Aluminum substrate green degreaser activity on-line monitoring and replenishing control system

Abstract

The invention discloses an on-line activity monitoring and replenishing control system for an aluminum substrate green degreaser, and relates to the technical field of degreaser activity detection. The system comprises a constant-temperature bypass sampling and preprocessing module, a multi-dimensional interface chemical parameter synchronous sensing module, an oil stain interference decoupling and activity correction calculation module, a bi-component asymmetric differentiation replenishing module, a bath solution aging trend prediction and solution change early warning module and a self-adaptive cleaning and zero calibration module. The method comprises the following steps: acquiring a dynamic surface tension spectrogram and scattered light turbidity, decoupling false influence of oil stain on tension measurement by using an interference model, inverting an effective activity index representing the real oil removal capacity of a surfactant, and independently supplementing with an electrolyte conductivity driving component; predictive liquid change early warning is realized through efficiency attenuation analysis; the problem of misalignment of a traditional monitoring method under oil contamination interference is solved, closed-loop management from accurate sensing and intelligent regulation to predictive maintenance is achieved, and the process stability and the chemical utilization rate are improved.

Description

Technical Field

[0001] This invention belongs to the field of degreasing agent activity detection technology, and specifically relates to an online monitoring and replenishment control system for green degreasing agent activity on aluminum substrates. Background Technology

[0002] During processing, aluminum and aluminum alloy substrates may retain stains such as lubricating oil and rust-preventive oil, which must be removed before subsequent treatment. Traditional degreasing processes often use strong alkalis or chemicals containing phosphorus or nitrogen, causing significant environmental pollution. In recent years, "green degreasing agents" composed of biodegradable surfactants, organic acids, and additives have become increasingly popular.

[0003] Traditional aluminum substrate degreasing processes involve the following steps: the aluminum substrate enters the degreasing tank via a conveyor belt or hanger, and is exposed to a degreasing agent at a temperature of 40℃-60℃ through spraying or immersion. The surfactant molecules in the degreasing agent rapidly migrate to the oil-liquid interface, and through wetting, penetration, emulsification, and solubilization, peel off and disperse the rolling oil and rust-preventive oil from the aluminum substrate surface into the bath solution. Subsequently, the aluminum substrate enters a water rinsing tank to remove residual agents. However, with increasing throughput, the accumulated oil and consumed active components in the bath solution directly affect the efficiency of the aforementioned physicochemical process.

[0004] This reveals the following challenges in the industrial application of green degreasing agents: 1. Difficulty in activity characterization: Green degreasing agents primarily rely on the wetting and emulsifying effects of surfactants for degreasing, rather than simple acid-base corrosion; traditional pH or conductivity monitoring cannot accurately reflect the remaining concentration and activity of surfactants; 2. Oil contamination interference: As production progresses, the amount of dissolved / emulsified oil in the bath increases, altering the physical properties of the bath and causing sensor readings to drift, leading to misjudgments; 3. Uneven component consumption: The ratio of oil carried out from the aluminum substrate surface to the additives (such as corrosion inhibitors and pH buffers) consumed in the chemical reaction is not fixed. A single "mother liquor replenishment" can lead to an imbalance in the bath composition (e.g., excessive surfactants and insufficient additives, or vice versa); therefore, a method that can resist oil contamination interference, accurately anchor degreasing activity, and achieve refined replenishment is urgently needed. Summary of the Invention

[0005] (a) Technical problems to be solved To address the problems in related technologies, this invention provides an online monitoring and replenishment control system for the activity of a green degreasing agent on aluminum substrates, thereby overcoming the aforementioned technical problems in existing related technologies.

