Method for determining production process parameters of mixed alcohol biological suckercide

Abstract

The invention provides a method for determining production process parameters of a mixed alcohol biological suckercide, and belongs to the technical field of mixed alcohol biological suckercide production, and the method comprises the following steps: firstly, determining main active components by analyzing physical properties, chemical composition, impurity content, stability and reaction activity of raw materials; according to the molecular information of the active component, a proper precursor compound is selected, and a chemical synthesis route is optimized through laboratory synthesis. A synthesis model equation set covering multiple parameters such as reaction temperature, time and catalyst dosage is established, and a synthesis parameter range is predicted. And designing an orthogonal test verification model, and searching an optimal synthesis parameter combination by using a genetic algorithm. The optimized parameters are verified and finely adjusted in a pilot plant test, and finally the optimal production process parameters of the fatty alcohol-mixed biological suckercide are determined. The method provided by the invention solves the technical problem that the optimal process parameter combination is difficult to obtain because the production process parameters of the existing mixed lipid alcohol biological suckercide are often set through artificial experience and repeated trial and error.

Description

Technical Field

[0001] The invention belongs to the technical field of production of mixed fat alcohol biological sprout inhibitors, and in particular relates to a method for determining production process parameters of mixed fat alcohol biological sprout inhibitors. Background Art

[0002] Mixed alcohol biological sprout inhibitors are a new type of plant growth regulator with broad application prospects in agricultural production. This type of substance can effectively inhibit the germination and growth of plant axillary buds, and plays an important role in tobacco sprout inhibition, crop storage, seed treatment and fruit tree pruning. In recent years, with the improvement of people's awareness of food safety and environmental protection, biological sprout inhibitors have gradually replaced traditional chemical sprout inhibitors and become a research hotspot.

[0003] At present, the production of mixed alcohol biological sprout inhibitors mainly relies on extraction from natural plants or chemical synthesis. Although the extraction method from plants is green and environmentally friendly, it has problems such as limited raw material sources, low extraction efficiency, and high cost. Although chemical synthesis can achieve large-scale production, it often has disadvantages such as harsh reaction conditions, many by-products, and low product purity.

[0004] In the existing production process of mixed alcohol biological sprout inhibitor, the determination of parameters usually relies on empirical rules and trial and error. This method is not only time-consuming and labor-intensive, but also difficult to obtain the optimal combination of process parameters. Especially when facing complex multi-component systems, traditional optimization methods often find it difficult to take into account the mutual influence between various parameters, resulting in the final process parameters being only a local optimal solution.

[0005] In addition, the analysis of raw materials and products in the prior art is often not comprehensive enough, which may lead to the neglect of certain key factors in the subsequent process optimization. For example, the presence of trace impurities in the raw materials may have a significant impact on the reaction process, and certain trace components in the product may be the key to determining the biological activity. Therefore, how to comprehensively and accurately analyze the composition of raw materials and products has become an important challenge in optimizing the production process.

[0006] Another common problem is that the process parameters optimized in the laboratory are often difficult to directly apply to industrial production. This is mainly due to the significant differences between laboratory tests and industrial production in terms of reactor scale, heat and mass transfer conditions, etc. How to establish a reliable method to effectively convert laboratory optimization results into industrial production parameters is an important technical problem currently faced.

[0007] In summary, the existing production process parameters of mixed lipid alcohol biological inhibitors are often set through manual experience and trial and error, and it is difficult to obtain the optimal combination of process parameters. Summary of the invention

[0008] In view of this, the present invention provides a method for determining the production process parameters of a mixed-fat alcohol biological sprout inhibitor, which can solve the technical problem that the existing production process parameters of a mixed-fat alcohol biological sprout inhibitor are often set through manual experience and repeated trial and error, and it is difficult to obtain the optimal process parameter combination.

[0009] The present invention is achieved in that:

[0010] The present invention provides a method for determining production process parameters of a mixed fat alcohol biological sprout inhibitor, which comprises the following steps:

[0011] S10. Conduct a comprehensive analysis of the raw materials for the production of mixed fat alcohol biological sprout inhibitor, including physical properties, chemical composition, impurity content, stability and reactivity, and determine the main active components of the mixed fat alcohol target fraction produced;

[0012] S20, selecting precursor compounds according to the molecular information of the main active components, based on the principles of structural similarity and reactivity, and determining the optimal chemical synthesis route through laboratory synthesis;

[0013] S30, establishing a group of synthesis parameter equations according to the physical properties and the optimal chemical synthesis route, including a material balance equation, a reaction kinetics equation, a heat balance equation, a phase balance equation, a mass transfer and heat transfer equation, and a mass transfer equation;

[0014] S40, based on the preset initial parameters, substituting into the synthesis parameter equation group, solving to obtain the range of the synthesis parameters;

[0015] S50, designing an orthogonal test according to the initial parameters and the range of the synthesis parameters, and obtaining orthogonal test results, including each combination of synthesis parameters and its corresponding product yield mass vector;

[0016] S60, using a genetic algorithm according to the orthogonal test results, taking the product yield as the fitness function, the synthesis parameter combination as the gene code, and the orthogonal test results as the initial population, iteratively executing to obtain the optimal multiple synthesis parameter combinations;

[0017] S70. Verify the multiple synthesis parameter combinations on a pilot scale, and make fine adjustments according to actual production conditions to obtain the optimal synthesis parameter combination as the optimal parameters for the production process of the mixed fat alcohol biological inhibitor.

[0018] Wherein, the method for determining the main active components of the produced mixed fat alcohol target fraction is a gas chromatography-mass spectrometry method.

[0019] Furthermore, the synthesis parameters include reaction temperature, reaction time, catalyst dosage, raw material ratio, reaction pressure, stirring speed, pH value, distillation temperature, distillation reflux ratio, solvent dosage, reactor size, feed rate, cooling water flow rate, and separation efficiency.

[0020] Furthermore, the preset initial parameters include initial concentration of reactants, initial temperature and pressure of the reactor, initial activity of the catalyst, purity and ratio of raw materials, and initial pH value of the reaction medium.

[0021] The material balance equation is specifically expressed as:

[0022]

[0023] In the formula, C i is the concentration of component i; t is time; u is the fluid velocity vector; D i is the diffusion coefficient of component i; R i is the reaction rate of component i;

[0024] Diffusion coefficient D i Estimation by Wilke-Chang equation:

[0025]

[0026] Where x is the binding factor of the solvent; M is the molecular weight of the solvent; T is the temperature; η is the viscosity of the solvent; V A is the molar volume of the solute at its boiling point.

[0027] The reaction kinetics equation is specifically expressed as:

[0028]

[0029] In the formula, r j is the rate of reaction j; k j is the rate constant of reaction j, n is the reaction order; C i is the concentration of reactant i; α ij is the reaction order of reactant i in reaction j; E a,j is the activation energy of reaction j; R is the gas constant; T is the temperature; K e is the equilibrium constant; β ij is the stoichiometric coefficient of product i in reaction j;

[0030] Rate constant k j Calculated by Arrhenius equation:

[0031]

[0032] Among them A jIt is the pre-factor obtained by fitting the experimental data.

