Preparation method of red mud composite phase change heat storage material

Abstract

The application discloses a preparation method of a red mud composite phase change heat storage material and relates to the technical field of material preparation process optimization. The method constructs a historical database containing temperature, power and product latent heat, calculates basic heating power based on data quality, collects current heating data in real time, calculates an inertia factor representing heat diffusion capacity and a temperature rise factor representing specific heat capacity characteristics, constructs a thermal response feature vector to quantify the composition similarity between the current batch and the historical record, dynamically determines the real-time target temperature for precise control by screening high-similarity high-quality historical records, and updates the database with new data. The application indirectly reflects the composition difference of red mud raw materials through thermal response characteristics, solves the problem of process window drift caused by composition fluctuation, realizes self-adaptive temperature control without complex composition detection, and significantly improves the phase change latent heat and batch consistency of the product.

Description

Technical Field

[0001] This application relates to the field of material preparation process optimization technology, and in particular to a method for preparing red mud composite phase change thermal storage material. Background Technology

[0002] Red mud is an industrial solid waste generated during alumina production. Its mineral composition is extremely complex, containing various components such as Al₂O₃, Fe₂O₃, SiO₂, and CaO. Due to its abundant pore structure and good thermal and chemical stability, red mud shows potential application value in the field of thermal energy storage.

[0003] Phase change thermal energy storage materials (PCEs) are functional materials that utilize the absorption or release of latent heat during phase change processes to achieve thermal energy storage and temperature regulation. The preparation of composite PCEs by combining properly treated red mud with PCEs shows promise for applications in solar energy utilization and building energy conservation.

[0004] The common industrial preparation process for red mud composite phase change thermal storage materials mainly utilizes the porous structure of red mud itself to physically adsorb and encapsulate the phase change material. A typical step involves: first, drying and grinding the red mud; then, mixing the ground red mud with phase change materials such as paraffin wax; finally, grinding the mixture in a grinding mill to ensure the paraffin wax is fully embedded in the pore structure of the red mud; and finally, heating during grinding to promote the uniform distribution and composite of the phase change material, resulting in the final product. This method is simple, easy to operate, and suitable for large-scale industrial production. It is particularly suitable for using the resulting composite material as building insulation material to reduce indoor temperature fluctuations and achieve building energy conservation.

[0005] Red mud has a complex composition, and the proportion of red mud components varies greatly depending on the process used in bauxite mining and alumina production. This leads to drastic fluctuations in the composition of red mud raw materials used in the preparation of red mud composite phase change thermal energy storage materials.

[0006] The thermal conductivity, specific heat capacity, surface acidity / alkalinity, and pore structure of red mud are all determined by its composition. Therefore, when the composition of the red mud raw material is different, the optimal composite temperature window for the preparation of red mud composite phase change thermal storage materials will also drift.

[0007] Existing methods for preparing red mud composite phase change thermal energy storage materials typically employ a fixed preset temperature, neglecting the impact of red mud composition differences on the optimal process window, resulting in high product scrap rates and poor batch-to-batch consistency. Summary of the Invention

[0008] Therefore, it is necessary to provide a method for preparing red mud composite phase change thermal energy storage materials to address the above-mentioned technical problems.

[0009] The present invention adopts the following technical solution: This invention provides a method for preparing a red mud composite phase change thermal storage material, comprising: A historical preparation process database is constructed, which contains temperature time series, heating power time series, and latent heat of phase change of the finished product for multiple historical records of the preparation process of red mud composite phase change thermal storage materials. The temperature time series is segmented according to the heating power time series, and the ideal temperature at each time moment is obtained by fitting the temperature time series of each segment; the temperature fluctuation coefficient of each historical record is obtained based on the degree of deviation of the temperature time series of each historical record from the ideal temperature; the heating reference weight of each historical record is obtained based on the latent heat of phase change and the temperature fluctuation coefficient, and the basic heating power of the current preparation process in each segment is calculated by weighting. Analyze the thermal response characteristics of each historical record and the current preparation process, calculate the inertia factor from the delay between power change and temperature rise, calculate the temperature rise factor from the temperature rise rate under unit heating power, construct a thermal response feature vector using the inertia factor and temperature rise factor, and calculate the thermal response similarity between the current preparation process and each historical record. The recommended weight is calculated based on the heating reference weight and thermal response similarity. The historical records with the highest recommended weight are selected as candidate targets. The temperature of the candidate target at the end of the current segment is taken as the real-time target temperature of the current preparation process. When the current temperature is detected to have reached the real-time target temperature, the control proceeds to the next stage; after heating is completed, the data of the current preparation process is appended to the historical preparation process database.

