Method for predicting concentration of volatile organic compounds in fermented grains in pit

Abstract

The invention discloses a method for predicting the concentration of volatile organic compounds in fermented grains in a pit, and belongs to the technical field of fermentation monitoring. The method comprises the following steps: acquiring the concentration of to-be-detected organic matters in the fermentation tail gas, the current fermentation time and the cellar temperature; and calculating the concentration of the to-be-detected organic matters in the fermented grains according to the constructed prediction model. According to the method, the prediction model is constructed by combining the volatile organic compounds in the fermentation tail gas, the fermentation temperature, the fermentation time and other parameters which are easy to collect, the concentration of the organic matters in the fermented grains can be accurately detected through the model, and the influence of sampling on the production process of the pit is avoided while efficient and high-accuracy concentration detection is realized; the mass transfer resistance correction term is introduced, so that the prediction model flexibly adapts to different stages of the fermentation process, and experimental results prove that the prediction accuracy of the model added with the correction term is remarkably improved.

Description

Technical Field

[0001] The invention relates to a method for predicting the concentration of volatile organic matter in fermented grains in a cellar, and belongs to the technical field of fermentation monitoring. Background Art

[0002] The mechanization level of traditional liquor brewing is constantly improving. The regulatory effect of the automation process on production depends on the stable acquisition of the internal parameters of the cellar, and the lack of corresponding detection methods makes it difficult to conduct a comprehensive analysis of the fermentation process. The traditional sampling method is cumbersome and will have a certain impact on the fermentation conditions and production cycle of the cellar. The shortcomings of this detection method also make it very difficult to comprehensively and accurately evaluate the fermentation conditions of the cellar. The change in gas concentration during the fermentation process contains very valuable process reaction information. The analysis of fermentation gas is simple to operate and has little impact on the fermentation environment of the cellar. It is a new way to obtain cellar parameters and analysis method.

[0003] However, there are some technical difficulties in analyzing the state of the fermentation process through gas concentration, including: 1. Gas is sensitive to factors such as pressure and temperature. Gas samples pay more attention to online detection to ensure the accuracy and representativeness of samples. However, desktop GC and GC-MS instruments are not portable enough to meet the needs of enterprises for online detection in workshops; 2. At present, there are few analyses of fermentation gases, which mainly focus on traditional inorganic gases such as oxygen and carbon dioxide, mainly the changes between simple phases such as solid-liquid phase and gas-liquid phase. However, the fermented mash of liquor fermentation is a mixed-phase substance with complex internal physical and chemical components and active material transfer. The isolated two-phase mass transfer theory is difficult to apply to the complete material transfer process, and there are also difficulties in application promotion and scale expansion; 3. The traditional mass transfer model is based on physical and chemical theories, lacks characteristic parameters for the dynamic process of biological fermentation, and is difficult to explain the relationship between the changes in various parameters during the reaction process and the fermentation state.

[0004] In response to the above difficulties, Li Huijuan et al. gave a correlation analysis between metabolic gases in the fermentation process and the quality of the original wine, but lacked a clear correlation model for each component. The influence of different ratios of grains to bran and different moisture content of the wine grains was only reflected from the change trend of metabolic gases, and the characterization of the fermentation state was not reasonable enough (Li Huijuan, Jin Yuanhao, Li Qunqing, et al. Correlation analysis of metabolic gases in the fermentation process of liquor and the quality of the original wine [J]. Food Science and Technology, 2022(004):047.); MACIEJEWSKA A et al. characterized the progress of the fermentation process and the sensory quality of the final wine by PCA analysis of the main flavor substances in the metabolic gases. There are many empirical parameters in this model, and the analysis object is mainly for the flavor substances of specific samples. The applicability is weak, and the model relies on human senses to verify. Summary of the invention

[0005] In order to accurately track and analyze the generation and mass transfer process of volatile organic compound concentration in the fermentation process according to the fermentation tail gas, the present invention provides a method for predicting the concentration of volatile organic compounds in the fermented grains in a cellar, and the technical scheme is as follows:

[0006] Step 1: Obtain the concentration of the organic matter to be measured in the fermentation tail gas, the current fermentation time and the cellar temperature;

[0007] Step 2: Calculate the concentration of the organic matter to be tested in the fermented grains according to the following formula:

[0008]

[0009] Where t represents the fermentation time, R represents the total mass transfer resistance coefficient, m represents the absorption equilibrium constant, and x g Indicates the concentration of the organic matter to be tested in the liquid phase, that is, the concentration of the organic matter to be tested in the fermented grains; y g It indicates the molar concentration of the organic matter to be measured in the gas phase, that is, the concentration of the organic matter to be measured in the fermentation tail gas.

