Fluent-based beer fermentation biochemical reaction simulation method and system

Abstract

The invention discloses a beer fermentation biochemical reaction simulation method and system based on Fluent. The method comprises the following steps: firstly, carrying out multiple groups of beer fermentation experiments under different initial conditions, and monitoring and recording the change condition of each key parameter along with time in the fermentation process; secondly, according to a group balance model, curve fitting is conducted on the collected parameters and time data, discrete data points are fitted into a smooth curve, the slope of each parameter is calculated through the fitted curve, and unknown parameters in the group balance model are solved through a least square method so as to optimize model parameters; then, the optimized group balance model is converted into a code form which can be recognized by a computer, and the code is compiled and integrated into Fluent software subjected to pretreatment; and finally, initial fermentation conditions are set in Fluent software, simulation calculation is operated, and the concentration of each substance and the flow condition of the fermentation liquor at any moment are obtained, so that all-time and all-directional simulation analysis of the beer fermentation process is realized.

Description

Technical Field

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

[0002] Beer fermentation is an important step in the beer production process. In industrial production, breweries use sugary wort as raw material, add yeast, and carry out fermentation in a sealed fermentation tank under anaerobic conditions. During the fermentation process, in addition to biochemical reactions, there are also relatively complex diacetyl oxidation-reduction reactions. Under the action of yeast, the wort will be converted into beer. During this process, sugar produces alcohol and various flavor substances under the action of yeast.

[0003] In order to deeply understand the changes in the content of yeast and various products during the yeast fermentation process, many scholars have devoted themselves to the research on beer fermentation. In actual production, beer manufacturers set sampling points at fixed positions in the fermentation tank to measure the content of various substances in the fermentation broth at a certain moment. However, due to the uneven distribution of substances inside the fermentation broth, the sampling results cannot represent the overall results, and at the same time, analyzing the samples also requires manpower and time.

[0004] With the rapid development of computers and the optimization and improvement of simulation technology, CFD (Computational Fluid Dynamics), that is, computational fluid dynamics, has come into people's view. The CFD technology combines fluid mechanics, numerical analysis, and computer science. By simulating and analyzing the behavior of fluids under various boundary conditions and forces, it solves fluid flow problems. Its core lies in converting complex fluid dynamics problems into solvable equation systems through mathematical models and algorithms, and then solving them on a computer.

[0005] Computational Fluid Dynamics (CFD), as a powerful simulation tool, has numerous advantages. It can predict and analyze the problems of fermentation broth flow and heat transfer without building a fermenter and the corresponding production line, thus significantly reducing the research and development costs. From the perspective of evaluation and optimization, CFD simulation can easily modify the boundary conditions and initial substance content parameters of the fermentation broth, making the design optimization and performance evaluation more flexible and rapid. In addition, CFD can reveal the flow conditions of the fermentation broth that are difficult to observe during production, such as the turbulent structure, streamline, and vortex of the fermentation broth. When analyzing the fermentation process, due to the fact that CFD software (such as Fluent, etc.) usually has powerful post-processing tools, the visualization and interpretation of the results are more intuitive. Generally speaking, the advantages of CFD lie in cost-effectiveness, design flexibility, in-depth insight into complex flows, prediction ability, and user operation convenience. However, current research on beer fermentation using CFD technology usually focuses on the cooling stage, with less simulation research on the main fermentation temperature control stage. At the same time, relying solely on CFD technology cannot simulate the biochemical reactions during the beer brewing process. Summary of the Invention

[0006] To solve the problems existing in the above-mentioned prior art, the present invention provides a method and system for simulating biochemical reactions in beer fermentation based on Fluent.

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

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

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

[0010] Step 3. Convert the optimized population balance model into a computer-recognizable code form, and compile and integrate this code into the pre-processed Fluent software;

[0011] Step 4. Set the initial conditions of fermentation in the Fluent software, run the simulation calculation, and obtain the concentrations of various substances and the flow conditions of the fermentation broth at any time, so as to realize the full-time and all-round simulation analysis of the beer fermentation process.

