Preparation method of nano magnesium-based hydrogen storage material based on parameter optimization

Abstract

The invention relates to the technical field of hydrogen storage material preparation, in particular to a preparation method of a nano magnesium-based hydrogen storage material based on parameter optimization. The preparation method comprises the following steps: preparing a solution A from functionalized graphene powder, tetrabutyl titanate and isopropanol, preparing a solution B from scandium nitrate monohydrate and isopropanol, mixing A and B to obtain gel, grinding, calcining, analyzing a state difference index, optimizing feedback error control temperature to obtain powder A by mixing a catalyst and magnesium powder, heating, preserving heat, cooling, preserving heat and cooling the powder A to obtain powder B, and preparing the graphene / scandium nitrate / magnesium powder composite material. And ball-milling the powder B to obtain the nano magnesium-based hydrogen storage material. According to the method, the influence of the heat transfer non-uniformity characteristic on the calcination treatment temperature error in the catalyst calcination process is analyzed, the temperature data at different time points are integrally divided, and the difference is analyzed, so that the influence of the non-uniformity characteristic on the feedback error analysis on the time interruption characteristic on the temperature data change is reduced; the capability of coping with the influence of the non-continuous difference of calcination treatment on feedback error analysis is improved, and the performance of the nano magnesium-based hydrogen storage material is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrogen storage material preparation, and more specifically, to a method for preparing a nano-magnesium-based hydrogen storage material based on parameter optimization. Background Art

[0002] Hydrogen is a zero-carbon fuel and renewable energy carrier with high energy density. After use as a fuel, hydrogen can be completely converted into water and is highly environmentally friendly. An ideal hydrogen storage material should have high hydrogen storage density, the characteristics of rapid absorption and release of hydrogen, and long-term cyclic stability. Magnesium-based hydrogen storage materials have the advantages of high hydrogen storage capacity, abundant magnesium resources, and low cost. They are considered to be a type of solid-state hydrogen storage material with great application prospects. However, their high hydrogen absorption and desorption enthalpy and low diffusion coefficient of hydrogen in magnesium hydride result in excessively high hydrogen absorption and desorption temperatures and slow hydrogen absorption and desorption rates, which limits their application in the field of hydrogen energy.

[0003] In recent years, a large number of research works have focused on the thermal / kinetic modification of magnesium-based hydrogen storage materials. In the process of preparing nano-magnesium-based hydrogen storage materials, the content and performance of the catalyst directly affect the hydrogen storage performance of the nano-magnesium-based hydrogen storage materials. Among them, using graphene-supported titanium dioxide and scandium trioxide composite materials as catalysts for the preparation of nano-magnesium-based hydrogen storage materials can prepare nano-magnesium-based hydrogen storage materials with high saturated hydrogen absorption capacity. During the calcination treatment of the graphene-supported titanium dioxide and scandium trioxide composite materials, the change in calcination temperature within the same time has a greater impact on the performance of the nano-magnesium-based hydrogen storage material prepared using the composite material. Therefore, the control accuracy of the calcination temperature of the above-mentioned composite material is low, which will reduce the hydrogen storage performance of the nano-magnesium-based hydrogen storage material. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, one aspect of the present invention is to provide a method for preparing a nano-magnesium-based hydrogen storage material based on parameter optimization. The specific steps of the preparation method are as follows: S1. mixing graphene nanosheets and concentrated nitric acid solution, cooling to room temperature after reflux, adjusting the mixed solution to neutral, and drying to obtain functionalized graphene powder; S2. The functionalized graphite powder and tetrabutyl titanate prepared in S1 are added to an isopropanol reagent and magnetically stirred to obtain a mixed solution A; scandium nitrate monohydrate is added to the isopropanol, and the solution is adjusted to acidic, and stirred to obtain a mixed solution B; S3. Place the mixed solution B prepared in S2 in a constant temperature bath with magnetic stirring, and drip the mixed solution A into the mixed solution B at a dripping speed of 1 to 3 drops / second, and stir to obtain a gel; S4. The gel prepared in S3 is placed in an oven for drying and then ground into powder, the powder is calcined, the change of calcination temperature in each region during the calcination process is analyzed to determine the calcination state difference index, the calcination error analysis weight is calculated based on the calcination state difference index, the optimization feedback error of the calcination process is obtained, the calcination temperature is controlled, and the graphene-loaded titanium dioxide and scandium trioxide composite material is obtained after calcination; S5. The graphene-loaded titanium dioxide and scandium trioxide composite material prepared in S4 was mixed with magnesium powder as a catalyst by ball milling to obtain a mixed powder A; S6. The mixed powder A prepared in S5 was heated under hydrogen and then kept warm, then cooled and kept warm, and then cooled to room temperature to obtain a mixed powder B; S7. Put the mixed powder B prepared in S6 into a ball mill and perform ball milling to obtain nano-magnesium-based hydrogen storage material.

