Quantitative analysis method for polarity of pentaerythritol ester molecules

Abstract

The invention provides a quantitative analysis method for the polarity of pentaerythritol ester molecules, and belongs to the technical field of synthetic ester base oil, the method comprises the following steps: constructing a pentaerythritol ester molecule polarity index prediction model; training the pentaerythritol ester molecule polarity index prediction model by using the pentaerythritol ester data of the determined molecular structure to obtain a trained pentaerythritol ester molecule polarity index prediction model; and predicting the molecular polarity index of the pentaerythritol ester by using the trained pentaerythritol ester molecular polarity index prediction model, and quantitatively evaluating the molecular polarity of the pentaerythritol ester. According to the method, a pentaerythritol ester molecular polarity index prediction model is constructed, and the molecular polarity index is used as an evaluation index to quantitatively analyze the molecular polarity of pentaerythritol ester; the purpose of rapidly and quantitatively analyzing the polarity of the pentaerythritol ester is achieved, formulation of a pentaerythritol ester research and development scheme and design of an oil product formula can be remarkably accelerated, manpower and material resources are saved, the research and development period is shortened, and the research and development cost is reduced.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of synthetic ester base oil, and particularly relates to a method for quantitatively analyzing the polarity of pentaerythritol ester molecules. BACKGROUND

[0002] Synthetic ester base oil belongs to V-class base oil according to the classification of the American Petroleum Institute (API), and becomes an important synthetic base oil at present due to its excellent lubricity, viscosity-temperature performance, thermal oxidation stability, low volatility, additive receptivity, biodegradability, low toxicity, and renewable raw materials. Among them, pentaerythritol ester is the most important and most widely used product in synthetic ester base oil due to its strong controllability of molecular structure. By reasonably selecting the carbon chain length and isomerization degree of fatty acids, pentaerythritol ester with different viscosity grades, excellent comprehensive performance, and meeting the lubrication requirements of different working conditions can be obtained. Pentaerythritol ester has stronger polarity than traditional mineral base oil due to the large number of ester functional groups in its structure, which on the one hand improves its adsorption capacity on the metal friction surface, thereby giving it better lubricity, and on the other hand helps to improve the solubility of functional additives in mineral oil or poly-alpha-olefin. Therefore, it has important application value in semi-synthetic or fully synthetic oil products. More importantly, the polarity of pentaerythritol ester makes it have good phase solubility with hydrofluorocarbon refrigerants, and has become the main base oil suitable for hydrofluorocarbon refrigerant chillers. On the other hand, the polarity of pentaerythritol ester also has a certain negative effect on its application. The main problem is that the polarity makes it have a strong swelling effect on the large number of rubber seals in the current lubricating oil system, thereby affecting the mechanical properties of rubber, and there is a risk of failure of the rubber seal and oil leakage. Therefore, a large number of rubber compatibility tests are often required before it can be applied. Similarly, although the polarity of pentaerythritol ester makes it have a certain phase solubility with hydrofluorocarbon refrigerants, it is also closely related to its molecular structure composition. Therefore, a pentaerythritol ester molecule with a specific polarity is required to make it have suitable phase solubility with the corresponding refrigerant, and a large number of experiments are also required for screening. As can be seen, the polarity of pentaerythritol ester is closely related to its application, but the polarity of pentaerythritol ester has been a macroscopic and vague concept for a long time, and has not been accurately quantified. How to analyze the polarity of pentaerythritol ester is still a difficult problem for many lubricating technology workers.

