formula is 65% SIC, 14% Al2O3, 3.5% TiO2, 3.5% Fe2O3, 14% MnO2, and keep warm at 1350 °C for 11 h.

[0075] Furthermore, experiments and tests were carried out on the above two schemes (A5B4C3D3E1F1 and A5B4C5D3E1F1), and the thermal conductivities reached 8.4 W / (m·K) and 8.7 W / (m·K) respectively, which were higher than the performance of any previous combination.

[0076] The method for optimizing the formulation design of the solar energy photothermal conversion composite material of the present invention can not only efficiently and scientifically select the formulation with the optimal target performance from all possible formulations, but also reduce the number of experiments and improve the formulation design efficiency, which is of great significance in the field of solar energy photothermal composite materials.

[0077] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications made without departing from the spirit and principle of the present invention shall be equivalent replacement methods and are all included in the protection scope of the present invention.

Claims

1. A method for optimizing the formulation design of a solar photothermal conversion composite material, characterized in that, It includes the following steps: S1) Determine the base materials to be selected for the solar thermo-conversion composite material, the embryo-making process, and the calcination conditions to be adopted. Select a single component in the base materials, the ratio between components, a single process in the embryo-making process, and a single condition in the calcination conditions as single-factor control variables, determine the target index, and design the selected m single-factor control variables and the selected one target index into a formula data set; S2) Designate the data of the single-factor variable corresponding to each single-factor control variable in the formula data set to be variable, while the single-factor data corresponding to the remaining single-factor control variables remains unchanged. Conduct experiments and performance tests. The change of the designated single-factor variable causes the change of the target index, and the selected target index shows the maximum or minimum performance; Take out and combine the single-factor optimal points corresponding to the maximum or minimum performance of the target index in each group of single-factor data to obtain the single-factor optimal combination; S3) Supplement k - 1 level points around each of the m single-factor optimal points in the single-factor optimal combination and fill them into the orthogonal experiment design table to obtain an m-factor k-level orthogonal experiment table; S4) Expand the m-factor k-level orthogonal experiment table. The expansion rule is as follows: under the condition that one level of each single factor remains unchanged, perform permutations and combinations on the different k levels of the other m - 1 factors to obtain k groups of experiments corresponding to this level of the single factor, and finally obtain the orthogonal experiment table of k groups of experiments for this single factor at k levels; S5) Calculate the average value of the target index of the k groups of experiments corresponding to each single factor at k levels, and select the optimal level of each single factor. Take out the optimal levels of each single factor and combine them into the optimal design formula for the composite material to select this target performance.

2. The method for optimizing the formulation design of the solar thermal conversion composite material according to claim 1, characterized in that: In S1), the base materials include simple substances, oxides, salts, and natural mixtures that make up the solar thermo-conversion composite material.

3. The formula design optimization method of the solar thermal conversion composite material according to claim 2, characterized in that: In S1), the embryo-making process includes the selection of the material mixing method, the forming method of the green embryo, and the drying method before calcination.

4. The method for optimizing the formulation design of the solar thermal conversion composite material according to claim 3, wherein: In S1), the calcination conditions include the calcination duration, the calcination temperature, the type of calcination furnace used, the position of the green embryo in the furnace, the heating rate, and the cooling rate.

5. The method for optimizing the formulation design of the solar thermal conversion composite material according to claim 4, wherein: In S1), the target index includes the spectral absorptivity, thermal conductivity, specific heat capacity, hardness, and oxidation resistance of the solar thermo-conversion composite material.

6. The method for optimizing the formulation design of the solar thermal conversion composite material according to claim 5, characterized in that: In S1), the selected single-factor control variables can make the solar thermo-conversion composite material show the maximum or minimum value of the determined target index.

7. The method for optimizing the formulation design of the solar thermal conversion composite material according to claim 1, characterized in that: In S3), the data of the k - 1 level points supplemented around the m single-factor optimal points in the single-factor optimal combination are selected from the historical database of solar conversion materials or obtained directly from experiments according to the design situation.

8. The method for optimizing the formulation design of the solar photovoltaic and thermal conversion composite material according to claim 1, characterized in that: In S4), when expanding the m-factor k-level orthogonal experiment table, under the condition that one level of each single factor remains unchanged, the different k levels of the other m - 1 factors need to be fully permuted and combined.

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