Numerical simulation method of beverage spray sterilization process

Abstract

The application provides a numerical simulation method of a beverage spray sterilization process, and relates to the field of food processing technology. The numerical simulation method comprises the following steps: collecting spray water temperature data of spray water temperature change with time in a spray sterilization machine through a temperature probe in real time; fitting the collected spray water temperature data into a segmented time function of bottle bottom temperature; setting a bottle bottom temperature boundary condition separately based on a temperature difference between measured spray water temperature and measured temperature zone conveyor chain plate temperature in different temperature zones; establishing a two-dimensional rotational symmetry model comprising a material liquid, an air layer, a bottle body and a bottle cap, and setting a temperature monitoring point; using a CFD simulation software to carry out grid division on the physical model and to carry out numerical simulation; calculating a sterilization intensity value of the temperature monitoring point, and comparing the sterilization intensity value with an actual heat penetration detection result to verify a sterilization effect. The application can quickly obtain the sterilization effect under different sterilization conditions, and provides guidance for setting of initial sterilization conditions of products or adjustment of later sterilization conditions.

Description

Technical Field

[0001] This invention relates to the field of food processing technology, and more specifically to a numerical simulation method for a beverage spray sterilization process. Background Technology

[0002] Spray sterilization is a common sterilization method in beverage production, with the advantages of simplicity, reliability, and virtually no possibility of recontamination. To improve production efficiency while ensuring sterilization effectiveness, the sterilization effect is often determined through heat penetration experiments. The heat penetration heat distribution experiment primarily measures the heating rate of the food under test to ensure that products of different specifications and types meet sterilization requirements under different equipment and operating conditions. Determining the initial sterilization conditions and optimizing them is a complex issue. In actual production, improving sterilization effectiveness through multiple heat penetration tests and adjustments to spray conditions is a common practice. However, this method suffers from incomplete data, high cost per experiment, and demanding requirements for testing instruments. Therefore, heat penetration experiments suffer from incomplete data, high cost per experiment, and high instrument requirements.

[0003] Computational Fluid Dynamics (CFD) simulation of sterilization is considered an economical and reliable research method and has been widely used in the thermal sterilization simulation of various foods to improve sterilization conditions. However, previous simulations generally treated the segmented temperatures of the sterilizer as the ambient water temperature, neglecting the impact of fluctuations in the spray water temperature on the simulation results. To address these issues, a numerical simulation method based on actual production process temperature data is needed. Summary of the Invention

[0004] In view of this, the purpose of this invention is to propose a numerical simulation method for the beverage spray sterilization process. The method uses numerical simulation to simulate the beverage spray sterilization process and establishes a computational fluid dynamics model that can be used to guide the setting of spray sterilization conditions in actual production. By detecting the heating conditions of the product during the actual sterilization process, the actual boundary conditions are obtained, and the process is numerically simulated using computational fluid dynamics to obtain a heating analysis model that can be used to quickly determine the product's heating conditions under different spraying conditions, thereby guiding the setting and optimization of product sterilization conditions.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] To achieve the above objectives, the present invention provides a numerical simulation method for a beverage spray sterilization process, comprising the following steps:

[0007] Step 1: Real-time boundary condition acquisition and dynamic fitting:

[0008] The spray water temperature data of the spray sterilizer is collected in real time by a temperature probe and the spray water temperature data changes over time. The collected spray water temperature data is fitted into a piecewise time function of bottle bottom temperature. Based on the measured spray water temperature and the measured temperature difference of the conveyor chain plate in different temperature zones, the bottle bottom temperature boundary condition is set separately.

[0009] Step 2: Physical Model Construction

[0010] A two-dimensional rotationally symmetric model including the liquid, air layer, bottle body and cap was established, and temperature monitoring points were set.

[0011] Step 3: CFD simulation solution:

[0012] CFD simulation software was used to mesh the physical model and conduct numerical simulations.

[0013] Step 4: Verification of sterilization effect:

[0014] Calculate the sterilization intensity value at the temperature monitoring point and compare it with the actual heat penetration test results to verify the sterilization effect.

