A method for predicting the specific heat capacity of soybeans and a soybean storage method
Patent Information
- Application Number
- CN202611235686.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-08-14
- Publication Date
- 2026-11-03
AI Technical Summary
Existing methods for determining the specific heat capacity of soybeans involve expensive equipment, complex operation, and time-consuming testing, making it difficult to meet the needs of grain depots for rapid and batch acquisition of soybean thermophysical parameters.
By constructing a predictive model, using the protein and fat content of soybean samples as characteristic data, these parameters were determined by the Kjeldahl nitrogen determination method and Soxhlet extraction method, and the specific heat capacity was determined by differential scanning calorimetry. A multiple linear regression model was constructed to predict the specific heat capacity, and the cooling process and ventilation parameters were adjusted in combination with the specific heat capacity value.
It enables rapid, accurate, and low-cost prediction of soybean specific heat capacity, supporting the design and adjustment of efficient storage processes at grain depots.
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Figure CN123029072A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of soybean storage, and particularly relates to a prediction method of specific heat capacity of soybean and a soybean storage method. BACKGROUND
[0002] Specific heat capacity is a key thermal property parameter for characterizing the heat storage capacity of materials, and its physical meaning is that the heat absorbed by unit mass of material to increase 1 DEG C. In the field of agricultural product processing engineering, specific heat capacity is not only the basic data for heat transfer calculation in drying, heat treatment, storage ventilation and other processes, but also an important input parameter for energy balance analysis, process equipment design and process simulation optimization.
[0003] The existing method for measuring the specific heat capacity of soybean mainly uses differential scanning calorimeter for direct measurement, but the equipment is expensive, the operation is complex and the test is time-consuming, which is difficult to meet the needs of rapid and batch acquisition of thermal property parameters of soybean in grain depot. SUMMARY
[0004] In order to overcome the shortcomings of the prior art, the purpose of the present application is to provide a prediction method of specific heat capacity of soybean and a soybean storage method, which solves the problems of expensive equipment, complex operation and long test time of the existing method.
[0005] To achieve the above-mentioned purpose, the present application provides the following solutions. A prediction method of specific heat capacity of soybean comprises: Sampling target soybean to obtain a soybean sample; Adjusting the moisture content of the soybean sample to a set level; Collecting characteristic data of the soybean sample; Inputting the characteristic data into a pre-constructed prediction model for calculation to obtain sample specific heat capacity; the expression of the prediction model is y=ka+mb+n; wherein y is the sample specific heat capacity; a is protein content; b is fat content; k, m and n are respectively first coefficient, second coefficient and third coefficient.
[0006] Preferably, the set level is 13%.
[0007] Preferably, the characteristic data comprises protein content and fat content.
[0008] Preferably, the determination method of the protein content adopts Kjeldahl nitrogen determination method.
[0009] Preferably, the determination method of the fat content adopts Soxhlet extraction method.
[0010] Preferably, the first coefficient adopts 0.003; the second coefficient adopts 0.043; and the third coefficient adopts 0.603.
[0011] Preferably, the construction process of the prediction model comprises: selecting a plurality of groups of soybean samples; respectively determining the protein content and fat content of each group of soybean samples; determining the specific heat capacity of each group of soybean samples by differential scanning calorimetry; performing multiple linear regression analysis on the determined protein content, fat content, and specific heat capacity to obtain the prediction model.
[0012] A soybean storage method, comprising: sampling, obtaining characteristic data, and performing prediction analysis on the soybeans stored in the warehouse by using the prediction method for the specific heat capacity of soybeans described above to obtain the predicted value of the specific heat capacity of the current batch of soybeans; determining the total heat to be dissipated per unit mass of soybeans according to the predicted value of the specific heat capacity and the difference between the real-time temperature and the target storage temperature; adjusting the cooling process parameters and ventilation parameters according to the total heat to be dissipated.