[0006] (II) Technical Solution To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution: This invention provides an online monitoring and replenishment control system for the activity of a green degreasing agent on aluminum substrates, comprising: Thermostatic bypass sampling and pretreatment module: It is configured to extract sample liquid from the main degreasing tank, perform multi-stage filtration and thermoelectric coupling temperature control, and output clean laminar flow sample liquid at a constant temperature; Multidimensional interface chemical parameter synchronous sensing: configured to simultaneously collect the dynamic surface tension value, scattered light turbidity value and electrolyte conductivity value of the clean laminar flow sample liquid at the constant temperature; the dynamic surface tension value is the value measured under a preset bubble lifetime; The oil pollution interference decoupling and effective activity index calculation module is configured to substitute the scattered light turbidity value into a predetermined turbidity-tension attenuation interference model to calculate the spurious reduction value of surface tension caused by oil pollution interference; sum the dynamic surface tension value and the spurious reduction value of surface tension to obtain the corrected effective surface tension value; and substitute the effective surface tension value into a predetermined concentration-surface tension standard curve to invert and calculate the effective activity index. A two-component asymmetric differential replenishment module is configured to compare the effective activity index with the target concentration of the surfactant component, and when the effective activity index is lower than a first trigger threshold, calculate and replenish the surfactant component; simultaneously, it compares the electrolyte conductivity value with the target conductivity of the auxiliary agent component, and when the electrolyte conductivity value is lower than a second trigger threshold, calculate and replenish the auxiliary agent component. The bath aging trend prediction and bath replacement early warning module is configured to record the cumulative replenishment amount of surfactant components and calculate the consumption per unit area in combination with the total area of ​​aluminum substrates processed at the same time; when the consumption per unit area exceeds the preset performance decay threshold, a bath replacement early warning signal is generated. Preferably, the isothermal bypass sampling and preprocessing module includes: The constant flow sampling unit stabilizes the flow rate of the extracted sample liquid within the laminar flow range through a constant flow valve; The multi-stage filtration subunit includes a coarse filter and a self-cleaning precision filter. The sample solution is filtered sequentially through the coarse filter and the self-cleaning precision filter to remove solid particles and obtain a clean laminar flow sample solution. Thermoelectric temperature control unit includes a Peltier effect semiconductor cooling / heating element and a PT100 temperature sensor; The PID temperature control subunit is used to read the temperature of the clean laminar flow sample in real time. When the temperature deviates from the preset temperature deviation, it makes the temperature of the clean laminar flow sample approach the reference temperature. The steady-state determination unit is used to determine the temperature change rate of the clean laminar flow sample liquid in real time. Only when the standard deviation of temperature fluctuation for N consecutive sampling cycles is less than the set threshold, the system determines that it has entered the baseline constant temperature and sends a measurement trigger signal to the multi-dimensional interface chemical parameter synchronous sensing module. Preferably, the multidimensional interface chemical parameter synchronous sensing module includes: The dynamic tension acquisition unit is used to generate a series of bubbles with different lifetimes by changing the gas flow rate using the maximum bubble pressure method, and to measure the pressure when each bubble bursts. Based on the Laplace equation, the dynamic surface tension value under the corresponding bubble lifetime is calculated, thereby forming a dynamic surface tension spectrum. The feature point extraction unit is used to extract the tension value under a specific bubble lifetime from the dynamic surface tension spectrum, define it as the reference dynamic tension, and transmit the reference dynamic tension to the oil pollution interference decoupling and activity correction calculation module. The turbidity and conductivity co-acquisition unit is used to measure the turbidity value of the sample liquid using an infrared scattering turbidity meter and the conductivity value of the sample liquid using a quadrupole conductivity electrode to obtain the scattered light turbidity and electrolyte conductivity of the clean laminar flow sample liquid; and transmits the scattered light turbidity to the oil pollution interference decoupling and activity correction calculation module, and transmits the electrolyte conductivity to the two-component asymmetric differential replenishment module; Preferably, the process of obtaining the concentration-surface tension standard curve in the oil pollution interference decoupling and activity correction module includes: In the sample preparation process, a series of clean degreasing agent standard solutions with different concentration gradients were prepared using pure water; During the data acquisition process, the dynamic surface tension values ​​of each standard solution were measured at the constant temperature. In the curve fitting process, the concentration of the degreasing agent is used as the independent variable and the dynamic surface tension value is used as the dependent variable. Logistic regression or linear regression algorithm is used to fit the curve, determine the mathematical expression and coefficients of the concentration-surface tension standard curve, and then solidify the concentration-surface tension standard curve into the operation logic of the controller. Preferably, the process of acquiring the turbidity-tension attenuation interference model in the reference model storage unit includes: To interfere with the sample preparation process, contaminants were added dropwise to a standard concentration of degreasing agent solution and emulsified to prepare a series of contaminated samples with different oil contents. During the synchronous measurement process, at the constant temperature, the scattered light turbidity value and dynamic surface tension value of each contaminated sample are measured simultaneously. The deviation calculation process calculates the decrease in dynamic surface tension value of each contaminated sample relative to the surface tension of the oil-free standard solution. In the model construction process, the turbidity of scattered light is used as the independent variable and the decrease in surface tension is used as the dependent variable. The least squares method is used to perform polynomial fitting to determine the mathematical expression and coefficients of the turbidity-tension attenuation interference model. The mathematical expression and coefficients of the turbidity-tension attenuation interference model are then solidified into the operation logic of the controller. Preferably, the two-component asymmetric differential supplementation module includes: The parameter definition subunit is used to define the target concentration of the surfactant component, the target conductivity of the auxiliary component, the replenishment dead zone, and the maximum limit for a single replenishment. The component A replenishment logic unit is used to receive the effective activity index, compare the effective activity index with the target concentration to obtain a first comparison value; if the first comparison value is lower than the difference between the target concentration and the replenishment dead zone, the required amount of surfactant component is calculated based on the difference and the total volume of the tank liquid, and the first metering pump is driven to perform dosing. The component B replenishment logic unit is used to receive the electrolyte conductivity, compare the electrolyte conductivity with the target conductivity to obtain a second comparison value; if the second comparison value is lower than the difference between the target conductivity and the replenishment dead zone, the required amount of the adjuvant component is calculated based on the second comparison value, and the second metering pump is driven to perform dosing. The cross-interlock control unit is used to monitor changes in turbidity during replenishment; if an abnormal surge in turbidity occurs after replenishing component A, the replenishment is stopped immediately; at the same time, it ensures that the replenishment of component A and component B are staggered on the time axis to avoid precipitation caused by excessively high local concentrations. Preferably, the two-component asymmetric differential supplementation module further includes: The supplementary response verification and efficiency evaluation subunit is used to restart the measurement process after a set mixing time following the supplementary action; and to calculate the activity recovery rate caused by each supplementation. The adaptive gain adjustment subunit is used to automatically correct the proportional coefficient in the replenishment algorithm if the recovery rate of three consecutive replenishments is lower than the preset efficiency threshold, thereby increasing the required amount of surfactant component calculated in the next replenishment and realizing adaptive iteration of control parameters. Preferably, the tank solution aging trend prediction and solution replacement early warning module includes: The historical data serialization unit is used to record the cumulative value of surfactant component demand for each execution by the two-component asymmetric differential replenishment module, with the production batch as the time axis. The dirt-holding capacity decay analysis unit is used to calculate the degreasing efficiency per unit surfactant consumption by combining the total processing area of ​​the aluminum substrate during the same period. The remaining lifetime estimation unit is used to perform regression analysis on the turbidity baseline value to predict the time point when the turbidity reaches the saturation threshold. The intelligent decision output unit is used to make a comprehensive judgment: if the oil removal efficiency of the unit surfactant consumption decreases by more than a set threshold year-on-year, or the turbidity is greater than the demulsification limit, then a forced liquid replacement command is generated, the replenishment pump is locked, and an alarm is triggered. Preferably, the contaminant-holding capacity decay analysis unit includes: The unit consumption calculation subunit is used to obtain the cumulative replenishment amount of surfactant component demand and the total processing area of ​​aluminum substrate within the current time window; the cumulative replenishment amount is divided by the total processing area to obtain the mass of surfactant consumed per unit area of ​​aluminum substrate; The efficiency decay determination subunit is used to take the mass of surfactant consumed per unit area of ​​aluminum substrate as a measurement index. When the index increases by more than a set percentage year-on-year, it is determined that the tank solution's dirt-holding capacity has decreased and an early warning state is entered. Preferably, the above-mentioned online monitoring and replenishment control system further includes: Adaptive cleaning and zero-point calibration module: It is configured to perform gas-liquid two-phase cleaning of the sensor after the monitoring cycle ends, and use pure water for measurement to generate zero-point drift compensation value, which is used to correct the measurement data of the next monitoring cycle. The adaptive cleaning and zero-point calibration module includes: The cleaning trigger subunit is used to trigger the cleaning process after each measurement task is completed or when data anomalies are detected. The gas-liquid two-phase cleaning unit is used to control the cleaning valve group to alternately inject pure water and compressed air into the measuring pipeline to form a gas-liquid plunger flow and peel off the oil film on the sensor surface. The dynamic calibration subunit is used to measure the actual surface tension of pure water after cleaning. The drift compensation subunit is used to compare the measured value with the standard value, calculate the deviation value, and generate the zero-point drift compensation value. If the value is within the allowable range, it is sent to the multi-dimensional interface chemical parameter synchronous sensing module for data correction. If it exceeds the range, an alarm is triggered.

[0007] (III) Beneficial Effects The present invention has the following beneficial effects: This invention uses dynamic surface tension monitoring, based on the core mechanism of the wetting speed of green degreasing agents, which is more accurate than simply measuring concentration. By introducing a turbidity correction algorithm, it eliminates the interference of oil accumulation during production on sensor readings. By adding two components independently, it avoids waste caused by excess of a certain component, extends the life of the tank solution, and achieves emission reduction.

[0008] The dynamic surface tension of this invention reflects the rate at which surfactant molecules diffuse from the bulk phase to the nascent gas-liquid interface. In high-speed spray lines, the interface lifetime is only tens to hundreds of milliseconds. Traditional static surface tension cannot characterize the ability of degreasers to wet aluminum plates instantaneously, while dynamic surface tension can accurately capture this kinetic characteristic. Oil stains themselves exhibit low surface tension characteristics, masking the reduction in effective surfactant concentration. This invention utilizes the linear correlation between scattered light turbidity and oil stain concentration to establish a physicochemical decoupling model, eliminating the nonlinear interference of oil stains on the tension signal, thereby restoring the true degreasing activity.

[0009] This invention achieves accurate and interference-resistant online monitoring of the true activity of green degreasing agents. It employs a technical approach combining isothermal bypass pretreatment with synchronous sensing of multi-dimensional interface parameters. By collecting dynamic surface tension spectra, scattered light turbidity, and conductivity, and utilizing a pre-set interference model to decouple and compensate for measurement deviations caused by oil contamination in real time, it ultimately derives an effective activity index unaffected by oil contamination. This overcomes the shortcomings of traditional single-parameter (such as pH and conductivity) monitoring methods, which suffer from severe inaccuracies in the presence of oil contamination, and for the first time achieves direct and quantitative characterization of the core functions of surfactants.

[0010] This invention establishes a refined and balanced replenishment control mechanism based on dual feedback of activity and conductivity. The system calculates the required amount of surfactant and additives according to two independent parameters: effective activity index and electrolyte conductivity, and performs differentiated replenishment independently through dual channels. The introduced cross-interlock and replenishment response verification mechanism ensures the safety and effectiveness of the replenishment action. It changes the traditional extensive mode of replenishing mother liquor based on experience, and can dynamically maintain the stoichiometric balance of each functional component in the bath, preventing performance degradation or resource waste caused by uneven consumption of components.