[0015] As a further aspect of the present invention, when calculating the sterilization intensity value at the temperature monitoring point, the sterilization intensity value... The calculation formula is:

[0016]

[0017] in, This refers to the bactericidal strength. For time; For temperature, For reference temperature; The temperature increase required to shorten the heating time by 90% in the thermal lethality time curve of the target bacteria during heat sterilization.

[0018] As a further aspect of the present invention, a separate temperature boundary condition is set for the bottom of the bottle, wherein the method for setting the temperature boundary condition for the bottom of the bottle is as follows:

[0019] The temperature difference between the conveyor chain plate and the spray water was measured in the heating, constant temperature and cooling sections of the spray sterilizer.

[0020] Within the same temperature zone, the bottle bottom temperature is set to the spray water temperature plus a fixed compensation value.

[0021] As a further aspect of the present invention, the temperature monitoring points set in the physical model construction include: point a and point b, where point a is the actual detection temperature point of the temperature measuring probe, and point b is the monitoring point of the center temperature of the liquid.

[0022] As a further aspect of the present invention, the verification of the sterilization effect also includes:

[0023] Simulations confirmed that the product's cold spot was always located at the bottom of the bottle, consistent with the actual temperature measurement point a.

[0024] Compare the simulated temperature curve at point a with the actual heat penetration curve, requiring a temperature error of ≤1℃.

[0025] As a further aspect of this invention, when using CFD simulation software to mesh the physical model and conduct numerical simulation, the following steps are included: employing a pressure-based solver, transient process simulation, selecting "Transient" for the Time option, and selecting the "Axisymmetric Swirl" option for the 2D Space option; activating gravity and setting the corresponding direction, activating the energy equation, using the K-ε turbulence model for turbulent viscosity, treating near-wall surfaces, and using standard wall functions; setting boundary conditions: setting the air-to-bottle, air-to-liquid, and liquid-to-bottle surfaces as coupling surfaces, and setting the outer wall temperature of the bottle according to the actual collected temperature; and using the SIMPLE algorithm to perform coupled calculations of pressure and velocity.

[0026] Compared with existing technologies, the numerical simulation method for beverage spray sterilization proposed in this invention has the following advantages:

[0027] This invention separately processes the temperature boundary at the bottom of the bottle, achieving a breakthrough in the accuracy of cold spot location. It solves the problem of traditional CFD models ignoring the temperature difference between the bottom and sidewalls, leading to misjudgment of the cold spot location. This ensures that the temperature probe placement coincides with the actual cold spot, thus resolving the problem of deviation in sterilization efficacy assessment. Furthermore, this invention uses dynamic boundary fitting and independent temperature control at the bottom of the bottle for calculation, verifying that the simulation error of the sterilization intensity value is less than the preset deviation, which is significantly better than traditional models.

[0028] This invention provides a numerical simulation method for the spray sterilization process of beverages. It analyzes the heating conditions of acidic milk beverages during spray sterilization under actual production conditions, obtaining the distribution of internal product temperature over time. The spray water temperature collected by a temperature probe is used as the boundary condition in the simulation, and the temperature boundary at the bottom of the bottle is treated separately, making the simulation closer to actual production conditions. It was found that the cold spot of the product is always located at the bottom during the high-efficiency sterilization stage; bottom monitoring and temperature measurement can effectively characterize heat penetration detection. The final model can be extended to different operating conditions, with a maximum deviation of no more than 4% in its F-value.

[0029] The numerical simulation method of this invention obtains the actual boundary conditions by detecting the heating conditions during the actual sterilization process of the product, and uses computational fluid dynamics to perform numerical simulation of the process to obtain a heating analysis model for quickly determining the product under different spraying conditions. This model guides the setting and optimization of product sterilization conditions, reduces experimental and testing instrument costs, and can quickly obtain the sterilization effect under different sterilization conditions. It provides guidance for setting the initial sterilization conditions or adjusting the subsequent sterilization conditions, and supports multi-scenario migration and parameter optimization.

[0030] These or other aspects of this application will become more apparent from the following description of embodiments. It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the application. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies, the accompanying drawings used in the description of the exemplary embodiments or related technologies will be briefly introduced below. The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention and do not constitute a limitation thereof. In the drawings:

[0032] Figure 1 This is a schematic diagram of a milk beverage bottle model in a numerical simulation method for a beverage spray sterilization process according to an embodiment of the present invention.

