A Fluent-based simulation method and system for biochemical reactions in beer fermentation

Abstract

This invention discloses a simulation method and system for the biochemical reaction of beer fermentation based on Fluent. First, multiple beer fermentation experiments under different initial conditions are conducted, monitoring and recording the changes of key parameters over time during fermentation. Second, based on a swarm equilibrium model, curve fitting is performed on the collected parameters versus time data, fitting discrete data points into smooth curves. The slope of each parameter is calculated using the fitted curves, and the unknown parameters in the swarm equilibrium model are solved using the least squares method to optimize the model parameters. Then, the optimized swarm equilibrium model is converted into computer-readable code, which is compiled and integrated into the pre-processed Fluent software. Finally, the initial fermentation conditions are set in the Fluent software, and simulation calculations are run to obtain the concentration of each substance and the flow of the fermentation liquid at any given time, thereby achieving full-time, all-round simulation analysis of the beer fermentation process.

Description

Technical Field

[0001] This invention belongs to the field of beer fermentation simulation, specifically a method and system for simulating the biochemical reactions of beer fermentation based on Fluent. Background Technology

[0002] Beer fermentation is a crucial step in the beer production process. In industrial production, breweries use sugary wort as a raw material, add yeast, and ferment it under anaerobic conditions in sealed fermentation tanks. Besides biochemical reactions, a complex diacetyl oxidation-reduction reaction occurs during fermentation. Under the action of yeast, the wort is converted into beer. During this process, sugar is converted into alcohol and various flavor compounds by the yeast.

[0003] To gain a deeper understanding of the changes in the content of yeast and its byproducts during yeast fermentation, many scholars have devoted themselves to the study of beer fermentation. In actual production, beer manufacturers set up sampling points at fixed locations on the fermentation tank to measure the content of various substances in the fermentation liquid at a specific moment. However, due to the uneven distribution of substances within the fermentation liquid, the sampling results cannot represent the overall results, and analyzing the samples also requires manpower and time.

[0004] With the rapid development of computers and the optimization and improvement of simulation technology, Computational Fluid Dynamics (CFD) has come into view. CFD technology integrates fluid mechanics, numerical analysis, and computer science. It solves fluid flow problems by simulating and analyzing the behavior of fluids under various boundary conditions and forces. Its core lies in transforming complex fluid dynamics problems into a set of solvable equations through mathematical models and algorithms, which can then be solved on a computer.

[0005] Computational Fluid Dynamics (CFD) offers numerous advantages as a powerful simulation tool. It can predict and analyze fermentation broth flow and heat transfer without building fermentation tanks and corresponding production lines, significantly reducing research and development costs. From an evaluation and optimization perspective, CFD simulations can easily modify boundary conditions and initial mass parameters of the fermentation broth, making design optimization and performance evaluation more flexible and rapid. Furthermore, CFD can reveal flow characteristics of the fermentation broth that are difficult to observe in production, such as turbulent structures, streamlines, and vortices. In analyzing the fermentation process, CFD software (such as Fluent) typically possesses powerful post-processing tools, making the visualization and interpretation of results more intuitive. In summary, the advantages of CFD lie in its cost-effectiveness, design flexibility, deep insight into complex flows, predictive capabilities, and ease of use. However, current research using CFD technology in beer fermentation typically focuses on the cooling stage, with limited simulation studies on the main fermentation temperature control stage. Additionally, CFD technology alone cannot simulate the biochemical reactions involved in the beer brewing process. Summary of the Invention

[0006] To address the problems existing in the prior art, this invention provides a method and system for simulating the biochemical reaction of beer fermentation based on Fluent.

[0007] In a first aspect, the present invention provides a method for simulating the biochemical reaction of beer fermentation based on Fluent, the method comprising the following steps:

[0008] Step 1. Conduct multiple beer fermentation experiments under different initial conditions, and monitor and record the changes of key parameters over time during the fermentation process;

[0009] Step 2. Based on the swarm equilibrium model, perform curve fitting on the collected parameters and time data to fit the discrete data points into a smooth curve, and calculate the slope of each parameter through the fitted curve; use the least squares method to solve for the unknown parameters in the swarm equilibrium model in order to optimize the model parameters.