[0010] Optionally, the total mass transfer resistance coefficient is calculated as follows:

[0011]

[0012] Among them, ∝ represents a proportional relationship, n represents an empirical constant, and D L represents the diffusion coefficient of the organic matter to be measured in the liquid phase, s represents the membrane contact area between the gas phase and the liquid phase, k L represents the liquid phase mass transfer coefficient, k G Represents the gas phase mass transfer coefficient.

[0013] Optionally, the absorption equilibrium constant m is:

[0014]

[0015] Among them, p s represents the saturated vapor pressure, γ represents the activity coefficient of the component, and p represents the total pressure of the system;

[0016] The activity coefficient of the component is calculated as:

[0017]

[0018] Among them, γ j represents the activity coefficient of the organic matter to be measured, x i and x j Respectively represent the molar fractions of water and the organic matter to be measured at the constant boiling point, Λ ij ,Λ ji They represent Wilson parameters respectively, expressed as:

[0019]

[0020] Among them, g ij -g ii Represents the interaction energy between water and the organic matter to be measured, which is a constant parameter; V i and V j They represent the molar volumes of water and the organic matter to be measured at the constant boiling point, T represents the temperature, and Q is the gas constant.

[0021] Optionally, the calculation formula of the saturated vapor pressure is:

[0022]

[0023] in, represents saturated vapor pressure, T represents temperature, and A, B, and C represent Antoine constants related to physical properties.

[0024] The present invention provides a prediction system for the concentration of volatile organic compounds in fermented grains, comprising:

[0025] The parameter acquisition module is configured to obtain the concentration of the organic matter to be measured in the fermentation tail gas, the current fermentation time and the cellar temperature; the concentration prediction module is configured to calculate the concentration of the organic matter to be measured in the fermented grains according to the following formula:

[0026]

[0027] Where t represents the fermentation time, R represents the total mass transfer resistance coefficient, m represents the absorption equilibrium constant, and x g Indicates the concentration of the organic matter to be measured in the liquid phase; y g Indicates the molar concentration of the organic matter to be measured in the gas phase.

[0028] Optionally, the total mass transfer resistance coefficient is calculated as follows:

[0029]

[0030] Among them, ∝ represents a proportional relationship, n represents an empirical constant, and D L represents the diffusion coefficient of the organic matter to be measured in the liquid phase, s represents the membrane contact area between the gas phase and the liquid phase, k L represents the liquid phase mass transfer coefficient, k G Represents the gas phase mass transfer coefficient.

[0031] Optionally, the absorption equilibrium constant m is:

[0032]

[0033] Among them, p srepresents the saturated vapor pressure, γ represents the activity coefficient of the component, and p represents the total pressure of the system;

[0034] The activity coefficient of the component is calculated as:

[0035]

[0036] Among them, γ j represents the activity coefficient of the organic matter to be measured, x i and x j Respectively represent the molar fractions of water and the organic matter to be measured at the constant boiling point, Λ ij ,Λ ji They represent Wilson parameters respectively, expressed as:

[0037]

[0038] Among them, g ij -g ii Represents the interaction energy between water and the organic matter to be measured, which is a constant parameter; V i and V j They represent the molar volumes of water and the organic matter to be measured at the constant boiling point, T represents the temperature, and Q is the gas constant.

[0039] Optionally, the calculation formula of the saturated vapor pressure is:

[0040]

[0041] in, represents saturated vapor pressure, T represents temperature, and A, B, and C represent Antoine constants related to physical properties.

[0042] The present invention provides a method for predicting the concentration of volatile organic matter in fermented grains, comprising a memory and a processor;

[0043] The memory is used to store computer programs;

[0044] The processor is configured to implement any of the methods described above when executing the computer program.

[0045] The present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method described in any one of the above items is implemented.