[0010] Further, the segmentation of the temperature time series according to the heating power time series includes: For each historical heating power time series, the slope change detection method is used to extract the times corresponding to the two turning points with the largest power changes. The temperature time series of the heating process is divided into the preheating stage, the immersion stage, and the heat preservation stage based on the times corresponding to these two turning points.

[0011] Furthermore, obtaining the temperature fluctuation coefficient for each historical record includes: For any historical record, obtain the deviation between the temperature and the ideal temperature at each moment in each segment of the historical record, and calculate the standard deviation of the deviation for all moments in each segment of the historical record; characterize the temperature fluctuation coefficient of the segment by the ratio of the standard deviation of the deviation to the range of the temperature at all moments in the segment; take the average of the temperature fluctuation coefficients of all segments of the historical record as the temperature fluctuation coefficient of the historical record.

[0012] Furthermore, obtaining the heating reference weight for each historical record includes: For any historical record, the product quality level of that historical record is characterized by the proportion of the maximum value of the latent heat of phase change of the historical record to the maximum value of the latent heat of phase change of all historical records. The product quality level is multiplied by the reciprocal of its temperature fluctuation coefficient, and the product is normalized proportionally. The normalized result is used as the heating reference weight of that historical record.

[0013] Furthermore, the weighted calculation yields the basic heating power for each stage of the current preparation process, including: Using the heating reference weight as the weight for the weighted average calculation, a weighted average calculation is performed on the heating power settings of all historical records in each segment, and the result of the weighted average calculation is used as the basic heating power of the current preparation process in each segment.

[0014] Furthermore, the calculation of the inertia factor based on the delay between power change and temperature rise includes: The normalized result of the delay of the response time of each historical segment relative to the start time of that segment is used as the inertia factor for that historical segment.

[0015] Furthermore, the method for obtaining the response time includes: The average of the temperature rise rate at all times in each historical record segment is recorded as the baseline state rate for that segment. When the ratio of the temperature rise rate of a historical record segment to the baseline state rate first exceeds a preset scaling factor, the corresponding time is recorded as the response time for that segment.

[0016] Furthermore, the calculation of the temperature rise factor based on the temperature rise rate per unit heating power includes: The instantaneous temperature rise factor is characterized by the ratio of the rate of temperature rise at each moment in the historical record to the average heating power of the segment in which the moment is located. A temperature rise window is preset, and the average instantaneous temperature rise factor of all moments within the temperature rise window with each moment as the boundary is calculated as the temperature rise factor of each moment in each segment of the historical record.

[0017] Furthermore, the calculation of the similarity between the current preparation process and the thermal response of each historical record includes: The inversely proportional normalized result of the Euclidean distance between the thermal response vector of the current fabrication process at each time step and the thermal response vector of the corresponding segment of each historical record at that time step is denoted as the thermal response similarity between the current fabrication process and the historical record at that time step.

[0018] Furthermore, the calculation of recommendation weights based on heating reference weights and thermal response similarity includes: Using the time sequence as weight, the weighted average of the heat response similarity between all times before the current time and each historical record is obtained, which represents the similarity level of the heat response features between the current time and each historical record. This is further multiplied by the heating reference weight of each historical record, and the product is used as the recommendation weight of each historical record at the current time.

[0019] The above-mentioned technical solution adopted in this invention can achieve the following beneficial effects: In the preparation method of red mud composite phase change thermal storage material provided by this invention, a historical database containing temperature, power and product quality is constructed; secondly, the basic heating power is determined by analyzing the fluctuation and quality of historical data; the core lies in real-time monitoring of the current preparation process, calculating the inertia factor reflecting the thermal diffusion capacity of the raw materials and the temperature rise factor reflecting the specific heat capacity characteristics, thereby constructing a thermal response feature vector and calculating the similarity with the historical records; then, the historical records with the closest composition characteristics and the best quality are selected as references to dynamically determine the real-time target temperature of the current process; finally, after the heating is completed, the new data is fed back into the database.