[0010] Step 3. Convert the optimized swarm equilibrium model into a computer-readable code form and compile and integrate the code into the pre-processed Fluent software;

[0011] Step 4. Set the initial fermentation conditions in Fluent software, run the simulation calculation, and obtain the concentration of each substance and the flow of the fermentation liquid at any time, thereby realizing the full-time and all-round simulation analysis of the beer fermentation process.

[0012] Furthermore, in step 1, the beer fermentation experiments conducted under different initial conditions include at least one set of temperature-controlled experiments and at least one set of uncontrolled experiments. In the temperature-controlled experiments, the temperature inside the fermentation tank is controlled within a preset temperature range through a cooling system. In the uncontrolled experiments, the temperature inside the fermentation tank changes naturally with the fermentation reaction.

[0013] Furthermore, in step 1, the key parameters include temperature, alcohol content, sugar content, and yeast count.

[0014] Furthermore, in step 2, when performing curve fitting on the collected parameters and time data, the fitting models used include quadratic polynomial models, linear models, Boltzmann function models, Slogistis function models, single-phase exponential decay models, and Bihill function models. The appropriate fitting model is selected according to different parameter change trends to improve fitting accuracy.

[0015] Furthermore, in step 2, when using the least squares method to solve for the unknown parameters in the group equilibrium model, multiple data points at different time points are selected for calculation to ensure the accuracy and reliability of the solution results.

[0016] Furthermore, in step 2, the unknown parameters include the yeast growth empirical constant, the yeast yield coefficient, the ethanol yield coefficient, and the yeast mortality rate.

[0017] Furthermore, in step 2, the selected data points at multiple different time points are evenly distributed across different stages of the fermentation process, including the initial, middle, and final stages of fermentation, in order to comprehensively reflect the parameter change trends during the fermentation process.

[0018] Furthermore, in step 2, when verifying the fitted curve, the coefficient of determination R of the fitted curve is also calculated. 2 And ensure that the coefficient of determination is greater than the preset threshold to further verify the goodness of fit of the fitted curve.

[0019] Furthermore, in step 3, the Fluent software pre-established a fermenter model and set the corresponding parameters and boundary conditions.

[0020] Secondly, the present invention provides a Fluent-based simulation system for the biochemical reaction of beer fermentation, comprising:

[0021] The experimental monitoring and data acquisition module is used to conduct multiple beer fermentation experiments under different initial conditions, and to monitor and record the changes of key parameters over time during the fermentation process.

[0022] The curve fitting and parameter optimization module is used to perform curve fitting on the collected parameters and time data according to the swarm equilibrium model, fit discrete data points into a smooth curve, and calculate the slope of each parameter through the fitted curve; and use the least squares method to solve the unknown parameters in the swarm equilibrium model to optimize the model parameters.

[0023] The model building and code implementation module is used to convert the optimized swarm equilibrium model into a computer-readable code form and compile and integrate the code into the pre-processed Fluent software;

[0024] The simulation calculation and result analysis module is used to set the initial fermentation conditions in Fluent software, run simulation calculations, and obtain the concentration of each substance and the flow of the fermentation liquid at any time, thereby realizing full-time and all-round simulation analysis of the beer fermentation process.

[0025] The beneficial effects of this invention are as follows: This invention aims to simulate the biochemical reactions in the beer fermentation process, and combine Fluent to analyze the flow of the fermentation liquid, so as to realize the full-time and all-round parameter monitoring and flow analysis of beer brewing, and provide guidance and reference for actual production. Attached Figure Description

[0026] Figure 1 The factors influencing the cell population balance equation of this invention are as follows.

[0027] Figure 2 This is a schematic diagram of cell growth according to the present invention.

[0028] Figure 3 This is a schematic diagram of cell division in this invention.

[0029] Figure 4 This is a schematic diagram of cell apoptosis according to the present invention.

[0030] Figure 5 This shows the temperature change over time in the uncontrolled fermentation experiment of this invention.