[0033] The heat balance equation is specifically expressed as:

[0034]

[0035] Where ρ is the fluid density; C p is the specific heat capacity of the fluid; T is the temperature; t is the time; u is the fluid velocity vector; k is the thermal conductivity; ΔH j is the heat of reaction j; r j is the rate of reaction j; Q is the external heat source term.

[0036] The phase equilibrium equation is specifically expressed as:

[0037]

[0038] In the formula, y i is the mole fraction of component i in the gas phase; P is the total pressure of the system; γ i is the activity coefficient of component i; x i is the mole fraction of component i in the liquid phase; is the saturated vapor pressure of component i; is the liquid phase molar volume of component i; R is the gas constant; T is the temperature.

[0039] The mass transfer and heat transfer equation is specifically expressed as:

[0040]

[0041] In the formula, C t is the total molar concentration; x i is the mole fraction of component i; N i is the molar flux of component i; is the Maxwell-Stefan diffusion coefficient between components i and j;

[0042] For heat transfer, Fourier's law is used:

[0043]

[0044] Where q is the heat flux; k is the thermal conductivity; and T is the temperature.

[0045] The mass transfer equation is specifically expressed as:

[0046]

[0047] Where N A is the mass transfer rate of component A; K L is the total mass transfer coefficient; a is the specific surface area; is the equilibrium concentration of component A at the interface; C A is the concentration of component A in the liquid phase;

[0048] Overall mass transfer coefficient K L Calculated by the following formula:

[0049]

[0050] where k L and k G are the mass transfer coefficients of the liquid phase and the gas phase, respectively, and H is the Henry constant.

[0051] Specifically, the step S10 includes:

[0052] Step 101, determining the physical state of the raw materials for the production of the mixed fat alcohol biological sprout inhibitor by physical property tests, such as specific gravity, boiling range, viscosity, etc.;

[0053] Step 102: using chemical analysis methods, such as elemental analysis, infrared spectroscopy, nuclear magnetic resonance, etc., to determine the chemical composition of the raw material;

[0054] Step 103, determining the content of inorganic impurities and organic impurities in the raw materials, and analyzing the possible effects of these impurities on subsequent reactions;

[0055] Step 104: Evaluate the thermal stability and reactivity of the raw materials to ensure their safety and reaction controllability during the production process;

[0056] Step 105: Analyze the produced mixed fat alcohol target fraction using gas chromatography-mass spectrometry to determine the molecular structure information of its main active components.

[0057] The specific steps of step S20 include:

[0058] Step 201: Based on the molecular structure of the target product and the reactivity of similar compounds, several potential synthetic routes are preliminarily designed from available raw materials;

[0059] Step 202: Under laboratory test conditions, the above-mentioned synthetic routes are verified and optimized, and the optimal chemical synthetic route is determined by comprehensive consideration of factors such as reaction conditions, yield, and by-products.

[0060] The specific implementation of step S30 includes:

[0061] Step 301: According to the determined optimal synthesis route, the material balance equation, reaction kinetic equation, heat balance equation, etc. of each reaction step are listed to establish a set of parameter equations describing the entire synthesis process;

[0062] Step 302: Establishing phase equilibrium equation, mass transfer and heat transfer equation and mass transfer equation to describe the phase equilibrium state and mass transfer and heat transfer behavior in the separation and purification process;

[0063] Step 303: Determine various physical parameters, such as viscosity, diffusion coefficient, thermal conductivity, etc. of the solvent, by empirical formulas or experimental measurements.

[0064] The specific implementation of step S40 includes:

[0065] Step 401, setting a set of reasonable initial parameters, including reactant concentration, reactor temperature and pressure, catalyst activity, raw material purity, etc.;

[0066] Step 402: Substitute the above initial parameters into the parameter equation group established in step S30, and obtain the value range of each process parameter by numerical solution;

[0067] Step 403: When determining the parameter value range, not only the requirements of product yield and quality must be met, but also the safety and economy of the process must be considered.

[0068] The specific implementation of step S50 includes:

[0069] Step 501: According to the parameter value range determined in step S40, a group of representative process parameter combinations are designed by using an orthogonal experimental design method;

[0070] Step 502: Conduct actual production on a pilot scale to measure the product yield and quality index corresponding to each parameter combination;

[0071] Step 503: Use the above experimental data as a basis for subsequent parameter optimization.

[0072] The specific implementation of step S60 includes:

[0073] Step 601: Encode each parameter combination into a gene sequence, and use the product yield as a fitness function;

[0074] Step 602: according to the orthogonal test result of step S50, set the initial population;

[0075] Step 603: Adopt genetic operators such as selection, crossover, and mutation to iteratively optimize the gene sequence to continuously improve the fitness function value, and finally obtain multiple optimized parameter combination schemes.

[0076] The specific implementation of step S70 includes:

[0077] Step 701, select several optimized parameter combinations obtained in step S60, and perform production verification on a pilot plant;

[0078] Step 702: During the pilot test, continue to observe and analyze key indicators such as product yield, quality stability, and energy consumption;

[0079] Step 703: According to the actual production situation, adjust the parameters appropriately and finally determine an optimal process parameter combination with the best comprehensive performance.

[0080] Compared with the prior art, the method for determining the production process parameters of a mixed fat alcohol biological sprout inhibitor provided by the present invention has the following beneficial effects:

[0081] First, the present invention adopts a comprehensive raw material and product analysis method, especially using gas chromatography-mass spectrometry technology to conduct detailed analysis of the target fraction, which can accurately identify the main active components in the product. This not only provides a reliable basis for the subsequent synthesis route design, but also helps to deeply understand the structure-activity relationship of the product and lay the foundation for further optimizing product performance. Compared with the existing technology, this analysis method is more comprehensive and accurate, and can capture trace components that may affect the reaction process and product performance.

[0082] Secondly, the present invention quantifies the complex chemical reaction and separation process by establishing a set of parameter equations covering the entire production process. Compared with the traditional empirical rules, this method can more accurately describe the relationship between various process parameters and provide a reliable theoretical basis for subsequent parameter optimization. In particular, this mathematical model can take into account multiple aspects such as reaction kinetics, mass transfer and heat transfer, phase equilibrium, etc., so as to achieve comprehensive optimization of the process.

[0083] Third, the present invention adopts orthogonal experimental design combined with genetic algorithm to optimize parameters, which has higher optimization efficiency and stronger global search ability than traditional single factor test or gradient descent method. Orthogonal experimental design can obtain maximum information within the minimum number of experiments, while genetic algorithm can effectively avoid falling into local optimal solution. This combined optimization method not only greatly improves the efficiency of parameter optimization, but also can obtain parameter combinations closer to the global optimal.

