Method for dynamically adjusting preparation temperature of ester-based metal working fluid

Abstract

The invention relates to the technical field of temperature regulation and control, in particular to a method for dynamically regulating the preparation temperature of an ester-based metal working fluid which comprises low-carbon ester base oil, an environment-friendly emulsifier, an anti-wear reagent at extreme pressure, a low-carbon preservative, a corrosion inhibitor and an auxiliary additive. The dynamic temperature adjusting method comprises the steps that basic heating power is set for low-carbon ester base oil, and the low-carbon ester base oil is heated to a first set temperature range through PID control; an environment-friendly emulsifier adding process and an extreme pressure anti-wear agent adding process are executed respectively, temperature adjustment is carried out based on a neural network and an image monitoring system in the adding process, and the temperature is controlled to reach a third set temperature range after adding is completed; a low-carbon preservative and a corrosion inhibitor are added at the same time, and temperature adjustment is conducted through PID control in the adding process; according to the method, real-time temperature feedback and self-adaptive adjustment can be achieved, and it is ensured that the temperature of each stage is kept within the optimal range needed by the material.

Description

Technical Field

[0001] The invention relates to the technical field of temperature control, and in particular to a method for dynamically adjusting the preparation temperature of an ester-based metalworking fluid. Background Art

[0002] As the global awareness of environmental protection increases, low-carbon and environmental protection has become an important direction for the development of all industries. In the field of metal processing, although traditional processing fluids can meet certain processing needs, their production raw materials mostly contain non-renewable resources, and they are prone to produce volatile organic compounds (VOCs) during use. They may also cause secondary pollution to the environment during waste treatment, which is contrary to the concept of low-carbon and environmental protection. Therefore, the development of a low-carbon and environmentally friendly metal processing fluid is of great significance to promoting the sustainable development of the metal processing industry.

[0003] Low-carbon and environmentally friendly metalworking fluids have the following characteristics: First, the selection of raw materials tends to be renewable resources or chemicals with low environmental impact; second, the preparation process should reduce energy consumption and carbon emissions; third, the emission of VOCs should be reduced during use, while having good lubrication, rust prevention and cooling properties; fourth, it should be easy to degrade or recycle during waste disposal to reduce the impact on the environment.

[0004] In the process of preparing low-carbon and environmentally friendly metalworking fluids, precise temperature control is particularly important; specifically, low-carbon and environmentally friendly metalworking fluids use renewable resources or chemicals with low environmental impact, such as bio-based ester compounds. The molecular structure of these materials is relatively complex, and temperature changes may cause decomposition or performance degradation. In addition, the accuracy of temperature control can not only improve the efficiency of the reaction, but also reduce unnecessary energy consumption. For example, during the heating or cooling process, if the temperature can be strictly controlled within the range required by the process, the energy waste caused by excessive heating or cooling can be avoided, thereby reducing carbon emissions. From the perspective of uniform mixing of ingredients, various additives in metalworking fluids need to be fully dissolved and evenly dispersed at a suitable temperature. Improper temperature control may lead to uneven emulsification, stratification or additive failure. Based on the above importance, how to achieve precise control of the preparation temperature of ester-based metalworking fluids has become an important technology to promote the widespread application of related low-carbon and environmentally friendly metalworking fluids. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a method for dynamically adjusting the temperature of an ester-based metalworking fluid preparation, which can realize real-time temperature feedback and adaptive adjustment to ensure that the temperature at each stage is maintained within the optimal range required by the material.

[0006] In a first aspect, the present invention provides a method for dynamically adjusting the temperature of an ester-based metalworking fluid, wherein the ester-based metalworking fluid comprises a low-carbon ester base oil, an environmentally friendly emulsifier, an extreme pressure anti-wear agent, a low-carbon preservative, a corrosion inhibitor, and an auxiliary additive, and the method for dynamically adjusting the temperature comprises:

[0007] Setting a basic heating power for the low-carbon ester base oil, and heating the low-carbon ester base oil to a first set temperature range through PID control;

[0008] The process of adding the environmentally friendly emulsifier and the extreme pressure anti-wear agent is performed separately, and the temperature is adjusted based on the neural network and the image monitoring system during the addition process, and the temperature is controlled to reach a third set temperature range after the addition is completed;

[0009] The low-carbon preservative and corrosion inhibitor are added at the same time, and the temperature is adjusted by PID control during the addition process, and the temperature is controlled to reach room temperature after the addition is completed;

[0010] Perform normal temperature control and complete the temperature control of adding auxiliary additives.

[0011] Further, a basic heating power is set for the low-carbon ester base oil, and the low-carbon ester base oil is heated to a first set temperature range through PID control, including:

[0012] Determine the first set temperature range according to the physical properties and process requirements of the low-carbon ester base oil; and extract the heating time requirement corresponding to the first set temperature range from the process requirements;

[0013] Calculating the basic heating power based on the first set temperature range and the heating time requirement;

[0014] Debugging the proportional, integral and differential parameters of the PID controller, monitoring the temperature of the low-carbon ester base oil in real time through a temperature sensor, and feeding back the real-time monitored temperature to the PID controller;

[0015] The heating device is turned on, and according to the instruction of the PID controller, the base oil is gradually heated to the first set temperature range.