[0031] Figure 6 This shows the change in alcohol content over time during the uncontrolled fermentation experiment of this invention.

[0032] Figure 7 This shows the change in sugar content over time during the uncontrolled fermentation experiment of this invention.

[0033] Figure 8 This shows the change in yeast count over time during the uncontrolled fermentation experiment of this invention.

[0034] Figure 9 This shows the temperature change over time in two fermentation experiments of this invention.

[0035] Figure 10This shows the change in yeast count over time in two fermentation experiments of this invention.

[0036] Figure 11 This shows the change in alcohol content over time in two fermentation experiments of this invention.

[0037] Figure 12 This shows the change in sugar content over time in two fermentation experiments of this invention.

[0038] Figure 13 This is the calculation and calibration result for the large tank of this invention. Detailed Implementation

[0039] The technical solution and basic principles of the present invention will be clearly and completely described below with reference to the accompanying drawings and specific embodiments.

[0040] This invention theoretically and formulatizes the biochemical reactions of beer fermentation based on a swarm equilibrium model, thereby simulating the main fermentation process of beer. During the biochemical reaction, the components of the fermentation broth and temperature changes interact with its flow state; therefore, it is necessary to establish a yeast biochemical reaction equation and solve it in conjunction with the flow and heat transfer equation. Furthermore, yeast cells are difficult to track individually, so the concept of a swarm is required to describe them. The yeast community reaches a dynamic equilibrium with parameters such as sugar concentration and temperature during growth, division, and decay. The biochemical reaction equation characterizing the dynamic equilibrium of these parameters at any given moment during beer fermentation is the swarm equilibrium equation.

[0041] Community equilibrium equations are a class of beer fermentation models based on biochemical reaction processes. They can output the consumption rate of each sugar component, the rate of temperature change, the yeast growth rate, and the alcohol concentration growth rate, based on input variables (sugar concentration, temperature, yeast count) and boundary conditions (heating or cooling). This allows for the determination of the sugar composition, temperature, yeast count, and alcohol concentration at the next time step. According to this model, during beer fermentation, the yeast community is constantly in a state of growth, division, and apoptosis. Therefore, the yeast community equilibrium equations during beer fermentation need to consider three processes: cell growth, cell division, and cell apoptosis. Figure 1 As shown.

[0042] (1) Cell growth

[0043] The process of cell growth does not change the total number of cells, only the cell quality, such as... Figure 2 As shown, cell growth follows the Monod equation, which describes the relationship between the specific proliferation rate of microorganisms and the concentration of organic substrates.

[0044] There are three main fermentation reactions in beer fermentation: glucose fermentation, maltose fermentation, and maltotriose fermentation.

[0045]

[0046]

[0047]

[0048] For the biochemical reaction process of glucose fermentation into alcohol and carbon dioxide, the reaction rate and the number of yeast cells are related. Related to writing:

[0049]

[0050] in The reaction rate constant can be calculated using the Michaelis-Menten kinetic formula.

[0051] Similarly, the above analytical method is applied to maltose and maltotriose, but it is necessary to add inhibition parameters for monosaccharides and maltobiose, which will not be elaborated here. With the above reaction parameters, the reaction rate of the biochemical reaction process can be calculated, thereby obtaining the exothermic power and ethanol production rate. Using yeast concentration as a simulated free substance, the reaction rate is as follows:

[0052]

[0053] in, It is the reaction rate constant, which depends on the three rate constants controlling the reaction as well as the inhibitory effect of high yeast concentration.

[0054] In summary, since the differential equation relating the fermentation rate to sugar concentration, temperature, and yeast count is known, it is not necessary to keep the sugar concentration, temperature, or yeast count constant during the actual experiment. Instead, the initial conditions (such as initial temperature, initial sugar concentration, and initial yeast count) and boundary conditions (such as maintaining no heat exchange between the inside and outside of the container) should be determined at the beginning of the experiment, and the changes of each variable over time should be continuously monitored during the experiment to correct some coefficients in the fermentation equation.