[0084] Fourth, the present invention effectively solves the problem that laboratory optimization results are difficult to directly apply to industrial production by verifying and fine-tuning the optimization parameters at a pilot scale. This method takes into account the scale effect that may occur in the process from small-scale to pilot-scale to industrial production, ensuring that the final process parameters are practical and reliable.

[0085] In summary, the present invention solves the technical problem that the production process parameters of the existing mixed fat alcohol biological sprout inhibitor are often set by manual experience and repeated trial and error, and it is difficult to obtain the optimal process parameter combination. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 A flow chart of the method provided by the present invention. DETAILED DESCRIPTION

[0087] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0088] like Figure 1 As shown, it is a flow chart of a method for determining production process parameters of a mixed fat alcohol biological sprout inhibitor provided by the present invention, and the method comprises the following steps:

[0089] S10. Conduct a comprehensive analysis of the raw materials for the production of mixed fat alcohol biological sprout inhibitor, including physical properties, chemical composition, impurity content, stability and reactivity, and use gas chromatography-mass spectrometry to determine the main active components of the mixed fat alcohol target fraction produced;

[0090] S20, selecting precursor compounds according to the molecular information of the main active components, based on the principles of structural similarity and reactivity, and determining the optimal chemical synthesis route through laboratory synthesis;

[0091] S30. According to the physical properties and the optimal chemical synthesis route, a group of synthesis parameter equations is established, including a material balance equation, a reaction kinetics equation, a heat balance equation, a phase balance equation, a mass transfer and heat transfer equation, and a mass transfer equation; wherein the synthesis parameters include reaction temperature, reaction time, catalyst dosage, raw material ratio, reaction pressure, stirring speed, pH value, fractionation temperature, fractionation reflux ratio, solvent dosage, reactor size, feed rate, cooling water flow rate, and separation efficiency;

[0092] S40, based on preset initial parameters, substituting into the synthesis parameter equation group, solving to obtain the range of the synthesis parameters; the preset initial parameters include the initial concentration of the reactants, the initial temperature and pressure of the reactor, the initial activity of the catalyst, the purity and ratio of the raw materials, and the initial pH value of the reaction medium;

[0093] S50, designing an orthogonal test according to the initial parameters and the range of the synthesis parameters, and obtaining orthogonal test results, including each combination of synthesis parameters and its corresponding product yield mass vector;

[0094] S60, using a genetic algorithm according to the orthogonal test results, taking the product yield as the fitness function, the synthesis parameter combination as the gene code, and the orthogonal test results as the initial population, iteratively executing to obtain the optimal multiple synthesis parameter combinations;

[0095] S70. Verify the multiple synthesis parameter combinations on a pilot scale, and make fine adjustments according to actual production conditions to obtain the optimal synthesis parameter combination as the optimal parameters for the production process of the mixed fat alcohol biological inhibitor.

[0096] The specific implementation methods of the above steps are described in detail below:

[0097] The specific implementation method of step S10 is to comprehensively analyze the raw materials for the production of mixed fat alcohol biological sprout inhibitor. First, the physical state of the raw materials is determined by physical property tests, such as specific gravity, boiling range, viscosity, etc. Then, chemical analysis methods, such as elemental analysis, infrared spectroscopy, nuclear magnetic resonance, etc., are used to determine the chemical composition of the raw materials. Next, the impurity content, including inorganic impurities and organic impurities, is determined, and the possible effects of these impurities on subsequent reactions are studied. At the same time, the thermal stability and reactivity of the raw materials are evaluated to ensure the safety of the raw materials during the production process and the controllability of the reaction.

[0098] Finally, the target fraction of the mixed fat alcohol produced was analyzed by gas chromatography-mass spectrometry to determine its main active components. The purpose of this step is to clarify the main components of the final product and its molecular structure information, laying the foundation for the optimization of the subsequent synthesis route. Gas chromatography-mass spectrometry technology can separate and identify the components in the sample. By analyzing the mass spectrum, the molecular weight information of each component can be obtained, thereby determining its molecular structure. This information is crucial for the design of subsequent synthesis routes and parameter optimization.

[0099] Step S20 is to select precursor compounds and determine the optimal synthesis route based on the molecular information of the main active components. First, based on the molecular structure of the target product and the reactivity of similar compounds, several potential synthesis routes are preliminarily designed based on the available raw materials. Then, under laboratory test conditions, these routes are verified and optimized, and the optimal chemical synthesis route is determined by considering factors such as reaction conditions, yield, and by-products. The key to this step is to give full play to chemical intuition and reaction activity laws, take into account raw material availability and reaction economy, and finally obtain a route that can efficiently synthesize the target product.

[0100] Step S30 is to establish a group of synthesis parameter equations. First, according to the determined optimal synthesis route, the material balance equations, reaction kinetic equations, heat balance equations, etc. of each reaction step are listed to quantify the material and energy conversion process in the synthesis process. These equations include multiple process parameters such as reaction temperature, time, pressure, pH value, etc. Next, it is necessary to establish phase equilibrium equations, mass transfer and heat transfer equations, and mass transfer equations to describe the phase equilibrium state and mass transfer and heat transfer behavior in the separation and purification process. Through these equations, a group of parameter equations covering the entire synthesis process can be established to lay a mathematical foundation for subsequent parameter optimization. The specific description is as follows:

[0101] 1.Material balance equation:

[0102]

[0103] In the formula, C i is the concentration of component i (mol / m 3 ), t is time (s), u is fluid velocity vector (m / s), D i is the diffusion coefficient of component i (m 2 / s); R i is the reaction rate of component i (mol / (m 3 ·s));

[0104] Diffusion coefficient D i It can be estimated by the Wilke-Chang equation:

[0105]

[0106] Where x is the binding factor of the solvent (dimensionless); M is the molecular weight of the solvent (g / mol); T is the temperature (K); η is the viscosity of the solvent (cP); V A is the molar volume of the solute at the boiling point (cm 3 / mol).

[0107] 2. Reaction kinetics equation:

[0108]

[0109] In the formula, r j is the rate of reaction j (mol / (m 3 ·s));k j is the rate constant of reaction j ((m 3 / mol)^(n-1) / s, n is the reaction order; C i is the concentration of reactant i (mol / m 3 ); α ij is the reaction order of reactant i in reaction j (dimensionless); E a,jis the activation energy of reaction j (J / mol); R is the gas constant (8.314 J / (mol·K)); T is the temperature (K); K e is the equilibrium constant (dimensionless); β ij is the stoichiometric coefficient of product i in reaction j (dimensionless);

[0110] Rate constant k j It can be calculated by the Arrhenius equation:

[0111]

[0112] Among them A j It is the pre-factor, which can be obtained by fitting the experimental data.