[0016] Further, the proportional, integral and differential parameters of the PID controller are tuned using one of the empirical methods, the Ziegler-Nichols method or the automatic tuning algorithm.

[0017] Furthermore, the process of adding the environmentally friendly emulsifier and the extreme pressure anti-wear agent is performed separately, and the temperature is adjusted based on the neural network and the image monitoring system during the addition process, including:

[0018] Load the neural network model into the control system and initialize the image monitoring device;

[0019] The environmentally friendly emulsifier is added, and during the process, the dispersed phase particle feature set after the environmentally friendly emulsion is added is detected in real time by an image monitoring system, and the neural network model predicts the change trend of the emulsification state in real time according to the data fed back by the image monitoring system, and adjusts the temperature according to the change trend;

[0020] After the addition of the environmentally friendly emulsifier is completed, the solution temperature is adjusted to a second set temperature range;

[0021] The extreme pressure anti-wear agent is added, and during the process, the distribution state of the extreme pressure anti-wear agent in the solution is detected in real time by an image monitoring system, the neural network model predicts the dispersion trend of the extreme pressure anti-wear agent in the solution according to the data fed back by the image monitoring system, and the temperature is adjusted according to the dispersion trend;

[0022] After the addition of the extreme pressure anti-wear agent is completed, the solution temperature is adjusted to a third set temperature range.

[0023] Furthermore, during the addition of the environmentally friendly emulsifier, the dispersed phase particle feature set after the addition of the environmentally friendly emulsion is detected in real time by an image monitoring system, and the neural network model predicts the change trend of the emulsified state in real time according to the data fed back by the image monitoring system, and adjusts the temperature according to the change trend, including:

[0024] According to the first set temperature range and the emulsifier addition speed, the monitoring frequency of the image monitoring system is set;

[0025] Based on the monitoring frequency, a microscopic image of the liquid after adding the environmentally friendly emulsifier is obtained in real time through an image monitoring system;

[0026] Perform feature recognition on liquid microscopic images to obtain a feature set of dispersed phase particles;

[0027] Inputting the dispersed phase particle feature set into a pre-built neural network model to obtain a dispersion indication result;

[0028] Calculate the dispersion fluctuation vector between the dispersion indication results obtained from two adjacent monitoring operations, wherein the dispersion fluctuation vector is the dispersion indication result obtained from the latest monitoring operation minus the dispersion indication result obtained from the previous monitoring operation; the dispersion fluctuation vector includes the fluctuation amount and the fluctuation direction;

[0029] Traversing the dispersed wave vector in a pre-built emulsified temperature control data space to obtain a temperature adjustment vector; the temperature adjustment vector includes a temperature adjustment amount and a temperature adjustment direction;

[0030] The temperature is dynamically adjusted according to the temperature adjustment vector.

[0031] Furthermore, the dispersed phase particle feature set includes particle size distribution, average particle size and dispersion uniformity.

[0032] Furthermore, the calculation formula of the dispersion indication result is:

[0033]

[0034] Where P represents the dispersion index result, n(r) represents the number of dispersed phase particles with a particle size of r, N represents the total number of dispersed phase particles, and R i represents the diameter of the i-th dispersed phase particle, m and n represent the number of rows and columns of the grid into which the image is divided, respectively; N ij represents the number of particles in the grid of row i and column j; represents the average particle size of all dispersed phase particles; ω1, ω2 and ω3 represent the weight coefficients corresponding to particle size distribution, average particle size and dispersion uniformity, respectively.

[0035] Furthermore, the setting factors of the second set temperature range include: thermal stability of the environmentally friendly emulsifier, optimal dispersion temperature of the extreme pressure anti-wear agent, and chemical balance of the solution.

[0036] Furthermore, the low-carbon preservative and the corrosion inhibitor are added simultaneously, the temperature is adjusted by PID control during the addition process, and the temperature is controlled to reach room temperature after the addition is completed, including:

[0037] According to the formula requirements of the metalworking fluid, determine the type of low-carbon preservative and corrosion inhibitor to be added, weigh the required preservative and corrosion inhibitor, and prepare to add the equipment;

[0038] Determine the temperature range required during the addition process based on the determined physical properties of the preservatives and corrosion inhibitors;

[0039] The temperature is controlled according to the temperature range required during the addition process; the weighed preservative and corrosion inhibitor are added to the solution at the same time; during the addition process, the temperature of the solution is monitored in real time by a temperature sensor and fed back to the PID controller;

[0040] The PID controller automatically adjusts the power of the heating equipment according to the real-time temperature data fed back by the temperature sensor;

[0041] After the preservatives and corrosion inhibitors are completely added and evenly dispersed, stop the heating equipment; start the cooling equipment to gradually reduce the solution temperature to the set temperature; during the cooling process, continue to use the PID controller to ensure that the temperature changes during the cooling process are stable.

[0042] Furthermore, the auxiliary additives include at least one of an antioxidant, a defoaming agent and a rust inhibitor.