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

[0013] Furthermore, in the said Step 1, the key parameters include temperature, alcohol content, sugar content, and the number of yeast cells.

[0014] Furthermore, in the said Step 2, when performing curve fitting on the collected parameter-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 corresponding fitting model is selected according to different parameter change trends to improve the fitting accuracy.

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

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

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

[0018] Furthermore, in the said Step 2, when verifying the fitting curve, the determination coefficient R 2 of the fitting curve is also calculated, and it is ensured that this determination coefficient is greater than a preset threshold to further verify the goodness of fit of the fitting curve.

[0019] Furthermore, in the said Step 3, the Fluent software has pre-established a fermentation tank model and set the corresponding parameters and boundary conditions.

[0020] Secondly, the present invention provides a beer fermentation biochemical reaction simulation system based on Fluent, including:

[0021] An experimental monitoring and data acquisition module, which is used to conduct multiple groups of beer fermentation experiments under different initial conditions, monitor and record the changes of each key parameter over time during the fermentation process;

[0022] The curve fitting and parameter optimization module is used to perform curve fitting on the collected parameter and time data according to the population balance model, 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 the unknown parameters in the population balance model to optimize the model parameters;

[0023] The model construction and code implementation module is used to convert the optimized population balance model into a computer-recognizable 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 the Fluent software, run the simulation calculation, obtain the concentrations of various substances at any time and the flow conditions of the fermentation broth, so as to realize the full-time and all-round simulation analysis of the beer fermentation process.

[0025] The beneficial effects of the present invention: The present invention aims to simulate the biochemical reactions in the beer fermentation process, combine the flow analysis of the fermentation broth by Fluent, realize the full-time and all-round parameter monitoring and flow analysis of beer brewing, and provide guidance and reference for actual production. Brief Description of the Drawings

[0026] Figure 1 It is the influencing factors of the cell population balance equation of the present invention.

[0027] Figure 2 It is the cell growth schematic diagram of the present invention.

[0028] Figure 3 It is the cell division schematic diagram of the present invention.

[0029] Figure 4 It is the cell apoptosis schematic diagram of the present invention.

[0030] Figure 5 It is the temperature change with time in the uncontrolled temperature fermentation experiment of the present invention.

[0031] Figure 6 It is the alcohol content change with time in the uncontrolled temperature fermentation experiment of the present invention.

[0032] Figure 7 It is the sugar content change with time in the uncontrolled temperature fermentation experiment of the present invention.

[0033] Figure 8 It is the yeast count change with time in the uncontrolled temperature fermentation experiment of the present invention.

[0034] Figure 9 It is the temperature change with time in the two fermentation experiments of the present invention.

[0035] Figure 10This shows the change of the number of yeast cells over time in two fermentation experiments of the present invention.

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

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

[0038] Figure 13 This is the calculation calibration result of the large tank of the present invention. Detailed implementation mode

[0039] The technical solution and basic principle of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the specification and specific embodiments.

[0040] Based on the population balance model, the present invention theorizes and formulates the biochemical reaction of beer fermentation, so as to realize the simulation of the main fermentation process of beer. In the biochemical reaction process, the components and temperature change of the fermentation broth interact with its flow state. Therefore, it is necessary to establish a yeast biochemical reaction equation and couple it with the flow and heat transfer equation for solution. At the same time, it is difficult to track yeast cells individually, so the concept of population needs to be used to describe them. The yeast population reaches a dynamic balance with parameters such as sugar concentration and temperature during the processes of growth, division, and withering. The biochemical reaction equation that characterizes the dynamic balance of each parameter at any moment during the beer fermentation process is the population balance equation.

[0041] The population balance equation is a type of beer fermentation model based on the biochemical reaction process. According to the input variables (sugar concentration, temperature, number of yeast cells) and boundary conditions (heat preservation or cooling conditions), it can output the consumption rate of each sugar component, the temperature change rate, the yeast growth rate, and the alcohol concentration growth rate, and then obtain the sugar component, temperature, number of yeast cells, and alcohol concentration at the next moment. According to this model description, during the beer fermentation process, the yeast community is constantly in the processes of growth, division and proliferation, and withering. Therefore, the population balance equation of the yeast community during the beer fermentation process needs to consider three processes: cell growth, cell division, and cell withering, as Figure 1 shown.