[0113] 3. Heat balance equation:

[0114]

[0115] Where ρ is the fluid density (kg / m 3 );C p is the specific heat capacity of the fluid (J / (kg·K)); T is the temperature (K); t is the time (s); u is the fluid velocity vector (m / s); k is the thermal conductivity (W / (m·K)); ΔH j is the heat of reaction j (J / mol); r j is the rate of reaction j (mol / (m 3 ·s)); Q is the external heat source term (W / m 3 );

[0116] The fluid density ρ can be calculated using the equation of state, for example for an ideal gas:

[0117]

[0118] Where P is the pressure (Pa) and M is the molecular weight (kg / mol).

[0119] 4. Phase equilibrium equation:

[0120] For liquid-vapor equilibrium, the modified Raoult's law can be used:

[0121]

[0122] In the formula, y i is the mole fraction of component i in the gas phase (dimensionless); P is the total pressure of the system (Pa); γ i is the activity coefficient of component i (dimensionless); x i is the mole fraction of component i in the liquid phase (dimensionless); is the saturated vapor pressure of component i (Pa); is the liquid phase molar volume of component i (m 3 / mol); R is the gas constant (8.314 J / (mol·K)); T is the temperature (K);

[0123] Activity coefficient γ i It can be calculated by UNIFAC method:

[0124]

[0125] in and They are the combination term and the residual term, respectively, and their default values ​​can be set based on experience.

[0126] 5. Mass transfer and heat transfer equations:

[0127] For mass transfer, the Maxwell-Stefan equation can be used:

[0128]

[0129] In the formula, C t is the total molar concentration (mol / m 3 ); x i is the mole fraction of component i (dimensionless); N i is the molar flux of component i (mol / (m 2 ·s)); is the Maxwell-Stefan diffusion coefficient between components i and j (m 2 / s);

[0130] For heat transfer, Fourier's law can be used:

[0131]

[0132] Where q is the heat flux (W / m 2 ), k is thermal conductivity (W / (m·K)), and T is temperature (K).

[0133] 6. Mass transfer equation:

[0134] For gas-liquid mass transfer, the double membrane theory can be used:

[0135]

[0136] Where N A is the mass transfer rate of component A (mol / (m 3 ·s)); K L is the total mass transfer coefficient (m / s); a is the specific surface area (m 2 / m 3 ); is the equilibrium concentration of component A at the interface (mol / m 3 );C A is the concentration of component A in the liquid phase (mol / m 3 );

[0137] Overall mass transfer coefficient K L It can be calculated by the following formula:

[0138]

[0139] where k L and k G are the mass transfer coefficients of the liquid and gas phases, respectively (m / s), and H is the Henry constant (dimensionless).

[0140] These equations constitute the synthesis parameter equations for the production process of mixed fat alcohol biological inhibitor. Each equation contains multiple parameters and involves partial derivatives, complex operations and power calculations. The solution of these equations requires the use of numerical methods, such as finite difference method or finite element method, combined with appropriate boundary conditions and initial conditions.

[0141] In the process of establishing a set of parametric equations, it is necessary to determine various physical parameters, such as the viscosity, diffusion coefficient, thermal conductivity, etc. of the solvent. These parameters can be estimated through empirical formulas or literature data. For some parameters that are difficult to determine, they need to be obtained through experimental measurements. For parameters that need to be fitted or obtained through experimental data, experiments and calculations can be performed according to the following steps:

[0142] 1. Reaction rate constant k j and activation energy E a,j Determination of:

[0143] Experimental steps:

[0144] (1) Conduct isothermal reaction kinetics experiments at different temperatures (at least 3-5 temperature points).

[0145] (2) At each temperature point, measure the change in reactant concentration at different reaction times.

[0146] (3) For each temperature point, draw a concentration-time curve.

[0147] Calculation method:

[0148] (1) For n-order reactions, use the integration method to process the data:

[0149] ln C=lnC 0 -kt (first order reaction);

[0150] (Secondary reaction);

[0151] (2) Obtain the k value at each temperature through linear regression.

[0152] (3) Linear regression using the Arrhenius equation:

[0153]

[0154] (4) Obtain E from the slope a , and get A from the intercept.

[0155] 2. Diffusion coefficient D i Determination of:

[0156] Experimental steps:

[0157] (1) Use the capillary method or membrane diffusion cell method to conduct the experiment.

[0158] (2) Measure the diffusion rate at different temperatures.

[0159] Calculation method:

[0160] (1) For the capillary method:

[0161] Where l is the capillary length, r is the capillary radius, c 0 and c are the initial concentration and the final concentration, respectively.

[0162] (2) Fit the experimental data to obtain the relationship between D and temperature.

[0163] 3. Reaction heat ΔH j Determination of:

[0164] Experimental steps:

[0165] (1) Perform the experiment using isothermal calorimetry or adiabatic calorimetry.

[0166] (2) Measure the temperature change or heat change during the reaction process.

[0167] Calculation method:

[0168] (1) For isothermal calorimetry: Where Q is the heat released or absorbed and n is the amount of reactant.

[0169] (2) For adiabatic calorimetry: Where m is the mass of the reaction mixture, c p is the specific heat capacity, ΔT is the temperature change. 4. Activity coefficient γ i Determination of:

[0170] Experimental steps:

[0171] (1) Measure the gas-liquid equilibrium data of liquid mixtures with different compositions.

[0172] (2) Record temperature, pressure, liquid phase composition and gas phase composition.

[0173] Calculation method:

[0174] (1) Using the modified Raoult's law:

[0175]

[0176] (2) Calculation

[0177] (3) Use models such as UNIFAC or NRTL to fit activity coefficient data.

[0178] 5. Mass transfer coefficient K L Determination of:

[0179] Experimental steps:

[0180] (1) Use a stirred tank or packed tower to conduct gas-liquid mass transfer experiments.

[0181] (2) Measure the mass transfer rate under different operating conditions (such as stirring speed, gas flow rate). Calculation method:

[0182] (1) Using the double membrane theory formula:

[0183] (2) Calculate K using experimental data L a value. ;

[0184] (3) If the specific surface area a is known, K can be obtained L .

[0185] 6. Catalyst activity A c Determination of:

[0186] Experimental steps:

[0187] (1) The catalytic reaction was carried out under standard reaction conditions.

[0188] (2) Measure reaction rate or conversion rate.

[0189] (3) Repeat the experiment to measure the stability and lifetime of the catalyst.

[0190] Calculation method:

[0191] (1) Definition of activity:

[0192] Where r is the actual reaction rate, r 0 is the reaction rate of fresh catalyst.

[0193] (2) Or use conversion rate:

[0194] Among them, X is the actual conversion rate, X 0 is the conversion rate of fresh catalyst.

[0195] 7. Determination of viscosity η:

[0196] Experimental steps:

[0197] (1) Use a capillary viscometer or a rotational viscometer.

[0198] (2) Measure the viscosity of the fluid at different temperatures.

[0199] Calculation method:

[0200] (1) For capillary viscometer:

[0201] Where r is the capillary radius, ρ is the liquid density, h is the height of the liquid column, t is the outflow time, V is the outflow volume, and l is the capillary length.