[0013] The present application discloses the following technical effects: The present application provides a prediction method for the specific heat capacity of soybeans and a soybean storage method, which calculates the characteristic data of soybean samples by using the constructed prediction model, solves the problems of expensive equipment, complex operation, and long testing time of existing methods, and realizes rapid, accurate, and low-cost prediction of the specific heat capacity of soybeans on site in the grain depot. BRIEF DESCRIPTION OF DRAWINGS
[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings described below are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.
[0015] Figure 1 The prediction process of the specific heat capacity of soybeans provided by the embodiments of the present application is shown in the schematic diagram. Figure 2 The soybean storage process provided by the embodiments of the present application is shown in the schematic diagram. DETAILED DESCRIPTION
[0016] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.
[0017] The application aims to provide a soybean specific heat capacity prediction method and a soybean storage method, which solves the problems of existing methods and devices, such as high cost, complex operation and long testing time.
[0018] In order to make the above-mentioned purposes, features and advantages of the application more obvious and easy to understand, the application will be further described in detail below in combination with the drawings and specific embodiments.
[0019] Figure 1 A soybean specific heat capacity prediction flowchart provided by the embodiment of the application is shown in FIG. 1, and the application provides a soybean specific heat capacity prediction method, which comprises the following steps. Figure 1 Step 100: Sampling target soybeans to obtain soybean samples; Step 200: Adjusting the water content of the soybean samples to a set level; Step 300: Collecting characteristic data of the soybean samples; Step 400: Inputting the characteristic data into a pre-constructed prediction model for calculation to obtain sample specific heat capacity; the expression of the prediction model is y=ka+mb+n; wherein y is the sample specific heat capacity; a is protein content; b is fat content; k, m and n are respectively a first coefficient, a second coefficient and a third coefficient.
[0020] Preferably, the set level is 13%.
[0021] Specifically, the characteristic data comprises protein content and fat content.
[0022] Further, the determination method of the protein content adopts the Kjeldahl method.
[0023] Further, the determination method of the fat content adopts the Soxhlet extraction method.
[0024] Preferably, the first coefficient adopts 0.003; the second coefficient adopts 0.043; and the third coefficient adopts 0.603.
[0025] Specifically, the construction process of the prediction model comprises the following steps. Selecting a plurality of groups of soybean samples; Respectively determining the protein content and fat content of each group of soybean samples; Determining the specific heat capacity of each group of soybean samples by using the differential scanning calorimetry method; Performing multiple linear regression analysis on the determined protein content, fat content and specific heat capacity to obtain the prediction model.
[0026] Further, a soybean storage method comprises the following steps. Step 500: Using the aforementioned method for predicting the specific heat capacity of soybeans, sample, acquire characteristic data, and perform predictive analysis on the soybeans stored in the warehouse to obtain the predicted specific heat capacity value of the current batch of soybeans. Step 600: Determine the total heat to be dissipated per unit mass of soybeans based on the predicted specific heat capacity and the difference between the real-time temperature and the target storage temperature; Step 700: Adjust the cooling process parameters and ventilation parameters according to the total heat to be dissipated.
[0027] Example 1: A method for predicting the specific heat capacity of soybeans includes the following steps: S1. Soybeans to be stored are sampled to obtain soybean samples. The soybean samples are initially cleaned to remove impurities and pretreated to adjust their moisture content to 13%. Characteristic data of the soybean samples are obtained, including protein content and fat content. That is, in this embodiment, the soybean samples are divided into two equal parts for protein content and fat content determination, respectively.
[0028] In this embodiment, the Kjeldahl method was used to determine the protein content of the soybean samples, referring to "GB5009.5—2025 National Food Safety Standard - Determination of Protein in Food". The Soxhlet extraction method was used to determine the fat content of the soybean samples, referring to "GB5009.6—2025 National Food Safety Standard - Determination of Fat in Food". Details are as follows: Weigh 2g to 5g of the uniform sample (accurate to 0.001g) and transfer it all into the filter paper tube.
