Method and device for online reaction monitoring of waterborne polyacrylate and polyurethane emulsion

Abstract

The invention discloses an online reaction monitoring method and device for waterborne polyacrylate and polyurethane emulsion, and relates to the field of material analysis and process control. The method characterizes the molecular structure of the emulsifier and the characteristics of the reactor wall by acquiring various process parameter data of the emulsification and mixing reaction process, including the molecular structure data of the emulsifier and the catalytic effect data of the reactor wall. Then, an emulsification and mixing reaction optimization model is established to predict the emulsion particle size distribution in the emulsification process, the product composition and conversion rate in the mixing reaction process, and the reaction process parameters are adjusted according to the prediction results. The method realizes real-time monitoring and intelligent optimization control of the emulsion preparation process.

Description

Technical Field

[0001] The invention relates to the field of material analysis and process control, and more specifically to an online reaction monitoring method and device for waterborne polyacrylate and polyurethane emulsion. Background Art

[0002] Aqueous polymer emulsions are widely used in coatings, adhesives, functional materials and other fields. Their preparation process usually involves key steps such as monomer / prepolymer emulsification, emulsion particle formation and polymerization reaction. Traditionally, these steps are often carried out in different reactors in steps, which have problems such as complex process, high energy consumption, and difficulty in product quality control. The existing technology has obvious deficiencies in the analysis and monitoring of the emulsion preparation process. There is a lack of in-depth understanding of the emulsification behavior of monomers / prepolymers, the particle formation mechanism and the polymerization reaction kinetics. It is difficult to achieve accurate measurement and real-time monitoring of key quality indicators such as particle size distribution, dispersity and degree of polymerization, which restricts the optimization of product performance and intelligent control of the process. Summary of the invention

[0003] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method and a device for online reaction monitoring of waterborne polyacrylate and polyurethane emulsion.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] On-line reaction monitoring method for waterborne polyacrylate and polyurethane emulsions, including:

[0006] Step S100: obtaining process optimization parameter data of the emulsification and mixing reaction process;

[0007] The step S100 includes:

[0008] Step S110: Acquire first relevant process optimization parameter data of the emulsification and mixing reaction process;

[0009] Step S120: obtaining second relevant process optimization parameter data of the emulsification and mixing reaction process, wherein the second relevant process optimization parameter data includes emulsifier dosage and emulsifier molecular structure data;

[0010] Step S130: obtaining third relevant process optimization parameter data of the emulsification and mixing reaction process, wherein the third relevant process optimization parameter data includes reactor wall catalytic effect data;

[0011] Step S200: obtaining an emulsifier molecular structure vector according to the emulsifier molecular structure data, wherein the emulsifier molecular structure vector includes seven features, namely, hydrophobic chain length, hydrophilic group number, hydrophilic group polarity, branching degree, conjugation degree, hydrophilic-hydrophobic balance factor and adsorption energy parameter;

[0012] Step S300: obtaining a reactor wall catalytic effect vector according to the reactor wall catalytic effect data, wherein the reactor wall catalytic effect vector includes six characteristics, namely, wall material composition, wall surface energy, wall specific surface area, wall adsorption capacity, wall reaction rate constant and wall catalytic selectivity;

[0013] Step S400: Step S400: predicting the emulsion particle size distribution, product composition and conversion rate in the emulsification and mixing reaction process according to the first relevant process optimization parameter data, the emulsifier molecular structure vector, the wall catalytic effect characteristic vector and the pre-constructed emulsification and mixing reaction optimization model;

[0014] Step S500: formulating a process adjustment strategy according to the predicted emulsion particle size distribution;

[0015] Step S600: formulating a process adjustment strategy based on the predicted product composition;

[0016] Step S700: formulating a process adjustment strategy according to the predicted conversion rate;

[0017] Step S800: Record the current process parameters as the optimal process parameters and proceed to the subsequent process.

[0018] Furthermore, the first relevant process optimization parameter data includes temperature, shear rate, emulsifier dosage, types and ratios of monomers and prepolymers, types and concentrations of initiators, and reaction time;

[0019] The emulsification and mixing reactions are carried out in the same reactor.

[0020] Furthermore, the step S200 includes:

[0021] Step S210: using the group contribution method to obtain the hydrophobic chain length;

[0022] Step S220: obtaining the number of hydrophilic groups by hydrogen nuclear magnetic resonance spectrum;

[0023] The number of hydrophilic groups, if the number of hydrophilic groups is 0, a warning message is issued, warning that the molecule may be a pure hydrophobic substance and is not suitable as an emulsifier, and then the step S280 is turned to generate an emulsifier molecular structure vector; if the number of hydrophilic groups is greater than 0, the step S230 is turned to obtain the polarity of the hydrophilic groups; the number of hydrophilic groups is an integer greater than or equal to 0;

[0024] Step S230: multiplying the Taft polarity constant (Taft stereo polarity constant) of each hydrophilic group by its number in the molecule, and dividing the sum of the products by the total number of hydrophilic groups to obtain the polarity of the hydrophilic group;

[0025] Step S240: Divide the number of branched carbon atoms by the total number of carbon atoms to obtain a branching degree;

[0026] If the degree of branching is within the set range, then go to step S250 to obtain the degree of conjugation; if the degree of branching exceeds the set range, then perform branching modification on the emulsifier molecule; after the modification is completed, go to step S240 to recalculate the degree of branching and determine whether it is within the set range;

[0027] Step S250: Dividing the number of conjugated bonds by the total number of bonds to obtain the degree of conjugation;

[0028] If the conjugation degree is within the set range, the process proceeds to step S260 to obtain the hydrophilic-hydrophobic balance factor; if the conjugation degree exceeds the set range, the emulsifier molecule is modified; after the modification is completed, the process proceeds to step S250 to recalculate the conjugation degree and determine whether it is within the set range;

[0029] Step S260: obtaining a hydrophilic-hydrophobic balance factor according to the relative length of the hydrophobic chain and the total number of carbon atoms;

[0030] The hydrophilic-hydrophobic balance factor, if the hydrophilic-hydrophobic balance factor is greater than the third preset value or less than the first preset value, it indicates that the hydrophilicity and hydrophobicity of the emulsifier molecule are seriously unbalanced, and a warning signal is issued to warn that the molecule may not be suitable as an emulsifier, and then the process goes to step S280 to generate an emulsifier molecular structure vector;

[0031] If the hydrophilic-hydrophobic balance factor is greater than or equal to the second preset value and less than or equal to the third preset value, it means that the emulsifier is more hydrophilic, and the process goes to step S270 to obtain an adsorption energy parameter;

[0032] If the hydrophilic-hydrophobic balance factor is greater than or equal to the first preset value and less than or equal to the second preset value, it means that the emulsifier is more lipophilic, and more hydrophilic groups are introduced to balance its hydrophilicity; after the molecular hydrophilicity is balanced, go to step S260 to calculate the hydrophilic-hydrophobic balance factor again and determine its range; wherein the first preset value is less than the second preset value, and the second preset value is less than the third preset value;

[0033] Step S270: taking the ratio of the adsorption energy of the emulsifier molecule on the oil-water interface to the total internal energy of the emulsifier molecule as an adsorption energy parameter;

[0034] Step S280: The length of the hydrophobic chain, the number of hydrophilic groups, the polarity of the hydrophilic groups, the degree of branching, the degree of conjugation, the hydrophilic-hydrophobic balance factor and the adsorption energy parameters are combined into an emulsifier molecular structure vector.

[0035] Furthermore, the step S210 includes:

[0036] Step S211: identifying and counting the number of methylene and methyl groups on the hydrophobic chain according to the chemical structure formula of the emulsifier molecule;

[0037] Step S212: Obtaining carbon chain length contribution values ​​of methylene and methyl groups;

[0038] The carbon chain length contribution value, when the temperature is higher than 50°C, uses the carbon chain length contribution value at high temperature; when the temperature is lower than or equal to 50°C, uses the carbon chain length contribution value at room temperature;

[0039] Step S213: multiplying the number of methylene groups by the carbon chain length contribution value of the methylene group to obtain the methylene chain length; multiplying the number of methyl groups by the carbon chain length contribution value of the methyl group to obtain the methyl chain length; summing the methylene chain length and the methyl chain length to obtain the initial hydrophobic chain length;

[0040] Step S214: introducing a branch chain correction factor and an unsaturation correction factor to correct the initial hydrophobic chain length to obtain a corrected hydrophobic chain length;

[0041] The branch correction factor is when the degree of branching is greater than D b2 When the degree of branching is D b1 To D b2 When the degree of branching is less than D b1 When the correction factor for low-branched or linear systems is selected, D b1 <D b2 ;

[0042] The unsaturation correction factor is when the number of unsaturated bonds is greater than N b2 When the number of unsaturated bonds is N, the correction factor for highly unsaturated systems is selected; when the number of unsaturated bonds is N b1 To N b2 When the number of unsaturated bonds is between 0 and 1, the correction factor for moderately unsaturated systems is used. When the number of unsaturated bonds is 0, the correction factor for saturated systems is used. b1 <N b2 ;

[0043] Step S215: introducing a conformation factor to perform a second correction on the corrected hydrophobic chain length to obtain a final hydrophobic chain length.