[0046] The beneficial effects of the present invention are:

[0047] The method for predicting the concentration of volatile organic matter in fermented mash of the present invention collects volatile organic matter in fermentation tail gas, and then constructs a prediction model in combination with easily collected parameters such as fermentation temperature and fermentation time. The model can accurately detect the concentration of organic matter in the fermented mash. Compared with the existing solution of obtaining mash for offline detection through a sampler or uncapping sampling, the process of collecting data in the present invention will not destroy the fermentation conditions of the cellar. The sampling method is fast and the detection efficiency is high. While achieving efficient and high-accuracy concentration detection, the influence of sampling on the cellar production process is avoided.

[0048] The prediction model of the present invention introduces a mass transfer resistance correction term, which incorporates the dynamically changing fermentation process parameters as an optimization term, and can reflect the changes in viscosity and temperature factors in the fermented grains in the cellar over time, and can track and analyze the generation of volatile organic compound concentrations and the mass transfer process during the fermentation process. The correction term is used to dynamically adjust the prediction process, so that the prediction model can flexibly adapt to different stages of the fermentation process. Experimental results show that the accuracy of model prediction is significantly improved after the correction term is added.

[0049] The prediction model of the present invention is applicable to volatile organic compounds that can be quantitatively detected in liquor fermentation tail gas and has a wide range of applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0051] Figure 1 The present invention is a flow chart of a method for predicting the concentration of volatile organic compounds in fermented grains in a cellar.

[0052] Figure 2 It is a comparison chart of the prediction effect of ethanol concentration before and after the model optimization in Example 1 of the present invention.

[0053] Figure 3 It is a comparison chart of the prediction effect of ethyl acetate concentration before and after the model optimization in Example 2 of the present invention. DETAILED DESCRIPTION

[0054] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0055] Embodiment 1:

[0056] This embodiment provides a method for predicting the concentration of volatile organic compounds based on tail gas parameters during a cellar fermentation process. Taking ethanol as an example, the ethanol concentration in the mash during the fermentation process is detected.

[0057] This example tracks and analyzes the production and mass transfer process of ethanol during liquor fermentation, and establishes a solid-liquid-gas three-phase mass transfer model, which is expressed as:

[0058]

[0059] Among them, x g , the concentration of organic matter in the liquid phase, mol / L; y g , the molar concentration of organic matter in the gas phase, mol / L; t, fermentation time, s; R, total mass transfer resistance coefficient, s; m, absorption equilibrium constant.

[0060] In this embodiment, the model characterizes the relationship between the ethanol concentration in the gas phase and the fermentation time and the ethanol concentration in the mash. That is, the ethanol concentration in the gas phase can be obtained according to the fermentation time and the ethanol concentration in the mash. Similarly, the ethanol concentration in the mash can be obtained according to the fermentation time and the ethanol concentration in the gas phase.

[0061] In the following models, the subscripts are 1 for water and 2 for ethanol.

[0062] The calculation process of the absorption equilibrium constant m is as follows:

[0063] (1) Consult the chemical manual to obtain the Antoine constants of ethanol and water respectively, and substitute them into formula (1) to obtain the saturated vapor pressure of water and ethanol at constant boiling point:

[0064]

[0065] Where: Saturated vapor pressure, mmHg; T, temperature, K; A, B, C represent Antoine constants related to physical properties.

[0066] The calculation can be obtained:

[0067] (2) Substituting the saturated vapor pressure of water and ethanol into formula (2) we can obtain the activity coefficients of ethanol and water:

[0068]

[0069] Where: γ i represents the activity coefficient of the component; p represents the total pressure of the system.

[0070] Calculation results show that the activity coefficient of water is γ1=2.31, and the activity coefficient of ethanol is γ2=1.01.

[0071] (3) The activity coefficients of ethanol and water, the composition of the water and ethanol system at the constant boiling point (x 水 =0.10597,x 乙醇 =0.89403) into equation (3) and equation (4) to calculate the Wilson parameter Λ 12 ,Λ 21 :

[0072]

[0073] In the formula, x1=x 水 , x2=x 乙醇 .

[0074] Calculated: Λ 12 =0.833,Λ 12 =0.206.