[0020] This invention transforms the differences in red mud raw material composition, which are difficult to measure directly, into the thermal response characteristics of the mixture during heating. It utilizes inertia factors and temperature rise factors to quantify the thermal conductivity and specific heat capacity of different batches of raw materials, thereby achieving dynamic optimization of the preparation process of red mud composite phase change thermal storage materials. This effectively solves the problem of optimal process window drift caused by fluctuations in red mud composition. By matching historical high-quality data to adjust the target temperature in real time, it significantly improves product quality and batch consistency, while avoiding the high cost of real-time chemical composition testing, achieving low-cost, high-efficiency intelligent temperature control. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0022] Figure 1 This is a schematic diagram of a method for preparing a red mud composite phase change thermal energy storage material provided in this specification; Figure 2 This is a schematic diagram of the heating process segmentation provided in this instruction manual; Figure 3 This is the logic diagram of the temperature control strategy provided in this manual; Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this application will be clearly and completely described below with reference to specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments in the specification without creative effort are within the scope of protection of this application.

[0024] Due to differences in the origin and production process of red mud, the composition of the raw materials used to prepare composite phase change thermal energy storage materials fluctuates drastically. The proportions of components such as Al2O3, Fe2O3, and SiO2 vary greatly, leading to changes in thermal conductivity and specific heat capacity, causing the fixed optimal process window to drift. Existing technologies typically use preset fixed temperature curves for heating, which cannot dynamically adjust the heating strategy according to the actual thermal response characteristics of the current batch of raw materials, resulting in high product scrap rates and poor batch consistency. To solve the problem of compositional differences, existing technologies usually rely on chemical composition analysis, which has the problems of high testing costs, low efficiency, and difficulty in providing real-time guidance for production.

[0025] The purpose of this invention is to reflect the compositional differences of red mud raw materials by using the temperature change state of the mixture during the heating process, and to dynamically optimize the temperature control in the preparation process of red mud composite phase change thermal storage materials, thereby improving product quality and batch consistency.

[0026] The technical solutions provided by the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0027] Figure 1 This is a schematic diagram of a method for preparing a red mud composite phase change thermal energy storage material as described in this specification, which specifically includes the following steps: S101: Construct a historical preparation process database, which contains temperature time series, heating power time series, and latent heat of phase change of the finished product for multiple historical records of the preparation process of red mud composite phase change thermal storage materials.

[0028] This step involves building a historical preparation process database, which will serve as the data foundation for subsequent analysis.

[0029] Historical records of the preparation process of several batches of red mud composite phase change thermal energy storage materials were collected. Each record includes the temperature time sequence during the heating process of that batch of red mud composite phase change thermal energy storage materials, the heating output power time sequence of the heating equipment, and the latent heat of phase change of the final red mud composite phase change thermal energy storage material product prepared in that batch. The specific data collection method is as follows: In the heating process of preparing red mud composite phase change thermal storage material, from the start of heating to the end of heat preservation, a sheathed thermocouple is inserted into the center of the mixed material, and the temperature of the mixed material is recorded at fixed sampling intervals in degrees Celsius. In this embodiment, the fixed sampling interval is preset to 1 second, which can be adjusted according to the specific implementation situation without specific limitation. The heating power of the heating equipment is directly read by the controller of the heating power supply and recorded synchronously with the temperature data in watts. The latent heat of phase change of the product is measured by differential scanning calorimetry in joules per gram, and the latent heat of phase change is used as an evaluation index of product quality.

[0030] A database of historical preparation processes was constructed using all historical temperature time series, heating power time series, and latent heat of phase change.

[0031] Thus, a historical preparation process database has been constructed, providing a reference for the heating settings of subsequent preparation processes by using heating process-related data and product quality from several historical preparation process records.