[0005] Preferably, the reflux temperature in S1 is 140-160° C., and the reflux time is 5-7 h.

[0006] Preferably, the mass ratio of graphene, titanium dioxide and scandium trioxide in the graphene nanosheets in S1 is 3~8:1~3.5:1~3.5.

[0007] Preferably, the mass ratio of the functionalized graphite powder to tetrabutyl titanate in S2 is 1:3, and the mass ratio of the total mass of the functionalized graphite powder and tetrabutyl titanate to scandium nitrate monohydrate is 8:5.

[0008] Preferably, the temperature of the thermostatic bath in S3 is 10-15°C, and the dripping speed is 1-3 drops / second. Preferably, the method for determining the calcination state difference index in S4 is: the calcination temperatures of all collection positions at the same collection time constitute a calcination feature judgment sample, and all calcination feature judgment samples are clustered; the calcination temperatures of all collection times at the same collection position in each cluster category are taken as a row vector, and each calcination feature sub-matrix is ​​constructed, and the mean of the difference between each row vector and other row vectors in the calcination feature sub-matrix is ​​analyzed as the calcination state difference index of each row vector.

[0009] Preferably, the method for calculating the calcination error analysis weight in S4 is: the calcination state difference indicators of all row vectors of each calcination uniform submatrix form a calcination feature vector, and the mean of the similarities between each calcination feature vector and other calcination feature vectors is recorded as the first similarity mean of each calcination uniform submatrix; the proportion of the first similarity mean of each calcination feature submatrix in the first similarity mean of all calcination feature submatrices is used as the calcination error analysis weight of each calcination feature submatrix.

[0010] Preferably, the method for controlling the calcination temperature in S4 is: analyzing the difference between the mean of each row vector in the calcination characteristic submatrix and the set temperature, recording the mean of all differences as the feedback error value of the calcination characteristic submatrix, averaging the product of the feedback error values ​​of all calcination characteristic submatrices and the calcination error analysis weights during the calcination process, and obtaining the optimized feedback error of the calcination process; using the optimized feedback error as the negative feedback result in the PID feedback adjustment process, and adopting PID to control the calcination temperature.

[0011] Preferably, the mass ratio of the magnesium powder in S5 to the obtained graphene-loaded titanium dioxide and scandium trioxide composite material is 19~1; the hydrogen pressure in S6 is 2~2.3MPa, the heating temperature is 580~600℃, the insulation time after heating is 90~120min, the cooling temperature is 340~350℃, and the insulation time after cooling is 4~5h.

[0012] Preferably, in S7, the ball milling speed is 400-500 r / min, the ball milling time is 10-15 h, and the mass ratio of steel balls to mixed powder B is 25-30:1.