[0042] (1) Cell growth

[0043] The process of cell growth does not change the total number of cells, but only changes the cell mass, as Figure 2 shown. The process of cell growth follows the Monod equation, which is used to describe the relationship between the specific growth rate of microorganisms and the concentration of organic substrates.

[0044] During the beer fermentation process, there are mainly three fermentation reaction formulas: 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 is related to the number of yeast cells and can be written as:

[0049]

[0050] where is the reaction rate constant, which can be calculated by the Michaelis-Menten kinetic equation.

[0051] Similarly, for maltose and maltotriose, the above analysis method is also applied, but the inhibition parameters of monosaccharides and the inhibition parameter of maltose need to be added, which will not be elaborated here. With the above reaction parameters, the reaction rate of the biochemical reaction process can be calculated, and thus the reaction heat release power and the ethanol production rate can be obtained. Simulating the yeast concentration as a free substance, the reaction rate is as follows:

[0052]

[0053] where is the reaction rate constant, which depends on the reaction rate constants of three reactions controlling the reaction and the inhibition effect of high yeast concentration.

[0054] In summary, since the differential equation form of the fermentation reaction rate with respect to sugar concentration, temperature, and the number of yeast cells is known, in the actual experimental process, it is not necessary to control the sugar concentration, temperature, or the yeast to be constant. Instead, determine the initial conditions at the beginning of the experiment (such as initial temperature, initial sugar concentration, initial number of yeast cells) and the boundary conditions during the experiment (such as keeping no heat exchange between the inside and outside of the container), and continuously monitor the changes of each variable with time during the experiment, so as to correct some coefficients in the above fermentation reaction equation.

[0055] (2) Cell division

[0056] The growth of yeast cells can be understood in this way through the Cell population model. The cell division in the budding yeast cell cycle is asymmetric. Among the newly generated cells produced by division, the smaller one is called the daughter cell, and the larger one is called the mother cell. The newly generated daughter cells must grow to a certain size (characterized by the cell transition mass here) before starting the budding cycle, while the newly generated mother cells start budding soon after birth. After budding occurs, the bud grows, while the mass of the mother cell remains basically unchanged. Cell division (characterized by the cell division mass here) produces a newly generated daughter cell and a newly generated mother cell (as Figure 3As shown in the figure), and then continue to develop in the cell cycle. Based on this simple cell cycle model, the equation representing the situation before and after cell division can be expressed as the two newly generated daughter cells minus the mother cell that disappears after division. At the same time, this process is also affected by environmental parameters such as temperature, pressure, and the concentration of nutrient components.

[0057] (3) Apoptosis

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

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

[0060] The population balance equation is the equation of the microscopic cell cycle model and the macroscopic cell rate of change, convection, and diffusion. It is worth mentioning that the population balance equation contains a total of four unknown parameters: the yeast growth empirical constant K x , the yeast yield coefficient Y R , the ethanol yield coefficient E R , and the yeast apoptosis rate Y D .

[0061] Based on the above analysis, the embodiments of the present application provide a method for simulating biochemical reactions in beer fermentation based on Fluent, including the steps:

[0062] Step 1: A total of 3 groups of non-temperature-controlled experiments and 1 group of temperature-controlled experiments were conducted in the embodiments of the present application. In order to verify the correctness of the population balance equation to the greatest extent and optimize the parameters in the equation, the initial conditions (yeast type, number of yeast, initial temperature, etc.) of each group of experiments were different. After recording the experimental parameters of each group at different time points, a parameter-time graph was plotted respectively. For the 3 groups of non-temperature-controlled experiments, the tank capacity was set to 300 L, and the diameter-height ratio was 1:1.5. The other initial conditions are shown in Table 1.

[0063] Table 1 Initial conditions of non-temperature-controlled experiments

[0064]

[0065] In the temperature-controlled experiment, the tank capacity was set to 600 L, the diameter-height ratio was 1:1.6, and the temperature was controlled to remain at 11 °C. The other initial conditions are shown in Table 2.