[0032] S102: The temperature time series is segmented according to the heating power time series, and the ideal temperature at each time moment is obtained by fitting the temperature time series of each segment; the temperature fluctuation coefficient of each historical record is obtained based on the degree of deviation of the temperature time series of each historical record from the ideal temperature; the heating reference weight of each historical record is obtained based on the latent heat of phase change and the temperature fluctuation coefficient, and the basic heating power of the current preparation process in each segment is calculated by weighting.

[0033] This step analyzes the volatility of each historical temperature time series, combines it with product performance, quantifies the reference value of each historical record, and then preliminarily determines the heating power for the current preparation process.

[0034] It should be noted that there may be some records in the historical records that are inherently unstable. Such records are of low reliability as a reference for the current preparation process. Therefore, the reliability of the historical records should be analyzed first.

[0035] The typical heating process can be divided into three main stages: Preheating stage, with low heating power and a slow temperature rise; the purpose of this stage is to achieve uniform preheating and avoid thermal shock. Wetting stage, with high heating power and a rapid temperature rise; in this stage, the phase change material melts extensively and penetrates into the pores of the red mud through capillary action. Holding stage, with low heating power and a relatively constant temperature; the purpose of this stage is to promote the filling of deep pores, such as… Figure 2 The diagram shows a segmented heating process. Different segments exhibit different temperature characteristics; therefore, based on the heating power, the temperature time series is divided into three segments for further analysis of the fluctuations in each segment.

[0036] Specifically, for each historical heating power time series, a slope change detection method is used to extract the times corresponding to the two turning points with the largest power changes. The temperature time series of the heating process is divided into three segments based on these two turning points. A least squares method is used to perform linear fitting on each of the three segments, and the resulting linear fitting is recorded as the ideal temperature curve for each segment. The temperature value at each moment on the ideal temperature curve is recorded as the ideal temperature of that historical record at that moment. The temperature time series of each historical record is compared with the ideal temperature, and the magnitude of the difference reflects the degree of fluctuation of each historical record, recorded as the temperature fluctuation coefficient of each historical record. Taking the k-th historical record as an example, the temperature fluctuation coefficient of the k-th historical record... The calculation method is as follows:

[0037] (1) in, This represents the temperature of the k-th historical record at the i-th time in the p-th segment. This represents the ideal temperature at time i in segment p of the k-th historical record. This represents the temperature range of the k-th historical record in the p-th segment. Let represent a positive real number close to 0, used to avoid a denominator of 0; here, we take . Adjustments can be made according to specific implementation conditions; this invention does not impose specific limitations in this regard. This represents the standard deviation function. This represents the function for taking the average value.

[0038] It should be noted that This represents all moments in the p-th segment of the heating process for the k-th historical record. The value is taken as the standard deviation, which represents the dispersion of the deviation between the actual temperature and the ideal temperature at all times in segment p. The larger the value, the greater the fluctuation of the actual temperature, and the lower the reliability of the data. This represents all segments of the k-th historical record. The values ​​are taken as average. It is used as the denominator here for normalization.

[0039] Furthermore, for historical records with better product quality, the heating settings are more likely to be optimal for the preparation process. Therefore, using these historical records as a reference for the current heating settings in the preparation process yields higher reliability. Combining the fluctuation level of each historical record with product quality, a heating reference weight is obtained for each historical record. The heating reference weight of the k-th historical record is... The calculation method is as follows:

[0040] (2) in, This represents the latent heat of phase transition for the k-th historical record. This represents the maximum value of the latent heat of phase transition across all historical records. This represents the temperature fluctuation coefficient of the k-th historical record. This represents the Sigmoid function, used here for proportional normalization.

[0041] It should be noted that The value represents the overall product quality level of the k-th historical record across all historical records. The larger the value, the more reliable the k-th historical record is. This value is combined with the temperature fluctuation level of the k-th historical record. The larger the temperature fluctuation level, the lower the reliability. This yields the heating reference weight of the historical record.

[0042] Furthermore, using the heating reference weight as the weight, the weighted average of the heating power settings for each segment across all historical records is obtained as the basic heating power for the current preparation process in each segment. When the current preparation process enters a corresponding segment, heating is performed using the basic heating power of that segment. The basic heating power for the p-th segment of the current preparation process... The calculation method is as follows:

[0043] (3) in, This represents the total number of all historical records. This represents the heating reference weight of the k-th historical record. This represents the average heating power of the k-th historical record in the p-th segment.

