Method for predicting service life of triaryl phosphate

Abstract

The invention belongs to the field of phosphate fire-resistant oil, and relates to a method for predicting the service life of triaryl phosphate, and the method specifically comprises the following steps: optimizing the geometrical configuration of triaryl phosphate molecules, and obtaining the performance parameters of triaryl phosphate to be detected; according to historical data analysis, establishing a life prediction model of triaryl phosphate; optimizing the life prediction model of the triaryl phosphate to obtain a life prediction model of standard triaryl phosphate; inputting the performance parameters, the related structure parameters and the molecular ionization energy into a life prediction model of standard triaryl phosphate to obtain a prediction result; according to the method, the service life of the triaryl phosphate can be accurately and effectively predicted, the detection frequency of fire-resistant oil of the phosphate is reduced, the state of the phosphate is early warned in time, and the safety and stability of unit operation are improved.

Description

Technical Field

[0001] The invention belongs to the field of phosphate fire-resistant oils and relates to a method for predicting the service life of a triaryl phosphate, which can timely predict the performance change of the triaryl phosphate during use. Background Art

[0002] Triaryl phosphates are important products and intermediates in the chemical industry. Due to their high auto-ignition point, they are often used as additives in transmission belts, cables, polyurethane foams, etc. to improve the flame retardant properties of the materials. In the power industry, with the continuous improvement of the parameters and capacity of steam turbine generator sets, the superheated steam temperature of the generator sets has also gradually increased, and the superheated steam temperature of some ultra-supercritical units can reach above 600°C. Because most steam turbine regulating systems are close to superheated steam pipelines, if mineral oil with a low auto-ignition point continues to be used as the hydraulic working medium of the steam turbine regulating system, once an oil leak occurs, the risk of fire is extremely high. Therefore, phosphate fire-resistant oil is now commonly used as the hydraulic working medium of the steam turbine speed control system.

[0003] However, although phosphate ester fire-resistant oil plays an irreplaceable role in the power industry, its service life is affected by many factors. Factors such as temperature, humidity, oxidation and mechanical stress will cause the gradual deterioration of phosphate ester fire-resistant oil, which is manifested as increased acid value, viscosity change, darker color and other phenomena. These changes will not only affect the performance of fire-resistant oil, but may also have an adverse effect on the normal operation of the unit. The main component of phosphate ester fire-resistant oil is triaryl phosphate, and its quality has an important influence on the service life of phosphate ester fire-resistant oil. Therefore, it is particularly important to accurately predict the life of triaryl phosphate and phosphate ester fire-resistant oil composed of it.

[0004] Through life prediction, we can timely understand the status of phosphate fire-resistant oil, predict its remaining service life, and take corresponding maintenance measures according to the prediction results, such as regeneration treatment, replacement of new oil, etc., to ensure the safe and stable operation of the unit. This can not only reduce the economic losses caused by unexpected downtime, but also improve the operating efficiency of the unit and reduce maintenance costs. However, at this stage, the methods for predicting its life are relatively scarce and not accurate enough. Traditional detection methods often rely on regular sampling and analysis, which is not only time-consuming and labor-intensive, but also difficult to capture subtle changes in the state of fire-resistant oil. In addition, phosphate fire-resistant oils from different batches and manufacturers may differ in composition and performance, which also increases the difficulty of life prediction. Therefore, the development of a highly accurate, convenient and fast method for predicting the life of triaryl phosphates has become an urgent problem to be solved. Summary of the invention

[0005] In order to overcome the shortcomings of the above-mentioned prior art, the purpose of the present invention is to provide a convenient and quick method for predicting the life of triaryl phosphate, which can accurately and effectively predict the service life of triaryl phosphate, reduce the frequency of phosphate fire-resistant oil detection, timely warn the phosphate status, and improve the safety and stability of unit operation.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: The present invention provides a method for predicting the service life of a triaryl phosphate, which specifically comprises the following steps: Optimize the geometric configuration of triaryl phosphate molecules and obtain the performance parameters of the triaryl phosphate to be tested; Based on historical data analysis, a life prediction model for triaryl phosphates was established; The life prediction model of triaryl phosphate is optimized to obtain the life prediction model of standard triaryl phosphate; The performance parameters, relevant structural parameters and molecular ionization energy are input into a lifetime prediction model of a standard triaryl phosphate to obtain a prediction result.