[0044] The conformation factor is 0.8-0.9 when the temperature is below 20°C; 0.95-1.05 when the temperature is between 20°C and 70°C; and 1.1-1.2 when the temperature is above 70°C.

[0045] Furthermore, the relative length of the hydrophobic chain is the ratio of the number of carbon atoms in the hydrophobic chain to the total number of carbon atoms, and the number of carbon atoms in the hydrophobic chain is obtained by subtracting the number of hydrophilic groups from the total number of carbon atoms;

[0046] The adsorption energy parameter is a relative quantity, and the total internal energy of the emulsifier molecule is the sum of the internal energy of the hydrophobic chain and the internal energy of the hydrophilic group.

[0047] Furthermore, the step S300 includes:

[0048] Step S310: analyzing the atomic percentage or mass percentage of different elements in the wall material to form the wall material composition characteristics;

[0049] Step S320: measuring the surface energy of the wall material by using a contact angle method, a surface tension method or a solvent extraction method;

[0050] Step S330: measuring the wall specific surface area by gas adsorption method;

[0051] Step S340: determining the wall adsorption capacity through an adsorption isotherm or an adsorption kinetic curve;

[0052] Step S350: determining the wall reaction rate constant by kinetic experiments or quantum chemical calculations;

[0053] Step S360: multiplying the ratio of the target product yield to the sum of all product yields by 100% to obtain the wall catalytic selectivity;

[0054] Step S370: The wall material composition, wall surface energy, wall specific surface area, wall adsorption capacity, wall reaction rate constant and wall catalytic selectivity are combined to form a reactor wall catalytic effect vector.

[0055] Furthermore, the method for measuring the wall specific surface area by gas adsorption method comprises:

[0056] Cut or break the wall material sample to be tested into small pieces, place them in a vacuum drying oven, dry them at 80-100°C for several hours, and then cool them to room temperature under a specific atmosphere;

[0057] The dried wall material sample is placed in a closed container of a bubble adsorption device, and the gas is controlled to flow into the container at a constant flow rate;

[0058] Carry out adsorption experiments at different pressures, maintain each pressure point for a certain time until the adsorption reaches equilibrium, and record the adsorption time and air intake at each pressure point, as well as the temperature and pressure changes in the container;

[0059] According to the state equation of the adsorbate gas, calculate the adsorption amount at each pressure point and draw the adsorption isotherm;

[0060] The surface roughness coefficient is obtained by dividing the actual specific surface area of ​​the surface roughness by the theoretical specific surface area when the surface is completely smooth;

[0061] The adsorption isotherm data are multiplied by the surface roughness coefficient to obtain the corrected adsorption isotherm data;

[0062] The adsorption model was used to fit the corrected adsorption isotherm to obtain the specific surface area data;

[0063] Furthermore, the specific atmosphere is a vacuum atmosphere or an inert atmosphere;

[0064] The adsorbate gas is an inert gas; the inert gas is nitrogen or argon;

[0065] The adsorption model is one of the following models: Langmuir model, BET model (Brunauer-Emmett-Teller model).

[0066] Furthermore, the method for formulating a process adjustment strategy based on the predicted emulsion particle size distribution includes:

[0067] If the predicted particle size distribution of the emulsion meets the preset particle size distribution requirement, then go to step S600 to formulate a process adjustment strategy based on the predicted product composition; if the predicted particle size distribution of the emulsion does not meet the preset particle size distribution requirement, then adjust the amount or type of the emulsifier, then go to step S120 to re-acquire the second relevant process optimization parameter data of the emulsification and mixing reaction process;

[0068] The method for formulating a process adjustment strategy according to the predicted product composition includes:

[0069] If the predicted product composition meets the preset product composition requirements, then go to step S700 to formulate a process adjustment strategy based on the predicted conversion rate; if the predicted product composition does not meet the preset product composition requirements, then adjust the monomer / prepolymer ratio or the type and concentration of the initiator, and then go to step S110 to re-acquire the first relevant process optimization parameter data of the emulsification and mixing reaction process;

[0070] The method for formulating a process adjustment strategy based on the predicted conversion rate includes:

[0071] If the predicted conversion rate reaches the preset conversion rate threshold, go to step S800, record the current process parameters as the optimal process parameters, and enter the subsequent process; if the predicted conversion rate does not reach the preset conversion rate threshold, adjust the reaction temperature, reaction time or shear rate, and then go to step S110 to re-acquire the first relevant process optimization parameter data of the emulsification and mixing reaction process.

[0072] The on-line reaction monitoring device of waterborne polyacrylate and polyurethane emulsion is used to implement the on-line reaction monitoring method of waterborne polyacrylate and polyurethane emulsion, comprising:

[0073] Process optimization parameter acquisition module: used to obtain process optimization parameter data of the emulsification and mixing reaction process, wherein the process optimization parameter data includes first related process optimization parameter data, second related process optimization parameter data and third related process optimization parameter data, wherein the second related process optimization parameter data includes emulsifier dosage and emulsifier molecular structure data, and the third related process optimization parameter data includes reactor wall catalytic effect data;

[0074] Feature conversion module: used to convert emulsifier molecular structure data into emulsifier molecular structure vectors, and to convert reactor wall catalytic effect data into reactor wall catalytic effect vectors;

[0075] Product prediction module: used to obtain sample data sets, and predict the emulsion particle size distribution, product composition and conversion rate in the emulsification and mixing reaction process based on the sample data sets and the pre-built emulsification and mixing reaction optimization model;

[0076] Emulsification and mixing reaction control module: used to formulate process adjustment strategies based on the emulsion particle size distribution, product composition and conversion rate during the emulsification and mixing reaction process.

[0077] An electronic device comprises a memory, a central processing unit and a computer program stored in the memory and executable on the central processing unit. When the central processing unit executes the computer program, the above-mentioned online reaction monitoring method of waterborne polyacrylate and polyurethane emulsion is realized.

[0078] A computer-readable storage medium stores a computer program, and when the computer program is executed, the above-mentioned method for online reaction monitoring of waterborne polyacrylate and polyurethane emulsion is implemented.

[0079] Compared with the prior art, the present invention has the following beneficial effects:

[0080] The present invention innovatively introduces new characteristic parameters such as the emulsifier molecular structure vector and the reactor material catalytic effect vector into the analysis of the emulsion preparation process, characterizes the influencing factors at the molecular level, and comprehensively improves the level of understanding of the preparation mechanism.

[0081] The present invention realizes real-time monitoring of key quality indicators such as emulsion particle size distribution, product composition, conversion rate, etc. through advanced data modeling means, providing a strong guarantee for the precise control of product quality.

[0082] The present invention provides an intelligent optimization control path for emulsion preparation, and can dynamically adjust process parameters according to real-time monitoring results to achieve efficient preparation of stable, controllable, high-quality emulsion.

[0083] The present invention does not require step-by-step operation, integrates emulsification and mixing reaction equipment, has a simple process flow, low energy consumption, is green and environmentally friendly, and is conducive to energy conservation, emission reduction and sustainable development.

[0084] The system monitoring method of the present invention is comprehensive and well-targeted, can be widely applied to the analysis and manufacture of various aqueous polymer emulsions, and has good promotion value.

[0085] In summary, the present invention provides an innovative process analysis and intelligent control technology for the preparation of emulsion materials, which not only deepens the understanding of the preparation mechanism at the theoretical level, but also provides a practical quality monitoring and optimization control method for industrial production, which is of great significance to promoting scientific and technological progress and industrial upgrading in this field. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 It is a flow chart of the on-line reaction monitoring method of waterborne polyacrylate and polyurethane emulsion in the present invention;

[0087] Figure 2 A flow chart of a method for obtaining a molecular structure vector of an emulsifier in an online reaction monitoring method of a waterborne polyacrylate and polyurethane emulsion of the present invention;

[0088] Figure 3 A flow chart of a method for obtaining a reactor wall catalytic effect vector in an online reaction monitoring method for waterborne polyacrylate and polyurethane emulsion of the present invention;

[0089] Figure 4 It is a functional module diagram of the online reaction monitoring device of waterborne polyacrylate and polyurethane emulsion in the present invention. DETAILED DESCRIPTION

[0090] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0091] Example 1

[0092] See also Figure 1 As shown, this embodiment provides an online reaction monitoring method for waterborne polyacrylate and polyurethane emulsion, comprising:

[0093] Step S100: obtaining process optimization parameter data of the emulsification and mixing reaction process;

[0094] The step S100 includes:

[0095] Step S110: Acquire first relevant process optimization parameter data of the emulsification and mixing reaction process;

[0096] Step S120: obtaining second relevant process optimization parameter data of the emulsification and mixing reaction process, wherein the second relevant process optimization parameter data includes emulsifier dosage and emulsifier molecular structure data;