[0055] (2) Cell division

[0056] Yeast cell growth can be understood using the Cell population model as follows: In budding yeast, cell division during the cell cycle is asymmetrical. Of the new cells produced through division, the smaller one is called the daughter cell, and the larger one is called the mother cell. The new daughter cell must grow to a certain size (characterized here by the cell transition mass) before the budding cycle begins, while the new mother cell begins budding shortly after birth. After budding, the bud grows, while the mass of the mother cell remains essentially unchanged. Cell division (characterized here by the mass of the divided cell) produces one new daughter cell and one new mother cell (e.g., ...). Figure 3As shown in the diagram, cells then continue to develop during the cell cycle. Based on this simple cell cycle model, the equation representing the cell before and after division can be expressed as the two newly generated daughter cells minus the mother cell that disappears after division. This process is also influenced by environmental parameters such as temperature, pressure, and nutrient concentration.

[0057] (3) Cell apoptosis

[0058] During apoptosis, the total number of cells and total mass decrease (e.g. Figure 4 As shown in the figure, this process is affected by environmental factors such as temperature and nutrient concentration.

[0059] Cell growth, cell division, and cell apoptosis constitute a simple cell cycle model. This model is actually the result of analysis from a microscopic perspective. From a macroscopic perspective, in addition to their own rate of change over time, cells are also subject to convection and diffusion.

[0060] The population equilibrium equation is essentially an equation relating the microscopic cell cycle model to the macroscopic cell rate of change, convection, and diffusion. It's worth noting that the population equilibrium equation contains a total of four unknown parameters, including the empirical constant K for yeast growth. x Yeast yield coefficient Y R Ethanol yield coefficient E R Yeast mortality rate Y D .

[0061] Based on the above analysis, this application provides a Fluent-based method for simulating the biochemical reactions of beer fermentation, including the following steps:

[0062] Step 1: In this embodiment, three groups of uncontrolled temperature experiments and one group of controlled temperature experiments were conducted. To verify the correctness of the group equilibrium equation and optimize the parameters in the equation to the greatest extent, the initial conditions (yeast type, yeast count, initial temperature, etc.) of each group of experiments were different. After recording the experimental parameters at different time points, parameter-time graphs were plotted. For the three groups of uncontrolled temperature experiments, the tank capacity was set to 300L, the diameter-to-height ratio was 1:1.5, and the other initial conditions are shown in Table 1.

[0063] Table 1 Initial conditions for uncontrolled temperature experiments

[0064]

[0065] In the temperature control experiment, the tank capacity was set to 600L, the diameter-to-height ratio was 1:1.6, and the temperature was maintained at 11℃. Other initial conditions are shown in Table 2.

[0066] Table 2 Initial conditions for temperature control experiment

[0067]

[0068] Step Two: Based on the formula in the population equilibrium model, perform curve fitting on each discrete point on the parameter-time graph (parameters include temperature, alcohol content, sugar content, and yeast count) to make it a smooth curve, and verify the degree of fit. First, analyze the three groups of uncontrolled temperature fermentation experiments, as follows:

[0069] (1) Temperature changes

[0070] In uncontrolled fermentation experiments, the fermenter can be considered a near-adiabatic container. Because the fermentation reaction is exothermic, the temperature inside the fermenter will continuously rise until the fermentation reaction is complete, at which point the temperature will slowly drop back to room temperature. Figure 5 As shown.

[0071] according to Figure 5 As a result, the fermentation process can be roughly divided into two stages: the uncontrolled free fermentation stage and the static cooling stage after the fermentation process is completed.

[0072] The first stage was fitted with a binomial curve, and the results are shown in Table 3.

[0073] Table 3. Temperature fitting results during the free fermentation stage

[0074]

[0075] Based on the above fitting results, When the value is greater than 0.98, the temperature over time can be approximated as a quadratic function, i.e. Taking the derivative with respect to time, we get According to the heat energy theorem, heat exchange is directly proportional to the first derivative of temperature with respect to time, i.e. This means that during uncontrolled fermentation, the heat released by the fermentation reaction changes linearly with time, which indirectly reflects that the fermentation rate changes linearly with time.