[0043] Compared with the prior art, the present invention has the following beneficial effects: the present invention can realize real-time temperature feedback and adaptive adjustment through PID control and neural network combined with image monitoring system, ensure that the temperature at each stage is kept within the optimal range required by the material, avoid decomposition or performance degradation caused by excessive temperature, maintain the chemical stability of the material, and thus improve the performance and sustainability of the processing fluid;

[0044] During the implementation process, the PID controller is used to accurately adjust the temperature, and the temperature adjustment strategy is predicted and optimized through the neural network, which can avoid unnecessary energy waste. At the same time, it can realize continuous monitoring and prediction of temperature and reaction status to ensure that the temperature fluctuations at each stage are controllable, which not only improves the stability of the production process, but also reduces the need for human intervention, making production more consistent and reducing potential process risks.

[0045] Based on the difference in importance, the addition process of emulsifiers and extreme pressure anti-wear agents is monitored and adjusted in real time through neural networks and image monitoring systems. The addition of emulsifiers has high temperature requirements, and the emulsification effect will directly affect the stability of the entire processing fluid. The extreme pressure anti-wear agent can be fully mixed with the base oil to achieve the best lubrication and anti-wear effects. The image monitoring system can accurately capture various states, and the neural network can adjust the temperature control strategy according to the feedback of real-time data to improve the quality of the final metalworking fluid; the heating of the base oil needs to be controlled within a certain temperature range, but because its fluidity and viscosity remain stable within a larger temperature range, this process has relatively low requirements for precise temperature control. Preservatives and corrosion inhibitors are usually added at lower temperatures. The temperature regulation is mainly to prevent uneven dispersion due to excessive cooling. Although temperature is important, the process is relatively stable. Therefore, the above temperature control is completed using PID control to reduce the difficulty of control. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a flow chart of the present invention;

[0047] Figure 2 It is a flow chart of temperature control during the process of adding the environmentally friendly emulsifier. DETAILED DESCRIPTION

[0048] The present application is described below in conjunction with the drawings in the present application.

[0049] like Figure 1 As shown, a method for dynamically adjusting the temperature of an ester-based metalworking fluid prepared by the present invention, the ester-based metalworking fluid includes a low-carbon ester base oil, an environmentally friendly emulsifier, an extreme pressure anti-wear agent, a low-carbon preservative, a corrosion inhibitor, and auxiliary additives. The method for dynamically adjusting the temperature specifically includes the following steps:

[0050] Step S100, setting a basic heating power for the low-carbon ester base oil, and heating the low-carbon ester base oil to a first set temperature range through PID control;

[0051] Step S200, respectively performing the process of adding an environmentally friendly emulsifier and an extreme pressure anti-wear agent, and performing temperature adjustment based on a neural network and an image monitoring system during the addition process, and controlling the temperature to reach a third set temperature range after the addition is completed;

[0052] Step S300, adding low-carbon preservative and corrosion inhibitor simultaneously, adjusting the temperature by PID control during the adding process, and controlling the temperature to reach room temperature after the adding is completed;

[0053] Step S400, executing normal temperature control to complete the temperature control of adding auxiliary additives.

[0054] In this embodiment, through PID control and neural network combined with image monitoring system, real-time temperature feedback and adaptive adjustment can be achieved to ensure that the temperature at each stage is kept within the optimal range required by the material, avoid decomposition or performance degradation due to excessive temperature, maintain the chemical stability of the material, and thus improve the performance and sustainability of the processing fluid;

[0055] During the implementation process, the PID controller is used to accurately adjust the temperature, and the temperature adjustment strategy is predicted and optimized through the neural network, which can avoid unnecessary energy waste. At the same time, it can realize continuous monitoring and prediction of temperature and reaction status to ensure that the temperature fluctuations at each stage are controllable, which not only improves the stability of the production process, but also reduces the need for human intervention, making production more consistent and reducing potential process risks.

[0056] Based on the difference in importance, the addition process of emulsifiers and extreme pressure anti-wear agents is monitored and adjusted in real time through neural networks and image monitoring systems. The addition of emulsifiers has high temperature requirements, and the emulsification effect will directly affect the stability of the entire processing fluid. The extreme pressure anti-wear agent can be fully mixed with the base oil to achieve the best lubrication and anti-wear effects. The image monitoring system can accurately capture various states, and the neural network can adjust the temperature control strategy according to the feedback of real-time data to improve the quality of the final metalworking fluid; the heating of the base oil needs to be controlled within a certain temperature range, but because its fluidity and viscosity remain stable within a larger temperature range, this process has relatively low requirements for precise temperature control. Preservatives and corrosion inhibitors are usually added at lower temperatures. The temperature regulation is mainly to prevent uneven dispersion due to excessive cooling. Although temperature is important, the process is relatively stable. Therefore, the above temperature control is completed using PID control to reduce the difficulty of control.

[0057] Described below Figure 1 How the various steps are performed.