[0066] Table 2 Initial conditions of temperature-controlled experiments

[0067]

[0068] Step 2: According to the formula in the population balance model, perform curve fitting on each discrete point on the parameter-time graph (parameters include temperature, alcohol content, sugar content, and the number of yeast cells) to make it a smooth curve and verify the fitting degree. First, analyze the three groups of unregulated temperature fermentation experiments as follows:

[0069] (1) Temperature change

[0070] In the unregulated temperature fermentation experiment, the fermentation tank can be regarded as an approximately adiabatic container. Due to the heat release of the fermentation reaction, the temperature in the fermentation tank will continuously rise until the fermentation reaction ends, and then the temperature in the container will slowly drop back to room temperature, as Figure 5 shown.

[0071] According to Figure 5 the results, the fermentation process can be roughly divided into two stages: the unregulated temperature free fermentation stage and the static cooling stage after the fermentation process ends.

[0072] Perform fitting on the first stage with a binomial curve, and the results are shown in Table 3.

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

[0074]

[0075] According to the above fitting results, is greater than 0.98, and the temperature can be approximately regarded as a quadratic function of time, that is , take the first derivative with respect to time, and get , and according to the heat energy theorem, the heat exchange is proportional to the first derivative of temperature with respect to time, that is , which means that during the unregulated temperature fermentation process, the heat release rate of the fermentation reaction changes linearly with time, indirectly reflecting that the fermentation reaction rate changes linearly with time.

[0076] Perform fitting on the second stage with a straight line, and the results are shown in Table 4.

[0077] Table 4 Temperature fitting results in the static cooling stage

[0078]

[0079] For the first experiment, the container temperature gradually approaches 22°C with time, so it is estimated that the room temperature condition for this experiment is 22°C, and since is lower than 0.98, the fitting result is inaccurate, so the temperature does not change linearly with time. For the second and third experiments, since is greater than 0.98, the temperature can be approximately regarded as changing linearly with time. Then, according to the slope in the fitting function, the temperature gradients under the two experimental conditions can be estimated , and then according to , it can be obtained that , substituting the density of water , specific heat capacity and the volume of the container into it, the change of reaction heat release with time in the first half of the fermentation reaction can be obtained: .

[0080] (2) Change of alcohol content

[0081] During the fermentation process, alcohol is a reaction product. Without considering losses such as alcohol volatilization, since the measurement accuracy of alcohol content is relatively high, the production of alcohol (or alcohol content) can reflect the progress of the fermentation reaction, as shown in Figure 6 .

[0082] From Figure 6 , it can be seen that during the fermentation reaction, the change of alcohol content with time shows a smooth growth curve, and its growth trend is to increase first and then decrease until the end of the fermentation reaction. According to the curve shape and the approximate linear change of the reaction rate with time in the previous section, it is estimated that the alcohol content follows a cumulative frequency distribution with time. Therefore, the Boltzmann function model is used for fitting, and the following results are obtained:

[0083] According to the fitting results (as shown in Table 5), the of the three experimental data are all above 0.98. Therefore, it satisfies the Boltzmann function distribution, and the fitting curves of the second and third experiments almost overlap. Among them, represents the final result of the fermentation reaction. The final cumulative alcohol contents produced in the three experiments are 5.58, 5.91, and 5.91 % (V / V) respectively. In addition, represents the position where the curve grows fastest. The results of the three experiments all show that it is around 79h. It is speculated that when the fermentation reaction proceeds to 79h, certain conditions reach the best state, resulting in the highest fermentation reaction rate.

[0084] Table 5 Fitting results of alcohol content

[0085]

[0086] (3) Change of sugar content

[0087] Similar to the alcohol content, the sugar content is a consumable in the fermentation reaction process. Without other factors affecting the sugar content, the result of the sugar content can also reflect the progress of the fermentation reaction, as shown in Figure 7 .