[0097] Step S130: obtaining third relevant process optimization parameter data of the emulsification and mixing reaction process, wherein the third relevant process optimization parameter data includes reactor wall catalytic effect data;

[0098] The first relevant process optimization parameter data includes temperature, shear rate, types and proportions of monomers and prepolymers, types and concentrations of initiators, reaction time, etc.;

[0099] The emulsification and mixing reactions are carried out in the same reactor, and the first relevant process optimization parameter data includes parameter data in the emulsification and mixing reaction processes, for example, the temperature includes the temperature in the emulsification process and the mixing reaction process;

[0100] It is understood that the temperature and shear rate can be directly measured by temperature sensors and shear force / speed sensors in the real-time temperature and shear rate of the reaction process, or the set temperature, stirring rate and other process parameter values ​​can be read from the control system of the reaction equipment; the amount of emulsifier can be obtained from the batching system; the type and proportion of monomers and prepolymers, the type and concentration of initiators can be read from the batching system according to the reaction formula; the reaction time can be read from the control system of the reaction equipment, or a timer can be used to record the time from the start to the completion of the reaction;

[0101] Step S100: Comprehensively obtain key process parameters such as temperature, shear rate, emulsifier dosage, monomer / prepolymer ratio, initiator type and concentration, reaction time, etc., to provide the necessary data basis for subsequent optimization. Obtaining emulsifier molecular structure data and reactor wall catalytic effect data can deeply analyze the influencing factors and reveal the essential mechanism of the reaction process. The system collects various parameter data, which is helpful to establish a data-driven machine learning model to achieve intelligent process control and optimization.

[0102] Step S200: obtaining an emulsifier molecular structure vector according to the emulsifier molecular structure data;

[0103] Emulsifier molecules usually consist of two parts: a lipophilic group (hydrophobic tail) and a hydrophilic group (polar head). The lipophilic group is mainly an alkyl chain, such as a straight chain, branched chain or cyclic alkane; the hydrophilic group can be ionic, such as carboxylate, sulfonate, quaternary ammonium salt, etc., or non-ionic, such as hydroxyl, ether, ester, etc. The molecular structure of the emulsifier can be described and characterized by the following parameters: hydrophobic chain length, number of hydrophilic groups, polarity of hydrophilic groups, number of branches and number of conjugated bonds.

[0104] The molecular structure vector of the emulsifier contains seven features, namely, the length of the hydrophobic chain, the number of hydrophilic groups, the polarity of the hydrophilic groups, the degree of branching, the degree of conjugation, the hydrophilic-hydrophobic balance factor and the adsorption energy parameter;

[0105] Furthermore, if Figure 2 As shown, step S200 of the present application includes:

[0106] Step S210: using the group contribution method to obtain the hydrophobic chain length;

[0107] The length of the hydrophobic chain indicates the number of carbon atoms in the hydrophobic alkyl chain in the emulsifier molecule. The length of the hydrophobic chain determines the arrangement and area occupied by the emulsifier molecules at the oil-water interface. The longer the hydrophobic chain, the larger the area occupied by the emulsifier molecule at the oil-water interface, which can better shield the surface of the oil droplets and improve the entropy stability of the emulsion. However, an overly long hydrophobic chain will also reduce the solubility of the emulsifier in the aqueous phase and affect the formation of the emulsion, so the length of the hydrophobic chain needs to be weighed.

[0108] Furthermore, step S210 of the present application includes:

[0109] Step S211: identifying and counting the number of methylene and methyl groups on the hydrophobic chain according to the chemical structure formula of the emulsifier molecule;

[0110] Step S212: Obtaining carbon chain length contribution values ​​of methylene and methyl groups;

[0111] Step S213: multiplying the number of methylene groups by the carbon chain length contribution value of the methylene group to obtain the methylene chain length; multiplying the number of methyl groups by the carbon chain length contribution value of the methyl group to obtain the methyl chain length; summing the methylene chain length and the methyl chain length to obtain the initial hydrophobic chain length;

[0112] Step S214: introducing a branch chain correction factor and an unsaturation correction factor to correct the initial hydrophobic chain length to obtain a corrected hydrophobic chain length;

[0113] Step S215: introducing a conformation factor to perform a secondary correction on the corrected hydrophobic chain length to obtain a final hydrophobic chain length;

[0114] Specifically, according to the chemical structure of the emulsifier molecule, the number of methylene (-CH2-) and methyl (-CH3) groups on the hydrophobic chain is identified and counted, which are recorded as and ;

[0115] Consult the literature or database to obtain the carbon chain length contribution values ​​of methylene and methyl, which are recorded as and , these contribution values ​​reflect the contribution of different types of groups to the length of the hydrophobic chain;

[0116] Among them, when the temperature is higher than 50°C, the carbon chain length contribution value at high temperature is selected;

[0117] When the temperature is lower than or equal to 50°C, the carbon chain length contribution value at room temperature is selected;

[0118] Calculate the initial hydrophobic chain length based on the number of groups and contribution values :

[0119] ;

[0120] Considering that the actual hydrophobic chain may have branched or unsaturated structures, a branch correction factor is introduced , considering the effect of branching on the length of the hydrophobic chain and the unsaturation correction factor , considering the effect of unsaturated bonds on the length of the hydrophobic chain, the corrected length of the hydrophobic chain is ; The value of the correction factor should be estimated or fitted according to the actual molecular structure and system conditions;

[0121] Among them, when the branching degree is greater than D b2 When , select the correction factor for highly branched systems;

[0122] When the branching degree is D b1 To D b2 When the ratio is between 0.01 and 0.02, the correction factor for the moderately branched system is selected;

[0123] When the branching degree is less than D b1 When the correction factor for low-branched or linear systems is selected, D b1 <D b2 ;

[0124] When the number of unsaturated bonds is greater than N b2 When using the correction factor for highly unsaturated systems;

[0125] When the number of unsaturated bonds is N b1 To N b2 When the ratio is between 2 and 5, the correction factor for moderately unsaturated systems is selected, where N b1 <Nb2 ;

[0126] When the number of unsaturated bonds is 0, the correction factor for the saturated system is selected;

[0127] The degree of branching of the hydrophobic chain will affect its extension and conformation in the aqueous medium, and thus affect the actual length of the hydrophobic chain participating in the interfacial behavior. The introduction of the branch correction factor can correct the "shortening effect" of the hydrophobic chain in the aqueous solution caused by the branch, making the calculated hydrophobic chain length more accurate, thereby better describing the behavior of the emulsifier at the interface. The unsaturated double bonds in the hydrophobic chain will increase the rigidity of the chain segment, affecting the conformation and effective length of the hydrophobic chain in the medium. The introduction of the unsaturated correction factor can take into account the influence of the presence of double bonds in the hydrophobic chain on the chain length and conformation, further improving the accuracy of the calculation of the hydrophobic chain length.

[0128] Considering the conformational effect of emulsifier molecules, the conformational factor is introduced , the corrected hydrophobic chain length is corrected twice to obtain the final hydrophobic chain length ;

[0129] Among them, when the temperature is below 20°C, the conformation factor takes the value of 0.8-0.9;

[0130] When the temperature is between 20°C and 70°C, the conformation factor takes the value of 0.95-1.05;

[0131] When the temperature is higher than 70°C, the conformation factor takes a value of 1.1-1.2.

[0132] It should be noted that identifying and counting the number of methylene (-CH2-) and methyl (-CH3) groups on the hydrophobic chain requires professionals to obtain them through chemical structural analysis or mass spectrometry;

[0133] Conformational effects refer to the influence of different possible conformations or configurations of molecules in space on their properties or behaviors. The conformation of a molecule refers to the three-dimensional arrangement of molecules in space. Different conformations may lead to changes in the properties, activity or reaction mode of the molecule. Conformational effects can be determined by factors such as bond angles, bond lengths, and chiral centers within the molecule, and are common in the fields of organic chemistry, biochemistry, and materials science.

[0134] The purpose of introducing conformation factors is to more accurately consider the effect of the conformation of a molecule on its properties. When estimating the length of the hydrophobic chain of an emulsifier, the introduction of conformation factors can take into account the effect of different possible arrangements of the hydrophobic chain on its length. Because the hydrophobic chain of a molecule may present different stereo configurations, such as rotation, twisting or helical, the existence of these conformations will affect the actual length of the hydrophobic chain. The introduction of conformation factors can more accurately reflect the actual situation of the molecule and improve the accuracy and reliability of the calculation results.