[0202] (2) Fitting the relationship between viscosity and temperature, such as using the Andrade equation:

[0203]

[0204] 8. Determination of thermal conductivity k:

[0205] Experimental steps:

[0206] (1) Perform the experiment using the plate method or hot wire method.

[0207] (2) Measure heat flow and temperature gradient under steady-state or transient conditions.

[0208] Calculation method:

[0209] (1) For the plate method:

[0210] Where Q is the heat flow, L is the sample thickness, A is the cross-sectional area, and ΔT is the temperature difference.

[0211] (2) For the hotline method:

[0212] Where q is the heat flow per unit length, t is the time, and T is the temperature.

[0213] Through these experimental and computational methods, various parameters required in the model can be obtained. Accurate determination of these parameters is crucial for establishing a reliable mathematical model. In actual operation, appropriate adjustments and optimizations may be required according to the specific reaction system and experimental conditions.

[0214] In general, the purpose of this step is to quantify the entire production process into a solvable mathematical model, laying the foundation for subsequent parameter optimization.

[0215] Step S40 is to solve the range of synthesis parameters based on the preset initial parameters. First, a set of reasonable initial parameters is set, including reactant concentration, reactor temperature and pressure, catalyst activity, raw material purity, etc. Then these initial parameters are substituted into the parameter equation group established in step S30, and the value range of each process parameter is obtained by numerical solution. The purpose of this step is to determine the reasonable value domain of the process parameters and provide a basis for the subsequent orthogonal experimental design. The obtained parameter range must not only meet the requirements of product yield and quality, but also consider the safety and economy of the process. In the numerical solution process, numerical algorithms such as Newton iteration method and Runge-Kutta method can be used. Based on the preset initial parameters, the specific implementation method of substituting into the synthesis parameter equation group and solving the synthesis parameter range is as follows:

[0216] 1. Initial parameter setting:

[0217] First, a set of initial parameters needs to be set, including:

[0218] C i,0 : Initial concentration of reactant i (mol / m 3 );

[0219] T 0 : Initial temperature of the reactor (K);

[0220] P 0 : Initial pressure of the reactor (Pa);

[0221] A c : The initial activity of the catalyst (dimensionless, between 0 and 1);

[0222] φ i : Purity of raw material i (dimensionless, between 0 and 1);

[0223] r i : The ratio of raw material i (dimensionless);

[0224] pH 0 : The initial pH value of the reaction medium;

[0225] These initial parameters can be determined through experiments or set based on production experience.

[0226] 2. Solving the parametric equations:

[0227] Next, we need to solve the parametric equations. Since the equations involve partial differential equations, numerical methods are usually required to solve them. Here, we take the finite difference method as an example to illustrate the solution process.

[0228] (1) Spatial discretization: Divide the reactor space into N x ×N y ×N z There are grids, and the size of each grid is Δx, Δy, Δz.

[0229] (2) Time discretization: Divide the reaction time into N t time steps, each time step is Δt.

[0230] (3) Difference format: Taking the material balance equation as an example, a mixed format of central difference and backward difference is adopted:

[0231]

[0232] in, represents the concentration of component i at time step n and spatial position (j, k, l).

[0233] (4) Boundary conditions: Set appropriate boundary conditions according to the reactor type. For example, for a completely mixed reactor, it can be assumed that the concentration is uniformly distributed in space.

[0234] (5) Initial conditions: Use the preset initial parameters as the initial conditions:

[0235]

[0236] (6) Iterative solution: For each time step, solve the differential equations to obtain the concentration, temperature and other parameters at each location. This usually requires the use of an iterative algorithm, such as the Gauss-Seidel method:

[0237]

[0238] Among them, m represents the number of iteration steps.

[0239] (7) Convergence judgment:

[0240] Set the convergence criterion ∈, when , the time step is considered to have converged.

[0241] (8) Parameter update:

[0242] After each time step, update the relevant parameters:

[0243] Diffusion coefficient D i :

[0244] Reaction rate constant k j :

[0245] Reaction rate r j :

[0246]

[0247] Temperature T: obtained by solving the heat balance equation

[0248] Pressure P: obtained by solving the state equation

[0249] pH value: calculated based on reaction progress and buffer capacity

[0250] 3. Determine the parameter range:

[0251] After completing the simulation of the entire reaction time, the changes of various parameters over time and space can be obtained. Based on these results, the reasonable range of each parameter can be determined:

[0252] Reaction temperature range: [T min ,T max ];

[0253] Reaction time range: [t min ,t max ];

[0254] Catalyst dosage range: [m cat,min ,m cat,max ];

[0255] Raw material ratio range: i,min ,r i,max ];

[0256] Reaction pressure range: [P min ,P max ];

[0257] Stirring speed range: [n min ,n max ];

[0258] pH range: [pH min ,pH max ];

[0259] Distillation temperature range: [T dist,min ,T dist,max ];

[0260] Distillation reflux ratio range: [R min ,R max ];

[0261] Solvent dosage range: [V solv,min ,Vsolv,max ];

[0262] Optionally, these ranges may be determined taking into account the following factors:

[0263] (1) Reaction yield and selectivity: The parameter range should ensure that the reaction yield and selectivity are within an acceptable range.

[0264] (2) Equipment limitations: The parameter range should not exceed the operating limits of the equipment.

[0265] (3) Safety: The parameter range should ensure the safety of the reaction process and avoid excessive temperature or pressure.

[0266] (4) Economical: The parameter range should take into account economic factors such as energy consumption and raw material consumption.

[0267] Through the above steps, a set of process parameter ranges based on theoretical calculations can be obtained to provide guidance for subsequent experimental optimization.

[0268] Step S50 is to design an orthogonal experiment to obtain product performance data corresponding to each parameter combination. First, according to the parameter value range determined in step S40, a set of representative process parameter combinations are designed using the orthogonal experiment design method. Orthogonal experiment design is an efficient experimental design method that can obtain maximum information within the minimum number of experiments. After determining the experimental plan, actual production is carried out on a pilot scale to determine the product yield and quality indicators corresponding to each parameter combination. These experimental data provide an important basis for subsequent parameter optimization.

[0269] Step S60 is to optimize the process parameters using a genetic algorithm. First, each parameter combination is encoded as a gene sequence, and the product yield is used as the fitness function. Then, according to the results of the orthogonal test, the initial population is set. Next, genetic operators such as selection, crossover, and mutation are used to iteratively optimize the gene sequence to continuously improve the fitness function value, and finally obtain multiple optimized parameter combination schemes. Genetic algorithm is an optimization algorithm that simulates the biological evolution process and can find solutions close to the global optimal solution in complex multi-objective optimization problems. Compared with traditional trial and error or gradient methods, genetic algorithms have stronger search capabilities and robustness.