[0058] For step S100:

[0059] The purpose of step S100 is to heat the low carbon ester base oil to a specific temperature range, focusing on the heating process of the low carbon ester base oil, and preparing for the subsequent additive addition and mixing process, which is specifically implemented as follows:

[0060] Step S110, determining a first set temperature range according to the physical properties and process requirements of the low-carbon ester base oil; and extracting a heating time requirement corresponding to the first set temperature range from the process requirements;

[0061] Among them, regarding physical properties, the physical properties of low-carbon ester base oils include melting point, boiling point, viscosity and thermal stability; the melting point determines the temperature at which the low-carbon ester base oil begins to change from solid to liquid, while the boiling point is the temperature at which it changes from liquid to gas. Viscosity is a measure of fluid flow resistance. For metalworking fluids, viscosity directly affects its lubrication performance. The viscosity of low-carbon ester base oils usually decreases with increasing temperature. Therefore, it is necessary to determine a temperature range so that the base oil has a suitable viscosity within this range to meet processing requirements; thermal stability refers to the need to determine a maximum heating temperature that will not cause the low-carbon ester base oil to decompose or degrade its performance;

[0062] In the process requirements, various additives will be added in the subsequent steps, such as environmentally friendly emulsifiers, extreme pressure anti-wear agents, etc. The above additives need to be fully dissolved and evenly dispersed in the base oil within a certain temperature range, and the heating temperature range needs to take into account the solubility of these additives; in addition, there may be chemical reactions between certain additives and the base oil, and chemical reactions usually require a certain temperature to accelerate. Therefore, the heating temperature range also needs to consider the kinetic requirements of the chemical reactions involved;

[0063] Based on the above analysis, a heating temperature range that meets both the physical property requirements of low-carbon ester base oil and the process requirements can be determined, namely the first set temperature range; after determining the first set temperature range, the corresponding heating time requirements need to be extracted from the process requirements. The determination of the heating time needs to consider factors such as the efficiency of the heating equipment, the quality of the base oil, and the heat capacity.

[0064] Step S120, based on the first set temperature range and the heating time requirement, calculate the basic heating power; specifically, it is necessary to estimate the energy required in the heating process according to the physical properties of the low carbon ester base oil and the required temperature range, and in combination with the heating time requirement, the heating power required to reach the first set temperature range can be calculated, and the calculation formula is:

[0065]

[0066] Wherein P is the heating power, m is the oil quality of the low-carbon ester base oil, c is the specific heat capacity of the low-carbon ester base oil, ΔT is the temperature difference between the real-time temperature of the low-carbon ester base oil and the minimum temperature in the first set temperature range, and t is the heating time. The basic heating power can be obtained by calculation to ensure that the heating process is both efficient and energy-saving. Reasonable heating power helps to maintain the stability and controllability of the entire preparation process, thereby improving the quality and performance of the metalworking fluid.

[0067] Step S130, debugging the proportional, integral and differential parameters of the PID controller, monitoring the temperature of the low-carbon ester base oil in real time through a temperature sensor, and feeding back the real-time monitored temperature to the PID controller; debugging the proportional, integral and differential parameters of the PID controller, using one of the empirical method, the Ziegler-Nichols method or the automatic tuning algorithm; accurately placing the temperature sensor in the low-carbon ester base oil to monitor its temperature change in real time, and the temperature sensor transmits the collected temperature data to the PID controller in real time;

[0068] Proportional action (P): adjust the heating power according to the deviation between the current temperature and the set temperature;

[0069] Integral action (I): accumulates historical data of temperature deviation to eliminate steady-state errors;

[0070] Derivative action (D): predict future temperature trends and make adjustments in advance to reduce overshoot;

[0071] By precisely debugging the proportional, integral and differential parameters of the PID controller, it is ensured that the temperature monitored in real time by the temperature sensor that detects the oil temperature can be accurately fed back to the PID controller, and through the instructions of the PID controller, precise control of the temperature of the low-carbon ester base oil can be achieved.

[0072] Step S140, turning on the heating device, and gradually heating the base oil to a first set temperature range according to the instruction of the PID controller.

[0073] For step S200:

[0074] In step S200, environmentally friendly emulsifier and extreme pressure anti-wear agent are added, and the changing trend of emulsification state and dispersion state is predicted and adjusted in real time through the image monitoring system and neural network model, so as to dynamically adjust the temperature. The specific implementation is as follows:

[0075] Step S210, loading the neural network model into the control system and initializing the image monitoring device; loading the pre-trained neural network model into the control system, the neural network model can predict the changing trend of the emulsification state and the dispersion state of the extreme pressure anti-wear agent based on the data provided by the image monitoring system, set up and start the image monitoring device to ensure that it can capture and transmit the microscopic image of the solution after adding the emulsifier and anti-wear agent in real time.

[0076] Step S220, adding environmentally friendly emulsifier, during which the dispersed phase particle feature set after adding the environmentally friendly emulsion is detected in real time by the image monitoring system, and the neural network model predicts the change trend of the emulsification state in real time based on the data fed back by the image monitoring system, and adjusts the temperature according to the change trend; more specifically, Figure 2 As shown, the temperature control method during the addition of the environmentally friendly emulsifier includes:

[0077] Step S221, according to the first set temperature range and the emulsifier addition speed, set the monitoring frequency of the image monitoring system; the selection of the monitoring frequency should ensure that the subtle changes in the emulsifier addition process can be captured, while not being too frequent to reduce the calculation burden; assuming that the first set temperature range is 45° C. to 55° C., and the emulsifier addition speed is 100 ml per minute, it can be selected to perform image monitoring every 10 seconds;

[0078] Step S222: When the environmentally friendly emulsifier is added, the image monitoring system is started; according to the set monitoring frequency, a microscopic image of the liquid after the environmentally friendly emulsifier is added is obtained in real time through a microscope or other high-resolution imaging equipment; the collected image data is stored in the control system for subsequent processing and analysis;