[0044] At this point, the basic heating power of each segment of the current preparation process is obtained. This step reflects the data quality of each historical record through the temperature fluctuation coefficient, and constructs a heating reference weight based on the product quality level. Based on the segmented processing of the preparation heating process, the reliable basic heating power of each segment of the current preparation process is obtained by weighting.

[0045] S103: Analyze the thermal response characteristics of each historical record and the current preparation process, calculate the inertia factor from the delay between power change and temperature rise; calculate the temperature rise factor from the temperature rise rate under unit heating power; construct a thermal response feature vector using the inertia factor and temperature rise factor, and calculate the thermal response similarity between the current preparation process and each historical record.

[0046] This step analyzes the temperature change characteristics of the current preparation process and historical records to reflect the different thermal responses of the mixture caused by different red mud raw material components. Based on the temperature change characteristics, the similarity between each historical record and the current process is quantified.

[0047] It should be noted that due to the varying compositions of red mud raw materials, particularly the presence of metal oxides such as Al2O3 and Fe2O3, a higher proportion of metal oxides results in stronger thermal conductivity, faster response to temperature changes, and a faster rate of temperature rise. Conversely, higher proportions of components like SiO2 and CaO lead to poorer thermal conductivity. In actual production, the composition of red mud raw materials fluctuates significantly, and measuring and analyzing the composition of all raw materials is costly and inefficient. Therefore, this step transforms the difficult-to-determine compositional differences of red mud raw materials into the thermal response characteristics of the mixture for analysis.

[0048] When controlling the temperature during the preparation of red mud composite phase change thermal storage materials, the different compositions of the red mud raw materials manifest in temperature changes specifically in the speed of temperature change response of the mixture during heating, as well as the difference in the degree of temperature rise. These differences in temperature change characteristics reflect the different heat transfer capabilities of the raw materials. By constructing characteristics based on the specific temperature changes of each historical record, quantitative comparisons of different batches of red mud raw materials can be achieved.

[0049] Specifically, the temperature change characteristics of the preheating and immersion sections for each historical record are analyzed. The temperature increase at each moment relative to the previous moment is divided by the sampling period to obtain the temperature rise rate at each moment.

[0050] Taking the preheating stage as an example, the average of the temperature rise rate of all historical records at all times in this stage is recorded as the reference state rate of this stage. When the temperature rise rate of the kth historical record in this stage exceeds 75% of the reference state rate for the first time, the corresponding time is recorded as the response time of this stage. Here, 75% is a preset proportional coefficient in this embodiment, which is used to specify the threshold for the temperature to enter the response state. This value can be adjusted according to the specific implementation situation, and this embodiment does not make specific limitations.

[0051] The inertia factor, denoted as the inertia factor, reflects the response speed of the mixture to temperature changes, based on the delay between power change and temperature rise. The inertia factor in the p-th historical record is shown in the k-th record. The calculation method is as follows:

[0052] (4) in, This indicates the response time of the k-th historical record in the p-th segment. This indicates the starting time of the k-th historical record in the p-th segment.

[0053] Obtain the inertia factor of the k-th historical record in the preheating and immersion stages, i.e. and time The average of the inertia factors of these two segments is denoted as the inertia factor of the k-th historical record. .

[0054] Furthermore, the temperature rise rate is reflected by the ratio of the temperature rise rate to the power, and is denoted as the temperature rise factor. The preset temperature rise window size is 5. Each time point is considered as a window spanning 5 time points backward from itself. The temperature rise level at each time point is analyzed within the temperature rise window. The temperature rise factor at time point j in segment p of the k-th historical record is then determined. The calculation method is as follows:

[0055] (5) in, This indicates the preset temperature rise window size. This represents the rate of temperature increase at time j in segment p of the k-th historical record. This represents the average heating power of the k-th historical record in the p-th segment; specifically, when the number of moments before the j-th moment in the p-th segment is less than the preset temperature rise window size, the maximum number of moments that can be obtained is taken.