[0011] This invention forms a closed-loop intelligent management system covering the entire lifecycle, from process control to predictive maintenance. The system not only achieves real-time monitoring and replenishment but also quantitatively analyzes and predicts trends in the degradation of the tank solution's dirt-holding capacity by continuously recording replenishment data and performance changes, thus proactively issuing liquid replacement warnings before performance critical points. Combined with periodic adaptive cleaning and zero-point calibration functions, it ensures the long-term stability and reliability of the sensing system. This represents a leap from precise sensing to intelligent control and then to predictive maintenance, significantly improving process stability, chemical utilization, and environmental management.

[0012] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0013] To more clearly illustrate the technical solutions of the embodiments of the invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the invention. For those skilled in the art, the drawings can be obtained from these drawings without creative effort.

[0014] Figure 1 This is a schematic diagram of a module of an online monitoring and replenishment control system for the activity of a green degreasing agent on an aluminum substrate according to the present invention; Figure 2 This is a schematic flowchart of an online monitoring and replenishment control method for a green degreasing agent on an aluminum substrate according to the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the invention, and not all embodiments. Based on the embodiments of the invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the invention.

[0016] The aluminum substrate degreasing production line mainly consists of a degreasing main tank (with a volume of 2000L), a main circulation pump, a heating and temperature control system, and spray / immersion pipelines. The degreasing main tank is filled with green degreasing agent working solution, and the main circulation pump is used to drive the tank solution to circulate and rinse the surface of the aluminum substrate.

[0017] To address the technical problems raised in the background section, please refer to [link / reference]. Figure 1 and Figure 2 This invention provides an online monitoring and replenishment control system for the activity of a green degreasing agent for aluminum substrates. The system is connected to the circulation pipeline of the main degreasing tank via a bypass pipeline. The online monitoring and replenishment control system includes a constant temperature bypass sampling and pretreatment module, a multi-dimensional interface chemical parameter synchronous sensing module, an oil pollution interference decoupling and effective activity index calculation module, a two-component asymmetric differential replenishment module, and a tank solution aging trend prediction and solution replacement early warning module. The constant-temperature bypass sampling and pretreatment module specifically extracts sample liquid from the main degreasing tank, performs multi-stage filtration and thermoelectric coupling temperature control, and outputs a clean laminar flow sample liquid at a constant temperature. The multi-dimensional interface chemical parameter synchronous sensing module specifically collects the dynamic surface tension value, scattered light turbidity value, and electrolyte conductivity value of the clean laminar flow sample liquid at the constant temperature; the dynamic surface tension value is measured under a preset bubble lifetime. The oil contamination interference decoupling and effective activity index calculation module specifically substitutes the scattered light turbidity value into a predetermined turbidity-tension attenuation interference model to calculate the false reduction value of surface tension caused by oil contamination interference; the dynamic surface tension value and the false reduction value of surface tension are summed to obtain the corrected effective surface tension value; the... The effective surface tension value is substituted into a predetermined concentration-surface tension standard curve to calculate the effective activity index. The two-component asymmetric differential replenishment module specifically compares the effective activity index with the target concentration of the surfactant component. When the effective activity index is lower than a first trigger threshold, the surfactant component is calculated and replenished. Simultaneously, the electrolyte conductivity value is compared with the target conductivity of the additive component. When the electrolyte conductivity value is lower than a second trigger threshold, the additive component is calculated and replenished. The bath aging trend prediction and bath replacement early warning module specifically records the cumulative replenishment amount of the surfactant component and, combined with the total area of ​​the aluminum substrates treated concurrently, calculates the consumption per unit area. When the consumption per unit area exceeds a preset performance decay threshold, a bath replacement early warning signal is generated. The above embodiments simultaneously acquire dynamic surface tension, turbidity, and conductivity data of clean laminar flow sample liquid through isothermal bypass sampling and multi-dimensional parameter sensing; real-time quantification of oil pollution interference is used with turbidity, and surface tension is compensated and corrected to calculate an effective activity index that truly reflects the oil removal capacity; based on this index and conductivity, differentiated replenishment through dual channels is driven to maintain the balance of tank liquid components, and predictive liquid replacement early warning is achieved by analyzing replenishment history and efficiency decay; this solves the problem of inaccurate monitoring caused by oil pollution interference in traditional methods, and realizes full-process management from accurate sensing and intelligent control to predictive maintenance, significantly improving process stability, chemical utilization rate, and environmental protection level.

[0018] To better implement the above embodiments, the above-mentioned isothermal bypass sampling and preprocessing module specifically includes: The constant flow sampling unit stabilizes the flow rate of the extracted sample liquid within the laminar flow range through a constant flow valve; it prevents pressure oscillations caused by flow rate fluctuations in the bubble pressure method measurement, thus obtaining a sample liquid flow without pressure oscillations. In specific implementation, the above-mentioned constant flow sampling unit is as follows: the system starts the sampling program, and the magnetically driven pump draws sample liquid from the central turbulent zone of the 2000L oil removal main tank at a flow rate of 2L / min; the sample liquid flows through the constant flow valve, and the flow rate is precisely limited to 500mL / min to ensure that the fluid entering the subsequent measurement chamber is in a laminar flow state (Reynolds number Re<2300), and to avoid the shearing interference of fluid turbulence on the microbubble formation process; The multi-stage filtration subunit includes a coarse filter and a self-cleaning precision filter. The sample solution is filtered sequentially through the coarse filter and the self-cleaning precision filter to remove solid particles and obtain a clean laminar flow sample solution. In practice, the multi-stage filtration sub-unit works as follows: the sample solution first passes through a 20-mesh stainless steel coarse filter to remove large oxide scale and tape residue; then it enters a 50-micron precision self-cleaning filter element; when the pressure difference between the inlet and outlet of the filter element exceeds 0.05MPa, the backwashing action is automatically triggered to ensure that the filtration channel is unobstructed. The thermoelectric coupling temperature control unit includes a Peltier effect semiconductor cooling / heating element and a PT100 temperature sensor; it is used to acquire the temperature of the clean laminar flow sample in real time and control the temperature of the clean laminar flow sample at the same reference temperature as when the concentration-surface tension standard curve was calibrated; when the standard deviation of temperature fluctuation is less than a set threshold for multiple consecutive sampling cycles, it is determined that the sample is in the test state at the constant temperature and a measurement trigger signal is sent to the multidimensional interface chemical parameter synchronous sensing module. In specific implementation, the thermoelectric coupling temperature control unit in the above embodiment is as follows: the PT100 sensor detects that the temperature of the sample liquid entering the measurement chamber is 45.5℃ (typical degreasing working temperature); the controller calculates the temperature difference ΔT = 20.5℃ and outputs a full-power cooling command; the semiconductor cooling chip quickly removes heat, causing the sample liquid to cool down as it flows through the heat exchange channel; when the temperature approaches 25.0℃, the PID algorithm automatically reduces the power and makes fine adjustments to lock the temperature at 25.0℃±0.1℃; N=10 (i.e., 10 seconds) is set, and the standard deviation threshold is set to 0.05℃; the system continuously monitors the temperature data within 10 seconds and calculates the standard deviation; only when the standard deviation is less than 0.05℃ is the system determined to have reached thermodynamic equilibrium, triggering subsequent surface tension measurements; The above embodiments provide a constant temperature bypass sampling and preprocessing method. Through the synergistic effect of magnetic pump sampling, multi-stage filtration and semiconductor precision temperature control, it creatively solves the problem of temperature fluctuations and impurities in industrial settings interfering with precision interface chemical measurements. The core is to establish a "standard laboratory-grade" measurement microenvironment independent of the production environment, ensuring the consistency of the comparison benchmark for all subsequent physical parameters (surface tension, conductivity), and laying the physical foundation for obtaining high-confidence data.