[0135] Step S220: obtaining the number of hydrophilic groups by hydrogen nuclear magnetic resonance spectrum;

[0136] The number of hydrophilic groups represents the number of hydrophilic groups in the emulsifier molecule, and the number of hydrophilic groups affects the hydrophilicity of the emulsifier molecule and the solubility in the aqueous phase. More hydrophilic groups mean that the emulsifier molecule has better hydrophilicity, can be better dissolved and propagated in the aqueous phase, and is conducive to the formation of emulsion. But too many hydrophilic groups may also cause the emulsifier to be excessively dissolved in the aqueous phase, and it is difficult to be enriched in the oil-water interface. The number of hydrophilic groups can be obtained by hydrogen nuclear magnetic resonance spectrum, and by analyzing the peak area ratio at different chemical shifts in the hydrogen magnetic resonance spectrum, the ratio of the number of methylene hydrogen atoms on the hydrophobic chain to the number of hydrogen atoms on the hydrophilic group can be obtained, thereby calculating the number of hydrophilic groups. Since obtaining the number of hydrophilic groups by hydrogen nuclear magnetic resonance spectrum is a common means for those skilled in the art, it will not be repeated too much in the present embodiment.

[0137] If the number of hydrophilic groups is 0, a warning message is issued, warning that the molecule may be a pure hydrophobic substance and is not suitable as an emulsifier, and then the process goes to step S280 to generate an emulsifier molecular structure vector;

[0138] If the number of hydrophilic groups is greater than 0, then go to step S230 to obtain the polarity of the hydrophilic groups; the number of hydrophilic groups is an integer greater than or equal to 0;

[0139] Step S230: multiply the Taft polarity constant (Taft stereo polarity constant) of each hydrophilic group by its number in the molecule, and divide the sum of the products by the total number of hydrophilic groups to obtain the polarity of the hydrophilic group.

[0140] The polarity of the hydrophilic group indicates the total polarity of the hydrophilic groups in the emulsifier molecule. Different types of hydrophilic groups have different polarities, which affect the interaction strength between the emulsifier molecule and the water molecule. The polarity of each hydrophilic group can be represented by the Taft polarity constant (σ*), and the total polarity of the emulsifier molecule is the weighted average of the polarities of each hydrophilic group. The more polar the hydrophilic group (such as carboxyl and sulfonic acid groups) is, the stronger the hydrogen bonding effect between the hydrophilic group and the water molecule is, and the better the hydrophilicity of the emulsifier molecule is. However, too high polarity may also cause the emulsifier to be over-dissolved, making it difficult to enrich the interface. Medium polarity (such as hydroxyl) is often the best choice.

[0141] The calculation methods of hydrophilic group polarity include:

[0142] ;

[0143] in, The hydrophilic group is polar. is the Taft polarity constant, which indicates the polarity of each hydrophilic group. is the number of the i-th hydrophilic group, is the number of hydrophilic groups, It is the total number of different types of hydrophilic groups in the emulsifier molecule.

[0144] The Taft polarity constant is an empirical parameter that reflects the polarity of different groups. The Taft polarity constant values ​​of many common groups have been obtained by fitting a large amount of experimental data and are included in chemical handbooks and databases, such as the Hansch constant database and the Taft polarity constant table.

[0145] Step S240: Divide the number of branched carbon atoms by the total number of carbon atoms to obtain a branching degree;

[0146] If the branching degree is within the set range, the process goes to step S250 to obtain the conjugation degree;

[0147] If the degree of branching exceeds the set range, the emulsifier molecule is subjected to branching modification; after the modification is completed, the process goes to step S240 to recalculate the degree of branching and determine whether it is within the set range;

[0148] The degree of branching can be fine-tuned by changing the number and length of branches in the molecule. Generally speaking, a moderate increase in the degree of branching helps improve the emulsification performance of the emulsifier, but too much branching may also cause the molecules to be arranged less tightly at the interface. The degree of branching can be optimized by selectively introducing or removing branches.

[0149] The number of branches indicates the number of branches on the hydrophobic alkyl chain in the emulsifier molecule. The presence of branches affects the conformational flexibility and interface arrangement of the emulsifier molecule. Moderate branches can increase the flexibility and conformational entropy of the emulsifier molecule, which is conducive to enrichment and forming a denser arrangement at the oil-water interface, but too many branches will reduce the tight stacking of the hydrophobic chain, which is not conducive to interface shielding; the degree of branching is the ratio of the number of branch carbon atoms to the total number of carbon atoms, reflecting the degree of branching of the molecule. The more branch carbon atoms there are, the higher the degree of branching.

[0150] Step S250: Dividing the number of conjugated bonds by the total number of bonds to obtain the degree of conjugation;

[0151] If the conjugation degree is within the set range, the process goes to step S260 to obtain a hydrophilic-hydrophobic balance factor;

[0152] If the conjugation degree exceeds the set range, the emulsifier molecule is modified; after the modification is completed, the process goes to step S250 to recalculate the conjugation degree and determine whether it is within the set range;

[0153] Chromophores or auxochromophores with strong light-absorbing ability are introduced for modification. Chromophores and auxochromophores are groups with special electronic structures that can affect the degree of conjugation and light-absorbing properties of molecules by extending the conjugated system, changing the distribution of electron clouds, and forming charge transfer absorption.

[0154] The number of conjugated bonds indicates the number of conjugated double bonds or benzene rings in the emulsifier molecule. The conjugated structure increases the rigidity of the emulsifier molecule and affects its orderly arrangement on the interface. The conjugated structure increases the rigidity of the molecule, allowing the emulsifier molecules to form an orderly and dense arrangement on the interface, thereby improving the thermodynamic stability of the emulsion, but too much conjugation may also reduce the flexibility of the molecule; the degree of conjugation is the ratio of the number of conjugated bonds to the total number of bonds, reflecting the degree of conjugation and rigidity of the molecule. The more conjugated bonds there are, the higher the degree of conjugation of the molecule.

[0155] Step S260: obtaining a hydrophilic-hydrophobic balance factor according to the polarity of the hydrophilic group, the number of the hydrophilic group and the total number of carbon atoms;

[0156] The hydrophilic-hydrophobic balance factor represents the ratio of the relative polarity of the hydrophilic group in the emulsifier molecule to the relative length of the hydrophobic chain, reflecting the hydrophilic-hydrophobic balance of the molecule. The mathematical expression of the hydrophilic-hydrophobic balance factor is:

[0157] ;

[0158] ;

[0159] in, is the hydrophilic-hydrophobic balance factor, The hydrophilic group is polar. is the reference polarity, is the relative length of the hydrophobic chain, is the number of hydrophilic groups, is the total number of carbon atoms. The larger the value, the longer the hydrophobic chain is relative to the total carbon chain, and the stronger the hydrophobicity of the emulsifier molecule is. The smaller the value, the more hydrophilic groups there are in the emulsifier molecule, and the stronger the hydrophilicity. The hydrophilic-hydrophobic balance factor is inversely proportional to the relative length of the hydrophobic chain; increasing the number of hydrophilic groups or using more polar hydrophilic groups will increase the hydrophilic-hydrophobic balance factor.

[0160] If the hydrophilic-hydrophobic balance factor is greater than the third preset value or less than the first preset value, or , indicating that the hydrophilicity and hydrophobicity are seriously unbalanced, a warning signal is issued to warn that the molecule may not be suitable as an emulsifier, and then the process goes to step S280 to generate an emulsifier molecular structure vector; wherein is the first preset value, is the third preset value;

[0161] If the hydrophilic-hydrophobic balance factor is greater than or equal to the second preset value and less than or equal to the third preset value, that is, it means that the emulsifier is more hydrophilic, the process goes to step S270 to obtain the adsorption energy parameter, where is a second preset value;

[0162] If the hydrophilic-hydrophobic balance factor is greater than or equal to the first preset value and less than or equal to the second preset value, that is, the emulsifier is more lipophilic, more hydrophilic groups are introduced to balance its hydrophilicity; after the molecular hydrophilicity is balanced, the process goes to step S260 to calculate the hydrophilic-hydrophobic balance factor again and determine its range; .

[0163] It should be noted that the hydrophilic-hydrophobic balance factor reflects the relative strength and balance state of the hydrophilic and hydrophobic parts of the emulsifier molecules. The hydrophilic-hydrophobic balance factor affects the directional arrangement and aggregation state of the emulsifier molecules at the oil-water interface. A moderate hydrophilic-hydrophobic balance is conducive to the formation of a regular monolayer of molecules at the interface, improving the stability of the interfacial film. However, a balance factor that is too high or too low may lead to uneven arrangement of molecules at the interface, forming a multi-molecular layer or micellar structure, and reducing the stability of the emulsion.