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

[0089] Table 6 Fitting Results of Brix Curves

[0090]

[0091] Although the brix of the first experimental result fluctuated up and down at the beginning of the reaction process, according to the fitting results, the values of all three experimental data were above 0.98. Therefore, it can be considered that they satisfy the Boltzmann function distribution. The measurement error of the brix in the first experiment was within the acceptable range, and the fitting curves of the second and third experiments almost overlapped. Among them, A2 represents the final result of the fermentation reaction, which means that there were sugars that could not be consumed by fermentation in the three experiments, resulting in a remaining brix of 1.86 o P, 2.31 o P, 2.34 o P; Parameter represents the position where the curve grows fastest. The results of all three experiments showed that it was around 77 h, which was very close to the result of the alcohol curve.

[0092] 4) Changes in the Number of Yeast Cells

[0093] During the fermentation process, yeast cells consume sugars for growth and reproduction, causing the number of yeast cells to continuously increase. When the number of yeast cells in the fermenter grows to a certain level and the sugar content decreases to a certain extent, the container environmental conditions are insufficient to support the growth of the yeast population, resulting in a decrease in the number of yeast cells, as Figure 8 shown.

[0094] Since the temperature is not controlled during the fermentation reaction process, the environmental conditions change greatly during the entire fermentation process. It is impossible to directly fit the number of yeast cells with a single curve. From the image, the data of 0 - 90 h in Experiment 1 and the data of 0 - 80 h in Experiments 2 and 3 were fitted with the Slogistis function, which conformed to the growth model of the yeast population under environmental constraints. The data of 100 - 150 h in Experiment 1 were fitted with exponential decay, and the data of 80 - 170 h in Experiments 2 and 3 were fitted linearly. The results are shown in Table 7:

[0095] Table 7 Fitting Results of Yeast Cell Number Curves

[0096]

[0097] It can be seen that the concavity and convexity of the curves in the first experiment are opposite to those in the second and third experiments. It can be seen that the initial situation in the first experiment was that the proliferation rate of yeast was relatively high, but as time passed, the proliferation rate slowly decreased, while the results of the second and third experiments were the opposite. In addition, when the fermentation was approaching the end, the number of yeast cells in Experiment 1 showed an exponential decay form and rapidly decreased to 20.8×10 6 / ml, while in Experiment 2 and Experiment 3, it linearly and slowly decreases to around 20×10 6 / ml. At the 80h mark, the fermentation reaction rate is still the highest, and this is exactly when the yeast population reaches its peak. After 80h, the number of yeast starts to decrease. The derivative of the fitting function shows that the withering rate is approximately 1 / 3 - 1 / 2 of the proliferation rate.

[0098] Next, the data of the temperature control experiment will be analyzed as follows:

[0099] The temperature change over time in the two fermentation experiments is plotted as Figure 9 shown. It can be seen that, compared with the non-temperature-controlled experiment, the temperature-controlled experiment takes longer, reaching 280h. The temperature inside the fermenter is controlled between 10~12℃ through the cooling system. For the convenience of subsequent analysis, it can be considered that the temperature remains approximately constant during the fermentation process.

[0100] When the temperature is kept constant, the factors restricting the reproduction and withering of yeast are only the sugar concentration, alcohol concentration in the environment, and the number of yeast itself. The yeast population curve of the temperature-controlled fermentation experiment and that of the third non-temperature-controlled experiment are plotted together as Figure 10 shown. It can be seen that after controlling the environmental temperature, the changes in proliferation and withering of the yeast growth curve in the 0 - 160h stage are overly 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 remains at a stable and relatively low value until the fermentation ends when the nutrients are consumed. The Bihill fitting results are shown in Table 8.

[0101] Table 8 Fitting Results of Yeast Population Curve

[0102]

[0103] It can be seen that the curve obtained by fitting with the Bihill function is in good agreement with the experimental data, reaching 0.95. However, the problem is that the peak of the curve is relatively low and the time corresponding to the peak is relatively late. Analyzing the curve function of the yeast population, the maximum number of yeast is 32×10 6 / ml, which occurs around 100h. This may mean that the highest point of the fermentation reaction rate is around 100h.