[0033] Figure 2 This is a heat penetration curve diagram in a numerical simulation method for a beverage spray sterilization process according to an embodiment of the present invention.

[0034] Figure 3 The diagram shows the actual and simulated temperatures of the spray water in a numerical simulation method for a beverage spray sterilization process according to an embodiment of the present invention.

[0035] Figure 4 This is a temperature distribution diagram of the product at different times in a numerical simulation method for a beverage spray sterilization process according to an embodiment of the present invention.

[0036] Figure 5 This is a graph showing the actual temperature and simulated temperature of a monitoring point in a numerical simulation method for a beverage spray sterilization process according to an embodiment of the present invention. Detailed Implementation

[0037] The present application will now be further described in conjunction with the accompanying drawings and specific embodiments. It should be noted that, without conflict, the various embodiments or technical features described below can be arbitrarily combined to form new embodiments.

[0038] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to specific examples and the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative and are not intended to limit the scope of this application.

[0039] It should be noted that all uses of "first" and "second" in the embodiments of the present invention are for the purpose of distinguishing two different entities or different parameters with the same name. Therefore, "first" and "second" are merely for convenience of expression and should not be construed as limiting the embodiments of the present invention. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, such as other steps or units inherent in a process, method, system, product, or device that includes a series of steps or units.

[0040] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0041] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the described order. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0042] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0043] To account for the impact of fluctuations in spray water temperature on simulation results, this invention proposes a numerical simulation method for the beverage spray sterilization process. The method simulates the beverage spray sterilization process, establishing a computational fluid dynamics model that can guide the setting of spray sterilization conditions in actual production. By detecting the actual heating conditions during the sterilization process, the actual boundary conditions are obtained, and computational fluid dynamics is used to numerically simulate the process, resulting in a model that can quickly determine the product's heating under different spray conditions, guiding the setting and optimization of product sterilization conditions.

[0044] See Figures 1 to 5 As shown, an embodiment of the present invention provides a numerical simulation method for a beverage spray sterilization process, comprising the following steps:

[0045] Step 1: Real-time boundary condition acquisition and dynamic fitting:

[0046] The spray water temperature data of the spray sterilizer is collected in real time by a temperature probe and the spray water temperature data changes over time. The collected spray water temperature data is fitted into a piecewise time function of bottle bottom temperature. Based on the measured spray water temperature and the measured temperature difference of the conveyor chain plate in different temperature zones, the bottle bottom temperature boundary condition is set separately.

[0047] Step 2: Physical Model Construction

[0048] A two-dimensional rotationally symmetric model including the liquid, air layer, bottle body and cap is established, and temperature monitoring points are set; the bottle body can be made of HDPE material.

[0049] Step 3: CFD simulation solution:

[0050] CFD simulation software was used to mesh the physical model and conduct numerical simulations.

[0051] Step 4: Verification of sterilization effect:

[0052] Calculate the sterilization intensity value at the temperature monitoring point and compare it with the actual heat penetration test results to verify the sterilization effect.

[0053] In this embodiment, the materials and instruments required to perform the numerical simulation method include:

[0054] Acidic dairy beverages: Acidic dairy beverages were selected as the research object. The outer packaging of the acidic dairy beverages was a high-density polyethylene bottle, the pH value of the contents was 4.1-4.3, the protein content was ≥1.02%, and the soluble solids content was ≥7.5%.

[0055] Temperature detection instrument: Temperature detection is performed using a wireless temperature verification system, wherein the wireless temperature verification system includes a temperature detection instrument, and the TrackSenser® Pro wireless temperature verification system is used as the temperature probe.

[0056] Spray sterilization equipment: The experiment was conducted using spray sterilization equipment, wherein the spray sterilization equipment is a tunnel-type spray sterilization machine of type SJJ01A (7+2) sections.

[0057] In this embodiment, when calculating the sterilization intensity value of the temperature monitoring point, the sterilization intensity value is... The calculation formula is:

[0058]

[0059] in, This refers to the bactericidal strength. For time; For temperature, For reference temperature; The temperature increase required to shorten the heating time by 90% in the thermal lethality time curve of the target bacteria during heat sterilization.