[0013] The beneficial effects of the present invention are as follows: The present invention uses a graphene-loaded titanium dioxide and scandium trioxide composite material as a catalyst for a nano-magnesium-based hydrogen storage material, which can further improve the saturated hydrogen absorption capacity of the prepared nano-magnesium-based hydrogen storage material, wherein the performance of the graphene-loaded titanium dioxide and scandium trioxide composite material is greatly affected by the stability of the calcination temperature, and the stability of the catalyst determines the saturated hydrogen absorption capacity of the prepared nano-magnesium-based hydrogen storage material, therefore, considering the influence of the heat transfer non-uniformity characteristics on the analysis of the calcination temperature error during the catalyst calcination process, the temperature data obtained from all temperature collection positions at different time points are divided as a whole, and the relative difference of the calcination temperature in the local area is analyzed through the division results, so as to reduce the influence of the time discontinuous characteristics of the non-uniformity characteristics on the feedback error analysis in the temperature data change, improve the ability to cope with the influence of the discontinuous difference of the calcination treatment on the feedback error analysis, and improve the performance of the prepared nano-magnesium-based hydrogen storage material.

[0014] Additional aspects and advantages of the invention will become apparent from the following description, or may be learned by practice of the invention. DETAILED DESCRIPTION

[0015] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with specific embodiments. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

[0016] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from the description. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0017] Embodiment 1 S1. Mix graphene nanosheets (the mass ratio of graphene, titanium dioxide and scandium trioxide is 3:1:1) and concentrated nitric acid solution, reflux at 140°C for 5 hours and then cool to room temperature, adjust the mixed solution to neutrality, and dry to obtain functionalized graphene powder; S2. The functionalized graphite powder prepared in S1 and tetrabutyl titanate were added to 10 ml of isopropanol reagent in a mass ratio of 1:3, and magnetic stirring was performed for one hour to obtain a mixed solution A; scandium nitrate monohydrate was added to 10 ml of isopropanol, and water and concentrated nitric acid were added to the solution to adjust the pH to 3, and the solution was stirred to obtain a mixed solution B; wherein the mass ratio of the total mass of the functionalized graphite powder and tetrabutyl titanate to the scandium nitrate monohydrate was 8:5; S3. Place the mixed solution B prepared in S2 in a 10°C constant temperature bath with magnetic stirring, and drip the mixed solution A into the mixed solution B at a dripping speed of 1 drop / second, and stir for 2 hours to obtain a gel; S4. Place the gel prepared in S3 in an oven at 50°C for half a day and dry it, then grind it into powder. Calcine the powder at 600°C for 4 hours, analyze the changes in calcination temperature in each area during the calcination process, determine the calcination state difference index, calculate the calcination error analysis weight based on the calcination state difference index, obtain the optimized feedback error of the calcination process, and control the calcination temperature. Step 1: Obtain the temperature data of the catalyst calcination during the preparation of nano-magnesium-based hydrogen storage materials.

[0018] During the preparation of nano-magnesium-based hydrogen storage materials, the performance of the graphene-loaded titanium dioxide and scandium trioxide composite material as a catalyst directly affects the saturated hydrogen absorption capacity of the prepared nano-hydrogen storage material. During the catalyst calcination process, a probe-type temperature sensor is used to collect temperature data. The specific position relationship between the position where the temperature data is collected and the calcined material is deployed by the implementer according to the actual application scenario. In this embodiment, the probe is inserted above the calcined material to collect calcination temperature data at multiple positions. The number of collection positions is , in order to accurately reflect the temperature control error during catalyst calcination, The value of the embodiment is set to 10.

[0019] The temperature data obtained at each sampling position during the catalyst calcination process are arranged in ascending time order as a temperature data sequence.

[0020] At this point, a temperature data sequence for each sampling position during the catalyst calcination process is obtained.

[0021] Step 2: Analyze the changes in calcination temperature in different areas during the calcination process to determine the calcination state difference index.

[0022] In the above preparation process, the obtained gel powder is calcined and the gel powder needs to be kept at a constant temperature of 600°C. However, due to the influence of heat transfer performance in the preparation process, the calcination temperature error accuracy judged only by the average temperature collected during the calcination process is low, and the optimal control parameters cannot be obtained through feedback adjustment. If the calcination of the catalyst is not reflected within the set temperature range, there is a stability difference in the saturated hydrogen absorption capacity of the prepared nano-magnesium-based hydrogen storage material due to the catalyst performance under the current preparation process.