[0270] Step S70 verifies the optimized parameter combination on a pilot scale, and makes fine adjustments according to the actual production situation to determine the final optimal parameters. First, select several optimized parameter combinations obtained in step S60 and conduct production verification on a pilot plant. In this process, it is also necessary to further observe and analyze some key indicators, such as product yield, quality stability, energy consumption, etc., adjust the parameters appropriately according to the actual situation, and finally determine a parameter combination with the best comprehensive performance. The purpose of this step is to ensure that the optimization results can be truly applicable to actual production, solve problems that may arise in the middle, and obtain the final optimal process parameters.

[0271] In general, this patent involves a systematic method for determining the production process parameters of mixed fat alcohol biological sprout inhibitors. First, a comprehensive analysis of the raw material properties and product composition is conducted to provide a basis for the design of the synthesis route. Then a mathematical model covering the entire process is established to lay the foundation for parameter optimization. Next, orthogonal experiments and genetic algorithms are used for optimization to obtain multiple alternative optimal parameter combinations. Finally, verification and fine-tuning are carried out under pilot conditions to determine the final optimal process parameters.

[0272] Specifically, the principle of the present invention is:

[0273] First, the present invention adopts a systematic analysis method to comprehensively examine the properties of raw materials and products. The scientific basis of this method is that the essence of chemical reactions is the interaction at the molecular level. Only by fully understanding the molecular structure and properties of reactants and products can the optimal reaction path be designed. In particular, the target fraction is analyzed using gas chromatography-mass spectrometry technology. This method can simultaneously achieve component separation and structural identification, and is one of the most advanced complex mixture analysis technologies. Through this analysis, the main active components in mixed fat alcohol can be accurately identified, providing a reliable basis for subsequent synthetic route design.

[0274] Secondly, the present invention quantifies the complex chemical reaction process by establishing a set of synthetic parameter equations. The theoretical basis of this method is the theory of transfer phenomena and reaction engineering theory in chemical engineering. The material balance equation is based on the law of conservation of mass and describes the concentration changes of each component in the reaction process; the reaction kinetics equation is based on the theory of chemical reaction rate and describes the relationship between the reaction rate and factors such as temperature and concentration; the heat balance equation is based on the law of conservation of energy and describes the heat changes in the reaction process; the phase balance equation is based on the theory of thermodynamic equilibrium and describes the distribution of each component in a multiphase system; the mass transfer and heat transfer equation is based on the theory of momentum, heat and mass transfer and describes the transfer process in the reactor. These equations together constitute a complete mathematical model that can comprehensively describe various phenomena in the reaction process and provide a theoretical basis for parameter optimization.

[0275] Third, the present invention adopts orthogonal test design combined with genetic algorithm to perform parameter optimization. The principle of orthogonal test design is to obtain maximum information within the minimum number of trials by reasonably arranging the test scheme. This method is based on mathematical statistics theory and can effectively handle multi-factor optimization problems. The principle of genetic algorithm is to simulate the selection, crossover and mutation mechanisms in the biological evolution process and find the optimal solution through iterative optimization. This algorithm is particularly suitable for handling nonlinear, multi-objective complex optimization problems. Combining these two methods can not only ensure the high efficiency of the test, but also realize global optimal search, so as to find the optimal parameter combination within a limited number of trials.

[0276] Fourth, the present invention determines the final process parameters through pilot verification and fine-tuning. The scientific basis of this method is the theory of scale-up in chemical engineering. From laboratory pilot to industrial production, the size change of the reactor will lead to significant changes in heat transfer and mass transfer conditions, which may cause changes in reaction kinetics and selectivity. Through pilot verification, problems caused by these scale effects can be discovered and solved in time, ensuring that the optimization results can be successfully applied to actual production.

[0277] In addition, each step of the method of the present invention conforms to scientific logic. From raw material analysis to synthesis route design, it follows a scientific research path from micro to macro, from theory to practice. The establishment of parameter equations is based on recognized chemical engineering theories, which ensures the scientificity and reliability of the model. The application of orthogonal experiments and genetic algorithms reflects the application of modern optimization theory in chemical engineering, and can effectively handle complex multi-parameter optimization problems. The final pilot verification and fine-tuning reflect the scientific idea of ​​combining theory with practice, ensuring the practicality of the optimization results.

[0278] In general, the technical solution of the present invention can solve the problem of optimizing the production process parameters of the mixed fat alcohol biological sprout inhibitor, mainly because it adopts a systematic, mathematical and intelligent method. Systematization is reflected in the comprehensive analysis of the properties of raw materials and products, taking into account various factors affecting the reaction; mathematicalization is reflected in the establishment of a complete set of parameter equations to accurately describe the reaction process; intelligence is reflected in the use of advanced optimization algorithms to efficiently search for the optimal solution. This method not only takes into account the essential laws of chemical reactions, but also combines the characteristics of modern chemical processes, so that more reliable and practical optimization results can be obtained.

[0279] In addition, the method of the present invention has strong logic and operability. From raw material analysis to final parameter determination, each step has a clear purpose and scientific basis, and the steps are closely connected to form a complete process parameter optimization system.

[0280] In order to better understand and implement the present invention, an embodiment of a specific application scenario of the present invention is provided below:

[0281] 1. Raw material analysis and determination of active components

[0282] The main raw materials of the mixed alcohol biological sprout inhibitor produced by a pesticide company are palm oil, soybean oil and other vegetable oils. In order to fully understand the properties of these raw materials, the company first conducted a detailed analysis of them.

[0283] Through physical property testing, it was found that the specific gravity of the raw material was between 0.91-0.93, the boiling range was between 280-320℃, and the dynamic viscosity was between 50-80cSt. These data show that the raw material is mainly composed of long-chain fatty acid glycerides, has a high viscosity, and is suitable as a reaction material.

[0284] Subsequently, the company used elemental analysis, infrared spectroscopy and other methods to determine the chemical composition of the raw materials. The results showed that the main components of palm oil were hexadecanoic acid glyceryl (about 50%) and octadecanoic acid glyceryl (about 40%), while soybean oil was mainly octadecanoic acid glyceryl (about 60%) and octadecenoic acid glyceryl (about 30%). In addition, a small amount of free fatty acids and trace inorganic salt impurities were also detected.

[0285] In order to further determine the main active ingredients of the final product, the company used gas chromatography-mass spectrometry to analyze the target fraction of the mixed alcohol produced. The results showed that the main active component in the fraction was dihydroxy dialkyl ether, accounting for about 75% of the total mass, and the rest was monohydroxy monoalkyl ether and a small amount of higher alcohol compounds. This information provides a key basis for the design of subsequent synthetic routes.

[0286] In summary, the comprehensive analysis of raw materials and products has laid a solid foundation for the process optimization of the present invention. The company has mastered the physical and chemical properties of the raw materials and the main component information of the target product, laying a good foundation for the subsequent synthesis route design and parameter optimization.

[0287] 2. Synthesis route design and optimization

[0288] Based on the above raw material and product analysis results, the company's R&D team designed several feasible synthesis routes and verified and optimized them under laboratory test conditions.