[0079] Step S223, perform feature recognition on the liquid microscopic image to obtain a dispersed phase particle feature set, which includes particle size distribution, average particle size and dispersion uniformity; perform preprocessing on the collected microscopic image, such as denoising, contrast enhancement, etc., to improve image quality; use image processing algorithms (such as edge detection, morphological operations, etc.) to identify dispersed phase particles in the image; use the OpenCV library to perform image processing, extract particle size distribution and average particle size, and calculate dispersion uniformity;

[0080] Step S224, organizing the extracted dispersed phase particle feature set (particle size distribution, average particle size and dispersion uniformity) into a format suitable for inputting into a neural network model; inputting the feature set into a pre-trained neural network model, and the model predicts the change trend of the current emulsification state based on the input data; using the TensorFlow or PyTorch framework to build a neural network model, input the feature set, and output the dispersion indication result;

[0081] Step S225, calculate the dispersion fluctuation vector between the dispersion indication results obtained by two adjacent monitoring operations, the dispersion fluctuation vector is the dispersion indication result obtained by the latest monitoring operation minus the dispersion indication result obtained by the previous monitoring operation; the dispersion fluctuation vector includes the fluctuation amount (i.e., the absolute value of the difference) and the fluctuation direction (i.e., the positive and negative signs of the difference); assuming that the dispersion indication result of the previous monitoring is D1, and the dispersion indication result of the current monitoring is D2, the fluctuation vector V is: V=D2-D1;

[0082] Step S226: pre-build a temperature control data space containing different temperature adjustment strategies, each strategy corresponds to a temperature adjustment vector, including a temperature adjustment amount and a temperature adjustment direction; traverse the dispersed fluctuation vector in the temperature control data space to find the temperature adjustment vector that best matches the current dispersed fluctuation vector; assuming that the temperature control data space contains multiple temperature adjustment strategies, such as increasing the temperature by 1°C, decreasing the temperature by 1°C, and keeping the temperature unchanged, select the strategy that best matches the current dispersed fluctuation vector;

[0083] Step S227, according to the selected temperature adjustment vector, the PID controller sends the corresponding adjustment instruction to adjust the power of the heating or cooling equipment; the temperature change of the base oil is monitored in real time by the temperature sensor to ensure that the temperature is adjusted according to the requirements of the adjustment vector; if the temperature adjustment vector indicates a temperature increase of 1°C, the PID controller adjusts the heating power to gradually increase the temperature by 1°C, and monitors the temperature change in real time by the temperature sensor to ensure that the temperature is stable within the new set range.

[0084] Furthermore, the calculation formula for the dispersion indication result is:

[0085]

[0086] Where P represents the dispersion index result, n(r) represents the number of dispersed phase particles with a particle size of r, N represents the total number of dispersed phase particles, and R i represents the diameter of the ith dispersed phase particle, m and n represent the number of rows and columns of the grid into which the image is divided, respectively; during the image processing process, the liquid microscopic image will be divided into multiple grids of the same size to facilitate the calculation of the number of particles in each grid; N ij represents the number of particles in the grid of row i and column j; represents the average particle size of all dispersed phase particles; ω1, ω2 and ω3 represent the weight coefficients corresponding to particle size distribution, average particle size and dispersion uniformity, respectively.

[0087] In step S220, the image monitoring system is used to detect the characteristics of the dispersed phase particles after adding the environmentally friendly emulsifier in real time, so that the subtle changes in the emulsification process can be quickly captured to ensure the controllability and stability of the emulsification process; the neural network model is used to predict the change trend of the emulsification state in real time according to the data fed back by the image monitoring system, and the temperature is adjusted according to the predicted results to improve the automation level and accuracy of the emulsification process; by accurately identifying and calculating the characteristics of the dispersed phase particles (such as particle size distribution, average particle size and dispersion uniformity), the emulsification effect can be more accurately evaluated, which is helpful to adjust the process parameters in time, optimize the emulsification process, and improve product quality; by setting the monitoring frequency of the image monitoring system, it is possible to capture important changes in the emulsification process and avoid too frequent data collection and calculation, thereby reducing the calculation burden of the system; pre-building a temperature control data space containing different temperature adjustment strategies, and selecting the most suitable temperature adjustment strategy according to the dispersion fluctuation vector, which can better adapt to various changes in the emulsification process and ensure the stability and efficiency of the emulsification process; sending corresponding adjustment instructions through the PID controller to adjust the power of the heating or cooling equipment, and monitoring the temperature changes in real time through the temperature sensor to ensure that the emulsification process is carried out within the set temperature range, thereby improving the quality and stability of the product.

[0088] Step S230: After the addition of the environmentally friendly emulsifier is completed, the solution temperature is adjusted to a second set temperature range; the second set temperature range is determined based on the temperature conditions that need to be reached after the emulsifier and the extreme pressure anti-wear agent are added to ensure that the above additives can be fully mixed with the base oil and play the best effect. The factors that need to be considered in determining the second set temperature range include:

[0089] a. Properties of environmentally friendly emulsifiers: Different environmentally friendly emulsifiers have different thermal stability and emulsification temperature ranges; therefore, when selecting the second set temperature range, it is necessary to refer to the technical specifications and recommended operating temperature of the emulsifier; the thermal stability of the emulsifier determines whether it can maintain a stable emulsification effect at high temperatures, thereby avoiding the occurrence of demulsification or stratification;

[0090] b. Requirements for extreme pressure anti-wear agents: The performance and dispersion effect of extreme pressure anti-wear agents are also affected by temperature; therefore, when determining the second set temperature range, the optimal dispersion temperature and stability of the extreme pressure anti-wear agent need to be considered; too high or too low a temperature may cause uneven dispersion of the extreme pressure anti-wear agent in the solution, thereby affecting the performance of the processing fluid;

[0091] c. Chemical balance of the solution: During the preparation process, the chemical components in the solution will reach a certain equilibrium state; this equilibrium state is affected by temperature, so it is necessary to maintain or optimize this equilibrium by adjusting the temperature; the selection of the second set temperature range should ensure that the chemical components in the solution can exist stably and exert the best synergistic effect.