[0023] Normally, the calcination temperature changes in different areas during the catalyst calcination process are consistent. In this case, the error between the collected temperature data and the set calcination parameters can accurately reflect the state of the calcination treatment. However, due to factors such as the influence of environmental heat transfer performance during the calcination process, using the comparison results of the collected temperature data and the set parameters as the feedback result of the calcination temperature may lead to poor control stability of the calcination temperature parameters.

[0024] Therefore, considering the time discontinuity characteristics of the calcination temperature caused by the non-uniformity characteristics of the heat transfer performance, the data change characteristics of different acquisition positions during the catalyst calcination process were relatively analyzed to further determine the temperature error during the calcination process. The specific analysis process is as follows: For the convenience of explanation and analysis, in the embodiment of the present invention, the temperature data sequence of each collection position during the calcination process is taken as a row vector in the matrix, and the matrix composed of the temperature data sequences obtained at all collection positions is recorded as a calcination uniformity feature matrix. Each column of data in the uniformity feature matrix is ​​taken as a calcination feature judgment sample, and all calcination feature judgment samples are taken as input. The agglomerative hierarchical clustering algorithm is used to obtain the division results of all calcination feature judgment samples, and the matrix composed of all samples in each cluster category according to the collection time of the corresponding samples is taken as each calcination feature sub-matrix.

[0025] It should be understood that the above clustering process can use the temperature data at different locations as overall features, and each calcination feature submatrix will be affected by the non-uniformity feature, resulting in data with similar influencing features at discontinuous time points being divided into the same group for comparative analysis.

[0026] For each calcination characteristic sub-matrix, the mean of the difference between each row vector and other row vectors in the calcination characteristic sub-matrix is ​​analyzed as the calcination state difference index of each row vector.

[0027] In an embodiment of the present invention, the Manhattan distance between each row vector and other row vectors in the calcination feature submatrix, that is, the difference between each row vector and other row vectors, is recorded as the first characteristic coefficient. The larger the first characteristic coefficient is, the greater the consistency change difference between local areas under the overall feature analysis of different acquisition positions.

[0028] It should be noted that, in this embodiment, Manhattan distance is used to measure the difference between two vectors. As other implementation methods, implementers can adopt other vector difference measurement methods in the prior art according to actual application scenarios, such as Euclidean distance, etc. The present invention does not impose any special restrictions on this.

[0029] In this embodiment, the calcination state difference index H of each row vector in the calcination characteristic submatrix is ​​calculated according to the first characteristic coefficient obtained by relative difference analysis of similar overall characteristic temperature data.

[0030] In this embodiment, the specific calculation relationship is: ; Among them, h ij represents the first characteristic coefficient between the i-th row vector and the j-th row vector, that is, the difference between the two vectors, It represents the number of acquisition positions, and its size is equal to the number of rows in the calcination feature submatrix; therefore, the larger the value of the calcination state difference index H calculated for each row vector, the greater the relative difference in the consistency of the temperature change in the local calcination area when the overall calcination characteristics change similarly.

[0031] At this point, according to the above process, the calcination feature submatrix of each cluster category can be obtained, and the calcination state difference index of each row vector in each calcination feature submatrix can be obtained.

[0032] Step 3: Calculate the calcination error analysis weight based on the calcination state difference index.

[0033] After the above clustering division, the temperature data with large overall feature differences are divided into various calcination feature sub-matrices. Therefore, the vector composed of the calcination state difference indicators corresponding to all row vectors in each calcination feature sub-matrix is ​​used as the calcination feature vector; therefore, based on the results of the overall feature division, a comparative analysis of the relative difference characteristics of the temperature consistency in the local area is performed, and the relative error characteristics of the calcination parameter changes in the calcination time are reflected through the comparative analysis results.

[0034] Specifically, the similarity between the calcination feature vectors corresponding to each calcination feature submatrix and each other calcination feature submatrix is ​​analyzed. The smaller the similarity, the greater the influence of the temperature non-uniformity in the calcination area on the relative feature comparison after the overall feature division. It should be noted that there are many methods for analyzing the similarity between calcination feature vectors. In this embodiment, cosine similarity is used for measurement. As other implementation methods, on the basis of ensuring the purpose of measuring the similarity between calcination feature vectors, other vector similarity measurement methods of the prior art can be selected according to the actual application scenario, and the present invention does not make any special restrictions.