[0289] First, considering that the raw materials are mainly glycerides, the company preliminarily designed a route to obtain a dihydroxy alcohol intermediate through a glycerolization reaction, and then obtain the target product through an alkylation reaction. The specific steps are as follows:

[0290] 1. Carry out transesterification reaction between palm oil or soybean oil and glycerol under alkaline conditions to obtain glycerol and fatty acid methyl esters;

[0291] 2. Carrying out a condensation reaction between the obtained glycerol and an aldehyde or ketone compound under acidic conditions to prepare a dihydroxy alcohol intermediate;

[0292] 3. The dihydroxy alcohol and alkyl halide undergo nucleophilic substitution reaction under alkaline conditions to obtain the target dihydroxy dialkyl ether product.

[0293] During the pilot test, the company conducted in-depth research on the key steps of the route, such as transesterification reaction conditions, condensation reaction kinetics, alkylation reaction mechanism, etc., and optimized the parameters of each step, such as temperature, time, pH value, material ratio, etc. After multiple rounds of experiments, the total yield of the synthetic route has been significantly improved to about 70%.

[0294] At the same time, the company's R&D team also considered the feasibility of preparing the target product directly from fatty alcohols through etherification reaction. The specific steps of this route are as follows:

[0295] 1. Hydrolyze palm oil or soybean oil at high temperature to obtain fatty alcohol;

[0296] 2. The obtained fatty alcohol is subjected to a condensation reaction with an aldehyde or ketone compound under acidic conditions to prepare a dihydroxy alcohol intermediate;

[0297] 3. The dihydroxy alcohol and alkyl halide undergo nucleophilic substitution reaction under alkaline conditions to obtain the target dihydroxy dialkyl ether product.

[0298] During the pilot test, the company also optimized the process parameters of each key step. Compared with the first route, this route directly starting from fatty alcohol has a slightly lower total yield (about 65%), but the reaction conditions are relatively mild and easy to control, and the separation and purification of intermediates is simpler.

[0299] Taking into account factors such as the source of raw materials, synthesis difficulty, and product quality, the company finally determined the synthesis route using fatty alcohol as raw material as the optimal process. This not only makes full use of the existing vegetable oil processing equipment, but also makes the process control relatively simple and reliable.

[0300] 3. Establishment and solution of parametric equations

[0301] In order to conduct in-depth analysis and optimization of the determined synthesis route, the company's R&D team established a mathematical model covering the entire process.

[0302] First, for the three main steps of glycerolization reaction, condensation reaction and alkylation reaction, the company listed the corresponding material balance equations. Taking alkylation reaction as an example, its material balance can be expressed as:

[0303]

[0304] In the formula, C i is the concentration of component i (mol / m 3 ), t is time (s), u is fluid velocity vector (m / s), D i is the diffusion coefficient of component i (m 2 / s); R i is the reaction rate of component i (mol / (m 3 ·s)).

[0305] For the diffusion coefficient D i , the company used the Wilke-Chang empirical formula for estimation:

[0306]

[0307] Where x is the binding factor of the solvent (dimensionless); M is the molecular weight of the solvent (g / mol); T is the temperature (K); η is the viscosity of the solvent (cP); V A is the molar volume of the solute at the boiling point (cm 3 / mol).

[0308] Next, the company established kinetic equations for each reaction step to describe the relationship between the reaction rate and parameters such as temperature and concentration. Taking the alkylation reaction as an example, its reaction kinetic equation is as follows:

[0309]

[0310] In the formula, r j is the rate of reaction j (mol / (m 3 ·s));k j is the rate constant of reaction j ((m 3 / mol)^(n-1) / s, n is the reaction order); C i is the concentration of reactant i (mol / m 3 ); α ij is the reaction order of reactant i in reaction j (dimensionless); E a,j is the activation energy of reaction j (J / mol); R is the gas constant (8.314 J / (mol·K)); T is the temperature (K); K e is the equilibrium constant (dimensionless); β ij is the stoichiometric coefficient of product i in reaction j (dimensionless).

[0311] In addition, the company has also established phase equilibrium equations, mass transfer and heat transfer equations, and mass transfer equations to describe the laws of material and energy transformation in the separation and purification process.

[0312] With the above series of equations, the company's R&D team can establish a set of parameter equations covering the entire production process. To solve these equations, they used numerical calculation methods such as finite difference method and Newton iteration method. By substituting the initial parameters and continuously optimizing and iterating, they finally determined the reasonable value range of each process parameter.

[0313] For example, for alkylation reactions, after numerical solution, the company determined that the optimal reaction temperature is 85-90°C, the reaction time is 2-3 hours, the catalyst dosage is 5-8 mol% of the reactants, etc. These parameters provide an important basis for the subsequent orthogonal experimental design.

[0314] In general, by establishing a comprehensive mathematical model, the company's R&D team has mastered the quantitative influence of various process parameters on product quality and production efficiency, laying a solid foundation for subsequent parameter optimization.

[0315] IV. Orthogonal Experimental Design and Genetic Algorithm Optimization

[0316] Based on the parameter value range determined in the previous step, the company adopted the orthogonal experimental design method to design a set of representative process parameter combinations and carried out verification production on the pilot plant. The main parameter settings are shown in Table 1:

[0317] Table 1 Parameter setting table

[0318]

[0319] As can be seen from the table above, with the increase of reaction temperature, time and catalyst dosage, the product yield gradually increases, while the impurity content also decreases. Among them, the parameter setting of the fourth combination has a yield of 75.2% and an impurity content of only 2.3%, which can initially meet the production requirements.

[0320] To further optimize the process parameters, the company adopted a genetic algorithm. First, each parameter combination was encoded as a gene sequence, and the product yield was used as the fitness function. Then, based on the above orthogonal test results, the initial population was set. Next, through genetic operations such as selection, crossover, and mutation, the gene sequence was iteratively optimized, the fitness function value was continuously improved, and finally multiple sets of optimized parameter combinations were obtained.

[0321] After multiple rounds of iterative calculations, the company finally determined the following parameter combination as the optimal process solution:

[0322] Reaction temperature: 88-90℃

[0323] Reaction time: 2.5-3.0 hours

[0324] Catalyst dosage: 6-7 mol%

[0325] Feed ratio (material ratio): 1:1.3-1:1.4

[0326] Stirring speed: 330-360r / min

[0327] This set of parameters not only achieves a product yield of 77.8% and controls the impurity content within 2.1%, but also has good operational stability and can meet actual production needs.

[0328] 5. Pilot verification and fine-tuning

[0329] To verify the applicability of the above optimal process parameters, the company carried out actual production at a pilot scale. During this process, the company further observed and analyzed key indicators such as product yield, quality stability, and energy consumption.