[0092] Step S240, the addition of extreme pressure anti-wear agent is to improve the extreme pressure performance and anti-wear performance of the processing fluid. The extreme pressure anti-wear agent usually has a specific chemical structure and properties, and can form a protective film during the metal processing process to reduce friction and wear between metals; when adding the extreme pressure anti-wear agent, the image monitoring system is used to detect the distribution state of the extreme pressure anti-wear agent in the solution in real time. The neural network model predicts the dispersion trend of the extreme pressure anti-wear agent in the solution based on the data fed back by the image monitoring system, and adjusts the temperature according to the dispersion trend; the specific implementation is similar to step S220, but this time it is for the addition process of the extreme pressure anti-wear agent; through the collaborative work of the image monitoring system and the neural network model, the dispersion state of the extreme pressure anti-wear agent in the solution is monitored and adjusted in real time; the core link of step S240 is similar to S221 to S227 in step S220, except that the object of attention is changed from the emulsifier to the extreme pressure anti-wear agent, the purpose is to ensure that it can be evenly dispersed in the solution, so as to achieve the best lubrication and anti-wear effect.

[0093] Step S250, after completing the addition of the extreme pressure anti-wear agent, adjust the solution temperature to a third set temperature range; first, the third set temperature range needs to be determined, and at the same time, a suitable temperature range needs to be determined based on the physical properties of the extreme pressure anti-wear agent (such as solubility, thermal stability, viscosity, etc.). It should be noted that the extreme pressure anti-wear agent is added at a lower temperature to prevent uneven dispersion caused by excessive cooling; consider the dispersibility and stability of the extreme pressure anti-wear agent in the solution, as well as the temperature requirements of subsequent steps, and determine an optimal temperature range; assuming that the optimal dispersion temperature range of the extreme pressure anti-wear agent is 40°C to 50°C, this range can be selected as the third set temperature range.

[0094] For step S300:

[0095] Step S300 is to add low-carbon preservatives and corrosion inhibitors at the same time on the basis of having added environmentally friendly emulsifiers and extreme pressure anti-wear agents, and adjust the temperature through PID control to ensure that the low-carbon preservatives and corrosion inhibitor additives are evenly dispersed in the solution, and finally adjust the solution temperature to room temperature; the specific implementation is as follows:

[0096] Step S310: Determine the type of low-carbon preservative and corrosion inhibitor to be added according to the formula requirements of the metalworking fluid, accurately weigh the required preservative and corrosion inhibitor, and prepare the equipment for adding;

[0097] Step S320: Determine the temperature range required during the addition process according to the physical properties of the preservative and corrosion inhibitor (such as solubility, thermal stability, viscosity, etc.); assuming that the optimal dispersion temperature range of the preservative and corrosion inhibitor is 30° C. to 40° C., this range can be selected as the temperature setting during the addition process;

[0098] Step S330, controlling the temperature according to the temperature range set in step S320; adding the weighed preservative and corrosion inhibitor to the solution at the same time; during the adding process, using a stirring device to ensure that the additives are evenly dispersed in the solution; using a temperature sensor to monitor the temperature of the solution in real time to ensure that the temperature remains within the set range;

[0099] Step S340, the PID controller automatically adjusts the power of the heating or cooling device according to the real-time temperature data fed back by the temperature sensor to ensure that the temperature is stable within the set range; if the temperature sensor detects that the solution temperature is lower than 30°C, the PID controller will increase the heating power; if the temperature exceeds 40°C, the PID controller will reduce the heating power or start the cooling device;

[0100] Step S350: After the preservative and corrosion inhibitor are completely added and evenly dispersed, stop the heating equipment; start the cooling equipment to gradually reduce the solution temperature to room temperature (usually 20°C to 25°C); continue to use the PID controller to ensure that the temperature changes smoothly during the cooling process to avoid a sharp drop in temperature that may cause solution stratification or additive failure; assuming that the room temperature is 25°C, the PID controller will gradually reduce the heating power and start the cooling equipment based on the feedback from the temperature sensor to ensure that the solution temperature slowly drops to 25°C.

[0101] Through step S300, it can be ensured that during the process of adding low-carbon preservatives and corrosion inhibitors, the solution temperature is accurately adjusted to the set range and finally drops to room temperature; this process not only improves the dispersibility and stability of preservatives and corrosion inhibitors in the solution, but also ensures that the metalworking fluid has good anti-rust and anti-corrosion properties; through PID control and real-time temperature monitoring, the temperature can be accurately controlled to avoid additive failure or solution stratification due to temperature fluctuations, which meets the requirements of low-carbon and environmental protection.