[0035] Furthermore, the mean of the similarities between each calcination feature vector and other calcination feature vectors is recorded as the first similarity mean of each calcination uniform submatrix, that is, the mean of the results of accumulating the similarities corresponding to each calcination feature submatrix on all calcination feature submatrices is taken as the first similarity mean of each calcination feature submatrix. The smaller the first similarity mean, the greater the difference in regional local relative changes reflected by different calcination feature submatrices after overall feature division, and the greater the error in the temperature data of all acquisition positions obtained at different acquisition times in the time interval during the calcination analysis process.

[0036] Due to the fluidity characteristics of heat transfer during the calcination process, the degree of influence of temperature non-uniformity at different positions has a time discontinuous characteristic. The calcination error analysis weight of the calcination feature submatrix is ​​obtained by calculating the proportion of the first similarity mean corresponding to each calcination feature submatrix in the first similarity mean corresponding to all calcination feature submatrices, which reflects the degree to which the divided data is affected by the calcination treatment non-uniformity.

[0037] The specific calculation formula is: ;in, Indicates The calcination error analysis weights of the calcination feature submatrices, and Respectively represent and The first similarity mean of the calcination feature submatrices, Represents the number of calcination feature sub-matrices; the larger the calculated calcination error analysis weight is, the less the temperature data corresponding to the current calcination feature sub-matrix is ​​affected by temperature uniformity after the overall feature division, and the accuracy of the calcination parameter error feedback analysis is relatively high.

[0038] At this point, the calcination error analysis weight of each calcination feature sub-matrix is ​​obtained.

[0039] Step 4: Determine the optimized feedback error of the calcination process through the calcination error analysis weight, and optimize the calcination temperature.

[0040] After the above analysis, the calcination error analysis weight of each calcination characteristic submatrix can be obtained, which reflects the relative accuracy of the temperature feedback error analysis of each group of temperature data used for calcination treatment after overall division. Therefore, the optimization feedback error in the catalyst calcination process is obtained based on the calcination error analysis weight. The specific calculation and analysis process is as follows: For each calcination feature submatrix, the difference between the mean of each row vector in the calcination feature submatrix and the set temperature is analyzed, and the mean of all differences is recorded as the feedback error value of the calcination feature submatrix. In the embodiment of the present invention, the absolute value of the difference between the mean of each row vector in the calcination feature submatrix and the set temperature is calculated. In the present invention, the set temperature is 600°C, and the mean of all the absolute values ​​is used as the feedback error value of the calcination feature submatrix. , according to the feedback error values ​​of all calcination feature sub-matrices and the corresponding calcination error analysis weights, the optimized feedback error V of the calcination process is obtained. The specific calculation relationship is: ; Wherein, V represents the optimization feedback error of the calcination treatment, and Respectively represent The feedback error value and calcination error analysis weight of the calcination characteristic submatrix, Represents the number of calcination feature submatrices.

[0041] The calculation of the optimized feedback error combines the temperature difference of the non-uniform area caused by heat transfer during the calcination process. Different feedback error analysis weights are assigned according to the difference in the impact of non-uniformity on temperature data analysis to obtain accurate calcination temperature parameter error analysis results.

[0042] During the catalyst calcination process, the temperature is maintained at a constant temperature by using PID feedback regulation, in which the control parameters of PID are determined by the attenuation curve method. The optimized feedback error calculated above is used as the negative feedback result in the PID feedback regulation process. PID is used to obtain the temperature during the calcination process to achieve precise control of the catalyst calcination temperature.