[0330] In terms of product yield, the average yield during the pilot production process was 77.3%, slightly lower than the 77.8% obtained by optimization calculation, mainly due to some factors that are difficult to fully control under pilot conditions, such as fluctuations in raw material batches, changes in equipment performance, etc. But overall, the pilot yield is still much higher than the initial level of 68%-73%, which fully demonstrates the superiority of the method of the present invention.

[0331] In terms of product quality stability, the impurity content of five consecutive batches was controlled between 2.0% and 2.3%, with small fluctuations, meeting the product quality standards. This is mainly due to the fine optimization of process parameters, which enables key indicators such as temperature and pH value to be accurately controlled within the optimal range.

[0332] In terms of energy consumption, the unit product energy consumption of the pilot production line has been reduced by about 15% compared with the initial production line, mainly due to the optimization of reaction time and fractionation temperature. For example, shortening the reaction time by 0.5 hours can save a lot of heating and cooling energy; and appropriately reducing the fractionation temperature by 2-3 degrees Celsius can also reduce the energy consumption of the distillation process.

[0333] Based on the pilot test, the company's R&D team also fine-tuned some parameters to further improve the stability and economy of the process. For example, the reaction temperature was controlled at 88-89°C, the reaction time was controlled at 2.6-2.8 hours, the catalyst dosage was controlled at 6.5-6.8 mol%, the feed ratio was maintained at about 1:1.35, and the stirring speed was controlled at 340-350 r / min. The fine-tuning of these parameters further optimized the reaction kinetics and mass transfer process, making the entire production process more controllable and efficient.

[0334] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for determining the production process parameters of a mixed fat alcohol biological sprout inhibitor, characterized in that: The following steps are involved: S10. Conduct a comprehensive analysis of the raw materials for the production of mixed fat alcohol biological sprout inhibitor, including physical properties, chemical composition, impurity content, stability and reactivity, and determine the main active components of the mixed fat alcohol target fraction produced; S20, selecting precursor compounds based on the molecular information of the main active components according to the principles of structural similarity and reactivity, and determining the optimal chemical synthesis route through laboratory synthesis; S30, establishing a group of synthesis parameter equations according to the physical properties and the optimal chemical synthesis route, including a material balance equation, a reaction kinetics equation, a heat balance equation, a phase balance equation, a mass transfer and heat transfer equation, and a mass transfer equation; S40, based on the preset initial parameters, substituting into the synthesis parameter equation group, solving to obtain the range of the synthesis parameters; S50, designing an orthogonal test according to the initial parameters and the range of the synthesis parameters, and obtaining orthogonal test results, including each combination of synthesis parameters and its corresponding product yield mass vector; S60, using a genetic algorithm according to the orthogonal test results, taking the product yield as the fitness function, the synthesis parameter combination as the gene code, and the orthogonal test results as the initial population, iteratively executing to obtain the optimal multiple synthesis parameter combinations; S70. Verify the multiple synthesis parameter combinations on a pilot scale, and make fine adjustments according to actual production conditions to obtain the optimal synthesis parameter combination as the optimal parameters for the production process of the mixed fat alcohol biological inhibitor.

2. The method for determining the production process parameters of a mixed fat alcohol biological sprout inhibitor according to claim 1, characterized in that: The method for determining the main active components of the produced mixed fat alcohol target fraction is a gas chromatography-mass spectrometry method.

3. The method for determining the production process parameters of a mixed fat alcohol biological sprout inhibitor according to claim 2, characterized in that: The synthesis parameters include reaction temperature, reaction time, catalyst dosage, raw material ratio, reaction pressure, stirring speed, pH value, fractionation temperature, fractionation reflux ratio, solvent dosage, reactor size, feed rate, cooling water flow rate, and separation efficiency.

4. The method for determining the production process parameters of a mixed fat alcohol biological sprout inhibitor according to claim 3, characterized in that: The preset initial parameters include the initial concentration of reactants, the initial temperature and pressure of the reactor, the initial activity of the catalyst, the purity and ratio of the raw materials, and the initial pH value of the reaction medium.

5. The method for determining the production process parameters of a mixed fat alcohol biological sprout inhibitor according to claim 4, characterized in that: The material balance equation is specifically expressed as: In the formula, C i is the concentration of component i; t is the time; u is the fluid velocity vector; D i is the diffusion coefficient of component i; R i is the reaction rate of component i; Diffusion coefficient D i Estimation by Wilke-Chang equation: Where x is the binding factor of the solvent; M is the molecular weight of the solvent; T is the temperature; η is the viscosity of the solvent; V A is the molar volume of the solute at its boiling point.

6. The method for determining the production process parameters of a mixed fat alcohol biological sprout inhibitor according to claim 5, characterized in that: The reaction kinetics equation is specifically expressed as: In the formula, r j is the rate of reaction j; k j is the rate constant of reaction j, n is the reaction order; C i is the concentration of reactant i; α ij is the reaction order of reactant i in reaction j; E a,j is the activation energy of reaction j; R is the gas constant; T is the temperature; K e is the equilibrium constant; β ij is the stoichiometric coefficient of product i in reaction j; Rate constant k j Calculated by Arrhenius equation: Among them A j It is the pre-factor obtained by fitting the experimental data.

7. The method for determining the production process parameters of a mixed fat alcohol biological sprout inhibitor according to claim 6, characterized in that: The heat balance equation is specifically expressed as: Where ρ is the fluid density; C p is the specific heat capacity of the fluid; T is the temperature; t is the time; u is the fluid velocity vector; k is the thermal conductivity; ΔH j is the heat of reaction j; r j is the rate of reaction j; Q is the external heat source term.

8. The method for determining the production process parameters of a mixed fat alcohol biological sprout inhibitor according to claim 7, characterized in that: The phase equilibrium equation is specifically expressed as: In the formula, y i is the mole fraction of component i in the gas phase; P is the total pressure of the system; γ i is the activity coefficient of component i; x i is the mole fraction of component i in the liquid phase; is the saturated vapor pressure of component i; is the liquid phase molar volume of component i; R is the gas constant; T is the temperature.

9. The method for determining the production process parameters of a mixed fat alcohol biological sprout inhibitor according to claim 8, characterized in that: The mass transfer and heat transfer equation is specifically expressed as: In the formula, C t is the total molar concentration; x i is the mole fraction of component i; N i is the molar flux of component i; is the Maxwell-Stefan diffusion coefficient between components i and j; For heat transfer, Fourier's law is used: Where q is the heat flux; k is the thermal conductivity; and T is the temperature.

10. The method for determining the production process parameters of a mixed fat alcohol biological sprout inhibitor according to claim 9, characterized in that: The mass transfer equation is specifically expressed as: Where N A is the mass transfer rate of component A; K L is the total mass transfer coefficient; a is the specific surface area; is the equilibrium concentration of component A at the interface; C A is the concentration of component A in the liquid phase; Overall mass transfer coefficient K L Calculated by the following formula: where k L and k G are the mass transfer coefficients of the liquid phase and the gas phase, respectively, and H is the Henry constant.

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