[0076] The second stage was fitted with a straight line, and the results are shown in Table 4.

[0077] Table 4 Temperature fitting results during the static cooling phase

[0078]

[0079] For the first experiment, the container temperature gradually approached 22℃ over time, therefore the estimated room temperature condition for this experiment was 22℃, and because Below 0.98, the fitting results are inaccurate, therefore the temperature does not show a linear relationship with time. For the second and third experiments, because... If the value is greater than 0.98, the temperature can be approximated as a linear change with time. Therefore, the temperature gradient under the two experimental conditions can be estimated based on the slope of the fitted function. And then according to , can be obtained The density of water Specific heat capacity and container volume Substituting the values, we can obtain the change in exothermic reaction over time during the first half of the fermentation process: .

[0080] (2) Changes in alcohol content

[0081] During fermentation, alcohol is a reaction product. Without considering losses such as alcohol evaporation, the alcohol yield (or alcohol content) can reflect the progress of the fermentation reaction due to the high accuracy of alcohol content measurement. Figure 6 As shown.

[0082] Depend on Figure 6 It can be seen that the change in alcohol content over time during the fermentation process follows a smooth increasing curve, with the trend being an initial increase followed by a decrease until the fermentation reaction ends. Based on the curve shape and the fact that the reaction rate changes approximately linearly with time as known in the previous section, the cumulative frequency distribution of alcohol content over time is estimated. Therefore, a Boltzmann function model is used for fitting, yielding the following results:

[0083] Based on the fitting results (as shown in Table 5), the data from the three experiments... All values ​​are above 0.98, thus satisfying the Boltzmann function distribution, and the fitting curves from the second and third experiments almost overlap. This represents the final result of the fermentation reaction; the cumulative alcohol content produced in the three experiments was 5.58%, 5.91%, and 5.91% (V / V), respectively. Furthermore, The curve represents the position where the growth is fastest. The results of the three experiments all showed that it was around 79 hours. It is speculated that certain conditions reached their optimal state when the fermentation reaction proceeded to 79 hours, resulting in the highest fermentation rate.

[0084] Table 5. Alcohol content fitting results

[0085]

[0086] (3) Changes in sugar content

[0087] Similar to alcohol content, sugar content is a byproduct of the fermentation process. In the absence of other factors affecting sugar content, the result can also reflect the progress of the fermentation reaction, such as... Figure 7 As shown.

[0088] Similarly, the distribution of sugar content over time follows a cumulative frequency distribution, so the Boltzmann function model is used for fitting, and the results are shown in Table 6.

[0089] Table 6. Results of Sugar Content Curve Fitting

[0090]

[0091] Although the sugar content in the first experiment fluctuated during the initial reaction, according to the fitting results, the data from the three experiments... All values ​​were above 0.98, therefore they can be considered to satisfy the Boltzmann function distribution. The measurement error of the sugar content in the first experiment was within acceptable limits, and the fitting curves of the second and third experiments almost overlapped. A2 represents the final result of the fermentation reaction, meaning that the three experiments included sugars that could not be consumed by fermentation, resulting in a residual sugar content of 1.86. o P, 2.31 o P, 2.34 o P; parameter This represents the position where the curve grows the fastest. The results of the three experiments all showed that it was around 77 hours, which is very close to the results of the alcohol curve.

[0092] 4) Changes in yeast count

[0093] During fermentation, yeast ingests sugar to grow and reproduce, causing its population to continuously increase. However, when the yeast population in the fermenter reaches a certain level and the sugar content decreases to a certain level, the container environment becomes insufficient to support the yeast population's growth, leading to a decrease in the yeast population. Figure 8 As shown.

[0094] Because no temperature control is performed during the fermentation process, the environmental conditions vary considerably throughout the fermentation, making it impossible to directly fit a single curve to the yeast cell count. Observation of the graphs shows that fitting the data from 0-90h in Experiment 1 and 0-80h in Experiments 2 and 3 with the Slogistis function conforms to the yeast population growth model under environmental constraints. Exponential decay fitting was performed on the data from 100-150h in Experiment 1, and linear fitting was performed on the data from 80-170h in Experiments 2 and 3. The results are shown in Table 7.