[0056] It should be noted that The instantaneous temperature rise factor of the k-th historical record at the j-th moment in segment p is represented. To reduce noise and smooth the data, a temperature rise window is preset here. The average value of the instantaneous temperature rise factor of all moments within the temperature rise window with the j-th moment of segment p as the boundary is used as the temperature rise factor at the j-th moment of segment p.

[0057] Obtain the temperature rise factor of the k-th historical record at all times during the preheating and immersion phases, and calculate the average of the temperature rise factors at all times during these two phases. This average is denoted as the first temperature rise factor of the k-th historical record. With the second temperature rise factor .

[0058] Furthermore, for the current preparation process, when the current preparation process enters the corresponding segment, it is heated with the basic heating power of that segment, and the response time of the current preparation process is detected. After entering the response time, the inertia factor and temperature rise factor of the current preparation process are obtained in the same way as the above process.

[0059] A thermal response feature vector is constructed based on the inertia factor and the temperature rise factor. The thermal response vector of the k-th historical record in the preheating stage is denoted as the first thermal response vector. The thermal response vector in the immersion section is denoted as the second thermal response vector. The thermal response vector of the current preparation process at time m is denoted as... ,in This represents the inertia factor of the current preparation process. This represents the temperature rise factor at time m in the current preparation process, and the thermal response vector characterizes the historical records and the temperature changes during the heating process in the current preparation process.

[0060] It should be noted that since the current preparation process is not a complete heating process, taking the average value of the temperature rise factor here may not accurately reflect the temperature change status of the current preparation process due to excessive local influence.

[0061] The distance between the thermal response vectors of historical records and the current preparation process reflects the similarity of temperature change characteristics between the two historical records. The inversely proportional normalized result of the Euclidean distance between the thermal response vector of the current preparation process at time m and either the first or second thermal response vector of the k-th historical record is denoted as the thermal response similarity between the current preparation process at time m and the k-th historical record. Here, the first thermal response vector is used when m belongs to the preheating stage, and the second thermal response vector is used when m belongs to the immersion stage. This process yields the thermal response similarity between the current preparation process at all times and all historical records.

[0062] Thus, the similarity between the current preparation process and historical records has been quantified. The specific heat capacity and thermal diffusivity of the raw materials are reflected by the temperature rise factor and inertia factor, respectively. The complex compositional differences are transformed into numerical thermal response characteristics, realizing the quantitative comparison of different batches of red mud raw materials. In actual production, similarity measurement can be performed in a low-cost and real-time manner using conventional thermocouple signals.

[0063] S104: Calculate the recommendation weight based on the heating reference weight and thermal response similarity, select the historical records with the highest recommendation weight as candidate targets, and use the temperature of the candidate target at the end of the current segment as the real-time target temperature of the current preparation process.

[0064] This step, based on thermal response similarity, selects several historical records from the historical database that are closest to the thermal response characteristics of the current preparation process, providing a reliable temperature setting reference for the current preparation process.

[0065] It should be noted that thermal response similarity characterizes the degree of similarity of thermal response characteristics between different batches of raw materials. In the preparation process of red mud composite phase change thermal storage materials, the thermal response characteristics are mainly affected by the proportion of metal oxides in the red mud raw materials. Therefore, by using thermal response similarity, we can screen out the historical records that are most similar to the current preparation process in terms of the proportion of metal oxides, thereby obtaining the experience that is closest to the current raw materials.

[0066] Specifically, obtain the recommendation weight of each historical record at the current moment in the current preparation process; the recommendation weight of the k-th historical record at the current moment. The calculation method is as follows: (6) in, This represents the heating reference weight of the k-th historical record. This represents the total number at the current moment. This represents the similarity of the thermal response at time m during the current preparation process to that of the k-th historical record.

[0067] It should be noted that This represents the weighted average of the similarity of heat responses, with weights assigned based on the time sequence. Weights are increased for times closer to the current time. It is related to the heating reference weights representing the k-th historical record itself and the product quality. Combined, it serves as the recommendation weight for the k-th historical record.