[0043] At this point, after the calcination treatment is completed, a graphene-loaded titanium dioxide and scandium trioxide composite material is obtained (wherein the mass ratio of graphene-loaded titanium dioxide to scandium trioxide is 19:1); S5. The graphene-loaded titanium dioxide and scandium trioxide composite material prepared in S4 was mixed with magnesium powder as a catalyst by ball milling. The ball milling process was carried out in an argon environment for 3 hours at a ball milling speed of 400 r / min. During the ball milling process, the ratio of the mass of the steel ball to the mixed material was 20:1 to obtain a mixed powder A; S6. The mixed powder A prepared in S5 was heated to 580°C under 2MPa hydrogen, kept warm for 90min, then cooled to 340°C and kept warm for 4h, and then cooled to room temperature to obtain a mixed powder B; S7. The mixed powder B prepared in S6 is placed in a ball mill for ball milling at a speed of 400 r / min for 10 hours. The mass ratio of steel balls to mixed powder B is 25:1 to obtain a nano-magnesium-based hydrogen storage material.

[0044] Embodiment 2 S1. Mix graphene nanosheets (the mass ratio of graphene, titanium dioxide and scandium trioxide is 6:2:2) and concentrated nitric acid solution, reflux at 150°C for 6 hours and then cool to room temperature, adjust the mixed solution to neutral, and dry to obtain functionalized graphene powder; S2. The functionalized graphite powder prepared in S1 and tetrabutyl titanate were added to 10 ml of isopropanol reagent in a mass ratio of 1:3, and magnetic stirring was performed for one hour to obtain a mixed solution A; scandium nitrate monohydrate was added to 10 ml of isopropanol, and water and concentrated nitric acid were added to the solution to adjust the pH to 3, and the solution was stirred to obtain a mixed solution B; wherein the mass ratio of the total mass of the functionalized graphite powder and tetrabutyl titanate to the scandium nitrate monohydrate was 8:5; S3. Place the mixed solution B prepared in S2 in a 12°C thermostatic bath with magnetic stirring, and drip the mixed solution A into the mixed solution B at a dripping speed of 2 drops / second, and stir for 2 hours to obtain a gel; S4. The gel prepared in S3 is placed in an oven at 50°C for half a day and then ground into powder, and the powder is calcined at 600°C for 4 hours, and the change of calcination temperature in each area during the calcination process is analyzed to determine the calcination state difference index, and the calcination error analysis weight is calculated based on the calcination state difference index, and the optimization feedback error of the calcination process is obtained to control the calcination temperature; (The specific method is as in Example 1) After the calcination treatment, a graphene-loaded titanium dioxide and scandium trioxide composite material is obtained (the mass ratio of graphene-loaded titanium dioxide to scandium trioxide is 19:1); S5. The graphene-loaded titanium dioxide and scandium trioxide composite material prepared in S4 was mixed with magnesium powder as a catalyst by ball milling. The ball milling process was carried out in an argon environment for 3 hours at a ball milling speed of 400 r / min. During the ball milling process, the ratio of the mass of the steel ball to the mixed material was 20:1 to obtain a mixed powder A; S6. The mixed powder A prepared in S5 was heated to 590°C under 2.2MPa hydrogen, kept warm for 100min, then cooled to 345°C and kept warm for 4.5h, and then cooled to room temperature to obtain a mixed powder B; S7. The mixed powder B prepared in S6 is placed in a ball mill for ball milling at a speed of 450 r / min for 12 hours. The mass ratio of steel balls to mixed powder B is 28:1 to obtain a nano-magnesium-based hydrogen storage material.