[0003] Currently, some related technologies have explored the polarity analysis of pentaerythritol esters. Van der Waal, G. first proposed using the non-polarity index (NPI) to measure the polarity of synthetic ester base oil, and the calculation formula is: NPI = (total number of carbon atoms x average molecular weight) / (number of ester groups x 100). Among them, the total number of carbon atoms is the total number of carbon atoms in the synthetic ester molecule, the average molecular weight is the average molecular weight of the synthetic ester, and the number of ester groups is the number of ester functional groups contained in the synthetic ester. The larger the non-polarity index, the smaller the polarity of the synthetic ester base oil. As can be seen from the calculation formula, the non-polarity index is still a macro and rough algorithm. The non-polarity index can roughly judge the polarity of the synthetic ester base oil, but the total number of carbon atoms, the average molecular weight, and the number of ester groups cannot reflect the specific structural differences of the synthetic ester, such as the influence of isomerization degree on polarity. Therefore, it can only be used for macro comparison of the polarity of synthetic esters with large differences in molecular structure or different types. For synthetic esters with similar total number of carbon atoms, average molecular weight, and number of ester groups but completely different isomerization degrees, it is impossible to distinguish and judge their polarity. Saint, V. et al. collected empirical data on the polarity of organic solvents to study the polarity of organic solvents, then based on the SMILES string representation of the organic solvents, used the open source toolkit Mordred to calculate the molecular descriptors, and then used the random forest machine learning algorithm to screen the key descriptors affecting the polarity of the organic solvent molecules and the molecular polarity to build a prediction model for predicting the empirical polarity of various organic solvents. However, this method can only be used for small-molecule organic solvents with simple structure, and it cannot be used for pentaerythritol esters with complex conformation and large molecular weight. In addition, this technology correlates molecular descriptors with molecular polarity to build a prediction model, rather than correlating molecular substructure or fine structure descriptors with molecular polarity, and cannot obtain knowledge about the influence of molecular substructure or fine structure properties on molecular polarity. SUMMARY

[0004] To solve the above problems of the prior art, the present application provides a quantitative analysis method for the molecular polarity of pentaerythritol esters to overcome the shortcomings of the prior art that are macro and rough and cannot reflect the influence of the specific structure of pentaerythritol esters on polarity.

[0005] To achieve the above-mentioned purpose, the present application provides a quantitative analysis method for the molecular polarity of pentaerythritol esters, comprising the following steps:

[0006] Step S1: building a pentaerythritol ester molecular polarity index prediction model;

[0007] Step S2: training the pentaerythritol ester molecular polarity index prediction model using pentaerythritol ester data that determines the molecular structure to obtain a trained pentaerythritol ester molecular polarity index prediction model;

[0008] Step S3: predicting the molecular polarity index of the pentaerythritol ester using the trained pentaerythritol ester molecular polarity index prediction model, and quantitatively evaluating the molecular polarity of the pentaerythritol ester.

[0009] Further, the step S2 further comprises: verifying the accuracy of the trained pentaerythritol ester molecular polarity index prediction model.

[0010] Further, the step S2 comprises:

[0011] Step S21: obtaining pentaerythritol ester data with a determined molecular structure;

[0012] Step S22: dividing the pentaerythritol ester data with a determined molecular structure into a training set and a verification set;

[0013] Step S23: training the pentaerythritol ester molecular polarity index prediction model using the training set;

[0014] Step S24: verifying the accuracy of the trained pentaerythritol ester molecular polarity index prediction model using the verification set, and if the accuracy does not meet the preset standard, jumping to step S23 until the accuracy meets the preset standard, and obtaining the trained pentaerythritol ester molecular polarity index prediction model.

[0015] Further, the pentaerythritol ester data with a determined molecular structure comprises: the molecular polarity index of the pentaerythritol ester with a determined molecular structure, and the quantum chemical parameter value of the corresponding carboxylic acid constituting the pentaerythritol ester with a determined molecular structure.

[0016] Further, the quantum chemical parameter value of the corresponding carboxylic acid comprises: the molecular structure, dipole moment, and polarizability of the carboxylic acid.

[0017] Further, the indicators of the preset standard comprise: the goodness of fit and the average absolute error of the predicted value of the molecular polarity index and the actual value of the molecular polarity index.

[0018] Further, the preset standard is that when the goodness of fit value of the predicted value of the molecular polarity index and the actual value of the molecular polarity index is the highest, the pentaerythritol ester molecular polarity index prediction model is taken as the final pentaerythritol ester molecular polarity index prediction model.

[0019] Further, the preset standard is that when the goodness of fit value of the predicted value of the molecular polarity index and the actual value of the molecular polarity index is in a preset interval, the pentaerythritol ester molecular polarity index prediction model is taken as the final pentaerythritol ester molecular polarity index prediction model.

[0020] Furthermore, the preset standard also includes a pentaerythritol ester molecular polarity index prediction model when the mean absolute error is less than a preset value, which is used as the final pentaerythritol ester molecular polarity index prediction model.

[0021] Furthermore, the number of training set samples and the number of validation set samples have a preset ratio, and the number of training set samples is greater than the number of validation set samples.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] This invention constructs a molecular polarity index prediction model for pentaerythritol esters, using the molecular polarity index as an evaluation index to quantitatively analyze the molecular polarity of pentaerythritol esters.