[0075] (4) The relationship between the molar volume of water and ethanol and temperature was obtained by consulting the literature and experiment:

[0076] V1=22.888-3.642×10 -2 T+6.85×10 -5 T 2 (5)

[0077] V2=27.866-0.1063T-7×10 -5 T 2 (6)

[0078] Substituting the calculated result of step (3) and the molar volumes of water and ethanol at the constant boiling point into equation (7), the interaction energy is obtained:

[0079]

[0080] Calculate g 12 -g 11 =3792.12 J / mol, g 21 -g 22 =1352.74 J / mol. The interaction energy is used as a constant parameter and substituted into equation (7) to obtain Λ 12 ,Λ 21 The relationship only related to temperature is the activity coefficient γ i The relationship between temperature and ethanol concentration. Where: Q, gas constant, 8.314 J / (mol·K); T, temperature, K.

[0081] (5) Calculation of absorption equilibrium constant of ethanol-water system:

[0082]

[0083] Among them, the activity coefficient γ2 of ethanol in formula (8) is obtained by formula (4); Obtained from formula (1); p is the atmospheric pressure 101.3 kPa. Input the above model into the engineering calculation software Mathcad, input the temperature and the molar concentration of ethanol, and the value of the absorption equilibrium constant m2 corresponding to ethanol can be obtained.

[0084] Note: Wilson equation is a semi-theoretical and semi-empirical equation, that is, it uses the canonical method of parameter calculation. When the objective function is different, the value of the parameter is different. In other words, due to different experimental methods and data processing methods, the parameters can have different values.

[0085] Mass transfer resistance correction The calculation process is as follows:

[0086] (1) Calculate the diffusion coefficient D:

[0087]

[0088] Wherein, μ represents viscosity, Pa·s; r represents the molecular radius of ethanol, m; k represents the Boltzmann constant, J / K.

[0089] The only parameters that need to be input are temperature T and corresponding phase viscosity μ. The temperature is obtained from the sensor reading. The viscosity μ can be measured. In this embodiment, the value in laboratory fermentation is 3 Pa·s.

[0090] (2) Calculate the mass transfer resistance coefficient R:

[0091]

[0092]

[0093] Where: ∝ represents a proportional relationship, n represents an empirical constant, the liquid phase is 0.50, and the gas phase is 0.5; D L represents the diffusion coefficient in the ethanol liquid phase, m 2 / s; s represents the membrane contact area between the gas phase and the liquid phase, m 2 ; R L Represents gas phase resistance, s; R G Indicates liquid phase resistance, s; k L Indicates the liquid phase mass transfer coefficient, m / s; k G Represents the gas phase mass transfer coefficient, m / s.

[0094] The mass transfer resistance coefficient R is a parameter related to temperature and viscosity in the same batch fermentation.

[0095] (3) Calculate the mass transfer resistance correction term:

[0096] The mass transfer resistance coefficient R is obtained from step (2). The above model is input into the engineering calculation software Mathcad, and the temperature, viscosity, membrane contact area between the gas phase and the liquid phase, and fermentation time are input to obtain the mass transfer resistance correction term:

[0097] In order to verify the accuracy of the ethanol concentration prediction model in this example, the correction term The predicted values ​​and actual values ​​before and after are fitted and compared. The comparison results are as follows Figure 2 As shown in Figure 2, it can be seen that compared with the traditional equilibrium model, After the model was built, it was found that the model could better predict the change of ethanol concentration in the mash with the fermentation time, and the root mean square error (RSME) decreased from 33.69 to 24.17, and the mean relative error (MRE) decreased from 0.72 to 0.54. Therefore, the prediction model accurately fits the fermentation time, ethanol concentration in the mash, and ethanol concentration in the fermentation tail gas, and can achieve accurate prediction of ethanol concentration in the mash.

[0098] Embodiment 2:

[0099] This example uses the method described in Example 1 to construct an ethyl acetate concentration prediction model to predict the concentration of ethyl acetate in the fermented grains. The prediction effect is as follows: Figure 3 shown.

[0100] It can be seen that compared with the traditional equilibrium model, After the model was built, it was also able to predict the change of ethyl acetate concentration in the fermented grains with the fermentation time. The root mean square error (RSME) decreased from 9.06 to 1.97, and the mean relative error (MRE) decreased from 1.42 to 1.32. Therefore, the prediction model accurately fits the fermentation time, ethyl acetate concentration in the fermented grains, and ethyl acetate concentration in the fermentation tail gas, and can achieve accurate prediction of ethyl acetate concentration in the fermented grains.