[0068] Furthermore, a preset recommendation threshold of 0.7 and a preset number of candidate targets of 3 are used. The three historical records with the largest recommendation weights (greater than the recommendation threshold) are selected as candidate targets. The average temperature of all candidate targets at the last moment of the segment in which they are located is taken as the target temperature of the current preparation process in that segment.

[0069] If the current temperature is greater than or equal to the target temperature of the segment, proceed to the next segment; otherwise, continue heating until the target temperature is reached. When the number of historical records with a recommendation weight greater than the recommendation threshold is less than the number of candidate targets, all historical records are used as the default candidate targets.

[0070] At this point, the real-time target temperature of the current preparation process is obtained. This step involves selecting historical records with high data quality that are similar to the thermal response state of the current preparation process, and making temperature recommendations based on clear physical similarity, thus making the setting of the target temperature physically interpretable.

[0071] S105: When the current temperature is detected to have reached the real-time target temperature, control proceeds to the next stage; after heating is completed, the data of the current preparation process is appended to the historical preparation process database.

[0072] This step involves constructing a complete temperature control strategy for the preparation process of red mud composite phase change thermal storage materials and performing data feedback.

[0073] Specifically, in the preparation process of red mud composite phase change thermal storage materials, temperature control is carried out according to the following strategies: Preheating and wetting sections: Heating is performed using the basic heating power of the section; after the response time of the section occurs, the target temperature of the section is calculated in real time. Once the target temperature is reached, the next section is entered; otherwise, heating continues. Insulation Section: Insulation is performed using the basic heating power of this section. The average temperature of all candidate targets at the end of the immersion section is used as the insulation temperature. A preset insulation time and allowable fluctuation range are set. When the difference between the current temperature and the insulation temperature exceeds the allowable fluctuation range, the temperature is adjusted back. The heating process ends when the total insulation time reaches the preset insulation time. Here, the preset insulation time is 30 minutes, and the allowable fluctuation range is 5°C. These can be adjusted according to specific implementation conditions; this invention does not impose specific limitations on them.

[0074] Furthermore, after the heating process is completed, the temperature time sequence, heating power time sequence, and latent heat of phase change test results of the current preparation process are added to the historical database, such as... Figure 3 The diagram shown is a logic diagram of the temperature control strategy.

[0075] Thus, the temperature control optimization of the preparation process of red mud composite phase change thermal storage material has been achieved.

[0076] based on Figure 1 The method for preparing red mud composite phase change thermal energy storage material shown can adaptively adjust the heating power and target temperature according to the actual temperature response state during the preparation process, effectively overcome the interference of red mud raw material composition fluctuations on the optimal preparation temperature, significantly improve the consistency of red mud composite phase change thermal energy storage material products, reduce the scrap rate, and avoid the high cost of raw material composition analysis. It has good industrial applicability and real-time control capabilities.

[0077] When applying the method for preparing a red mud composite phase change thermal storage material provided in this specification, it is not necessary to follow the instructions. Figure 1 The steps shown are executed in sequence. The specific execution order of each step can be determined as needed, and this manual does not impose any restrictions on it.

[0078] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A method for preparing a red mud composite phase change thermal storage material, characterized in that, include: A historical preparation process database is constructed, which contains temperature time series, heating power time series, and latent heat of phase change of the finished product for multiple historical records of the preparation process of red mud composite phase change thermal storage materials. The temperature time series is segmented according to the heating power time series, and the ideal temperature at each time moment is obtained by fitting the temperature time series of each segment; the temperature fluctuation coefficient of each historical record is obtained based on the degree of deviation of the temperature time series of each historical record from the ideal temperature; the heating reference weight of each historical record is obtained based on the latent heat of phase change and the temperature fluctuation coefficient, and the basic heating power of the current preparation process in each segment is calculated by weighting. Analyze the thermal response characteristics of each historical record and the current preparation process, calculate the inertia factor from the delay between power change and temperature rise, calculate the temperature rise factor from the temperature rise rate under unit heating power, construct a thermal response feature vector using the inertia factor and temperature rise factor, and calculate the thermal response similarity between the current preparation process and each historical record. The recommended weight is calculated based on the heating reference weight and thermal response similarity. The historical records with the highest recommended weight are selected as candidate targets. The temperature of the candidate target at the end of the current segment is taken as the real-time target temperature of the current preparation process. When the current temperature is detected to have reached the real-time target temperature, the control proceeds to the next stage; after heating is completed, the data of the current preparation process is appended to the historical preparation process database.