[0019] To better implement the above embodiments, the above-mentioned multidimensional interface chemical parameter synchronous sensing module specifically includes: The dynamic tension acquisition unit is used to generate a series of bubbles with different lifetimes by changing the gas flow rate using the maximum bubble pressure method, and to measure the pressure when each bubble bursts. Based on the Laplace equation, the dynamic surface tension value under the corresponding bubble lifetime is calculated, thereby forming a dynamic surface tension spectrum. In specific implementation, the dynamic tension acquisition unit in the above embodiment is as follows: the controller drives the piezoelectric ceramic air pump to generate bubble flows with bubble lifetimes of 10ms, 50ms, 100ms, 500ms and 1000ms in sequence according to the preset program; among them, the bubble lifetime of 100ms is set as the key monitoring point because the degreasing process of aluminum substrate is usually completed in a few seconds to a few minutes, and the time scale of 100ms is consistent with the interface renewal frequency of aluminum substrate when it is sprayed or immersed in the degreasing tank; while the time scale of surfactant molecules diffusing from the bulk phase to the interface is usually on the order of tens to hundreds of milliseconds, and the dynamic surface tension at 100ms can most realistically simulate the instantaneous wetting performance of the degreasing agent on the aluminum plate surface; Capturing the maximum bubble pressure at the moment of bubble burst using a high-frequency pressure sensor (sampling rate > 1 kHz) P max According to the Yang-Laplace equation P max = P 0 +2 c / r + ρgh ;in, P 0 Atmospheric pressure r Where is the capillary radius. r The liquid density of the clean laminar flow sample solution. g It is the acceleration due to gravity. h The surface tension is calculated in real time to determine the capillary immersion depth. c In this embodiment, the capillary radius... r =0.1mm, immersion depth h The constant value is 10mm; The feature point extraction unit is used to extract the tension value under a specific bubble lifetime from the dynamic surface tension spectrum, define it as the reference dynamic tension, and transmit the reference dynamic tension to the oil pollution interference decoupling and activity correction calculation module. In specific implementation, the feature point extraction unit in the above embodiment is specifically: t Taking 100ms as an example, the system automatically locks the bubble lifespan. t The data at 100ms was recorded as follows: c m=27.5 mN / m; This value represents the dynamic adsorption capacity of the degreasing agent molecules within 100 ms; The turbidity and conductivity co-acquisition unit is used to measure the turbidity value of the sample liquid using an infrared scattering turbidity meter and the conductivity value of the sample liquid using a quadrupole conductivity electrode to obtain the scattered light turbidity and electrolyte conductivity of the clean laminar flow sample liquid; and transmits the scattered light turbidity to the oil pollution interference decoupling and activity correction calculation module, and transmits the electrolyte conductivity to the two-component asymmetric differential replenishment module; In specific implementation, the turbidity and conductivity co-acquisition unit in the above embodiment is as follows: In this embodiment, the infrared turbidity meter uses the ISO 7027 standard 90-degree scattering light method to measure the current tank liquid turbidity. T turb =150 NTU; conductivity measured by a quadrupole conductivity electrode. K m =12.5 mS / cm; The above embodiments provide a method for synchronous sensing of multidimensional interface chemical parameters. By scanning the dynamic surface tension spectrum using the maximum bubble pressure method and combining turbidity and conductivity monitoring, the activity of the degreasing agent is creatively characterized from the perspective of microscopic molecular dynamics. This method not only measures static indicators but also captures the dynamic rate of surfactant molecule migration to the interface (i.e., the tension at a bubble lifetime of 100ms). This is directly related to the degreasing agent's ability to instantaneously wet and remove oil stains from the aluminum substrate surface, and is more meaningful for process guidance than traditional static parameters.

[0020] To better implement the above embodiments, the oil contamination interference decoupling and activity correction calculation module aims to solve the false activity phenomenon caused by oil contamination interference. In actual production, as aluminum plates are processed, oil contaminants such as stretching oil and cutting fluid emulsify and enter the degreasing tank. These oil contaminants typically have low surface tension (or contain surface-active components). Therefore, when the tank solution becomes dirty, the measured surface tension value may become very low (appearing to be highly active), but in reality, the effective degreasing agent (surfactant) has been consumed or encapsulated by the oil contaminants, losing its ability to further remove oil. The oil contamination interference decoupling and activity correction calculation module in the above embodiments specifically includes: The process of obtaining the concentration-surface tension standard curve: In the sample preparation process, a series of clean degreasing agent standard solutions with different concentration gradients were prepared using pure water; During the data acquisition process, the dynamic surface tension values ​​of each standard solution were measured at the constant temperature. In the curve fitting process, the concentration of the degreasing agent is used as the independent variable and the dynamic surface tension value is used as the dependent variable. Logistic regression or linear regression algorithm is used to fit the curve, determine the mathematical expression and coefficients of the concentration-surface tension standard curve, and then solidify the concentration-surface tension standard curve into the operation logic of the controller. In specific implementation, the process of obtaining the concentration-surface tension standard curve in the above embodiment is as follows: Using deionized water and fresh degreasing agent stock solution, five sets of clean standard solutions with concentration gradients of 1.0%, 2.7%, 7.4%, 20.0%, and 54.6% are precisely prepared; these standard solutions are sequentially passed into the constant temperature bypass sampling and pretreatment module, controlling the temperature to be constant at 25℃; using the dynamic tension acquisition unit in the multi-dimensional interface chemical parameter synchronous sensing module, the baseline dynamic tension value of each solution is measured at a bubble lifetime of 100ms; the recorded data points are as follows: (1.0%, 20.0 mN / m), (2.7%, 15.0 mN / m)... (The degreasing agent concentration is...) C (unit: %) is the independent variable ( x (axis), with dynamic surface tension value ( c (unit: mN / m) is the dependent variable. y The standard curve was obtained by performing logarithmic function regression analysis using the least squares method (axis). In this embodiment, the intercept term was determined to be 20 and the slope term to be -5 based on the regression calculation of the experimental data. The final mathematical expression of the fitted standard curve is as follows: c =-5 ln( C +20; This formula and its coefficients (-5, 20) are embedded in the controller's operational logic; During online monitoring, the system substitutes the corrected effective surface tension into this formula to calculate the current effective activity index. The process of obtaining the turbidity-tension attenuation interference model includes: To interfere with the sample preparation process, contaminants were added dropwise to a standard concentration of degreasing agent solution and emulsified to prepare a series of contaminated samples with different oil contents. During the synchronous measurement process, at the constant temperature, the scattered light turbidity value and dynamic surface tension value of each contaminated sample are measured simultaneously. The deviation calculation process calculates the decrease in dynamic surface tension value of each contaminated sample relative to the surface tension of the oil-free standard solution. In the model construction process, the turbidity of scattered light is used as the independent variable and the decrease in surface tension is used as the dependent variable. The least squares method is used to perform polynomial fitting to determine the mathematical expression and coefficients of the turbidity-tension attenuation interference model. The mathematical expression and coefficients of the turbidity-tension attenuation interference model are then solidified into the operation logic of the controller. In specific implementation, the process of obtaining the turbidity-tension attenuation interference model in the above embodiment is as follows: Take the prepared standard concentration degreasing agent solution (in this embodiment, a 1.0% solution is taken, with an oil-free reference tension) c s=20.0mN / m) as the substrate; stretching oil was added dropwise to it and fully emulsified to prepare a series of contaminated samples with turbidities of 100 NTU, 200 NTU and 300 NTU respectively; in the simultaneous measurement step, the scattered light turbidity value of each contaminated sample was measured simultaneously under the constant temperature condition of 25℃. T ) and measured dynamic surface tension value ( c m The measured data are as follows: Sample 1: T=100 NTU, c m =18.0 mN / m; Sample 2: T=200 NTU, c m =14.0 mN / m; Sample 3: T=300 NTU, c m =8.0 mN / m; Calculate the decrease in surface tension Δ for each sample relative to the oil-free baseline. c oil ;Δ c oil = c s -c m The decrease in turbidity of sample 1 was 20.0 − 18.0 = 2.0 mN / m; the decrease in turbidity of sample 2 was 20.0 − 14.0 = 6.0 mN / m; the decrease in turbidity of sample 3 was 20.0 − 8.0 = 12.0 mN / m; based on the turbidity value of scattered light ( T ) is the independent variable, and the decrease in surface tension (Δ) is the independent variable. c oil ) is the dependent variable, and a quadratic polynomial Δ is used. c oil = r 1T 2 + r Fit using 2T; substitute the above data into the solution for coefficients: when T When =100, Δ c oil =2.0, which matches 0.0001×(100) 2 +0.01×100=1+1=2; when T When =200, Δ c oil =6.0, which matches 0.0001×(200) 2 +0.01×200=2=4+2=6; Final determination of coefficients r 1 = 0.0001 r 2=0.01; the obtained turbidity-tension attenuation interference model is: Δ c oil =0.0001 T 2 + 0.01 T This mathematical relationship is incorporated into the control algorithm to calculate the false tension reduction caused by oil contamination in real time. When applying the above turbidity-tension attenuation interference model, specifically: the measured turbidity... T turb Substituting 150 NTU into the formula, Δ c oil =0.0001 (150) 2 +0.01 150 = 2.25 + 1.5 = 3.75 mN / m; This means that the current amount of oil contamination causes the measured value to be 3.75 mN / m lower than expected; The value here is only the value calculated in this embodiment, used to demonstrate the algorithm logic. In specific implementation, the actual value and coefficient need to be calibrated according to the specific chemicals. Calculate the corrected surface tension c corr = c m +Δ c oil =27.5 + 3.75 = 31.25 mN / m; Although the measured value of 27.5 seems to indicate high activity (close to 28.0 for fresh liquid), the turbidity-corrected value of 31.25 reveals that the actual activity is actually insufficient; This successfully identified the false activity and prevented the system from misjudging and not replenishing the feed; Will c corr Substituting 31.25 into the concentration-surface tension standard curve, the effective concentration can be calculated. C eff The concentration was approximately 4.1%, lower than the target concentration of 5.0%. The above embodiments provide a method for decoupling oil pollution interference and correcting activity. By establishing a mathematical model of turbidity and surface tension deviation, the method creatively solves the problem of distorted surface tension data in contaminated tank solutions. This method quantifies the masking effect of oil pollution on surface tension and restores the true effective concentration of surfactants through a reverse compensation algorithm. It avoids the risk of underdosing due to falsely low data and ensures the accuracy of activity judgment under oil pollution accumulation conditions.