[0007] Preferably, the specific method for optimizing the geometric configuration of the triaryl phosphate molecule and obtaining the performance parameters of the triaryl phosphate to be tested is: using advanced quantum chemical calculation software to globally optimize the geometric configuration of the triaryl phosphate molecule to ensure that the energy is in the lowest state, and then obtaining the performance parameters of the triaryl phosphate to be tested through computational simulation.

[0008] Preferably, the specific steps of establishing the life prediction model of triaryl phosphate are: Based on historical data analysis, the lifespan of triaryl phosphates under different performance parameters is obtained; Based on the linear and nonlinear relationships between different variables and macroscopic properties, regression coefficients are obtained; A functional relationship was established based on the relevant regression coefficient and standard deviation, and the life prediction model of triaryl phosphate under different performance parameters was obtained.

[0009] Preferably, the specific steps of optimizing the life prediction model of triaryl phosphate to obtain the life prediction model of standard triaryl phosphate are: The accuracy of the functional relationship is evaluated and tested using a test set; the model is adjusted and optimized based on the evaluation results, and ultimately a standard triaryl phosphate lifetime prediction model is obtained.

[0010] Preferably, before establishing the life prediction model of triaryl phosphates, the historical data is cleaned, outliers and missing values ​​are removed, and normalization is performed.

[0011] Preferably, the life prediction model of triaryl phosphate under different performance parameters is established based on the correlation coefficient and the standard deviation, specifically comprising the following steps: Obtain the surface electrostatic potential of different triaryl phosphate molecules, obtain the corresponding triaryl phosphate operating temperature, ambient humidity and triaryl phosphate acid value life prediction model set, and divide the data in the set into a training set and a test set; examine the linear or nonlinear relationship between different variables of the triaryl phosphate molecules and the macroscopic properties of the molecules in the training set; optimize the model performance by adjusting the learning rate, and establish a life prediction model for triaryl phosphates.

[0012] Preferably, the historical data includes lifespan data sets of triaryl phosphates under different performance parameters.

[0013] Preferably, the performance parameters include triaryl phosphate ester molecular surface electrostatic potential, environmental parameters, acid value and molecular ionization energy.

[0014] Preferably, the environmental parameters refer to operating temperature and ambient humidity.

[0015] Preferably, the calculation formula of the life prediction model of the standard triaryl phosphate is: L=α0+α1*T a +α2*H b +α3*P c +α3*I d +α4*A; Wherein, L is the service life of triaryl phosphate, T is the operating temperature, H is the ambient humidity, P is the surface electrostatic potential of triaryl phosphate molecule, A is the acid value, I is the molecular ionization energy, α0 is the correction value, α1 is the first regression coefficient, α2 is the second regression coefficient, α3 is the third regression coefficient, α4 is the fourth regression coefficient; a, b, c and d are all exponents.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a method for predicting the service life of triaryl phosphates. By establishing a set of prediction models for the lifespan of different triaryl phosphate molecules, the relationship between parameters such as molecular surface electrostatic potential, operating temperature and ambient humidity and the macroscopic properties of the triaryl phosphates is established, and finally a prediction model is obtained. The model can more accurately and effectively predict the service life of new phosphate fire-resistant oils and operating phosphate fire-resistant oils, fill the gap in related prediction methods, timely warn the operating status of phosphate fire-resistant oils, and improve the safety and stability of unit operation. DETAILED DESCRIPTION

[0017] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solution of the present invention will be clearly and completely described below in conjunction with 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 should fall within the scope of protection of the present invention.

[0018] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0019] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0020] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0021] The present invention provides a method for predicting the service life of a triaryl phosphate, which specifically comprises the following steps: Optimize the geometric configuration of triaryl phosphate molecules and obtain the performance parameters of the triaryl phosphate to be tested; Based on historical data analysis, a life prediction model for triaryl phosphates was established; The life prediction model of triaryl phosphate is optimized to obtain the life prediction model of standard triaryl phosphate; The performance parameters, relevant structural parameters and molecular ionization energy are input into a lifetime prediction model of a standard triaryl phosphate to obtain a prediction result.

[0022] Among them, the specific method for optimizing the geometric configuration of triaryl phosphate molecules and obtaining the performance parameters of the triaryl phosphate to be tested is: using advanced quantum chemical calculation software to globally optimize the geometric configuration of triaryl phosphate molecules to ensure that the energy is in the lowest state, and then obtaining the performance parameters of the triaryl phosphate to be tested through computational simulation; global optimization of the geometric configuration of triaryl phosphate molecules by advanced quantum chemical calculation software can ensure that the molecules are in their lowest energy stable state. The molecular configuration in this stable state is closer to the actual situation. Therefore, the performance parameters calculated based on this configuration will be more accurate, which improves the prediction accuracy of the model and greatly reduces the experimental cost and time.