[0104] Next, the alcohol content and sugar content are plotted, as shown in Figure 11 、 Figure 12 respectively. It can be seen that the 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 Fitting Results of Sugar Content Curve

[0106]

[0107] As can be seen from the table, whether it is alcohol content or sugar content, both are greater than 0.98, and the values are 91.9 and 94.2 respectively, representing the moments with the maximum reaction rate, and both correspond to the moments with the maximum number of yeast cells.

[0108] The above values are the experimental results of the small tank samples. To ensure the calculation accuracy of the large production tanks, the parameters of this model need to be adjusted for the large tanks. The adjustment method is similar to the above temperature control experimental results. The comparison curve of sugar content simulation and actual production can be seen in Figure 13 , and the R 2 for the comparison of experimental and simulation results is 0.95.

[0109] (2) Step 3: After verifying that the curve fitting degree meets the calculation requirements, select multiple groups of data points on the material curves of each experiment and obtain the parameters of each substance at the corresponding time by checking Figure 5-12 and calculate the slope of the data points (the rate of change of substance R x ). As mentioned above, there are 4 unknown parameters in the equation, namely the yeast growth empirical constant K X , the yeast yield coefficient Y R , the ethanol yield coefficient E R , and the yeast decay rate Y D . These four unknown parameters jointly form the specific growth rate k fx through relatively complex operations. The specific growth rate will change with time, while the four unknown parameters will not change.

[0110] According to the population balance model: R x = C y* k fx . To ensure the accuracy of the unknown parameters, multiple points can be selected and substituted into the equation, and the least squares method can be used to solve the system of equations, so as to find the value of one unknown, making the sum of the squares of the residuals of all equations the smallest. The specific equations are as follows:

[0111]

[0112]

[0113] In the above equations, the first line calculates the ethanol growth rate, and the second line calculates the change rate of yeast concentration. E R represents the ethanol yield coefficient, and Y EG represents how many moles of ethanol are produced per mole of sugar. This value can be obtained by converting according to the composition ratio of glucose, maltose, and maltotriose at the initial stage of the experiment and the reaction equations for the production of ethanol from the three sugars. Y RDenotes the yeast production rate coefficient, Y XG Indicates how many moles of yeast are produced per mole of carbohydrate. The calculation method of this value is the same as that of Y EG , K X Is the yeast growth empirical constant, K f1 Represents the change rate of sugar content, which can be obtained by calculating the slope of the sugar content curve, c y Denotes the yeast concentration, Y D Represents the yeast withering rate, and the unknown parameter therein is the yeast growth empirical constant K X , the yeast production rate coefficient Y R , the ethanol production rate coefficient E R And the yeast withering rate Y D .

[0114] In the actual calculation process, to ensure the accuracy of the unknown parameters, multiple points can be taken and calculated using the least squares method. Here, for the sake of simplifying the calculation process, only four points are taken for the equation operation. The following four points can be taken for calculation:

[0115] 1. Experiment 1 proceeds to 40 hours;

[0116] 2. Experiment 2 proceeds to 60 hours;

[0117] 3. Experiment 3 proceeds to 80 hours;

[0118] 4. Experiment 4 proceeds to 100 hours.

[0119] Taking point 2 as an example, read Figure 5-12 The parameters in it. It can be known that the alcohol content is 1.28% (V / V), the sugar content is 10.9 o P, the yeast concentration is 26×10 6 / ml. Take the derivatives of the ethanol and yeast concentration curves at point 2 to obtain And . At this point, the equations at point 2 have been formed. Similarly, 3 sets of equations for the other 3 points are obtained, and a total of 4 sets of equations can be used to calculate 4 unknown parameters.

[0120] Step 4: The current yeast growth empirical constant K x , the yeast production rate coefficient Y R , the ethanol production rate coefficient E R And the yeast withering rate Y DThese four parameters have all been obtained. Therefore, the population balance model has been perfected. It is only necessary to convert the population balance equation into C language code and further compile the code file into the pre-processed Fluent software (this software has established a fermenter model and set various parameters and boundary conditions). After compilation, set the initial concentrations of glucose, maltose, maltotriose, yeast, and alcohol, and then further calculations can be performed to obtain the concentrations of various substances and the flow conditions of the fermentation broth at any time.