[0021] To better implement the above embodiments, the aforementioned two-component asymmetric differential supplementation module is responsible for performing specific supplementation actions, aiming to solve the component imbalance problem; the aforementioned two-component asymmetric differential supplementation module specifically includes: The parameter definition subunit is used to define the target concentration of the surfactant component, the target conductivity of the auxiliary component, the replenishment dead zone, and the maximum limit for a single replenishment. In specific implementation, the parameter definition subunits of the above embodiments are as follows: Storage tank A1, containing a high-concentration surfactant compound solution (such as isomeric alcohol ether + APG); storage tank B1, containing an auxiliary agent compound solution (such as citric acid, sodium gluconate, and corrosion inhibitor); the actuator includes two precision electromagnetic diaphragm metering units, namely a first metering pump and a second metering pump, with a flow accuracy of ±1%; the target surfactant concentration is set. C ta =5.0%, corresponding to a target tension of 28.0 mN / m; set the target conductivity of the additive. K ta =13.0 mS / cm; the replenishment dead zones are set at 0.2% and 0.5 mS / cm respectively; the purpose of setting the replenishment dead zones is to eliminate oscillations caused by sensor measurement noise and tank liquid mixing delay; the specific calculation method is: take the standard deviation of 100 consecutive measurement data under steady-state operation of the system ( s The dead zone range is set to ±3. s To ±5 s For example, if the noise fluctuation of concentration measurement is 0.05%, considering the mixing inhomogeneity, the dead zone is set to a standard deviation. s Four times, or 0.2%, to ensure that the pump is only triggered when the actual concentration deviation exceeds the control range, thus avoiding wear and control instability caused by frequent start-stop of the metering pump; The component A replenishment logic unit is used to receive the effective activity index, compare the effective activity index with the target concentration to obtain a first comparison value; if the first comparison value is lower than the difference between the target concentration and the replenishment dead zone, the required amount of surfactant component (component A) is calculated based on the difference and the total volume of the tank liquid, and the first metering pump is driven to perform the dosing. In specific implementation, the logic unit for adding component A in the above embodiment is specifically as follows: based on the current effective concentration C eff =4.1%, target 5.0%; calculated deviation 0.9% > 0.2% (dead zone); calculated the volume of component A (high concentration surfactant) to be added. V A =(5.0%-4.1%) 2000L K λ / Concentration factor, the initial concentration factor in this embodiment K λ The value is 1, and it will be continuously adjusted during subsequent use; the controller drives the first metering pump to add the corresponding amount of medicine; The component B replenishment logic unit is used to receive the electrolyte conductivity, compare the electrolyte conductivity with the target conductivity to obtain a second comparison value; if the second comparison value is lower than the difference between the target conductivity and the replenishment dead zone, the required amount of adjuvant component (component B) is calculated based on the second comparison value, and the second metering pump is driven to perform dosing. In specific implementation, the logic unit added to component B of the above embodiment is specifically: the current measured conductivity. K m =12.5 mS / cm, target 13.0 mS / cm; deviation 0.5 mS / cm ≤ 0.5 mS / cm (dead zone); the additive concentration is determined to meet the requirements (possibly due to water evaporation leading to concentration), therefore the second metering pump is not started; The cross-interlock control unit is used to monitor changes in turbidity during replenishment; if an abnormal surge in turbidity occurs after replenishing component A, the replenishment is stopped immediately; at the same time, it ensures that the replenishment of component A and component B are staggered on the time axis to avoid precipitation caused by excessively high local concentrations. In specific implementation, the cross-interlock control unit of the above embodiment is specifically configured as follows: when the first metering pump is working, the second metering pump is forcibly locked; after the first metering pump finishes working, a 5-minute delay is made to allow the tank solution to circulate evenly before determining whether the second metering pump needs to be started; this logic avoids the risk of salt precipitation caused by direct mixing of two high-concentration mother liquors at the dosing port; through this asymmetric addition, the system only added surfactant in the above case, without adding additives; if traditional single-component mother liquor is used for addition, excess additives will inevitably be introduced in order to replenish surfactants, leading to additive accumulation, cost waste, and even affecting the cleaning effect. This system perfectly solves this problem; To better implement the above embodiments, the two-component asymmetric differential supplementation module of the above embodiments further includes: The supplementary response verification and efficiency evaluation subunit is used to restart the measurement process after a set mixing time following the supplementary action; and to calculate the activity recovery rate caused by each supplementation. In specific implementation, the above embodiment's response verification and efficiency evaluation subunit works as follows: After each addition of component A (surfactant), the system waits for a preset mixing time. Once the tank solution is fully mixed by the main circulation pump, a complete monitoring process is automatically initiated to obtain the corrected surface tension value after addition. c corr ; Calculate the activity recovery rate of this supplement. l 1. Calculate the theoretical expected recovery value. l 2 = (Target concentration corresponding tension - Effective concentration corresponding tension before replenishment); Calculate the actual recovery value. l 3= l 2-Before adding ccorr The recovery rate l 1= l 3 / l 2) × 100%; In this embodiment, before supplementation c corr The concentration was 31.25 mN / m (corresponding to an effective concentration of 4.1%), and theoretically, it is expected to recover to 28.0 mN / m (5.0%) after supplementation. l 2 = -3.25mN / m; if the actual measurement is after adding... l 3 is 29.0 mN / m, then the actual recovery value is... l 3 = -2.25 mN / m, recovery rate l 1 = (-2.25 / -3.25) × 100% ≈ 69.2%; An adaptive gain adjustment subunit is used to automatically correct the proportional coefficient in the replenishment algorithm if the recovery rate of three consecutive replenishments is lower than a preset efficiency threshold, thereby increasing the required amount of surfactant component calculated in the next replenishment and realizing adaptive iteration of control parameters. The efficiency threshold in this embodiment is 80%, which is determined based on the empirical ratio between the theoretical stoichiometry of the degreasing agent and the actual engineering losses (such as carry-out from the hanger and volatilization). Setting "three consecutive times" as the trigger condition is based on the principle of statistical process control (SPC) and aims to distinguish between single random errors (such as bubble interference and instantaneous uneven mixing) and systematic deviations of the system (such as pump wear and changes in the buffering capacity of the tank liquid). Only when the deviation shows a continuous trend is it determined to be a model parameter mismatch, thereby triggering the correction of the algorithm gain and preventing system over-adjustment caused by single misjudgment. In specific implementation, the aforementioned adaptive gain adjustment subunit is: a system preset supplementary efficiency threshold. l y The calculated recovery rate is 80%; if three consecutive additions of component A are performed, the recovery rate is... l If all values ​​are below 80%, then the concentration-addition ratio coefficient in the current addition algorithm is determined. K λ (That is, the formula for calculating the supplementary amount) V A = (C ta - C eff ) × tank volume × K λ coefficients in K λ The solution may be too small due to aging or changes in composition, resulting in insufficient replenishment. In this case, the adaptive gain adjustment subunit automatically activates the correction logic to adjust the proportional coefficient. K λ Multiply by a correction factor greater than 1 (e.g., 1.2), i.e. Kλ new = K λ old×1.2; The updated coefficient will be used to calculate the next replenishment amount, thereby dynamically amplifying the replenishment command to overcome the problem of "replenishment efficiency decay" caused by changes in the tank solution system, until the replenishment efficiency recovers to above the threshold; At the same time, this correction event will be recorded as an auxiliary criterion for judging the aging degree of the tank solution; The above embodiments provide a two-component asymmetric differential replenishment method, which creatively solves the composition imbalance problem in the traditional single-component replenishment mode by splitting the degreasing agent into two independent control loops: surfactant and additive. In actual aluminum processing, surfactants are mainly consumed through carry-out and emulsification, while additives (such as acid-base buffers) are consumed more slowly and are greatly affected by water evaporation. This method achieves the goal of replenishing what is lacking. In the above case, only surfactant was added and no additive was added, which ensured the degreasing effect and avoided the chemical waste and waste liquid treatment pressure caused by excessive additives.