[0023] The specific steps for establishing the life prediction model of triaryl phosphates are: Based on historical data analysis, the lifespan of triaryl phosphates under different performance parameters is obtained; Based on the linear and nonlinear relationships between different variables and macroscopic properties, regression coefficients are obtained; A functional relationship was established based on the relevant regression coefficient and standard deviation, and the life prediction model of triaryl phosphate under different performance parameters was obtained.

[0024] In the process of model building, by exploring the linear and nonlinear relationships between different variables (such as operating temperature, ambient humidity, molecular surface electrostatic potential, acid value, molecular ionization energy, etc.) and the life of triaryl phosphates, the influence of these variables on the life can be accurately quantified; based on the regression coefficients obtained from these relationships, a high-precision prediction model can be constructed to improve the accuracy and reliability of the prediction. By considering multiple performance parameters and the complex relationships between them, the established life prediction model can more comprehensively reflect the service life of triaryl phosphates under different conditions.

[0025] The specific steps of optimizing the life prediction model of triaryl phosphate to obtain the life prediction model of standard triaryl phosphate are as follows: The accuracy of the functional relationship is evaluated and tested using a test set; the model is adjusted and optimized based on the evaluation results, and ultimately a standard triaryl phosphate lifetime prediction model is obtained.

[0026] In the model optimization stage, we first need to use an independent test set to verify the initially established life prediction model. This test set contains a series of data points that are not involved in the training of the life prediction model. By inputting the data in the test set into the model, we can obtain a series of predicted life values ​​and compare them with the actual life data, and calculate the error indicators (such as mean square error, mean absolute error, etc.) to evaluate the accuracy of the model. According to the evaluation results of the test set, if it is found that the prediction effect of the model in some aspects is not ideal (such as large errors, obvious deviations, etc.), the model needs to be adjusted and optimized.

[0027] The adjustment directions include: Modify the model structure, adjust the number, type or interaction of variables in the model to better capture the patterns in the data; Optimize the regression coefficients and recalculate the regression coefficients by gradient descent or genetic algorithm to make the model prediction results more consistent with the actual data; Introduce regularization terms to prevent model overfitting and improve the generalization ability of the model; Handle outliers, identify and handle outliers in the test set to ensure the reliability of the evaluation results.

[0028] Through test set evaluation and adjustment optimization, the prediction error of the model can be significantly reduced and the prediction accuracy can be improved.

[0029] Before establishing the life prediction model of triaryl phosphates, the historical data was cleaned, outliers and missing values ​​were removed, and normalization was performed; outliers are caused by measurement errors, data entry errors, or abnormal experimental conditions, which will distort the distribution of data and affect the training effect of the model. By identifying and removing these outliers, the purity and consistency of the data set can be significantly improved. The existence of missing values ​​will cause the model to be unable to fully utilize all available information during the training process, and even introduce bias. Through reasonable missing value processing strategies (such as filling, deleting, or interpolating, etc.), the integrity of the data can be maintained and information loss can be reduced. The cleaned data set removes unnecessary noise and interference factors, allowing the model to focus more on capturing the core features and laws in the data, thereby improving the accuracy and stability of the prediction.

[0030] After removing redundant and invalid data, the amount of data that the model needs to process is reduced, and the training process is accelerated, which not only improves modeling efficiency but also reduces the consumption of computing resources.

[0031] A life prediction model for triaryl phosphates under different performance parameters is established based on the correlation coefficient and the standard deviation, which specifically includes the following steps: Obtain the surface electrostatic potential of different triaryl phosphate molecules, obtain the corresponding triaryl phosphate operating temperature, ambient humidity and triaryl phosphate acid value life prediction model set, and divide the data in the set into a training set and a test set; examine the linear or nonlinear relationship between different variables of the triaryl phosphate molecules and the macroscopic properties of the molecules in the training set; optimize the model performance by adjusting the learning rate, and establish a life prediction model for triaryl phosphates.

[0032] By deeply analyzing the complex relationship between variables and life expectancy and selecting appropriate algorithms to build models, the accuracy of predictions can be significantly improved. Introducing standard deviation to evaluate prediction uncertainty makes the prediction results more reliable and comprehensive. Using an independent test set to verify the model ensures that the model can still maintain good prediction performance on unknown data. By adjusting hyperparameters such as the learning rate, the model training process is optimized to reduce overfitting and improve the generalization ability of the model.