[0164] Step S270: taking the ratio of the adsorption energy of the emulsifier molecule on the oil-water interface to the total internal energy of the emulsifier molecule as an adsorption energy parameter;

[0165] The adsorption energy parameter is the ratio of the adsorption energy of the emulsifier molecule at the oil-water interface to its total internal energy, reflecting the interfacial adsorption tendency of the molecule. The mathematical expression is:

[0166] ;

[0167] in is the adsorption energy parameter, which is a relative quantity. is the total internal energy of the emulsifier molecule, It refers to the adsorption energy of emulsifier molecules on the oil-water interface. is the internal energy of the hydrophobic chain, is the internal energy of the hydrophilic group; Will follow increases with the increase of Will follow and decreases and increases;

[0168] The adsorption energy parameter characterizes the adsorption driving force and adsorption strength of the emulsifier molecules at the oil-water interface. Higher adsorption energy is conducive to the rapid adsorption and arrangement of molecules on the interface, forming a stable interface film. However, too high adsorption energy may also lead to excessive aggregation of molecules on the interface, forming a multi-molecular layer or micellar structure, reducing the stability of the emulsion. At the same time, the adsorption energy also affects the desorption and exchange process of the emulsifier molecules on the interface, thereby affecting the dynamic stability of the emulsion. Therefore, it is necessary to select appropriate adsorption energy parameters according to the temperature, pH and other conditions of the emulsification system.

[0169] The calculation of adsorption energy parameters requires the adsorption energy of molecules at the oil-water interface and the internal energy of the hydrophobic chain and the hydrophilic group ( and ). These energy parameters are usually obtained through molecular dynamics simulation or quantum chemical calculation. In the simulation or calculation process, it is necessary to construct a reasonable molecular model according to the specific structure of the emulsifier molecule, select appropriate force field parameters and solvation model, and set appropriate boundary conditions and sampling methods to obtain reliable energy data. In addition, experimental methods such as isothermal titration calorimetry can also be used to directly measure the adsorption enthalpy change of emulsifier molecules at the interface to verify or correct the calculation results.

[0170] Step S280: The length of the hydrophobic chain, the number of hydrophilic groups, the polarity of the hydrophilic groups, the degree of branching, the degree of conjugation, the hydrophilic-hydrophobic balance factor and the adsorption energy parameters are combined into an emulsifier molecular structure vector.

[0171] The molecular structure vector expression of emulsifier is:

[0172] ;

[0173] in, is the molecular structure vector of the emulsifier, is the length of the hydrophobic chain, is the number of hydrophilic groups, The hydrophilic group is polar. is the degree of branching, is the conjugation degree, is the hydrophilic-hydrophobic balance factor, is the adsorption energy parameter;

[0174] Step S200 digitizes the complex molecular structure of the emulsifier into a structural vector, providing a standardized and quantitative molecular characterization method. The structural vector includes key features such as hydrophobic chain length, number of hydrophilic groups, polarity, degree of branching, degree of conjugation, etc., and comprehensively describes the physical and chemical properties of the emulsifier. It provides theoretical guidance for the rational selection, molecular design and performance optimization of emulsifiers, and improves the quality and stability of emulsions.

[0175] Step S300: obtaining a reactor wall catalytic effect vector according to reactor wall catalytic effect data;

[0176] The reactor wall catalytic effect vector includes six characteristics, namely, wall material composition, wall surface energy, wall specific surface area, wall adsorption capacity, wall reaction rate constant and wall catalytic selectivity.

[0177] Furthermore, if Figure 3 As shown, step S300 of the present application includes:

[0178] Step S310: analyzing the atomic percentage or mass percentage of different elements in the wall material to form the wall material composition characteristics;

[0179] Different elements have different chemical properties and electronic structures, which lead to different ways and strengths of interaction with reactants and intermediates. Differences in wall material composition can affect the mechanism, rate and selectivity of catalytic reactions, and thus affect the efficiency and product quality of the mixed reaction process.

[0180] Wall material composition ,in, is the atomic percentage or mass percentage of each element in the wall material;

[0181] It should be noted that the composition of the wall material reflects the chemical composition of the wall material. Obtaining information on the composition of the wall material can help understand the nature of the wall catalytic effect and provide a theoretical basis and experimental guidance for the design and screening of efficient catalytic materials. By optimizing the composition of the wall material, the activity and selectivity of the catalytic reaction can be improved, the generation of by-products can be reduced, and the economic and environmental benefits of the mixed reaction process can be improved. Characterization methods such as X-ray photoelectron spectroscopy or energy dispersive X-ray spectroscopy can be used to analyze the content and distribution of different elements in the wall material.

[0182] Step S320: measuring the surface energy of the wall material by using a contact angle method, a surface tension method or a solvent extraction method;

[0183] The surface energy of the wall is a macroscopic manifestation of the intermolecular forces on the surface of the material, and is closely related to factors such as the chemical composition, microstructure and electronic properties of the material. The surface energy of the wall will affect the adsorption orientation, coverage and activation state of reactants and intermediates on the wall, and thus affect the rate and selectivity of the catalytic reaction. The surface energy of the wall material can be measured by methods such as the contact angle method, the surface tension method or the solvent extraction method. Exemplarily, the method of obtaining the surface energy of the wall by solvent extraction can be:

[0184] ;

[0185] in, is the wall surface energy, is the solvent polarity parameter corresponding to the critical surface energy, and is an empirical constant;

[0186] It should be noted that the wall surface energy reflects the interaction strength between the wall and the reactants and intermediates, affecting the adsorption and surface reaction process. Obtaining wall surface energy information can help understand the thermodynamic driving force of the wall catalytic effect and provide theoretical guidance for optimizing the surface properties of catalytic materials. By regulating the wall surface energy, the adsorption and activation process of reactants and intermediates can be optimized, the surface reaction can be promoted, and the efficiency and selectivity of the catalytic reaction can be improved. and It can be obtained by consulting the literature or by fitting standard substances, that is, selecting several standard solid materials with known surface energy, testing their interaction with different solvents by solvent extraction, substituting the test data into the formula, and obtaining the best fitting a and e values ​​by nonlinear fitting. The solvent polarity parameters corresponding to the critical surface energy can be directly obtained by consulting the solvent parameter manual.

[0187] Step S330: measuring the wall specific surface area by gas adsorption method;

[0188] The wall specific surface area is closely related to the microstructural characteristics of the material, such as particle size, porosity and surface roughness, and is one of the important indicators of catalytic material performance. The larger the specific surface area, the more active sites on the wall surface are available for reactant adsorption and activation, and the higher the rate and conversion rate of the catalytic reaction. The wall specific surface area can be measured by gas adsorption method.

[0189] Methods for determining wall specific surface area by gas adsorption include:

[0190] Cut or break the wall material sample to be tested into small pieces, place them in a vacuum drying oven, dry them at 80-100°C for several hours, and then cool them to room temperature under a specific atmosphere;

[0191] The dried wall material sample is placed in a closed container of a bubble adsorption device, and the gas is controlled to flow into the container at a constant flow rate;

[0192] Carry out adsorption experiments at different pressures, maintain each pressure point for a certain time until the adsorption reaches equilibrium, and record the adsorption time and air intake at each pressure point, as well as the temperature and pressure changes in the container;

[0193] According to the state equation of the adsorbate gas, calculate the adsorption amount at each pressure point and draw the adsorption isotherm;

[0194] The surface roughness coefficient is obtained by dividing the actual specific surface area of ​​the surface roughness by the theoretical specific surface area when the surface is completely smooth;

[0195] The adsorption isotherm data are multiplied by the surface roughness coefficient to obtain the corrected adsorption isotherm data;

[0196] The adsorption model was used to fit the corrected adsorption isotherm to obtain the specific surface area data.

[0197] Furthermore, the specific atmosphere is a vacuum atmosphere or an inert atmosphere;

[0198] The adsorbate gas is an inert gas, and the inert gas is nitrogen or argon;

[0199] The adsorption model can be a Langmuir model or a BET model;

[0200] It should be noted that, according to the chemical composition and crystal structure of the wall material, the theoretical specific surface area per unit mass or unit volume of the material is obtained by using theoretical calculation or simulation methods (such as molecular dynamics simulation, first principle calculation, etc.); the surface morphology of the wall material is directly observed and analyzed by characterization means such as atomic force microscopy and scanning electron microscopy to obtain quantitative parameters of surface roughness, such as arithmetic mean roughness, root mean square roughness, etc., and then the actual specific surface area of ​​the surface roughness is estimated according to empirical formulas or theoretical models, such as fractal theory;

[0201] It is understandable that obtaining wall specific surface area information can help evaluate the reaction activity and efficiency of catalytic materials and provide a quantitative basis for optimizing the microstructure of catalytic materials. By increasing the wall specific surface area, the utilization efficiency of catalytic materials can be improved, the catalytic reaction can be accelerated, and the energy consumption and cost of the mixed reaction process can be reduced.

[0202] Step S330 can accurately measure the actual specific surface area value of the solid material by standardizing the sample pretreatment, experimental test and data processing, and provide reliable parameter support for subsequent analysis such as surface catalytic behavior. Pretreatment of the sample in a vacuum or inert atmosphere can remove impurity gases and moisture adsorbed on the sample surface to avoid their interference with the gas adsorption test. Controlling experimental conditions such as gas flow rate and pressure, and recording changes in parameters such as temperature and pressure in real time can ensure the accuracy of the experimental data. Considering that the roughness of the surface of the actual solid material will lead to an increase in the specific surface area, the introduction of a surface roughness correction coefficient to correct the measured data can obtain a specific surface area value that is closer to the actual situation.