[0024] Using a limited number of pentaerythritol ester samples, the pentaerythritol ester molecular polarity index prediction model was trained and validated based on the quantum chemical parameter values ​​of the carboxylic acid that constitutes pentaerythritol ester and the molecular polarity index of pentaerythritol ester, thus obtaining the final pentaerythritol ester molecular polarity index prediction model.

[0025] This final pentaerythritol ester molecular polarity index prediction model can be applied to predict the molecular polarity index of pentaerythritol esters with uncertain molecular structures; or to quickly calculate the molecular polarity index of pentaerythritol esters with known molecular structures.

[0026] This achieves the goal of rapid quantitative analysis of pentaerythritol ester polarity, overcoming the shortcomings of existing technologies that are macroscopic and crude, making it difficult to accurately quantify the molecular polarity of pentaerythritol ester and failing to reflect the influence of the specific structure of pentaerythritol ester on polarity. It helps to reduce the workload of artificial synthesis and testing, can significantly accelerate the formulation of pentaerythritol ester R&D plans and the design of oil formulations, save manpower and resources, shorten the R&D cycle, and reduce R&D costs. Attached Figure Description

[0027] Figure 1 This is a flowchart of one embodiment of the present invention;

[0028] Figure 2 This is a schematic diagram of the molecular structure of pentaerythritol ester according to an embodiment of the present invention. In the diagram, acid1, acid2, acid3, and acid4 represent different carboxylic acid groups, and R... 1 R 2 R 2 R 4 Alkyl carbon chains representing different carboxylic acid groups;

[0029] Figure 3 This is a graph showing the linear correlation between the predicted and actual values ​​of a molecular polarity prediction model according to an embodiment of the present invention on the training set.

[0030] Figure 4 This is a graph showing the linear correlation between the predicted and actual values ​​of a molecular polarity prediction model according to an embodiment of the present invention on a test set. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0033] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0034] Furthermore, the use of "and / or" or "and / or" throughout the text implies three parallel solutions. For example, "A and / or B" includes solution A, solution B, or a solution that simultaneously satisfies both A and B. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0035] To quantify molecular polarity, Tian Lu et al. first proposed and defined the parameter of the "Molecular Polarity Index" (MPI), the calculation formula of which is as follows:

[0036] MPI=(1 / A)∫∫S∣V(r)∣dS

[0037] Where A is the van der Waals surface area, V(r) is the molecular electrostatic potential, and S is the molecular surface area. Based on this, the molecular polarity indices of representative molecules such as ethane, ethylene, benzene, and cyclic carbon

[18] were calculated to be 2.6, 6.7, 8.4, and 2.6 kcal / mol, respectively. It is believed that the molecular polarity index is a reliable index for measuring molecular polarity, and the larger the index, the higher the polarity.

[0038] However, current calculations of the molecular polarity index (MPI) are limited to small organic molecules with simple structures, and remain difficult for pentaerythritol esters with complex conformations and large molecular weights. Therefore, it is of great significance to find a convenient way to analyze and quantify the polarity of pentaerythritol esters based on their structural composition information, providing convenient and accurate guidance for the development of pentaerythritol ester research and development plans and oil formulation design, reducing a large amount of trial and error work, and thus improving research and development efficiency.

[0039] This invention provides a method for the quantitative analysis of the polarity of pentaerythritol ester molecules, which can quickly and quantitatively analyze the polarity of pentaerythritol esters. In order to better understand the purpose, structure and function of this invention, a detailed description is given below with reference to the accompanying drawings.

[0040] Example 1

[0041] like Figure 1 As shown in this embodiment, the present invention provides a method for quantitatively analyzing the polarity of pentaerythritol ester molecules, comprising the following steps:

[0042] Step S1: Construct a model for predicting the polarity index of pentaerythritol ester molecules;

[0043] Step S2: Use the pentaerythritol ester data with determined molecular structure to train the pentaerythritol ester molecular polarity index prediction model to obtain the trained pentaerythritol ester molecular polarity index prediction model.

[0044] Step S3: Using a trained pentaerythritol ester molecular polarity index prediction model, predict the molecular polarity index of pentaerythritol ester and quantitatively evaluate the molecular polarity of pentaerythritol ester.

[0045] Furthermore, step S2 also includes: verifying the accuracy of the trained pentaerythritol ester molecular polarity index prediction model.