2. The method for preparing a red mud composite phase change thermal storage material as described in claim 1, characterized in that, The segmentation of the temperature time series based on the heating power time series includes: For each historical heating power time series, the slope change detection method is used to extract the times corresponding to the two turning points with the largest power changes. The temperature time series of the heating process is divided into the preheating stage, the immersion stage, and the heat preservation stage based on the times corresponding to these two turning points.

3. The method for preparing a red mud composite phase change thermal storage material as described in claim 1, characterized in that, The process of obtaining the temperature fluctuation coefficient for each historical record includes: For any historical record, obtain the deviation between the temperature and the ideal temperature at each moment in each segment of the historical record, and calculate the standard deviation of the deviation for all moments in each segment of the historical record; characterize the temperature fluctuation coefficient of the segment by the ratio of the standard deviation of the deviation to the range of the temperature at all moments in the segment; take the average of the temperature fluctuation coefficients of all segments of the historical record as the temperature fluctuation coefficient of the historical record.

4. The method for preparing a red mud composite phase change thermal storage material as described in claim 1, characterized in that, The process of obtaining the heating reference weight for each historical record includes: For any historical record, the product quality level of that historical record is characterized by the proportion of the maximum value of the latent heat of phase change of the historical record to the maximum value of the latent heat of phase change of all historical records. The product quality level is multiplied by the reciprocal of its temperature fluctuation coefficient, and the product is normalized proportionally. The normalized result is used as the heating reference weight of that historical record.

5. The method for preparing a red mud composite phase change thermal storage material as described in claim 1, characterized in that, The weighted calculation yields the basic heating power for each stage of the current preparation process, including: Using the heating reference weight as the weight for the weighted average calculation, a weighted average calculation is performed on the heating power settings of all historical records in each segment, and the result of the weighted average calculation is used as the basic heating power of the current preparation process in each segment.

6. The method for preparing a red mud composite phase change thermal storage material as described in claim 1, characterized in that, The calculation of the inertia factor based on the delay between power change and temperature rise includes: The normalized result of the delay of the response time of each historical segment relative to the start time of that segment is used as the inertia factor for that historical segment.

7. The method for preparing a red mud composite phase change thermal storage material as described in claim 6, characterized in that, The method for obtaining the response time includes: The average of the temperature rise rate at all times in each historical record segment is recorded as the baseline state rate for that segment. When the ratio of the temperature rise rate of a historical record segment to the baseline state rate first exceeds a preset scaling factor, the corresponding time is recorded as the response time for that segment.

8. The method for preparing a red mud composite phase change thermal storage material as described in claim 1, characterized in that, The calculation of the temperature rise factor based on the rate of temperature rise per unit heating power includes: The instantaneous temperature rise factor is characterized by the ratio of the rate of temperature rise at each moment in the historical record to the average heating power of the segment in which the moment is located. A temperature rise window is preset, and the average instantaneous temperature rise factor of all moments within the temperature rise window with each moment as the boundary is calculated as the temperature rise factor of each moment in each segment of the historical record.

9. The method for preparing a red mud composite phase change thermal storage material as described in claim 1, characterized in that, The calculation of the similarity between the current preparation process and the thermal response of each historical record includes: The inversely proportional normalized result of the Euclidean distance between the thermal response vector of the current fabrication process at each time step and the thermal response vector of the corresponding segment of each historical record at that time step is denoted as the thermal response similarity between the current fabrication process and the historical record at that time step.

10. The method for preparing a red mud composite phase change thermal storage material as described in claim 1, characterized in that, The calculation of recommendation weights based on heating reference weights and thermal response similarity includes: Using the time sequence as weight, the weighted average of the heat response similarity between all times before the current time and each historical record is obtained, which represents the similarity level of the heat response features between the current time and each historical record. This is further multiplied by the heating reference weight of each historical record, and the product is used as the recommendation weight of each historical record at the current time.

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