[0029] Place the filter paper tube into the extraction chamber of the Soxhlet extractor, and connect it to a receiving flask that has been dried to constant weight (accurate to 0.0001 g). Add anhydrous diethyl ether or petroleum ether through the top of the extractor condenser until the flask is two-thirds full. Heat in a water bath at 50°C to 60°C, continuously refluxing the anhydrous diethyl ether or petroleum ether (6 to 8 times / hour), generally for 6 to 10 hours. At the end of the extraction, collect one drop of the extract using a ground glass rod; the absence of oil spots on the ground glass rod indicates that the extraction is complete.
[0030] Remove the receiving bottle and recover anhydrous diethyl ether or petroleum ether. When 1 to 2 mL of solvent remains in the receiving bottle, evaporate it to dryness in a 60°C water bath, then dry it at 100°C ± 5°C for 1 hour. Cool it to room temperature in a desiccator and weigh it (accurate to 0.0001 g). Repeat the above operation until constant weight is achieved (until the difference between two weighings does not exceed 2 mg), and take the smallest weighing result. Calculate the fat content based on the weighing results, following the publicly available technology.
[0031] The method for obtaining the moisture content of soybean samples refers to GB5009.3—2016 National Food Safety Standard - Determination of Moisture in Food.
[0032] S2, Based on the feature data, the specific heat capacity of the soybean sample was calculated using a prediction model. The prediction model is as follows: y = ka + mb + n.
[0033] Where a is the protein content, b is the fat content, and k, m, and n are coefficients.
[0034] In this embodiment, the prediction model is constructed as follows: Eight soybean samples were selected from the grain depot. These eight samples had different protein and fat contents. The protein and fat contents of each of the eight soybean samples were measured, as shown in Table 1. Differential scanning calorimetry (DSC) was used to determine the specific heat capacity of multiple soybean samples, as shown in Table 1. DSC is a thermal analysis technique based on the principle of heat flow compensation. By precisely controlling the heating process of the soybean sample and a reference material, the heat flow difference between the two is measured to calculate the specific heat capacity of the soybean sample. This method has advantages such as small sample volume, fast measurement speed, high sensitivity, and wide temperature range. The specific heat capacity of the sample is calculated using the standard substance comparison method. That is, by comparing the heat flow curves of a standard substance (sapphire) with the sample to be tested, the specific heat capacity of the sample at any temperature can be calculated. The calculation formula is as follows: .
[0035] in, This indicates the specific heat capacity of a soybean sample at a certain temperature. This indicates the specific heat capacity of a standard substance at a certain temperature. Indicates the mass of the soybean sample; Indicates the mass of the standard substance; This represents the signal value of a soybean sample at a certain temperature. This indicates the signal value of a standard substance at a certain temperature. This represents the signal value of the baseline at a certain temperature.
[0036] The specific process for determining specific heat capacity is as follows: Baseline determination: Empty crucibles (covered) are placed on both sides of the instrument's two sample cells to ensure similar mass and reduce systematic error. The instrument's heating program is initiated: the initial temperature is set to 20℃, isothermal for 10 min, then heated to 90℃ at a rate of 10℃ / min, held at this temperature for 10 min, and subsequently cooled to the initial temperature using a programmed cooling method. Under computer program control, the instrument automatically records the heat flow difference required to maintain the same temperature in both sample cells throughout the heating process, obtaining the baseline curve. The purpose of baseline determination is to eliminate the influence of instrument system errors and crucible heat capacity differences on subsequent measurements.
[0037] Standard reference determination: Place a crucible containing the standard reference (sapphire, α-Al₂O₃) in the sample cell on the right side of the instrument, while keeping an empty crucible on the left. The mass of the standard reference is controlled within the range of 5 mg to 10 mg, and recorded accurately to 0.01 mg. Use the same temperature program as the baseline determination (20℃ for 10 min, 20℃ → 90℃, 10℃ / min, 10 min, cooling). The instrument automatically records the heat flow difference between the sample cell and the reference cell during the temperature rise process to obtain the standard reference curve. The specific heat capacity data of sapphire is provided by the built-in database of the instrument software, or calculated according to the specific heat capacity-temperature relationship given in the national standard material certificate.