[0060] For the milk beverage in this embodiment, the pH value is between 4.1 and 4.3, and the corresponding reference temperature is 93.3 °C. The value is 8.3 ℃. It is 5 minutes, which is the calculated result. A value greater than 5 minutes can basically guarantee the microbial safety of the product.

[0061] In this embodiment, when calculating the sterilization intensity value at the temperature monitoring point and comparing it with the actual heat penetration detection results, the specific operation of heat penetration detection is as follows:

[0062] First, the temperature detectors are calibrated to determine the deviation between the detected temperature and the actual temperature for each probe, and this deviation is recorded for later data analysis. Before formal testing, the temperature probes are set to automatic start to ensure no data loss. Two different probes are placed inside and outside the empty bottle of the product to be tested, respectively, and then the liquid is poured into the bottle and sealed. After the spray sterilizer is running stably, the product to be tested is placed inside the sterilizer. After the product undergoes normal heating, holding, and cooling cycles, the probes are removed and the data is read to obtain the actual internal temperature change curve.

[0063] When constructing the physical model, a common 220mL volume dairy beverage was selected as the research object, including the bottle cap, air, liquid, HDPE bottle (0.5 mm thick), etc. The model diagram is shown below. Figure 1 As shown in the figure (all dimensions are in mm), the material properties of each part are shown in Table 1. Except for density, the parameters of the liquid material are referenced to pure water. To ensure the accuracy of the calculation results while minimizing the calculation time, a two-dimensional rotational model was used to simulate the process. Point a represents the actual temperature detected by the temperature probe, and point b represents the monitoring point for the center temperature of the liquid material.

[0064] Table 1. Thermophysical properties of liquid, air, bottle body and aluminum foil

[0065]

[0066] In this embodiment, a separate temperature boundary condition is set for the bottom of the bottle. The method for setting the temperature boundary condition for the bottom of the bottle is as follows: the temperature difference between the conveyor chain plate and the spray water is measured in the heating section, constant temperature section and cooling section of the spray sterilizer; within the same temperature zone, the temperature of the bottom of the bottle is set to the spray water temperature plus a fixed compensation value.

[0067] In the CFD simulation solution stage, the physical model was meshed using CFD simulation software, resulting in a mesh count of 3858. Numerical simulations were then performed, including: using a pressure-based solver, transient process simulation (with the Time option set to Transient and the 2D Space option set to Axisymmetric Swirl); activating gravity and setting the corresponding direction; activating the energy equation; using the K-ε turbulence model for turbulent viscosity; near-wall treatment; and using standard wall functions. Material properties are shown in Table 1. Boundary conditions were set: air and bottle body, air and liquid, and liquid and bottle body were all set as coupling surfaces, and the bottle outer wall temperature was set based on the actual collected temperature. The SIMPLE algorithm was used for coupled calculation of pressure and velocity.

[0068] This invention separately processes the temperature boundary at the bottom of the bottle, achieving a breakthrough in the accuracy of cold spot location. It solves the problem of traditional CFD models ignoring the temperature difference between the bottom and sidewalls, leading to misjudgment of the cold spot location. This ensures that the temperature probe placement coincides with the actual cold spot, thus resolving the problem of deviation in sterilization efficacy assessment. Furthermore, this invention uses dynamic boundary fitting and independent temperature control at the bottom of the bottle for calculation, verifying that the simulation error of the sterilization intensity value is less than the preset deviation, which is significantly better than traditional models.

[0069] The verification of sterilization effect also includes:

[0070] Simulations confirmed that the product's cold spot was always located at the bottom of the bottle, consistent with the actual temperature measurement point a.

[0071] Compare the simulated temperature curve at point a with the actual heat penetration curve, requiring a temperature error of ≤1℃.