[0033] The historical data includes lifespan data sets of triaryl phosphates under different performance parameters, the performance parameters including the surface electrostatic potential of triaryl phosphate molecules, environmental parameters, acid value and molecular ionization energy; the environmental parameters refer to the operating temperature and ambient humidity.

[0034] The electrostatic potential of the triaryl phosphate molecule surface is an important physical quantity to measure the charge distribution of the triaryl phosphate molecule surface. It reflects the electron density and charge distribution state of different regions on the molecular surface. For triaryl phosphate, the electrostatic potential of its molecular surface is related to its chemical stability, reactivity and interaction with the surrounding environment, which in turn affects its service life; acid value is an indicator to measure the content of free fatty acids in oils or organic compounds, usually expressed as the number of milligrams of potassium hydroxide required to neutralize 1 gram of sample. For triaryl phosphate, the increase in acid value usually means that it has undergone oxidative degradation, which is one of the main reasons for the decline in performance and shortened life of triaryl phosphate; molecular ionization energy refers to the minimum energy required to remove an electron from a molecule. It reflects the degree of electron binding in the molecule and the stability of the molecule. For triaryl phosphate, the size of the ionization energy is related to its antioxidant capacity, thermal stability and reactivity with other substances, which indirectly affects its service life. The use temperature is one of the main environmental factors faced by triaryl phosphate in practical applications. High temperature will accelerate the oxidative degradation process of triaryl phosphate, resulting in adverse consequences such as increased acid value and viscosity changes, thereby shortening its service life. Therefore, it is crucial to understand the life performance of triaryl phosphates at different temperatures. Ambient humidity is also one of the important factors affecting the performance of triaryl phosphates. A high humidity environment may promote the hydrolysis reaction of triaryl phosphates, generate corrosive substances, and cause damage to the equipment. At the same time, humidity may also affect the oxidative degradation rate of triaryl phosphates, further affecting their service life.

[0035] The calculation formula of the lifetime prediction model of standard triaryl phosphates is: L=α0+α1*T a +α2*H b +α3*P c +α3*I d +α4*A; Wherein, L is the service life of triaryl phosphate, T is the operating temperature, H is the ambient humidity, P is the surface electrostatic potential of triaryl phosphate molecule, A is the acid value, I is the molecular ionization energy, α0 is the correction value, α1 is the first regression coefficient, α2 is the second regression coefficient, α3 is the third regression coefficient, α4 is the fourth regression coefficient; a, b, c and d are all exponents.

[0036] The first regression coefficient α1, the second regression coefficient α2, the third regression coefficient α3 and the fourth regression coefficient α4 are all obtained through regression analysis and are used to quantify the influence of each variable on the life of the triaryl phosphate. Their positive or negative sign and size reflect the positive or negative impact of different factors on the lifespan and the strength of the impact. The correction value α0 is used to adjust the overall prediction level of the model to better match the actual data, reflecting the slight impact of other unconsidered factors on the lifespan except for the above-mentioned independent variables. The power a of temperature T is used to represent the nonlinear degree of the effect of temperature on the lifespan, reflecting the changes in the degradation mechanism in different temperature ranges; the power b of ambient humidity H reflects the sensitivity of humidity to the lifespan; the power c of the surface electrostatic potential P of triaryl phosphate molecules represents the degree of influence of the molecular surface electrostatic potential on the lifespan, revealing how the charge distribution affects the degradation process of triaryl phosphates; the acid value A is directly involved in the calculation as an independent variable without the need for power transformation, because it itself is an indicator that directly reflects the degradation state of triaryl phosphates; the power d of the molecular ionization energy I represents the nonlinear degree of the effect of ionization energy on the lifespan, involving complex mechanisms such as electron transfer and chemical reaction kinetics.

[0037] The prediction model set established by the present invention can highly accurately predict the service life of newly produced and in-service triaryl phosphate fire-resistant oils, providing a scientific basis for the replacement cycle of fire-resistant oils in power systems and industrial equipment, and effectively avoiding resource waste or safety hazards caused by premature or late replacement.

[0038] By combining real-time monitoring with model prediction, the present invention can identify the trend of declining performance of phosphate fire-resistant oils in advance, achieve early warning of operating status, provide a valuable time window for equipment maintenance, and significantly improve the safety and stability of unit operation; through accurate prediction, it reduces environmental pollution and resource waste caused by improper handling (such as excessive replacement), which meets the requirements of the era of green development.