[0203] Step S340: determining the wall adsorption capacity through an adsorption isotherm or an adsorption kinetic curve;

[0204] The wall adsorption capacity is closely related to factors such as the specific surface area, pore structure and surface functional groups of the wall material. The greater the adsorption capacity, the higher the concentration of reactants and intermediates available for reaction on the wall surface, and the stronger the concentration driving force of the surface reaction, which is conducive to the catalytic reaction. The adsorption capacity of the wall material for reactants and intermediates can be determined by adsorption isotherms or adsorption kinetics curves.

[0205] Exemplarily, the method for calculating the wall adsorption capacity in this embodiment includes:

[0206] ;

[0207] in, is the wall adsorption capacity, is the initial concentration, is the equilibrium concentration, is the volume of the solution, is the sample mass; the initial concentration refers to the concentration of the reactant in the solution at the beginning of the adsorption process, and the equilibrium concentration refers to the concentration of the reactant remaining in the solution after the adsorption process reaches equilibrium;

[0208] Obtaining information on wall adsorption capacity can help us evaluate the adsorption performance and reaction driving force of catalytic materials. By increasing the wall adsorption capacity, we can increase the concentration of reactants and intermediates on the wall, accelerate the surface reaction, and improve the efficiency and rate of the catalytic reaction.

[0209] Step S350: determining the wall reaction rate constant by kinetic experiments or quantum chemical calculations;

[0210] The reaction rate constant is closely related to the chemical composition, electronic structure and surface active sites of the catalytic material. It is one of the core indicators for measuring catalyst performance. The larger the reaction rate constant, the faster the catalytic reaction proceeds, and the higher the efficiency and capacity of the mixed reaction process. The reaction rate constant of wall-catalyzed reactions is usually determined through kinetic experiments or quantum chemical calculations.

[0211] Exemplarily, the method for calculating the wall reaction rate constant in this embodiment includes:

[0212] ;

[0213] in, is the wall reaction rate constant, A is the pre-exponential factor, is the apparent activation energy, R is the ideal gas constant, and T is the reaction temperature. In practical applications, kinetic experiments or computational chemistry methods can be selected according to specific circumstances to obtain the required parameters A, Etc., combined with the reaction temperature T, the wall reaction rate constant can be calculated by substituting it into the formula The numerical value of .

[0214] It is understandable that the wall reaction rate constant reflects the intrinsic rate of the wall catalytic reaction and determines the kinetic characteristics of the catalytic reaction. By measuring and comparing the reaction rate constants of different catalytic materials, the catalytic materials with the highest activity and the best efficiency can be screened out, and the structure-activity relationship between the composition, structure and performance of the catalytic materials can be studied. This not only helps to deeply understand the microscopic mechanism and kinetic characteristics of the wall catalytic reaction, but also provides theoretical guidance and experimental basis for the design and development of new and efficient catalytic materials. By increasing the wall reaction rate constant, the catalytic reaction can be accelerated, the time of the mixed reaction process can be shortened, and the production efficiency and economic benefits can be improved.

[0215] Step S360: The ratio of the target product yield to the sum of all product yields is multiplied by 100% to obtain the wall catalytic selectivity.

[0216] Wall catalytic selectivity is closely related to factors such as the chemical composition, surface structure and reaction conditions of the catalytic material. The higher the catalytic selectivity, the higher the yield and purity of the target product, the less by-products generated, and the better the atomic economy and environmental friendliness of the mixed reaction process.

[0217] Methods for calculating wall catalytic selectivity include:

[0218] ;

[0219] in, is the wall catalytic selectivity, is the target product yield, is the yield of the ith product, is the total number of all products. The higher the wall catalytic selectivity, the higher the yield of the target product.

[0220] Wall catalytic selectivity reflects the selective preference of the wall for specific products. Obtaining wall catalytic selectivity information can help evaluate the chemical selectivity and product quality of catalytic materials. By improving wall catalytic selectivity, the generation of by-products can be reduced, the separation and purification steps of the mixed reaction process can be simplified, and the production cost and environmental load can be reduced.

[0221] Step S370: The wall material composition, wall surface energy, wall specific surface area, wall adsorption capacity, wall reaction rate constant and wall catalytic selectivity are combined into a reactor wall catalytic effect vector;

[0222] The characteristic vector expression of the wall catalytic effect can be:

[0223] ;

[0224] in, is the characteristic vector of the catalytic effect of the reactor wall, The wall material is composed of is the wall surface energy, is the wall specific surface area, is the wall adsorption capacity, is the wall reaction rate constant, It is wall catalytic selectivity.

[0225] Step S300 quantitatively characterizes key parameters such as reactor wall material composition, surface energy, specific surface area, adsorption capacity, reaction rate constant and catalytic selectivity, reveals the catalytic effect mechanism of the reactor wall on emulsification and mixing reactions, provides a basis for improving reaction efficiency and selectivity, assists in the rational selection and optimal design of reactor materials, reduces catalyst usage, and reduces side reactions and waste emissions.

[0226] Step S400: predicting the emulsion particle size distribution, product composition and conversion rate in the emulsification and mixing reaction process according to the first relevant process optimization parameter data, the emulsifier molecular structure vector, the wall catalytic effect characteristic vector and the pre-built emulsification and mixing reaction optimization model;

[0227] The method for constructing the emulsification and mixing reaction optimization model comprises:

[0228] Obtain a sample data set, the sample data set including historical first relevant process optimization parameter data, historical emulsifier dosage, historical emulsifier molecular structure vector, historical wall catalytic effect characteristic vector, historical emulsion particle size distribution, historical product composition and historical conversion rate;

[0229] The sample data set is divided into a sample training set, a sample verification set and a sample test set, and an optimization neural network is constructed. The historical first relevant process optimization parameter data, historical emulsifier dosage, historical emulsifier molecular structure vector and historical wall catalytic effect feature vector in the sample training set are used as input data of the optimization neural network. The historical emulsion particle size distribution, historical product composition and historical conversion rate in the sample training set are used as output data of the optimization neural network. The optimization neural network is trained to obtain an initial optimization neural network for predicting emulsion particle size distribution, product composition and conversion rate in the emulsification and mixing reaction process. The sample verification set is used to adjust the parameters of the initial optimization neural network, and the output is an optimization neural network that satisfies the preset error value as the emulsification and mixing reaction optimization model. Finally, the sample test set is used to test the emulsification and mixing reaction optimization model.

[0230] The particle size distribution of the emulsion can be measured using equipment such as dynamic light scattering and laser diffraction particle size analyzer.

[0231] Product composition and conversion rate can be determined using analytical techniques such as gas chromatography, high performance liquid chromatography, and nuclear magnetic resonance.

[0232] Step S400 fuses multi-source heterogeneous data such as emulsifier structure vectors, reactor wall catalytic effect vectors, and process condition parameters to construct high-dimensional feature input, and uses machine learning algorithms to automatically capture nonlinear mapping relationships in high-dimensional data to establish an accurate process model. The model can intelligently predict key performance indicators such as particle size distribution, product composition, and conversion rate, guide process control, analyze the model to explain the impact of important features on performance, and explore potential paths for process optimization, greatly improve optimization efficiency, reduce trial and error costs, and realize intelligent manufacturing of emulsification and mixing reaction processes.

[0233] Step S500: formulating a process adjustment strategy according to the predicted emulsion particle size distribution;

[0234] The method for formulating a process adjustment strategy based on the predicted emulsion particle size distribution includes:

[0235] If the predicted particle size distribution of the emulsion meets the preset particle size distribution requirement, the process goes to step S600 to formulate a process adjustment strategy according to the predicted product composition;

[0236] If the predicted particle size distribution of the emulsion does not meet the preset particle size distribution requirement, the amount or type of the emulsifier is adjusted, and then the process goes to step S120 to re-acquire the second relevant process optimization parameter data of the emulsification and mixing reaction process;

[0237] It is understandable that the particle size distribution of the emulsion can be effectively controlled by adjusting the amount or type of emulsifier. The role of the emulsifier is to reduce the interfacial tension between the monomer / prepolymer and water, so that it forms stable emulsion droplets, and acts as a stabilizer in the subsequent polymerization process. Properly increasing the amount of emulsifier can disperse more monomers / prepolymers to form small droplets, thereby obtaining a smaller particle size; conversely, reducing the amount of emulsifier will increase the particle size. Different types of emulsifiers have different abilities to reduce interfacial tension and stabilize droplets due to differences in molecular structure, which will also affect the final particle size distribution.