[0022] To better implement the above embodiments, the bath aging trend prediction and bath replacement early warning module specifically includes: The historical data serialization unit is used to record the cumulative value of surfactant component demand for each execution by the two-component asymmetric differential replenishment module, with the production batch as the time axis. The historical data serialization unit in the above embodiment is specifically as follows: the system database automatically creates a record table with "tank change cycle" as the ID; whenever the two-component asymmetric differential replenishment module completes a dosing action (e.g., 2.0L of component A is added), this unit immediately records the timestamp and replenishment amount, and updates the cumulative surfactant consumption for the current cycle; in this embodiment, the current cycle has been running for 14 days, and the cumulative consumption... E VA The record is 240L; The dirt-holding capacity decay analysis unit is used to calculate the degreasing efficiency per unit surfactant consumption by combining the total processing area of ​​the aluminum substrate during the same period. The above embodiment of the contaminant-holding capacity attenuation analysis unit includes: The unit consumption calculation subunit is used to obtain the cumulative replenishment amount of surfactant component demand and the total processing area of ​​aluminum substrate within the current time window; the cumulative replenishment amount is divided by the total processing area to obtain the mass of surfactant consumed per unit area of ​​aluminum substrate; The efficiency decay determination subunit is used to take the mass of surfactant consumed per unit area of ​​aluminum substrate as a measurement index. When the index increases by more than a set percentage year-on-year, it is determined that the tank solution's dirt-holding capacity has decreased and an early warning state is entered. The above embodiment's contamination-holding capacity decay analysis unit specifically involves obtaining the total cumulative processed area of ​​the aluminum substrate within the current cycle from the production line's MES system via an industrial communication protocol. S ta =12000m 2 The unit consumption index is calculated using the unit consumption calculation formula. E de1 = E VA / S ta =240L / 12000m 2 =0.02L / m 2 This is used to characterize the reagent cost required to treat each square meter of aluminum; the system retrieves the unit consumption value from the previous comparable time period (e.g., the previous 72 hours). E de2 =0.018L / m 2 ; Calculate the unit consumption growth rate = (0.02L / m 2 -0.018L / m 2 ) / 0.018 100% = 11%; The preset growth rate warning threshold for this implementation is 10%, so the current growth rate of 11% has exceeded the threshold; the system determines that the tank solution's dirt-holding capacity (i.e., the amount of oil dirt that a unit of agent can hold and remove) is significantly decreasing. Even if the concentration is maintained at 5.0% by replenishment, its actual oil removal efficiency has decreased, and a tank solution performance degradation warning signal is triggered. The remaining lifetime estimation unit is used to perform regression analysis on the turbidity baseline value to predict the time point when the turbidity reaches the saturation threshold. The remaining lifetime estimation unit in the above embodiment specifically involves: extracting the baseline turbidity value at each day's settling time (excluding interference from production bubbles) to form a time series: Day 1 (10 NTU), Day 7 (150 NTU), and Day 14 (600 NTU); performing data fitting with time as the horizontal axis and turbidity as the vertical axis, and using an exponential regression algorithm to fit the turbidity growth curve. By fitting the time series data, we can obtain... k The value is 0.334; the turbidity demulsification limit threshold is set to... T max=800 NTU (at this point, the oil will be reverse-absorbed onto the aluminum plate surface); based on the extrapolation of the fitted curve, the predicted time node to reach 800 NTU is Day 16; that is, the system calculates the remaining effective lifespan to be 2 days; the turbidity demulsification limit threshold is obtained through a laboratory calibration experiment for maximum dirt holding capacity; by continuously adding pollutants to the standard degreasing agent and stirring, while monitoring the continuity of the water film (wetting angle) and the state of the tank solution on the surface of the aluminum plate after degreasing; when an undispersible floating oil layer (demulsification) is observed on the surface of the tank solution, or when oil spots remain on the cleaned aluminum plate (reverse adsorption), the turbidity value at this time is recorded (measured as 850 NTU in this embodiment); to retain a safety margin, 90%-95% of this critical value (therefore, the value is 800 NTU in this embodiment) is taken as the system's liquid replacement alarm threshold to ensure that the replacement is completed before the tank solution completely fails; The intelligent decision output unit is used to make a comprehensive judgment: if the oil removal efficiency of the unit surfactant consumption decreases by more than a set threshold year-on-year, or the turbidity is greater than the demulsification limit, then a "forced liquid replacement command" is generated, the replenishment pump is locked and an alarm is triggered. The intelligent decision output unit in the above embodiment is specifically as follows: The system presets the standard unit consumption benchmark for new tank liquid to be 0.01 L / m³. 2 Logical judgment 1: The currently calculated unit consumption is 0.02L / m 2 The value is 200% of the baseline, exceeding the set degradation threshold (150%). This indicates that most of the added surfactant is consumed by aging products or adsorbed by sludge and is not effectively used for oil removal. Continuing to add more surfactant is an ineffective cost investment. Logic judgment two: The current turbidity of 600 NTU is approaching the limit of 800 NTU. Execution action: Based on logic judgment one (excessive consumption) and logic judgment two (end of life), the unit immediately generates a forced liquid replacement command with the highest priority. The forced liquid replacement command is converted into two concurrent actions: a red pop-up window appears on the HMI main interface saying "Battery solution failed, tank replacement recommended"; an electrical signal is sent to forcibly cut off the metering pump power of the two-component asymmetric differential replenishment module to prevent further waste of reagents in the failed tank solution. The above embodiments provide a method for predicting the aging trend of the bath solution and providing early warning of solution replacement. By introducing the economic and technical indicator of unit consumption efficiency, it creatively elevates chemical management from simple concentration control to the level of full life cycle efficiency management. This method avoids the arbitrariness of workers judging the bath replacement based on experience (such as looking at the liquid color and smelling the odor) in traditional processes, eliminates the risk of "secondary pollution" on the aluminum surface caused by excessive aging of the bath solution, and also prevents the premature discharge of still active bath solution, thus achieving the optimal balance between economic benefits and process quality.