[0072] In this embodiment, the simulation results are compared with the actual results. The heat penetration test results are obtained according to the heat penetration test method, and the resulting heat penetration curve is shown below. Figure 2 As shown, Figure 2 In the heat penetration curve diagram, segment A represents the period before the detector is turned on, completing the initial instrument debugging, and the product is filled into the bottle. At this time, the temperature detected by the temperature probe increases from room temperature to the liquid temperature. Segment AB represents the period after filling, when the detector is placed outside the bottle and the spray sterilizer is running stably. At this time, the liquid temperature decreases. Segment B represents the period after the product enters the sterilizer and completes the heating, temperature rise, and cooling steps. Segment BC represents the cooling process after the product has completed the temperature rise.

[0073] Data on the temperature variation of the sprayed water on the outer wall of the bottle over time was obtained based on heat penetration testing data. To simplify the calculation process, only the data from segment BC is used for simulation. Following the CFD simulation method, the curve of the sprayed water temperature on the outer wall of the bottle collected by the temperature detector over time is fitted into a function, and the R-squared value for each segment is calculated. 2The values ​​are all not less than 0.9. The temperature difference between the bottom of the bottle and the spray water in different regions are as follows: heating section 1 (-4 ℃), heating section 1 (-2 ℃), constant temperature section (-1 ℃), and cooling section (+4 ℃). The simulated and actual conditions of the obtained spray water temperature are shown in Table 2, and the fitting effect between the two is shown in [reference needed]. Figure 3 As shown.

[0074] Table 2. Calculation formulas for spray water temperature in different temperature ranges

[0075]

[0076] In this embodiment, the temperature distribution of the product inside the bottle at different times is shown in the figure. Figure 4 As shown, during the heating time of 120s, the temperature of the liquid material was mainly between 45-47℃. After that, the liquid material continued to heat up, and at the end of the heating section (t=840s), the main temperature of the liquid material had reached above 87℃. Since the spray water temperature was close to 90℃, the product continued to heat up in the constant temperature section, and the main temperature rose to about 90℃. After leaving the heating section, the product was cooled down, and at 2060 seconds, the product temperature dropped to 85℃. At about 2400 seconds, the main temperature of the product dropped to 63℃.

[0077] The actual detection point (point a) of the temperature detector probe and the temperature monitoring point (point b) at the center of the product were monitored to obtain the temperature change at the corresponding locations over time. The temperature at point a was then compared with the actual detected temperature. (See [reference]). Figure 5 As shown.

[0078] Comparing the heat penetration detection process and results with the numerical simulation results reveals that the temperature error between the two methods is less than 1 ℃ for the vast majority of the time, with a larger deviation only occurring near the end of the cooling process. Calculations based on the F-value method show that the actual measured F-value is 7.82 min, while the numerical simulation yields an F-value of 7.76 min, with an error of 0.77%. This indicates that the model closely approximates the actual heating process and can effectively reflect the continuous changes in the internal temperature distribution of the product.

[0079] According to the simulation results, the F-value at monitoring point b is 7.96, indicating that the sterilization intensity in the center of the product is slightly higher than that at the bottom. This is because, compared with other similar sterilization simulations, the biggest difference in this process is that the cold zone inside the product remains at the bottom of the product during the high-efficiency sterilization phase (before t=2250s, the main body temperature of the product is above 80℃), rather than moving as the sterilization process progresses. In many other simulations, the temperature at the bottom of the bottle and the temperature on the sides of the bottle are often simulated at the same temperature, but in reality, there is a certain temperature difference between the two. The different temperature conditions at the bottom will obviously affect the temperature of the liquid near the wall, thus leading to different distributions of the cold zone inside the product. To ensure the overall sterilization efficacy of the product, the temperature probe needs to be placed at the cold point of the product. Different cold point positions in the simulation results will directly affect the accuracy of the product sterilization efficacy simulation. In this simulation, the boundary conditions at the bottom of the bottle were treated separately to make them closer to the actual situation in production, making the simulation results more reliable.

[0080] The simulation results show that the actual test results are quite close to the numerical simulation results. However, there are significant differences in the testing process between the two methods. Heat penetration testing requires a considerable preparation time, during which the initial temperature of the tested product will differ from the actual initial temperature of the product. For example, in this embodiment, the product's initial temperature was reduced from 42°C to 35°C before subsequent operations began, resulting in a temperature difference of approximately 7°C. Even with expedited preparation for each test, unavoidable anomalies during production, such as "ship blockages" and "bottle tipping," can lead to excessive product cooling. This difference in initial temperature significantly affects the actual sterilization effect. Numerical simulation, on the other hand, can simulate the product's temperature rise under different initial temperature conditions, effectively avoiding this problem.