[0045] Embodiment 3 S1. Mix graphene nanosheets (the mass ratio of graphene, titanium dioxide and scandium trioxide is 8:3.5:3.5) and concentrated nitric acid solution, reflux at 160°C for 7 hours and then cool to room temperature, adjust the mixed solution to neutrality, and dry to obtain functionalized graphene powder; S2. The functionalized graphite powder prepared in S1 and tetrabutyl titanate were added to 10 ml of isopropanol reagent in a mass ratio of 1:3, and magnetic stirring was performed for one hour to obtain a mixed solution A; scandium nitrate monohydrate was added to 10 ml of isopropanol, and water and concentrated nitric acid were added to the solution to adjust the pH to 3, and the solution was stirred to obtain a mixed solution B; wherein the mass ratio of the total mass of the functionalized graphite powder and tetrabutyl titanate to the scandium nitrate monohydrate was 8:5; S3. Place the mixed solution B prepared in S2 in a 15°C constant temperature bath with magnetic stirring, and drip the mixed solution A into the mixed solution B at a dripping speed of 3 drops / second, and stir for 2 hours to obtain a gel; S4. The gel prepared in S3 is placed in an oven at 50°C for half a day and then ground into powder, and the powder is calcined at 600°C for 4 hours, and the change of calcination temperature in each area during the calcination process is analyzed to determine the calcination state difference index, and the calcination error analysis weight is calculated based on the calcination state difference index, and the optimization feedback error of the calcination process is obtained to control the calcination temperature; (The specific method is as in Example 1) After the calcination treatment, a graphene-loaded titanium dioxide and scandium trioxide composite material is obtained (the mass ratio of graphene-loaded titanium dioxide to scandium trioxide is 19:1); S5. The graphene-loaded titanium dioxide and scandium trioxide composite material prepared in S4 was mixed with magnesium powder as a catalyst by ball milling. The ball milling process was carried out in an argon environment for 3 hours at a ball milling speed of 400 r / min. During the ball milling process, the ratio of the mass of the steel ball to the mixed material was 20:1 to obtain a mixed powder A; S6. The mixed powder A prepared in S5 was heated to 600°C under 2.3MPa hydrogen, kept warm for 120min, then cooled to 350°C and kept warm for 5h, and then cooled to room temperature to obtain a mixed powder B; S7. The mixed powder B prepared in S6 is placed in a ball mill for ball milling at a speed of 500 r / min for 15 hours. The mass ratio of steel balls to mixed powder B is 30:1 to obtain a nano-magnesium-based hydrogen storage material.

[0046] To analyze the calcination temperature adjustment effect in the embodiment of the present invention, the specific results are shown in Table 1 below.

[0047] Table 1 The present invention compares and analyzes the hydrogen absorption amounts of the nano-magnesium-based hydrogen storage materials produced in the three embodiments of the present invention and the comparative example under a hydrogen pressure of 3.0 MPa in different time periods (100 s, 10 min, 30 min). The comparative example does not use the process of steps 1 to 4 of the present invention to control and adjust the calcination temperature. Correspondingly, the three embodiments of the present invention use the stability optimization control steps of the calcination temperature in the present invention, that is, the process of steps 1 to 4 to control the calcination temperature. By comparing and analyzing the hydrogen absorption amounts of the nano-magnesium-based hydrogen storage materials obtained in the comparative example and the three embodiments, it is found that the nano-magnesium-based hydrogen storage materials produced by controlling the calcination temperature in the three embodiments of the present invention have higher hydrogen storage capacity and higher hydrogen absorption amount than the nano-magnesium-based hydrogen storage materials produced by fixing the calcination temperature.

[0048] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may also have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for preparing a nano-magnesium-based hydrogen storage material based on parameter optimization, characterized in that: The specific steps of the preparation method are as follows: S1. mixing graphene nanosheets and concentrated nitric acid solution, cooling to room temperature after reflux, adjusting the mixed solution to neutral, and drying to obtain functionalized graphene powder; S2. The functionalized graphite powder and tetrabutyl titanate prepared in S1 are added to the isopropanol reagent and magnetically stirred to obtain a mixed solution A; Adding scandium nitrate monohydrate into isopropanol, adjusting the solution to be acidic, and stirring to obtain a mixed solution B; S3. Place the mixed solution B prepared in S2 in a constant temperature bath with magnetic stirring, and drip the mixed solution A into the mixed solution B at a dripping speed of 1 to 3 drops / second, and stir to obtain a gel; S4. The gel prepared in S3 is placed in an oven for drying and then ground into powder, the powder is calcined, the change of calcination temperature in each region during the calcination process is analyzed to determine the calcination state difference index, the calcination error analysis weight is calculated based on the calcination state difference index, the optimization feedback error of the calcination process is obtained, the calcination temperature is controlled, and the graphene-loaded titanium dioxide and scandium trioxide composite material is obtained after calcination; S5. The graphene-loaded titanium dioxide and scandium trioxide composite material prepared in S4 was mixed with magnesium powder as a catalyst by ball milling to obtain a mixed powder A; S6. The mixed powder A prepared in S5 was heated under hydrogen and then kept warm, then cooled and kept warm, and then cooled to room temperature to obtain a mixed powder B; S7. Put the mixed powder B prepared in S6 into a ball mill and perform ball milling to obtain nano-magnesium-based hydrogen storage material.