[0023] To better implement the above embodiments, the online monitoring and replenishment control system for the activity of a green degreasing agent on an aluminum substrate in this embodiment further includes: Adaptive cleaning and zero-point calibration module: It is configured to perform gas-liquid two-phase cleaning of the sensor after the monitoring cycle ends, and use pure water for measurement to generate zero-point drift compensation value, which is used to correct the measurement data of the next monitoring cycle; This module is designed to ensure long-term maintenance-free operation of the system because the degreasing agent itself contains high viscosity components, and oil stains are easy to adhere to the capillary wall and turbidimeter window, causing measurement drift. The adaptive cleaning and zero-point calibration module in the above embodiments specifically includes: The cleaning trigger subunit is used to trigger the cleaning process after each measurement task is completed or when data anomalies are detected. The gas-liquid two-phase cleaning unit is used to control the cleaning valve group to alternately inject pure water and compressed air into the measuring pipeline to form a gas-liquid plunger flow and peel off the oil film on the sensor surface. In specific implementation, the gas-liquid two-phase cleaning unit in the above embodiment is as follows: open the pure water valve and the compressed air valve, and switch at a frequency of 2Hz through the solenoid valve to form a plunger flow of alternating water-air-water-air in the pipeline; the shear stress of this flow on the pipe wall is 5-10 times that of simple water flow, which can effectively peel off the adhering oil stains. The dynamic calibration subunit is used to measure the actual surface tension of pure water after cleaning. The drift compensation subunit is used to compare the measured value with the standard value, calculate the deviation value, and generate the zero-point drift compensation value. If the value is within the allowable range, it is sent to the multi-dimensional interface chemical parameter synchronous sensing module for data correction. If it exceeds the range, an alarm is triggered. In specific implementation, the drift compensation subunit of the above embodiment is as follows: the surface tension of pure water at 25°C is measured, and the standard value is 72.0 mN / m; if the measured value is 71.5 mN / m, it indicates that there is a system error of -0.5; the system automatically corrects all subsequent measurement results by adding 0.5; if the measured value is lower than 60 mN / m, it is determined that the capillary is seriously contaminated, triggering secondary cleaning or an alarm. The above embodiments provide an adaptive cleaning and zero-point calibration method. By using gas-liquid plunger flow cleaning and pure water benchmark comparison, the method creatively achieves long-term maintenance-free operation of the online monitoring system. In view of the characteristics of high viscosity of degreasing agent and easy contamination of probe, the method ensures that the sensor is always in a clean state, and eliminates the error caused by instrument aging through zero-point drift compensation, thus ensuring the long-term stability and repeatability of monitoring data.

[0024] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0025] The preferred embodiments of the invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention.

Claims

1. A control system for online monitoring and replenishment of green degreasing agent on aluminum substrates, characterized in that, include: Thermostatic bypass sampling and pretreatment module: It is configured to extract sample liquid from the main degreasing tank, perform multi-stage filtration and thermoelectric coupling temperature control, and output clean laminar flow sample liquid at a constant temperature; Multidimensional interface chemical parameter synchronous sensing: configured to simultaneously collect the dynamic surface tension value, scattered light turbidity value and electrolyte conductivity value of the clean laminar flow sample at the constant temperature; The dynamic surface tension value is a value measured under a preset bubble lifetime. Oil pollution interference decoupling and effective activity index calculation module: configured to substitute the scattered light turbidity value into a predetermined turbidity-tension attenuation interference model to calculate the false reduction value of surface tension caused by oil pollution interference; The corrected effective surface tension value is obtained by summing the dynamic surface tension value with the spurious decrease in surface tension. The effective surface tension value is substituted into a predetermined concentration-surface tension standard curve, and the effective activity index is calculated by inversion. A two-component asymmetric differential replenishment module is configured to compare the effective activity index with the target concentration of the surfactant component, and when the effective activity index is lower than a first trigger threshold, calculate and replenish the surfactant component; simultaneously, it compares the electrolyte conductivity value with the target conductivity of the auxiliary agent component, and when the electrolyte conductivity value is lower than a second trigger threshold, calculate and replenish the auxiliary agent component. The bath aging trend prediction and bath replacement early warning module is configured to record the cumulative amount of surfactant components added and, in conjunction with the total area of ​​aluminum substrates treated in the same period, calculate the consumption per unit area. When the consumption per unit area exceeds a preset performance decay threshold, a liquid replacement warning signal is generated.

2. The online monitoring and replenishment control system for the activity of a green degreasing agent on an aluminum substrate as described in claim 1, characterized in that, The isothermal bypass sampling and preprocessing module includes: The constant flow sampling unit stabilizes the flow rate of the extracted sample liquid within the laminar flow range through a constant flow valve; The multi-stage filtration subunit includes a coarse filter and a self-cleaning precision filter. The sample solution is filtered sequentially through the coarse filter and the self-cleaning precision filter to remove solid particles and obtain a clean laminar flow sample solution. The thermoelectric coupling temperature control unit acquires the temperature of the clean laminar flow sample liquid in real time and controls the temperature of the clean laminar flow sample liquid at the same reference temperature as when the concentration-surface tension standard curve was calibrated. When the standard deviation of temperature fluctuation in multiple consecutive sampling cycles is less than the set threshold, it is determined that the sample is in the test state at the constant temperature and a measurement trigger signal is sent to the multidimensional interface chemical parameter synchronous sensing module.