[0081] To obtain temperature changes within the product at different locations within a sterilizer, or under different sterilization conditions, a sufficient number of temperature detectors are often required to monitor the process. Numerical simulation technology, however, can obtain the internal temperature changes of the product under different operating conditions by changing the boundary conditions. This saves on instrument costs and also reduces energy consumption and raw material costs during testing.

[0082] In summary, the method of this invention is applicable to a wider range of production conditions and can be more widely used in determining sterilization conditions and evaluating sterilization effects. Therefore, the model of this invention can be used to verify the test results of different batches.

[0083] In the embodiments of this invention, for the simulation of heat penetration detection under multiple working conditions, heat penetration detection and numerical simulation were carried out on spray sterilization equipment at different times and on different production lines. The experimental results are shown in Table 3. Since monitoring point a is closer to the cold spot of the product, if this point can meet the sterilization requirements, the safety of the product can be basically guaranteed; at the same time, its position can also correspond to the actual probe detection point. Therefore, the sampling points mentioned below are all monitoring point a. The equipment mentioned below is the SJJ01A (7+2) tunnel spray sterilization machine of Hangzhou Wahaha Precision Machinery Equipment Co., Ltd., and the total width of the equipment is about 4 meters. The samples to be tested were placed at 0.5 m, 1.5 m, 2.5 m, and 3.5 m (distance from the left edge) of the sterilization machine to determine the actual heating and temperature rise of the product, and to verify the accuracy of the CFD model based on this. Sampling and simulation were carried out on other points of the spray sterilization equipment where the above sampling points are located. The actual F value and simulated F value are shown in Table 3 below.

[0084] Table 3. Actual and simulated F values ​​at different sampling points of the same equipment

[0085]

[0086] As shown in Table 3, the model demonstrates good simulation performance at different sampling points on the same device. The actual measured F-value is the F-value at the bottom of the product, with an error of less than 4% compared to the simulated bottom F-value. The simulated F-value at the center of the product is slightly higher than the bottom F-value.

[0087] Sampling and testing were conducted on sterilization equipment in another workshop, during another season, and on another production line. The actual F-values ​​and simulated F-values ​​are shown in Table 4 below:

[0088] Table 4. Actual and simulated F values ​​at different sampling points within another device.

[0089]

[0090] As shown in Table 4, the model also demonstrates good simulation results in different workshops, different seasons, and on another production line of the same model. The error between the actual measured bottom F value and the simulated value is less than 4%. Therefore, this method has significant potential for widespread application.

[0091] Based on the above simulations of heat penetration testing under multiple operating conditions, the results from different sampling points on the same equipment, and the results from sterilization equipment at different times and on different production lines, it can be seen that the model has good fitting results under different operating conditions, with a maximum deviation of no more than 4% for its F-value. This indicates that the numerical simulation model can simulate the sterilization of products under various operating conditions and can be extended to different production lines, providing guidance for the setting and optimization of equipment parameters. The biggest reason for the deviation lies in the discrepancy between the actual spray water temperature and the numerical simulation.

[0092] In summary, the numerical simulation method for the beverage spray sterilization process of this invention analyzes the product heating situation of acidic milk beverages during the spray sterilization process under actual production conditions, obtaining the distribution of internal product temperature changes over time. The spray water temperature collected by a temperature probe is used as the boundary condition in the simulation, and the temperature boundary at the bottom of the bottle is treated separately, making the entire simulation closer to actual production conditions. It was found that the cold spot of the product is always located at the bottom of the product during the high-efficiency sterilization stage, and bottom monitoring temperature measurement can effectively characterize heat penetration detection. The finally constructed model can be extended to different operating conditions, with a maximum deviation of no more than 4% in its F-value.