[0101] Some steps in the embodiments of the present invention may be implemented using software, and the corresponding software program may be stored in a readable storage medium, such as a CD or a hard disk.

[0102] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for predicting the concentration of volatile organic compounds in fermented grains, characterized in that: The method comprises: Step 1: Obtain the concentration of the organic matter to be measured in the fermentation tail gas, the current fermentation time and the cellar temperature; Step 2: Calculate the concentration of the organic matter to be tested in the fermented grains according to the following formula: Where t represents the fermentation time, R represents the total mass transfer resistance coefficient, m represents the absorption equilibrium constant, and x g Indicates the concentration of the organic matter to be measured in the liquid phase; y g Indicates the molar concentration of the organic matter to be measured in the gas phase.

2. The method according to claim 1, characterized in that: The calculation method of the total mass transfer resistance coefficient is: Among them, ∝ represents a proportional relationship, n represents an empirical constant, and D L represents the diffusion coefficient of the organic matter to be measured in the liquid phase, s represents the membrane contact area between the gas phase and the liquid phase, k L represents the liquid phase mass transfer coefficient, k G Represents the gas phase mass transfer coefficient.

3. The method according to claim 1, characterized in that The absorption equilibrium constant m is: Among them, p s represents the saturated vapor pressure, γ represents the activity coefficient of the component, and p represents the total pressure of the system; The activity coefficient of the component is calculated as: Among them, γ j represents the activity coefficient of the organic matter to be measured, x i and x j Respectively represent the molar fractions of water and the organic matter to be measured at the constant boiling point, Λ ij ,Λ ji They represent Wilson parameters respectively, expressed as: Among them, g ij -g ii Represents the interaction energy between water and the organic matter to be measured, which is a constant parameter; V i and V j They represent the molar volumes of water and the organic matter to be measured at the constant boiling point, T represents the temperature, and Q is the gas constant.

4. The method according to claim 1, characterized in that: The calculation formula of the saturated vapor pressure is: in, represents saturated vapor pressure, T represents temperature, and A, B, and C represent Antoine constants related to physical properties.

5. A prediction system for the concentration of volatile organic compounds in fermented grains, characterized in that: The system comprises: The parameter acquisition module is configured to obtain the concentration of the organic matter to be measured in the fermentation tail gas, the current fermentation time and the cellar temperature; The concentration prediction module is configured to calculate the concentration of the organic matter to be tested in the fermented grains according to the following formula: Where t represents the fermentation time, R represents the total mass transfer resistance coefficient, m represents the absorption equilibrium constant, and x g Indicates the concentration of the organic matter to be measured in the liquid phase; y g Indicates the molar concentration of the organic matter to be measured in the gas phase.

6. The prediction system according to claim 5, characterized in that The calculation method of the total mass transfer resistance coefficient is: Among them, ∝ represents a proportional relationship, n represents an empirical constant, and D L represents the diffusion coefficient of the organic matter to be measured in the liquid phase, s represents the membrane contact area between the gas phase and the liquid phase, k L represents the liquid phase mass transfer coefficient, k G Represents the gas phase mass transfer coefficient.

7. The prediction system according to claim 5, characterized in that The absorption equilibrium constant m is: Among them, p s represents the saturated vapor pressure, γ represents the activity coefficient of the component, and p represents the total pressure of the system; The activity coefficient of the component is calculated as: Among them, γ j represents the activity coefficient of the organic matter to be measured, x i and x j Respectively represent the molar fractions of water and the organic matter to be measured at the constant boiling point, Λ ij ,Λ ji They represent Wilson parameters respectively, expressed as: Among them, g ij -g ii Represents the interaction energy between water and the organic matter to be measured, which is a constant parameter; V i and V j They represent the molar volumes of water and the organic matter to be measured at the constant boiling point, T represents the temperature, and Q is the gas constant.

8. The prediction system according to claim 5, characterized in that The calculation formula of the saturated vapor pressure is: in, represents saturated vapor pressure, T represents temperature, and A, B, and C represent Antoine constants related to physical properties.

9. A method for predicting the concentration of volatile organic compounds in fermented grains, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is configured to implement the method according to any one of claims 1 to 4 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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