3. The online monitoring and replenishment control system for the activity of a green degreasing agent on an aluminum substrate as described in claim 2, characterized in that, The multidimensional interface chemical parameter synchronous sensing module includes: The dynamic tension acquisition unit is used to generate a series of bubbles with different lifetimes by changing the gas flow rate using the maximum bubble pressure method, and to measure the pressure when each bubble bursts. Based on the Laplace equation, the dynamic surface tension value under the corresponding bubble lifetime is calculated, thereby forming a dynamic surface tension spectrum. The feature point extraction unit is used to extract the tension value under a specific bubble lifetime from the dynamic surface tension spectrum, define it as the reference dynamic tension, and transmit the reference dynamic tension to the oil pollution interference decoupling and activity correction calculation module. The turbidity and conductivity co-acquisition unit is used to measure the turbidity value of the sample liquid using an infrared scattering turbidity meter and the conductivity value of the sample liquid using a quadrupole conductivity electrode to obtain the scattered light turbidity and electrolyte conductivity of the clean laminar flow sample liquid; and transmits the scattered light turbidity to the oil pollution interference decoupling and activity correction calculation module, and transmits the electrolyte conductivity to the two-component asymmetric differential replenishment module.

4. The online monitoring and replenishment control system for the activity of a green degreasing agent on an aluminum substrate as described in claim 1, characterized in that, The process of obtaining the concentration-surface tension standard curve in the oil pollution interference decoupling and effective activity index calculation module includes: In the sample preparation process, a series of clean degreasing agent standard solutions with different concentration gradients were prepared using pure water; During the data acquisition process, the dynamic surface tension values ​​of each standard solution were measured at the constant temperature. In the curve fitting process, the concentration of the degreasing agent is used as the independent variable and the dynamic surface tension value is used as the dependent variable. Logistic regression or linear regression algorithm is used to fit the curve, determine the mathematical expression and coefficients of the concentration-surface tension standard curve, and then solidify the concentration-surface tension standard curve into the operation logic of the controller.

5. The online monitoring and replenishment control system for the activity of a green degreasing agent on an aluminum substrate according to claim 1, characterized in that, The process of obtaining the turbidity-tension attenuation interference model in the oil pollution interference decoupling and effective activity index calculation module includes: To interfere with the sample preparation process, contaminants were added dropwise to a standard concentration of degreasing agent solution and emulsified to prepare a series of contaminated samples with different oil contents. During the synchronous measurement process, at the constant temperature, the scattered light turbidity value and dynamic surface tension value of each contaminated sample are measured simultaneously. The deviation calculation process calculates the decrease in dynamic surface tension value of each contaminated sample relative to the surface tension of the oil-free standard solution. In the model construction process, the turbidity of scattered light is used as the independent variable and the decrease in surface tension is used as the dependent variable. The least squares method is used for polynomial fitting to determine the mathematical expression and coefficients of the turbidity-tension attenuation interference model. The mathematical expression and coefficients of the turbidity-tension attenuation interference model are then solidified into the operation logic of the controller.

6. The online monitoring and replenishment control system for the activity of a green degreasing agent on an aluminum substrate as described in claim 4, characterized in that, The two-component asymmetric differential supplementation module includes: The parameter definition subunit is used to define the target concentration of the surfactant component, the target conductivity of the auxiliary component, the replenishment dead zone, and the maximum limit for a single replenishment. The component A replenishment logic unit is used to receive the effective activity index, compare the effective activity index with the target concentration to obtain a first comparison value; if the first comparison value is lower than the difference between the target concentration and the replenishment dead zone, the required amount of surfactant component is calculated based on the difference and the total volume of the tank liquid, and the first metering pump is driven to perform dosing. The component B replenishment logic unit is used to receive the electrolyte conductivity, compare the electrolyte conductivity with the target conductivity to obtain a second comparison value; if the second comparison value is lower than the difference between the target conductivity and the replenishment dead zone, the required amount of the adjuvant component is calculated based on the second comparison value, and the second metering pump is driven to perform dosing. The cross-interlock control unit is used to monitor changes in turbidity during replenishment. If an abnormal surge in turbidity occurs after replenishing component A, the replenishment is stopped immediately. At the same time, it ensures that the replenishment of component A and component B are staggered on the time axis to avoid precipitation caused by excessively high local concentrations.

7. The online monitoring and replenishment control system for the activity of a green degreasing agent on an aluminum substrate as described in claim 6, characterized in that, The two-component asymmetric differential supplementation module also includes: The supplementary response verification and efficiency evaluation subunit is used to restart the measurement process after a set mixing time following the supplementary action; and to calculate the activity recovery rate caused by each supplementation. The adaptive gain adjustment subunit is used to automatically correct the proportional coefficient in the replenishment algorithm if the recovery rate of three consecutive replenishments is lower than the preset efficiency threshold, thereby increasing the required amount of surfactant component calculated in the next iteration and realizing adaptive iteration of control parameters.

8. The online monitoring and replenishment control system for the activity of a green degreasing agent on an aluminum substrate as described in claim 6, characterized in that, The tank solution aging trend prediction and solution replacement early warning module includes: The historical data serialization unit is used to record the cumulative value of surfactant component demand for each execution by the two-component asymmetric differential replenishment module, with the production batch as the time axis. The dirt-holding capacity decay analysis unit is used to calculate the degreasing efficiency per unit surfactant consumption by combining the total processing area of ​​the aluminum substrate during the same period. The remaining lifetime estimation unit is used to perform regression analysis on the turbidity baseline value to predict the time point when the turbidity reaches the saturation threshold. The intelligent decision output unit is used to make a comprehensive judgment: if the oil removal efficiency of the unit surfactant consumption decreases by more than a set threshold year-on-year, or the turbidity is greater than the demulsification limit, then a forced liquid replacement command is generated, the replenishment pump is locked, and an alarm is triggered.

9. The online monitoring and replenishment control system for the activity of a green degreasing agent on an aluminum substrate as described in claim 8, characterized in that, The contaminant-holding capacity attenuation analysis unit includes: The unit consumption calculation subunit is used to obtain the cumulative replenishment amount of surfactant component demand and the total processing area of ​​aluminum substrate within the current time window; the cumulative replenishment amount is divided by the total processing area to obtain the mass of surfactant consumed per unit area of ​​aluminum substrate; The efficiency decay determination subunit is used to measure the mass of surfactant consumed per unit area of ​​aluminum substrate. When the mass of surfactant consumed per unit area increases by more than a set percentage year-on-year, the tank solution’s dirt-holding capacity is determined to have decreased, and an early warning state is entered.

10. The online monitoring and replenishment control system for the activity of a green degreasing agent on an aluminum substrate as described in claim 1, characterized in that, Also includes: Adaptive cleaning and zero-point calibration module: It is configured to perform gas-liquid two-phase cleaning of the sensor after the monitoring cycle ends, and use pure water for measurement to generate zero-point drift compensation value, which is used to correct the measurement data of the next monitoring cycle. The adaptive cleaning and zero-point calibration module includes: The cleaning trigger subunit is used to trigger the cleaning process after each measurement task is completed or when data anomalies are detected. The gas-liquid two-phase cleaning unit is used to control the cleaning valve group to alternately inject pure water and compressed air into the measuring pipeline to form a gas-liquid plunger flow and peel off the oil film on the sensor surface. The dynamic calibration subunit is used to measure the actual surface tension of pure water after cleaning. The drift compensation subunit is used to compare the measured value with the standard value, calculate the deviation value, and generate the zero-point drift compensation value. If the value is within the allowable range, it is sent to the multi-dimensional interface chemical parameter synchronous sensing module for data correction. If it exceeds the range, an alarm is triggered.

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