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

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

[0123] A curve fitting and parameter optimization module, which is used to perform curve fitting on the collected parameter-time data according to the population balance model, 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 the unknown parameters in the population balance model to optimize the model parameters;

[0124] A model construction and code implementation module, which is used to convert the optimized population balance model into a computer-recognizable code form and compile and integrate this code into the pre-processed Fluent software;

[0125] A simulation calculation and result analysis module, which is used to set the fermentation initial conditions in the Fluent software, run the simulation calculation, obtain the concentrations of various substances and the flow conditions of the fermentation broth at any time, so as to realize the full-time and all-round simulation analysis of the beer fermentation process.

[0126] The above embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several deformations and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention patent should be subject to the appended claims.

Claims

1. A method for simulating the biochemical reaction of beer fermentation based on Fluent, characterized in that, The method includes the following steps: Step 1. Conduct beer fermentation experiments under multiple groups of different initial conditions, and monitor and record the changes of each key parameter with time during the fermentation process; Step 2. According to the population balance model, perform curve fitting on the collected parameter-time data, 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 the unknown parameters in the population balance model to optimize the model parameters; Step 3. Convert the optimized population balance model into a computer-recognizable code form, and compile and integrate this code into the pre-processed Fluent software; Step 4. Set the initial conditions of fermentation in the Fluent software, run the simulation calculation, and obtain the concentrations of various substances and the flow conditions of the fermentation broth at any time, so as to realize the full-time and all-round simulation analysis of the beer fermentation process.

2. The Fluent-based simulation method for beer fermentation biochemical reaction according to claim 1, wherein In the said Step 1, the multiple groups of beer fermentation experiments under different initial conditions include at least one temperature-controlled experiment and at least one non-temperature-controlled experiment. In the temperature-controlled experiment, the temperature in the fermentation tank is controlled within a preset temperature range through a cooling system. In the non-temperature-controlled experiment, the temperature in the fermentation tank changes naturally with the fermentation reaction.

3. The Fluent-based simulation method for beer fermentation biochemical reaction according to claim 1 or 2, characterized in that In the said Step 1, the key parameters include temperature, alcohol content, sugar content, and the number of yeast cells.

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

5. The Fluent-based simulation method for beer fermentation biochemical reactions according to claim 4, characterized in that, In the said Step 2, when using the least squares method to solve the unknown parameters in the population balance model, select data points at multiple different time points for calculation to ensure the accuracy and reliability of the solution result.

6. The Fluent-based simulation method for beer fermentation biochemical reactions according to claim 1 or 5, characterized in that, In the said Step 2, the unknown parameters include yeast growth empirical constant, yeast yield coefficient, ethanol yield coefficient, and yeast decay rate.

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

8. The Fluent-based simulation method for biochemical reactions in beer fermentation according to claim 7, wherein, In step 2, when validating the fitting curve, the coefficient of determination R of the fitting curve is also calculated 2 , and it is ensured that the coefficient of determination is greater than a preset threshold to further verify the goodness of fit of the fitting curve.

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

10. A Fluent-based simulation system for beer fermentation biochemical reactions, characterized in that, It includes: An experimental monitoring and data acquisition module, which is used to conduct beer fermentation experiments under multiple groups of different initial conditions, and monitor and record the changes of each key parameter with time during the fermentation process; A curve fitting and parameter optimization module, which is used to perform curve fitting on the collected parameter-time data according to the population balance model, 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 the unknown parameters in the population balance model to optimize the model parameters; A model construction and code implementation module, which is used to convert the optimized population balance model into a computer-recognizable code form, and compile and integrate this code into the pre-processed Fluent software; The simulation calculation and result analysis module is used to set the initial fermentation conditions in the Fluent software, run the simulation calculation, and obtain the concentrations of various substances and the flow conditions of the fermentation broth at any time, so as to realize the full-time and all-round simulation analysis of the beer fermentation process.

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