[0038] Soybean sample determination: The Brazilian soybean sample to be tested was placed in the crucible in the sample cell on the right, with the sample mass controlled within the range of 5 to 10 mg, and recorded accurately to 0.01 mg. An empty crucible was placed on the left as a reference. The same heating program as the baseline determination was used (20℃ for 10 min, 20℃→90℃, 10℃ / min, 10 min, cooling). The instrument automatically recorded the heat flow difference between the sample and the reference during the heating process to obtain the sample curve. Once the instrument has cooled to the initial temperature (25°C), repeat this step for the next measurement.
[0039] Table 1
[0040] Multiple linear regression analysis was performed on the above data to construct a predictive model.
[0041] Example 2: A soybean storage method includes the following steps: According to the prediction method described in Example 1, the soybeans stored in the warehouse are sampled and their characteristic data are acquired to calculate the predicted specific heat capacity of the batch of soybeans.
[0042] Based on the predicted specific heat capacity and the difference between the real-time temperature and the target storage temperature, the total heat required to dissipate per unit mass of soybeans is obtained, and the cooling process parameters and ventilation parameters are adjusted according to the total heat dissipation.
[0043] The beneficial effects of this invention are as follows: This invention provides a method for predicting the specific heat capacity of soybeans and a soybean storage method. At the grain depot site, samples of soybeans to be stored are taken. By simply measuring the moisture content, temperature, protein content, and fat content of the soybeans, the specific heat capacity of soybeans can be predicted quickly, accurately, and at low cost using a prediction model. Based on the predicted specific heat capacity, the soybean storage process can be designed and adjusted.
[0044] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.
[0045] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.
Claims
1. A method for predicting the specific heat capacity of soybeans, characterized in that, include: Samples were taken from the target soybeans to obtain soybean samples; The moisture content of the soybean sample was adjusted to the set level; Collect characteristic data of the soybean samples; The feature data is input into a pre-constructed prediction model for calculation to obtain the specific heat capacity of the sample; the expression of the prediction model is: y=ka+mb+n; where y is the specific heat capacity of the sample; a is the protein content; b is the fat content; k, m, and n are the first coefficient, the second coefficient, and the third coefficient, respectively.
2. The method for predicting the specific heat capacity of soybeans according to claim 1, characterized in that, The set level is 13%.
3. The method for predicting the specific heat capacity of soybeans according to claim 1, characterized in that, The characteristic data include: protein content and fat content.
4. The method for predicting the specific heat capacity of soybeans according to claim 3, characterized in that, The protein content was determined using the Kjeldahl method.
5. The method for predicting the specific heat capacity of soybeans according to claim 3, characterized in that, The fat content was determined using the Soxhlet extraction method.
6. The method for predicting the specific heat capacity of soybeans according to claim 1, characterized in that, The first coefficient is 0.003; the second coefficient is 0.043; and the third coefficient is 0.
603.
7. The method for predicting the specific heat capacity of soybeans according to claim 1, characterized in that, The process of constructing the prediction model includes: Select several groups of soybean samples; The protein and fat content of each soybean sample group were measured separately. The specific heat capacity of each soybean sample group was determined using differential scanning calorimetry. The predicted model was obtained by performing multiple linear regression analysis on the measured protein content, fat content, and specific heat capacity.
8. A soybean storage method, characterized in that, include: The specific heat capacity prediction method for soybeans according to claim 1 is used to sample, acquire characteristic data and perform predictive analysis on soybeans stored in warehouses to obtain the predicted specific heat capacity value of the current batch of soybeans. The total heat to be dissipated per unit mass of soybeans is determined based on the predicted specific heat capacity and the difference between the real-time temperature and the target storage temperature. Adjust the cooling process parameters and ventilation parameters according to the total heat to be dissipated.