[0039] The prediction method of the present invention is not only applicable to a specific type of triaryl phosphate fire-resistant oil, but its principle and framework can also be extended to the life prediction of other types of fire-resistant oils or similar chemicals. No matter whether it is mineral oil-based, synthetic ester-based or other new fire-resistant oils, as long as the changing patterns of its key performance parameters and their dependence on environmental conditions can be accurately captured and quantified, the method of the present invention can be effectively applied and adjusted, thereby providing equally accurate and reliable life prediction results.

[0040] In addition, in view of the widespread existence of similar chemical systems in the chemical industry, the prediction model of the present invention also has broad application potential. By appropriately adjusting the model parameters and input variables, the model can be adapted to the life prediction of various chemicals such as lubricating oil, hydraulic oil, insulating oil, etc.

[0041] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A method for predicting the service life of a triaryl phosphate, characterized in that: The specific steps include: Optimize the geometric configuration of triaryl phosphate molecules and obtain the performance parameters of the triaryl phosphate to be tested; Based on historical data analysis, a life prediction model for triaryl phosphates was established; The life prediction model of triaryl phosphate is optimized to obtain the life prediction model of standard triaryl phosphate; The performance parameters, relevant structural parameters and molecular ionization energy are input into a lifetime prediction model of a standard triaryl phosphate to obtain a prediction result.

2. A method for predicting the service life of triaryl phosphate according to claim 1, characterized in that: The specific method for optimizing the geometric configuration of the triaryl phosphate molecule and obtaining the performance parameters of the triaryl phosphate to be tested is: using advanced quantum chemical calculation software to globally optimize the geometric configuration of the triaryl phosphate molecule to ensure that the energy is in the lowest state, and then obtaining the performance parameters of the triaryl phosphate to be tested through calculation simulation.

3. A method for predicting the service life of triaryl phosphate according to claim 1, characterized in that: The specific steps of establishing the life prediction model of triaryl phosphate are: Based on historical data analysis, the lifespan of triaryl phosphates under different performance parameters is obtained; Based on the linear and nonlinear relationships between different variables and macroscopic properties, regression coefficients are obtained; A functional relationship was established based on the relevant regression coefficient and standard deviation, and the life prediction model of triaryl phosphate under different performance parameters was obtained.

4. A method for predicting the service life of triaryl phosphate according to claim 3, characterized in that: The specific steps of optimizing the life prediction model of triaryl phosphate to obtain the life prediction model of standard triaryl phosphate are: The accuracy of the functional relationship is evaluated and tested using a test set; the model is adjusted and optimized based on the evaluation results, and ultimately a standard triaryl phosphate lifetime prediction model is obtained.

5. A method for predicting the service life of triaryl phosphate according to claim 1, characterized in that: Before establishing the life prediction model of triaryl phosphates, the historical data were cleaned, outliers and missing values ​​were removed, and normalization was performed.

6. A method for predicting the service life of triaryl phosphate according to claim 1, characterized in that: The life prediction model of triaryl phosphate under different performance parameters is established based on the correlation coefficient and the standard deviation, and specifically comprises the following steps: Obtain the surface electrostatic potential of different triaryl phosphate molecules, obtain the corresponding triaryl phosphate operating temperature, ambient humidity and triaryl phosphate acid value life prediction model set, and divide the data in the set into a training set and a test set; examine the linear or nonlinear relationship between different variables of the triaryl phosphate molecules and the macroscopic properties of the molecules in the training set; optimize the model performance by adjusting the learning rate, and establish a life prediction model for triaryl phosphates.

7. A method for predicting the service life of triaryl phosphate according to claim 1, characterized in that: The historical data includes a lifetime data set of triaryl phosphates under different performance parameters.

8. A method for predicting the service life of triaryl phosphate according to claim 1, characterized in that: The performance parameters include triaryl phosphate ester molecular surface electrostatic potential, environmental parameters, acid value and molecular ionization energy.

9. A method for predicting the service life of triaryl phosphate according to claim 8, characterized in that: The environmental parameters refer to operating temperature and ambient humidity.

10. A method for predicting the service life of triaryl phosphate according to claim 1, characterized in that: The calculation formula of the life prediction model of the standard triaryl phosphate is: L=α0+α1*T a +α2*H b +α3*P c +α3*I d +α4*A; Wherein, L is the service life of triaryl phosphate, T is the operating temperature, H is the ambient humidity, P is the surface electrostatic potential of triaryl phosphate molecule, A is the acid value, I is the molecular ionization energy, α0 is the correction value, α1 is the first regression coefficient, α2 is the second regression coefficient, α3 is the third regression coefficient, α4 is the fourth regression coefficient; a, b, c and d are all exponents.