[0238] Step S600: formulating a process adjustment strategy based on the predicted product composition;

[0239] The method for formulating a process adjustment strategy according to the predicted product composition includes:

[0240] If the predicted product composition meets the preset product composition requirements, then go to step S700 to formulate a process adjustment strategy based on the predicted conversion rate;

[0241] If the predicted product composition does not meet the preset product composition requirements, the ratio of the monomer / prepolymer or the type and concentration of the initiator are adjusted, and then the process goes to step S110 to re-acquire the first relevant process optimization parameter data of the emulsification and mixing reaction process;

[0242] It is understandable that the composition and structure of the product can be effectively controlled by adjusting the ratio of monomers / prepolymers or the type and concentration of initiators. Different monomers / prepolymers have different reactivity and polymerization behavior. Changing their ratio will affect the composition, molecular weight and performance of the final product. The type and concentration of the initiator determine the start and rate of the polymerization reaction, thereby affecting the molecular weight, molecular weight distribution, etc. of the product. Reasonable adjustment of these parameters can accurately control the product composition to meet different application requirements.

[0243] Step S700: formulating a process adjustment strategy according to the predicted conversion rate;

[0244] The method for formulating a process adjustment strategy based on the predicted conversion rate includes:

[0245] If the predicted conversion rate reaches the preset conversion rate threshold, the process goes to step S800 to record the current process parameters as the optimal process parameters and enter the subsequent process;

[0246] If the predicted conversion rate does not reach the preset conversion rate threshold, the reaction temperature, reaction time or shear rate is adjusted, and then the process goes to step S110 to re-acquire the first relevant process optimization parameter data of the emulsification and mixing reaction process.

[0247] It is understandable that the conversion rate can be optimized by adjusting the reaction temperature, reaction time or shear rate. Temperature is a key factor affecting the reaction rate. Properly increasing the temperature can accelerate the polymerization of monomers / prepolymers, but too high a temperature will lead to serious chain termination and mass transfer and diffusion difficulties; the reaction time determines the extent of the reaction. Prolonging the time is conducive to improving the conversion rate, but too long a time will waste energy and induce side reactions; stirring and shearing not only improves the mass transfer process, but also affects the particle size and particle size distribution. The best conversion rate can be achieved by comprehensively balancing these factors.

[0248] Step S800: Record the current process parameters as the optimal process parameters and proceed to the subsequent process.

[0249] Through the intelligent optimization of the above steps S500-S700, it is possible to achieve precise control of key quality indicators such as particle size distribution, product composition and conversion rate to ensure that the product meets the design requirements. When all these indicators reach the optimal level, the corresponding process parameter combination is the optimal process parameter, which can be fixed for mass production. In the future, you only need to operate according to this process parameter to continuously obtain the ideal product, which greatly reduces the cycle and cost of process development.

[0250] Example 2

[0251] This embodiment provides an online reaction monitoring device for water-based polyacrylate and polyurethane emulsion on the basis of embodiment 1, such as Figure 4 As shown, including:

[0252] Process optimization parameter acquisition module: used to obtain process optimization parameter data of the emulsification and mixing reaction process, wherein the process optimization parameter data includes first related process optimization parameter data, second related process optimization parameter data and third related process optimization parameter data, wherein the second related process optimization parameter data includes emulsifier dosage and emulsifier molecular structure data, and the third related process optimization parameter data includes reactor wall catalytic effect data;

[0253] Feature conversion module: used to convert emulsifier molecular structure data into emulsifier molecular structure vectors, and to convert reactor wall catalytic effect data into reactor wall catalytic effect vectors;

[0254] Product prediction module: used to obtain sample data sets, and predict the emulsion particle size distribution, product composition and conversion rate in the emulsification and mixing reaction process based on the sample data sets and the pre-built emulsification and mixing reaction optimization model;

[0255] Emulsification and mixing reaction control module: used to formulate process adjustment strategies based on the emulsion particle size distribution, product composition and conversion rate during the emulsification and mixing reaction process.

[0256] Example 3

[0257] This embodiment discloses an electronic device, including a memory, a central processing unit, and a computer program stored in the memory and executable on the central processing unit. When the central processing unit executes the computer program, the above-mentioned online reaction monitoring method for water-based polyacrylate and polyurethane emulsion is implemented.

[0258] Since the electronic device introduced in this embodiment is an electronic device used to implement the online reaction monitoring method of water-based polyacrylate and polyurethane emulsion in the embodiment of the present application, based on the online reaction monitoring method of water-based polyacrylate and polyurethane emulsion introduced in the embodiment of the present application, the technical personnel of the field can understand the specific implementation of the electronic device of the present embodiment and its various variations, so how the electronic device implements the method in the embodiment of the present application is not described in detail here. As long as the technical personnel of the field implement the electronic device used in the online reaction monitoring method of water-based polyacrylate and polyurethane emulsion in the embodiment of the present application, it belongs to the scope of protection of the present application.

[0259] Example 4

[0260] This embodiment discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed, the above-mentioned online reaction monitoring method of waterborne polyacrylate and polyurethane emulsion is implemented.

[0261] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters, weights and thresholds in the formula are set by technicians in this field according to actual conditions.

[0262] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired network or a wireless network. The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD) or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0263] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0264] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0265] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic, for example, the division of the units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0266] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0267] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0268] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0269] Finally: 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 principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for monitoring the online reaction of waterborne polyacrylate and polyurethane emulsion, characterized in that: include: Step S100: obtaining process optimization parameter data of the emulsification and mixing reaction process; The step S100 includes: Step S110: Acquire first relevant process optimization parameter data of the emulsification and mixing reaction process; Step S120: obtaining second relevant process optimization parameter data of the emulsification and mixing reaction process, wherein the second relevant process optimization parameter data includes emulsifier dosage and emulsifier molecular structure data; Step S130: obtaining third relevant process optimization parameter data of the emulsification and mixing reaction process, wherein the third relevant process optimization parameter data includes reactor wall catalytic effect data; Step S200: obtaining an emulsifier molecular structure vector according to the emulsifier molecular structure data, wherein the emulsifier molecular structure vector includes seven features, namely, hydrophobic chain length, hydrophilic group number, hydrophilic group polarity, branching degree, conjugation degree, hydrophilic-hydrophobic balance factor and adsorption energy parameter; Step S300: obtaining a reactor wall catalytic effect vector according to the reactor wall catalytic effect data, wherein the reactor wall catalytic effect vector includes six characteristics, namely, wall material composition, wall surface energy, wall specific surface area, wall adsorption capacity, wall reaction rate constant and wall catalytic selectivity; Step S400: predicting the emulsion particle size distribution, product composition and conversion rate in the emulsification and mixing reaction process according to the first relevant process optimization parameter data, the emulsifier molecular structure vector, the wall catalytic effect characteristic vector and the pre-built emulsification and mixing reaction optimization model; Step S500: formulating a process adjustment strategy according to the predicted emulsion particle size distribution; Step S600: formulating a process adjustment strategy based on the predicted product composition; Step S700: formulating a process adjustment strategy according to the predicted conversion rate; Step S800: Record the current process parameters as the optimal process parameters and enter the subsequent process; The method for formulating a process adjustment strategy based on the predicted emulsion particle size distribution includes: If the predicted particle size distribution of the emulsion meets the preset particle size distribution requirement, then go to step S600 to formulate a process adjustment strategy based on the predicted product composition; if the predicted particle size distribution of the emulsion does not meet the preset particle size distribution requirement, then adjust the amount or type of the emulsifier, then go to step S120 to re-acquire the second relevant process optimization parameter data of the emulsification and mixing reaction process; The method for formulating a process adjustment strategy according to the predicted product composition includes: If the predicted product composition meets the preset product composition requirements, then go to step S700 to formulate a process adjustment strategy based on the predicted conversion rate; if the predicted product composition does not meet the preset product composition requirements, then adjust the monomer / prepolymer ratio or the type and concentration of the initiator, and then go to step S110 to re-acquire the first relevant process optimization parameter data of the emulsification and mixing reaction process; The method for formulating a process adjustment strategy based on the predicted conversion rate includes: If the predicted conversion rate reaches the preset conversion rate threshold, go to step S800, record the current process parameters as the optimal process parameters, and enter the subsequent process; if the predicted conversion rate does not reach the preset conversion rate threshold, adjust the reaction temperature, reaction time or shear rate, and then go to step S110 to re-acquire the first relevant process optimization parameter data of the emulsification and mixing reaction process.

2. The method for online reaction monitoring of waterborne polyacrylate and polyurethane emulsion according to claim 1, characterized in that: The first relevant process optimization parameter data include temperature, shear rate, emulsifier dosage, monomer and prepolymer types and ratios, initiator types and concentrations, and reaction time; the emulsification and mixing reactions are carried out in the same reactor.