[0102] For step S400:

[0103] The goal of step S400 is to perform normal temperature control and complete the addition of auxiliary additives on the basis of having added environmentally friendly emulsifiers, extreme pressure anti-wear agents, low-carbon preservatives and corrosion inhibitors, as follows:

[0104] According to the formula requirements of the metalworking fluid, select appropriate auxiliary additives; these additives may include antioxidants, defoamers, rust inhibitors, etc. to further improve the performance of the processing fluid;

[0105] Step S410, selecting appropriate auxiliary additives, according to the formula requirements of the metalworking fluid, selecting appropriate auxiliary additives; auxiliary additives include antioxidants, defoaming agents, rust inhibitors, etc., to further improve the performance of the processing fluid, accurately weigh the required auxiliary additives, and prepare to add them to the equipment;

[0106] Step S420, determine the normal temperature range, generally between 20°C and 25°C, to ensure that the solution is within this temperature range when the auxiliary additive is added, so as to avoid adverse effects of temperature fluctuations on the dispersibility and performance of the additive; assuming that the room temperature is 25°C, 20°C to 25°C can be selected as the normal temperature control range;

[0107] Step S430, ensure that the PID controller is in working state, and perform temperature control according to the set normal temperature range; add the weighed auxiliary additives to the solution; during the addition process, ensure that the additives are evenly dispersed in the solution through a stirring device; monitor the temperature of the solution in real time through a temperature sensor to ensure that the temperature remains within the normal temperature range;

[0108] Step S440, the PID controller automatically adjusts the power of the heating or cooling device according to the real-time temperature data fed back by the temperature sensor to ensure that the temperature is stable within the normal temperature range; if the temperature sensor detects that the solution temperature is lower than 20°C, the PID controller will increase the heating power; if the temperature exceeds 25°C, the PID controller will reduce the heating power or start the cooling device;

[0109] Step S450, after the auxiliary additives are completely added and evenly dispersed, ensure that the solution temperature is stable within the normal temperature range for a period of time, usually 15 to 30 minutes, to ensure that all additives are fully mixed; assuming that the normal temperature range is 20°C to 25°C, the PID controller will ensure that the temperature is stable within this range for 30 minutes;

[0110] Step S460, verify the uniformity of the dispersion of the auxiliary additive in the solution by sampling and testing; a microscope or other testing equipment may be used for inspection; the temperature change curve during the addition process and the final temperature data are recorded to ensure the traceability and quality control of the process; after sampling, the dispersion of the auxiliary additive in the solution is checked using a microscope to ensure that it is evenly dispersed.

[0111] In step S400, by selecting and adding auxiliary additives such as antioxidants, defoamers, and rust inhibitors, the antioxidant, anti-foaming and anti-rust properties of the processing fluid can be significantly improved, thereby extending the service life of the processing fluid and improving processing efficiency and product quality; step S400 clearly stipulates the normal temperature range (20°C to 25°C), and uses a PID controller for precise temperature control, which helps to ensure that the additives can be evenly dispersed when added, avoiding the impact of temperature fluctuations on the dispersion and performance of the additives; using a PID controller and a temperature sensor for real-time monitoring and automatic adjustment realizes the automation and intelligence of temperature control, which not only improves production efficiency, but also reduces errors caused by human intervention, ensuring the consistency and stability of the processing fluid preparation process; recording the temperature change curve during the addition process and the final temperature data, as well as the sampling and detection results, helps to achieve process traceability and quality control.

[0112] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for dynamically adjusting the temperature of an ester-based metalworking fluid, characterized in that: The ester-based metalworking fluid includes a low-carbon ester base oil, an environmentally friendly emulsifier, an extreme pressure anti-wear agent, a low-carbon preservative, a corrosion inhibitor, and auxiliary additives. The temperature dynamic adjustment method includes: Setting a basic heating power for the low-carbon ester base oil, and heating the low-carbon ester base oil to a first set temperature range through PID control; The process of adding the environmentally friendly emulsifier and the extreme pressure anti-wear agent is performed separately, and the temperature is adjusted based on the neural network and the image monitoring system during the addition process, and the temperature is controlled to reach a third set temperature range after the addition is completed; The low-carbon preservative and corrosion inhibitor are added at the same time, and the temperature is adjusted by PID control during the addition process, and the temperature is controlled to reach room temperature after the addition is completed; Perform normal temperature control and complete the temperature control of adding auxiliary additives.

2. A method for dynamically adjusting the temperature of an ester-based metalworking fluid according to claim 1, characterized in that: The basic heating power is set for the low-carbon ester base oil, and the low-carbon ester base oil is heated to a first set temperature range through PID control, including: Determine the first set temperature range according to the physical properties and process requirements of the low-carbon ester base oil; and extract the heating time requirement corresponding to the first set temperature range from the process requirements; Calculating the basic heating power based on the first set temperature range and the heating time requirement; Debugging the proportional, integral and differential parameters of the PID controller, monitoring the temperature of the low-carbon ester base oil in real time through a temperature sensor, and feeding back the real-time monitored temperature to the PID controller; The heating device is turned on, and according to the instruction of the PID controller, the base oil is gradually heated to the first set temperature range.