[0046] Furthermore, step S2 includes:

[0047] Step S21: Obtain data on pentaerythritol esters with determined molecular structures;

[0048] Step S22: Divide the pentaerythritol ester data with determined molecular structure into a training set and a validation set;

[0049] Step S23: Train the pentaerythritol ester molecular polarity index prediction model using the training set;

[0050] Step S24: Use the validation set to verify the accuracy of the trained pentaerythritol ester molecular polarity index prediction model. If the accuracy does not meet the preset standard, proceed to step S23 until the accuracy meets the preset standard, and obtain the trained pentaerythritol ester molecular polarity index prediction model.

[0051] Furthermore, the data for pentaerythritol esters with determined molecular structures include: the molecular polarity index of pentaerythritol esters with determined molecular structures, and the quantum chemical parameter values ​​of the corresponding carboxylic acids that constitute pentaerythritol esters with determined molecular structures.

[0052] Furthermore, the quantum chemical parameters corresponding to carboxylic acids include: the molecular structure, dipole moment, and polarizability of the carboxylic acid.

[0053] Furthermore, the preset standard indicators include: the degree of agreement between the predicted value of the molecular polarity index and the actual value of the molecular polarity index, as well as the mean absolute error.

[0054] In some preferred embodiments of this example, the preset standard is: the pentaerythritol ester molecular polarity index prediction model with the highest degree of agreement between the predicted value and the actual value of the molecular polarity index is used as the final pentaerythritol ester molecular polarity index prediction model.

[0055] In some preferred embodiments of this example, the preset standard is: the pentaerythritol ester molecular polarity index prediction model when the degree of agreement between the predicted value and the actual value of the molecular polarity index is within a preset range is used as the final pentaerythritol ester molecular polarity index prediction model.

[0056] The preset standard also includes a pentaerythritol ester molecular polarity index prediction model with a mean absolute error less than a preset value as the final pentaerythritol ester molecular polarity index prediction model.

[0057] Furthermore, the number of training set samples and the number of validation set samples have a preset ratio, and the number of training set samples is greater than the number of validation set samples.

[0058] Example 2

[0059] The difference from Example 1 is that, in this example, the quantitative analysis method for the polarity of pentaerythritol ester molecules includes the following steps:

[0060] Step 1: Collect molecular structure data of different pentaerythritol esters. In this example, 11 carboxylic acids, including 2-methylpropionic acid, 2,2-dimethylpropionic acid, 3-methylbutyric acid, 4-methylvaleric acid, 2-ethylhexanoic acid, 3,5,5-trimethylhexanoic acid, n-butyric acid, n-valeric acid, n-hexanoic acid, n-octanoic acid, and n-nonanoic acid, were selected as the basic raw materials for composing pentaerythritol esters. Through permutation and combination, 196 pentaerythritol esters were constructed.

[0061] Step 2: As Figure 2 As shown, a 3D molecular structure model of pentaerythritol ester was constructed using GaussView software. The geometric structure of the constructed 3D molecular structure model of pentaerythritol ester was optimized and frequency analyzed using the M06 / 6-31G(d) theoretical method in Gaussian16 software based on density functional theory, resulting in a thermodynamically stable 3D molecular structure model of pentaerythritol ester.

[0062] Step 3: Based on the optimization of the pentaerythritol ester molecular structure using density functional theory (DFT), the molecular polarity index of pentaerythritol ester was calculated using the Multiwfn program. All geometric optimizations and frequency analyses were performed using the Gaussian 16 program at the M06 / 6-31G(d) level.

[0063] Step 4: Obtain the quantum chemical parameters of the carboxylic acids that make up each pentaerythritol ester. This embodiment uses the type of carboxylic acid that makes up the pentaerythritol ester as a characteristic to distinguish different pentaerythritol esters, and calculates the dipole moment and polarizability of each carboxylic acid, providing a quantitative description of the properties of each carboxylic acid. The geometric structure optimization and calculation of molecular properties such as dipole moment and polarizability of all carboxylic acid molecules were performed using the Gaussian 16 program at the M06 / 6-31G(d) level.