[0093] The numerical simulation method of this invention obtains the actual boundary conditions by detecting the heating conditions during the actual sterilization process of the product, and uses computational fluid dynamics to perform numerical simulation of the process to obtain a heating analysis model for quickly determining the product under different spraying conditions. This model guides the setting and optimization of product sterilization conditions, reduces experimental and testing instrument costs, and can quickly obtain the sterilization effect under different sterilization conditions. It provides guidance for setting the initial sterilization conditions or adjusting the subsequent sterilization conditions, and supports multi-scenario migration and parameter optimization.

[0094] The above are exemplary embodiments disclosed in this invention. However, it should be noted that various changes and modifications can be made without departing from the scope of the embodiments of this invention as defined by the claims. The functions, steps, and / or actions of the methods according to the disclosed embodiments described herein do not need to be performed in any particular order. Furthermore, although the elements disclosed in the embodiments of this invention may be described or claimed individually, they may be understood as multiple unless explicitly limited to a singular number.

[0095] It should be understood that, as used herein, the singular form "a" is intended to include the plural form as well, unless the context clearly supports an exception. It should also be understood that, as used herein, "and / or" refers to any and all possible combinations of one or more of the associatedly listed items. The embodiment numbers disclosed above are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0096] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention (including the claims) is limited to these examples. Within the framework of the invention, technical features of the above embodiments or different embodiments can be combined, and many other variations of different aspects of the invention exist, which are not provided in the details for the sake of brevity. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the invention should be included within the protection scope of the invention.

Claims

1. A numerical simulation method for a beverage spray sterilization process, characterized in that, The method includes: Step 1: Real-time boundary condition acquisition and dynamic fitting: The spray water temperature data of the spray sterilizer is collected in real time by a temperature probe, showing the change of spray water temperature over time. The collected spray water temperature data is then fitted into a piecewise time function of the bottle bottom temperature. Based on the measured spray water temperature and the measured temperature difference between the conveyor chain plate and the temperature zone in different temperature zones, separate boundary conditions for the bottle bottom temperature are set. The method for setting the bottle bottom temperature boundary is as follows: the temperature difference between the conveyor chain plate and the spray water is measured in the heating, constant temperature, and cooling sections of the spray sterilizer. Within the same temperature zone, the bottle bottom temperature is set to the spray water temperature plus a fixed compensation value. Step 2: Physical Model Construction A two-dimensional rotationally symmetric model including the liquid, air layer, bottle body and cap is established, and temperature monitoring points are set. The temperature monitoring points set in the physical model construction include: point a and point b. Point a is the actual detection temperature point of the temperature probe, and point b is the monitoring point of the center temperature of the liquid. Step 3: CFD simulation solution: CFD simulation software was used to mesh the physical model and conduct numerical simulations. Step 4: Verification of sterilization effect: Calculate the sterilization intensity value at the temperature monitoring point and compare it with the actual heat penetration test results to verify the sterilization effect. The verification of the sterilization effect also includes: determining through simulation that the cold point of the product is always located in the bottom area of ​​the bottle, consistent with the actual temperature measurement point a; comparing the simulated temperature curve at point a with the actual heat penetration curve, requiring the temperature error to be ≤1℃.

2. The numerical simulation method for the beverage spray sterilization process as described in claim 1, characterized in that, When calculating the sterilization intensity value at the temperature monitoring point, the sterilization intensity value The calculation formula is: in, This refers to the bactericidal strength. For time; For temperature, For reference temperature; The temperature increase required to shorten the heating time by 90% in the thermal lethality time curve of the target bacteria during heat sterilization.

3. The numerical simulation method for the beverage spray sterilization process as described in claim 1, characterized in that, When using CFD simulation software to mesh the physical model and conduct numerical simulations, the following steps are taken: A pressure-based solver is used, transient process simulation is performed, the Time option is set to Transient, and the 2D Space option is set to Axisymmetric Swirl; gravity is activated and the corresponding direction is set; the energy equation is activated; the K-ε turbulence model is used for turbulent viscosity, with near-wall treatment using standard wall functions; boundary conditions are set: air and bottle body, air and liquid, and liquid and bottle body are all coupling surfaces; the bottle outer wall temperature is set according to the actual collected temperature; the SIMPLE algorithm is used for coupled calculation of pressure and velocity.

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