3. A method for dynamically adjusting the temperature of an ester-based metalworking fluid according to claim 2, characterized in that: The proportional, integral and differential parameters of the PID controller are tuned using either the empirical method, the Ziegler-Nichols method or an automatic tuning algorithm.

4. The method for dynamically adjusting the temperature of an ester-based metalworking fluid according to claim 1, characterized in that: The process of adding the environmentally friendly emulsifier and the extreme pressure anti-wear agent is performed separately, and the temperature is adjusted based on the neural network and the image monitoring system during the addition process, including: Load the neural network model into the control system and initialize the image monitoring device; The environmentally friendly emulsifier is added, and during the process, the dispersed phase particle feature set after the environmentally friendly emulsion is added is detected in real time by an image monitoring system, and the neural network model predicts the change trend of the emulsification state in real time according to the data fed back by the image monitoring system, and adjusts the temperature according to the change trend; After the addition of the environmentally friendly emulsifier is completed, the solution temperature is adjusted to a second set temperature range; The extreme pressure anti-wear agent is added, and during the process, the distribution state of the extreme pressure anti-wear agent in the solution is detected in real time by an image monitoring system, the neural network model predicts the dispersion trend of the extreme pressure anti-wear agent in the solution according to the data fed back by the image monitoring system, and the temperature is adjusted according to the dispersion trend; After the addition of the extreme pressure anti-wear agent is completed, the solution temperature is adjusted to a third set temperature range.

5. A method for dynamically adjusting the temperature of an ester-based metalworking fluid according to claim 4, characterized in that: The environmentally friendly emulsifier is added, and during the process, the dispersed phase particle feature set after the environmentally friendly emulsion is added is detected in real time by the image monitoring system. The neural network model predicts the change trend of the emulsification state in real time according to the data fed back by the image monitoring system, and adjusts the temperature according to the change trend, including: According to the first set temperature range and the emulsifier addition speed, the monitoring frequency of the image monitoring system is set; Based on the monitoring frequency, a microscopic image of the liquid after adding the environmentally friendly emulsifier is obtained in real time through an image monitoring system; Perform feature recognition on liquid microscopic images to obtain a feature set of dispersed phase particles; Inputting the dispersed phase particle feature set into a pre-built neural network model to obtain a dispersion indication result; Calculate the dispersion fluctuation vector between the dispersion indication results obtained from two adjacent monitoring operations, wherein the dispersion fluctuation vector is the dispersion indication result obtained from the latest monitoring operation minus the dispersion indication result obtained from the previous monitoring operation; the dispersion fluctuation vector includes the fluctuation amount and the fluctuation direction; Traversing the dispersed wave vector in a pre-built emulsified temperature control data space to obtain a temperature adjustment vector; the temperature adjustment vector includes a temperature adjustment amount and a temperature adjustment direction; The temperature is dynamically adjusted according to the temperature adjustment vector.

6. A method for dynamically adjusting the temperature of an ester-based metalworking fluid according to claim 5, characterized in that: The dispersed phase particle feature set includes particle size distribution, average particle size and dispersion uniformity.

7. A method for dynamically adjusting the temperature of an ester-based metalworking fluid according to claim 6, characterized in that: The calculation formula of the dispersion indication result is: Where P represents the dispersion index result, n(r) represents the number of dispersed phase particles with a particle size of r, N represents the total number of dispersed phase particles, and R i represents the diameter of the i-th dispersed phase particle, m and n represent the number of rows and columns of the grid into which the image is divided, respectively; N ij represents the number of particles in the grid of row i and column j; represents the average particle size of all dispersed phase particles; ω1, ω2 and ω3 represent the weight coefficients corresponding to particle size distribution, average particle size and dispersion uniformity, respectively.

8. A method for dynamically adjusting the temperature of an ester-based metalworking fluid according to claim 4, characterized in that: The setting factors of the second set temperature range include: the thermal stability of the environmentally friendly emulsifier, the optimal dispersion temperature of the extreme pressure anti-wear agent and the chemical balance of the solution.

9. The method for dynamically adjusting the temperature of an ester-based metalworking fluid according to claim 1, characterized in that: The low-carbon preservative and corrosion inhibitor are added at the same time, and the temperature is adjusted by PID control during the addition process. After the addition is completed, the temperature is controlled to reach room temperature, including: According to the formula requirements of the metalworking fluid, determine the type of low-carbon preservative and corrosion inhibitor to be added, weigh the required preservative and corrosion inhibitor, and prepare to add the equipment; Determine the temperature range required during the addition process based on the determined physical properties of the preservatives and corrosion inhibitors; The temperature is controlled according to the temperature range required during the addition process; the weighed preservative and corrosion inhibitor are added to the solution at the same time; during the addition process, the temperature of the solution is monitored in real time by a temperature sensor and fed back to the PID controller; The PID controller automatically adjusts the power of the heating equipment according to the real-time temperature data fed back by the temperature sensor; After the preservatives and corrosion inhibitors are completely added and evenly dispersed, stop the heating equipment; start the cooling equipment to gradually reduce the solution temperature to the set temperature; during the cooling process, continue to use the PID controller to ensure a smooth temperature change during the cooling process.

10. The method for dynamically adjusting the temperature of an ester-based metalworking fluid according to claim 1, characterized in that: The auxiliary additives include at least one of an antioxidant, a defoaming agent and a rust inhibitor.

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