[0095] Table 7. Results of yeast count curve fitting

[0096]

[0097] It can be seen that the curves of the first experiment and the second and third experiments have opposite concavity and convexity, indicating that the yeast proliferation rate was high initially in the first experiment, but gradually slowed down over time, while the results of the second and third experiments were the opposite. Furthermore, as fermentation neared its end, the yeast count in Experiment 1 exhibited an exponential decay, rapidly decreasing to 20.8 × 10⁻⁶. 6 / ml, while Experiments 2 and 3 showed a linear and slow decrease to 20×10. 6 The fermentation rate remains highest around 80 hours, coinciding with the peak yeast cell count. After 80 hours, the yeast cell count begins to decrease, and the derivative of the fitted function shows that the apoptosis rate is approximately 1 / 3 to 1 / 2 of the proliferation rate.

[0098] The temperature control experiment data will be analyzed next, as follows:

[0099] The temperature changes over time in the two fermentation experiments are plotted as follows: Figure 9 As shown, the temperature-controlled experiment lasted longer than the uncontrolled experiment, reaching 280 hours, with the temperature inside the fermenter controlled between 10 and 12°C through a cooling system. For ease of subsequent analysis, it can be assumed that the temperature remained approximately constant during the fermentation process.

[0100] Under constant temperature conditions, the only factors limiting yeast reproduction and mortality are the sugar concentration, alcohol concentration, and the yeast cell count itself in the environment. The yeast cell count curves from the temperature-controlled fermentation experiment are plotted together with those from the third, uncontrolled temperature experiment, as shown below. Figure 10 As shown in the figure, after controlling the ambient temperature, the changes in the proliferation and decline of yeast in the 0-160h stage are excessively smooth. Therefore, the data of the first half of the fermentation stage can be fitted with the Bihill function. In the last half stage, the number of yeast cells remains at a stable low value until the end of light fermentation due to nutrient consumption. The Bihill fitting results are shown in Table 8.

[0101] Table 8. Results of yeast count curve fitting

[0102]

[0103] It can be seen that the curve obtained by fitting with the Bihill function is in good agreement with the experimental data. The value reached 0.95, but the problem is that the peak value of the curve is too low and the time corresponding to the peak value is too late. Analysis of the curve function of yeast count shows that the maximum yeast count is 32 × 10⁻⁶. 6 / ml, which occurred around 100h, may mean that the fermentation reaction rate peaked around 100h.

[0104] Next, the alcohol content and sugar content will be plotted, as shown below. Figure 11 , Figure 12 As shown in Table 9, alcohol content and sugar content still follow the cumulative frequency distribution. Therefore, the Boltzmann function model is used for fitting, and the results are shown in Table 9.

[0105] Table 9. Results of Sugar Content Curve Fitting

[0106]

[0107] As can be seen from the table, both alcohol content and sugar content... All are greater than 0.98, and The values ​​of 91.9 and 94.2 represent the moments when the reaction rate is at its maximum, which also correspond to the moments when the yeast count is at its maximum.

[0108] The above values ​​are from small-scale sample experiments. To ensure the accuracy of calculations for large-scale production tanks, the model parameters need to be adjusted for large tanks. The adjustment method is similar to the temperature control experiment results described above. A comparison curve of simulated saccharide content with actual production can be found... Figure 13 R-squared results comparing experimental and simulation results 2 It is 0.95.

[0109] (2) Step 3: After verifying that the curve fitting degree meets the calculation requirements, select multiple sets of data points on the material curves of each experiment and check them. Figure 5-12 Obtain the material parameters at the corresponding time points, and simultaneously calculate the slope (rate of change of matter R) of the data points. x The above text mentions that there are four unknown parameters in the equation, namely the empirical constant for yeast growth, K. X Yeast yield coefficient Y R Ethanol yield coefficient E R Yeast mortality rate Y D These four unknown parameters, through relatively complex calculations, together constitute a specific growth rate k. fx The specific growth rate will change over time, while the four unknown parameters will not change.