2. The method for preparing a nano-magnesium-based hydrogen storage material based on parameter optimization according to claim 1, characterized in that: The reflux temperature in S1 is 140-160° C., and the reflux time is 5-7 h.

3. The method for preparing a nano-magnesium-based hydrogen storage material based on parameter optimization according to claim 1, characterized in that: The mass ratio of graphene, titanium dioxide and scandium trioxide in the graphene nanosheets in S1 is 3-8:1-3.5:1-3.

5.

4. The method for preparing a nano-magnesium-based hydrogen storage material based on parameter optimization according to claim 1, characterized in that: The mass ratio of the functionalized graphite powder to tetrabutyl titanate in S2 is 1:3, and the mass ratio of the total mass of the functionalized graphite powder and tetrabutyl titanate to scandium nitrate monohydrate is 8:

5.

5. The method for preparing a nano-magnesium-based hydrogen storage material based on parameter optimization according to claim 1, characterized in that: The temperature of the thermostatic bath in S3 is 10-15° C., and the dropping speed is 1-3 drops / second.

6. The method for preparing a nano-magnesium-based hydrogen storage material based on parameter optimization according to claim 1, characterized in that: The method for determining the calcination state difference index in S4 is: the calcination temperatures of all collection positions at the same collection time constitute a calcination feature judgment sample, and all calcination feature judgment samples are clustered; the calcination temperatures of all collection times at the same collection position in each cluster category are taken as a row vector, and each calcination feature sub-matrix is ​​constructed, and the mean of the difference between each row vector and other row vectors in the calcination feature sub-matrix is ​​analyzed as the calcination state difference index of each row vector.

7. The method for preparing a nano-magnesium-based hydrogen storage material based on parameter optimization according to claim 1, characterized in that: The method for calculating the calcination error analysis weight in S4 is: the calcination state difference indicators of all row vectors of each calcination uniform submatrix form a calcination feature vector, and the mean of the similarities between each calcination feature vector and other calcination feature vectors is recorded as the first similarity mean of each calcination uniform submatrix; the proportion of the first similarity mean of each calcination feature submatrix in the first similarity mean of all calcination feature submatrices is used as the calcination error analysis weight of each calcination feature submatrix.

8. The method for preparing a nano-magnesium-based hydrogen storage material based on parameter optimization according to claim 1, characterized in that: The method for controlling the calcination temperature in S4 is: analyzing the difference between the mean of each row vector in the calcination characteristic submatrix and the set temperature, recording the mean of all differences as the feedback error value of the calcination characteristic submatrix, averaging the product of the feedback error values ​​of all calcination characteristic submatrices and the calcination error analysis weights during the calcination process, and obtaining the optimized feedback error of the calcination process; using the optimized feedback error as the negative feedback result in the PID feedback adjustment process, and adopting PID to control the calcination temperature.

9. The method for preparing a nano-magnesium-based hydrogen storage material based on parameter optimization according to claim 1, characterized in that: The mass ratio of the magnesium powder in the S5 to the obtained graphene-loaded titanium dioxide and scandium trioxide composite material is 19~1; the hydrogen pressure in the S6 is 2~2.3MPa, the heating temperature is 580~600℃, the insulation time after heating is 90~120min, the cooling temperature is 340~350℃, and the insulation time after cooling is 4~5h.

10. The method for preparing a nano-magnesium-based hydrogen storage material based on parameter optimization according to claim 1, characterized in that: In the S7, the ball milling speed is 400-500 r / min, the ball milling time is 10-15 h, and the mass ratio of steel balls to mixed powder B is 25-30:1.

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