[0064] Step 5: Based on the polarity index of each pentaerythritol ester molecule and the corresponding quantum chemical parameter values ​​of the carboxylic acid, a prediction model for the pentaerythritol ester molecule polarity index is constructed. In this example, 126 pentaerythritol ester molecules are used as the training set sample, and 70 pentaerythritol ester molecules are used as the test set sample. Based on the training set sample, this embodiment constructs a multiple linear regression prediction model by correlating the carboxylic acid dipole moment, polarizability, and the corresponding pentaerythritol ester molecule polarity index, and applies the model to the test set for external validation.

[0065] The model's fitting results on the training and test sets are as follows: Figure 3 and Figure 4 As shown, by appendix Figure 3 and attached Figure 4As can be seen, the model's predicted values ​​agree well with the actual values, with a coefficient of determination (R²) exceeding 0.9, and a mean absolute error of less than 0.5 kcal / mol, indicating a small error. This demonstrates that the quantitative analysis method for the pentaerythritol ester molecular polarity of this invention can achieve the goal of accurately quantifying the polarity of pentaerythritol esters.

[0066] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention are included within the scope of protection of the present invention.

Claims

1. A method for quantitatively analyzing the molecular polarity of pentaerythritol esters, characterized in that, Includes the following steps: Step S1: Construct a model for predicting the polarity index of pentaerythritol ester molecules; Step S2: Use the pentaerythritol ester data with determined molecular structure to train the pentaerythritol ester molecular polarity index prediction model to obtain the trained pentaerythritol ester molecular polarity index prediction model. Step S3: Using a trained pentaerythritol ester molecular polarity index prediction model, predict the molecular polarity index of pentaerythritol ester and quantitatively evaluate the molecular polarity of pentaerythritol ester.

2. The method for quantitative analysis of the polarity of pentaerythritol ester molecules as described in claim 1, characterized in that, Step S2 further includes: verifying the accuracy of the trained pentaerythritol ester molecular polarity index prediction model.

3. The method for quantitative analysis of the polarity of pentaerythritol ester molecules as described in claim 2, characterized in that, Step S2 includes: Step S21: Obtain data on pentaerythritol esters with determined molecular structures; Step S22: Divide the pentaerythritol ester data with determined molecular structure into a training set and a validation set; Step S23: Train the pentaerythritol ester molecular polarity index prediction model using the training set; Step S24: Use the validation set to verify the accuracy of the trained pentaerythritol ester molecular polarity index prediction model. If the accuracy does not meet the preset standard, proceed to step S23 until the accuracy meets the preset standard, and obtain the trained pentaerythritol ester molecular polarity index prediction model.

4. The method for quantitative analysis of the polarity of pentaerythritol ester molecules as described in claim 3, characterized in that, Data for pentaerythritol esters with determined molecular structures include: the molecular polarity index of the pentaerythritol ester with determined molecular structures, and the quantum chemical parameter values ​​of the corresponding carboxylic acids that make up the pentaerythritol ester with determined molecular structures.

5. The method for quantitative analysis of the polarity of pentaerythritol ester molecules as described in claim 4, characterized in that, The quantum chemical parameters of the corresponding carboxylic acid include: molecular structure, dipole moment, and polarizability.

6. The method for quantitative analysis of the polarity of pentaerythritol ester molecules as described in claim 3, characterized in that, The preset standard indicators include: the degree of agreement between the predicted value and the actual value of the molecular polarity index, and the mean absolute error.

7. The method for quantitative analysis of the polarity of pentaerythritol ester molecules as described in claim 6, characterized in that, The preset standard is: the pentaerythritol ester molecular polarity index prediction model with the highest degree of agreement between the predicted value and the actual value of the molecular polarity index is used as the final pentaerythritol ester molecular polarity index prediction model.

8. The method for quantitative analysis of the polarity of pentaerythritol ester molecules as described in claim 6, characterized in that, The preset standard is: the pentaerythritol ester molecular polarity index prediction model when the degree of agreement between the predicted value and the actual value of the molecular polarity index is within a preset range is used as the final pentaerythritol ester molecular polarity index prediction model.

9. The method for quantitative analysis of the polarity of pentaerythritol ester molecules as described in claim 8, characterized in that, The preset standard also includes a pentaerythritol ester molecular polarity index prediction model with a mean absolute error less than a preset value as the final pentaerythritol ester molecular polarity index prediction model.

10. The method for quantitative analysis of the polarity of pentaerythritol ester molecules as described in claim 3, characterized in that, The number of samples in the training set and the number of samples in the validation set have a preset ratio, and the number of samples in the training set is greater than the number of samples in the validation set.

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