3. The method for online reaction monitoring of waterborne polyacrylate and polyurethane emulsion according to claim 1, characterized in that: The step S200 includes: Step S210: using the group contribution method to obtain the hydrophobic chain length; Step S220: obtaining the number of hydrophilic groups by hydrogen nuclear magnetic resonance spectrum; The number of hydrophilic groups, if the number of hydrophilic groups is 0, a warning message is issued, warning that the molecule may be a pure hydrophobic substance and is not suitable as an emulsifier, and then the step S280 is turned to generate an emulsifier molecular structure vector; if the number of hydrophilic groups is greater than 0, the step S230 is turned to obtain the polarity of the hydrophilic groups; the number of hydrophilic groups is an integer greater than or equal to 0; Step S230: multiplying the Taft polarity constant of each hydrophilic group by its number in the molecule, and dividing the sum of the products by the total number of hydrophilic groups to obtain the polarity of the hydrophilic group; Step S240: Divide the number of branched carbon atoms by the total number of carbon atoms to obtain a branching degree; If the degree of branching is within the set range, then go to step S250 to obtain the degree of conjugation; if the degree of branching exceeds the set range, then perform branching modification on the emulsifier molecule; after the modification is completed, go to step S240 to recalculate the degree of branching and determine whether it is within the set range; Step S250: Dividing the number of conjugated bonds by the total number of bonds to obtain the degree of conjugation; If the conjugation degree is within the set range, the process proceeds to step S260 to obtain the hydrophilic-hydrophobic balance factor; if the conjugation degree exceeds the set range, the emulsifier molecule is modified; after the modification is completed, the process proceeds to step S250 to recalculate the conjugation degree and determine whether it is within the set range; Step S260: obtaining a hydrophilic-hydrophobic balance factor according to the relative length of the hydrophobic chain and the total number of carbon atoms; The hydrophilic-hydrophobic balance factor, if the hydrophilic-hydrophobic balance factor is greater than the third preset value or less than the first preset value, it indicates that the hydrophilicity and hydrophobicity of the emulsifier molecule are seriously unbalanced, and a warning signal is issued to warn that the molecule may not be suitable as an emulsifier, and then the process goes to step S280 to generate an emulsifier molecular structure vector; If the hydrophilic-hydrophobic balance factor is greater than or equal to the second preset value and less than or equal to the third preset value, it means that the emulsifier is more hydrophilic, and the process goes to step S270 to obtain an adsorption energy parameter; If the hydrophilic-hydrophobic balance factor is greater than or equal to the first preset value and less than or equal to the second preset value, it means that the emulsifier is more lipophilic, and more hydrophilic groups are introduced to balance its hydrophilicity; after the molecular hydrophilicity is balanced, go to step S260 to calculate the hydrophilic-hydrophobic balance factor again and determine its range; wherein the first preset value is less than the second preset value, and the second preset value is less than the third preset value; Step S270: taking the ratio of the adsorption energy of the emulsifier molecule on the oil-water interface to the total internal energy of the emulsifier molecule as an adsorption energy parameter; Step S280: The length of the hydrophobic chain, the number of hydrophilic groups, the polarity of the hydrophilic groups, the degree of branching, the degree of conjugation, the hydrophilic-hydrophobic balance factor and the adsorption energy parameters are combined into an emulsifier molecular structure vector.

4. The method for online reaction monitoring of waterborne polyacrylate and polyurethane emulsion according to claim 3, characterized in that: The step S210 includes: Step S211: identifying and counting the number of methylene and methyl groups on the hydrophobic chain according to the chemical structure formula of the emulsifier molecule; Step S212: Obtaining carbon chain length contribution values ​​of methylene and methyl groups; The carbon chain length contribution value, when the temperature is higher than 50°C, uses the carbon chain length contribution value at high temperature; when the temperature is lower than or equal to 50°C, uses the carbon chain length contribution value at room temperature; Step S213: multiplying the number of methylene groups by the carbon chain length contribution value of the methylene group to obtain the methylene chain length; multiplying the number of methyl groups by the carbon chain length contribution value of the methyl group to obtain the methyl chain length; summing the methylene chain length and the methyl chain length to obtain the initial hydrophobic chain length; Step S214: introducing a branch chain correction factor and an unsaturation correction factor to correct the initial hydrophobic chain length to obtain a corrected hydrophobic chain length; The branch correction factor is when the degree of branching is greater than D b2 When the degree of branching is D b1 To D b2 When the degree of branching is less than D b1 When the correction factor for low-branched or linear systems is selected, D b1 <D b2 ; The unsaturation correction factor is when the number of unsaturated bonds is greater than N b2 When the number of unsaturated bonds is N, the correction factor for highly unsaturated systems is selected; when the number of unsaturated bonds is N b1 To N b2 When the number of unsaturated bonds is between 0 and 1, the correction factor for moderately unsaturated systems is used. When the number of unsaturated bonds is 0, the correction factor for saturated systems is used. b1 <N b2 ; Step S215: introducing a conformation factor to perform a secondary correction on the corrected hydrophobic chain length to obtain a final hydrophobic chain length; The conformation factor is 0.8-0.9 when the temperature is below 20°C; 0.95-1.05 when the temperature is between 20°C and 70°C; and 1.1-1.2 when the temperature is above 70°C.

5. The method for online reaction monitoring of waterborne polyacrylate and polyurethane emulsion according to claim 3, characterized in that: The relative length of the hydrophobic chain is the ratio of the number of carbon atoms in the hydrophobic chain to the total number of carbon atoms, and the number of carbon atoms in the hydrophobic chain is obtained by subtracting the number of hydrophilic groups from the total number of carbon atoms; The adsorption energy parameter is a relative quantity, and the total internal energy of the emulsifier molecule is the sum of the internal energy of the hydrophobic chain and the internal energy of the hydrophilic group.

6. The method for online reaction monitoring of waterborne polyacrylate and polyurethane emulsion according to claim 1, characterized in that: The step S300 includes: Step S310: analyzing the atomic percentage or mass percentage of different elements in the wall material to form the wall material composition characteristics; Step S320: measuring the surface energy of the wall material by using a contact angle method, a surface tension method or a solvent extraction method; Step S330: measuring the wall specific surface area by gas adsorption method; Step S340: determining the wall adsorption capacity through an adsorption isotherm or an adsorption kinetic curve; Step S350: determining the wall reaction rate constant by kinetic experiments or quantum chemical calculations; Step S360: multiplying the ratio of the target product yield to the sum of all product yields by 100% to obtain the wall catalytic selectivity; Step S370: The wall material composition, wall surface energy, wall specific surface area, wall adsorption capacity, wall reaction rate constant and wall catalytic selectivity are combined to form a reactor wall catalytic effect vector.

7. The method for online reaction monitoring of waterborne polyacrylate and polyurethane emulsion according to claim 6, characterized in that: The method for measuring the wall specific surface area by gas adsorption method comprises: Cut or break the wall material sample to be tested into small pieces, place them in a vacuum drying oven, dry them at 80-100°C for several hours, and then cool them to room temperature under a specific atmosphere; The dried wall material sample is placed in a closed container of a bubble adsorption device, and the gas is controlled to flow into the container at a constant flow rate; Carry out adsorption experiments at different pressures, maintain each pressure point for a certain time until the adsorption reaches equilibrium, and record the adsorption time and air intake at each pressure point, as well as the temperature and pressure changes in the container; According to the state equation of the adsorbate gas, calculate the adsorption amount at each pressure point and draw the adsorption isotherm; The surface roughness coefficient is obtained by dividing the actual specific surface area of ​​the surface roughness by the theoretical specific surface area when the surface is completely smooth; The adsorption isotherm data are multiplied by the surface roughness coefficient to obtain the corrected adsorption isotherm data; The adsorption model was used to fit the corrected adsorption isotherm to obtain the specific surface area data; The specific atmosphere is a vacuum atmosphere or an inert atmosphere; The adsorbate gas is an inert gas; the inert gas is nitrogen or argon; The adsorption model is one of the following models: Langmuir model and BET model.

8. An on-line reaction monitoring device for waterborne polyacrylate and polyurethane emulsion, which is used to implement the on-line reaction monitoring method for waterborne polyacrylate and polyurethane emulsion according to any one of claims 1 to 7, characterized in that: include: Process optimization parameter acquisition module: used to obtain process optimization parameter data of the emulsification and mixing reaction process, wherein the process optimization parameter data includes first related process optimization parameter data, second related process optimization parameter data and third related process optimization parameter data, wherein the second related process optimization parameter data includes emulsifier dosage and emulsifier molecular structure data, and the third related process optimization parameter data includes reactor wall catalytic effect data; Feature conversion module: used to convert emulsifier molecular structure data into emulsifier molecular structure vectors, and to convert reactor wall catalytic effect data into reactor wall catalytic effect vectors; Product prediction module: used to obtain sample data sets, and predict the emulsion particle size distribution, product composition and conversion rate in the emulsification and mixing reaction process based on the sample data sets and the pre-built emulsification and mixing reaction optimization model; Emulsification and mixing reaction control module: used to formulate process adjustment strategies based on the emulsion particle size distribution, product composition and conversion rate during the emulsification and mixing reaction process.

9. An electronic device comprising a memory, a central processing unit, and a computer program stored in the memory and executable on the central processing unit, characterized in that: When the central processing unit executes the computer program, the online reaction monitoring method of waterborne polyacrylate and polyurethane emulsion described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the method for online reaction monitoring of waterborne polyacrylate and polyurethane emulsion according to any one of claims 1 to 7 is implemented.

Images