[0110] According to the group equilibrium model, we have: R x =C y* k fx To ensure the accuracy of the unknown parameters, multiple points can be selected and substituted into the equations. The least squares method can then be used to solve the system of equations, thus finding a value for the unknown that minimizes the sum of squared residuals of all equations. The specific equations are as follows:

[0111]

[0112]

[0113] The first row of the above equation calculates the ethanol growth rate, and the second row calculates the yeast concentration change rate, E. R Y represents the ethanol yield coefficient. EG Y represents the number of moles of ethanol produced per mole of sugar. This value can be calculated from the initial composition ratio of glucose, maltose, and maltotriose, and the reaction equation for the formation of ethanol from these three substances. RY represents the yeast yield coefficient. XG This indicates how many moles of yeast are produced per mole of sugar; the calculation method is the same as for Y. EG K X K is an empirical constant for yeast growth. f1 The rate of change in sugar content can be obtained by calculating the slope of the sugar content curve, c. y Y represents the concentration of yeast cells. D This represents the yeast mortality rate, where the unknown parameter is the empirical yeast growth constant K. X Yeast yield coefficient Y R Ethanol yield coefficient E R and yeast mortality rate Y D .

[0114] In actual calculations, to ensure the accuracy of unknown parameters, multiple points can be selected for calculation using the least squares method. Here, to simplify the calculation process, only four points are used for equation operations. The following four points can be selected for calculation:

[0115] 1. Experiment 1 was conducted for 40 hours;

[0116] 2. Experiment 2 was conducted for 60 hours;

[0117] 3. Experiment 3 was conducted for 80 hours;

[0118] 4. Experiment 4 was conducted for 100 hours.

[0119] Taking point 2 as an example, read Figure 5-12 Based on the parameters, the alcohol content is 1.28% (V / V) and the sugar content is 10.9%. o P, yeast concentration is 26 × 10⁻⁶ 6 / ml, the derivative of the ethanol and yeast concentration curves at point 2 is obtained. and Thus, the system of equations at point 2 is complete. Similarly, the system of equations at the other three points is obtained. Together, these three systems of equations form four sets of equations, which can be used to calculate the four unknown parameters.

[0120] Step 4: Current empirical constant K for yeast growth x Yeast yield coefficient Y R Ethanol yield coefficient E R and yeast mortality rate Y DAll four parameters have been obtained, so the group equilibrium model is complete. The group equilibrium equation can be converted into C language and then compiled into the pre-processed Fluent software (which has already established the fermenter model and set various parameters and boundary conditions). After compilation, the initial concentrations of glucose, maltose, maltotriose, yeast, and alcohol can be set, and calculations can be performed to obtain the concentrations of each substance and the flow of the fermentation broth at any given time.

[0121] Based on the same concept as the above method, this application also provides a Fluent-based beer fermentation biochemical reaction simulation system, including:

[0122] The experimental monitoring and data acquisition module is used to conduct multiple beer fermentation experiments under different initial conditions, and to monitor and record the changes of key parameters over time during the fermentation process.

[0123] The curve fitting and parameter optimization module is used to perform curve fitting on the collected parameters and time data according to the swarm equilibrium model, fit discrete data points into a smooth curve, and calculate the slope of each parameter through the fitted curve; and use the least squares method to solve the unknown parameters in the swarm equilibrium model to optimize the model parameters.

[0124] The model building and code implementation module is used to convert the optimized swarm equilibrium model into a computer-readable code form and compile and integrate the code into the pre-processed Fluent software;

[0125] The simulation calculation and result analysis module is used to set the initial fermentation conditions in Fluent software, run simulation calculations, and obtain the concentration of each substance and the flow of the fermentation liquid at any time, thereby realizing full-time and all-round simulation analysis of the beer fermentation process.

[0126] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A Fluent-based simulation method for the biochemical reactions of beer fermentation, characterized in that, The method includes the following steps: Step 1. Conduct multiple sets of beer fermentation experiments under different initial conditions, monitor and record the changes of key parameters over time during the fermentation process; the key parameters include temperature, alcohol content, sugar content and yeast count; Step 2. Based on the population balance model that includes yeast growth, division, and death, perform curve fitting on the collected parameters and time data to fit the discrete data points into a smooth curve, and calculate the slope of each parameter through the fitted curve; use the least squares method to solve for the unknown parameters in the population balance model in order to optimize the model parameters. The unknown parameters include the yeast growth empirical constant, yeast yield coefficient, ethanol yield coefficient, and yeast die-off rate. Step 3. Convert the optimized swarm equilibrium model into a computer-readable code form and compile and integrate the code into the pre-processed Fluent software; Step 4. Set the initial fermentation conditions in Fluent software, run the simulation calculation, and obtain the concentration of each substance and the flow of the fermentation liquid at any time, thereby realizing the full-time and all-round simulation analysis of the beer fermentation process.

2. The Fluent-based biochemical reaction simulation method for beer fermentation according to claim 1, characterized in that, In step 1, the beer fermentation experiments conducted under different initial conditions include at least one set of temperature-controlled experiments and at least one set of uncontrolled experiments. In the temperature-controlled experiments, the temperature inside the fermentation tank is controlled within a preset temperature range by a cooling system. In the uncontrolled experiments, the temperature inside the fermentation tank changes naturally with the fermentation reaction.

3. The Fluent-based biochemical reaction simulation method for beer fermentation according to claim 1, characterized in that, In step 2, when performing curve fitting on the collected parameters and time data, the fitting models used include quadratic polynomial model, linear model, Boltzmann function model, Slogistis function model, single-phase exponential decay model and Bihill function model. The appropriate fitting model is selected according to different parameter change trends to improve fitting accuracy.

4. The Fluent-based biochemical reaction simulation method for beer fermentation according to claim 3, characterized in that, In step 2, when solving for the unknown parameters in the group equilibrium model using the least squares method, multiple data points at different time points are selected for calculation to ensure the accuracy and reliability of the solution results.

5. The Fluent-based biochemical reaction simulation method for beer fermentation according to claim 4, characterized in that, In step 2, the selected data points at multiple different time points are evenly distributed across different stages of the fermentation process, including the initial, middle, and final stages of fermentation, in order to comprehensively reflect the parameter change trends during the fermentation process.

6. The Fluent-based biochemical reaction simulation method for beer fermentation according to claim 5, characterized in that, In step 2, when verifying the fitted curve, the coefficient of determination R of the fitted curve is also calculated. 2 And ensure that the coefficient of determination is greater than the preset threshold to further verify the goodness of fit of the fitted curve.

7. The Fluent-based biochemical reaction simulation method for beer fermentation according to claim 1, characterized in that, In step 3, the Fluent software pre-established a fermenter model and set the corresponding parameters and boundary conditions.

8. A Fluent-based simulation system for the biochemical reaction of beer fermentation, characterized in that, include: The experimental monitoring and data acquisition module is used to conduct multiple sets of beer fermentation experiments under different initial conditions, monitor and record the changes of key parameters over time during the fermentation process; the key parameters include temperature, alcohol content, sugar content and yeast count; The curve fitting and parameter optimization module is used to perform curve fitting on the collected parameter and time data based on the population balance model that includes yeast growth, division, and apoptosis. It fits discrete data points into a smooth curve and calculates the slope of each parameter through the fitted curve. It uses the least squares method to solve for the unknown parameters in the population balance model in order to optimize the model parameters. The unknown parameters include the yeast growth empirical constant, yeast yield coefficient, ethanol yield coefficient, and yeast apoptosis rate. The model building and code implementation module is used to convert the optimized swarm equilibrium model into a computer-readable code form and compile and integrate the code into the pre-processed Fluent software; The simulation calculation and result analysis module is used to set the initial fermentation conditions in Fluent software, run simulation calculations, and obtain the concentration of each substance and the flow of the fermentation liquid at any time, thereby realizing full-time and all-round simulation analysis of the beer fermentation process.

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