System and method for judging air scrubbing end point in regeneration process of fine treatment resin

Abstract

The embodiment of the invention provides a system and a method for judging an air scrubbing end point in a regeneration process of fine treatment resin, and the method comprises the following steps: obtaining background turbidity of demineralized water, carrying out pollutant component analysis on invalid resin, and determining a characteristic absorption peak of a main pollutant; a scrubbing step is started, and turbidity data and pollutant characteristic UV absorption spectrum data of scrubbing drainage in the drainage main pipe are collected in real time; on the basis of an AI prediction model, the scrubbing end point is dynamically judged in combination with the turbidity, the UV absorption peak attenuation trend and temperature compensation in the drainage mother pipe; and the scrubbing end point is verified, after verification is passed, UV point scanning analysis is carried out, if the absorption peak value is lower than the initial peak value, scrubbing is ended, and otherwise, an abnormal alarm signal is output.

Description

TECHNICAL FIELD

[0001] The present document relates to the technical field of condensate polishing resin regeneration, and particularly relates to an air scrubbing endpoint judgment system and method in a polishing resin regeneration process. BACKGROUND

[0002] In a condensate polishing system of a power plant, in-vitro regeneration of resin is a key link to ensure water quality, and an air scrubbing step is used to remove corrosion products, suspended solids and fine resin adsorbed on the surface of the resin to improve the regeneration effect.

[0003] At present, most power plants in China still use the air scrubbing method based on a fixed number of cycles in the resin regeneration process, that is, the scrubbing number is preset according to operating experience. This method has significant defects: when the water quality fluctuates and the degree of resin contamination changes, the fixed number of scrubbing cannot be adjusted adaptively, which easily leads to insufficient or excessive scrubbing. When the scrubbing is insufficient, the remaining pollutants affect the regeneration efficiency and the water quality; when the scrubbing is excessive, the resin is accelerated to wear, the service life is shortened, and the operating cost is increased. Although there are individual attempts to monitor turbidity or a single indicator to determine the endpoint in the prior art, they often rely on manual experience, lack multi-parameter fusion analysis and intelligent decision-making capability, and cannot realize precise and adaptive scrubbing control. In addition, the traditional method does not consider the differences in spectral characteristics of different pollutants, and it is difficult to realize targeted scrubbing strategies, resulting in waste of water resources and resin resources. Therefore, developing a method and system capable of real-time monitoring, intelligent judgment and automatic adjustment of the scrubbing endpoint has become a key technical requirement to improve the resin regeneration efficiency and reduce the operating cost. SUMMARY

[0004] One or more embodiments of the present specification provide an air scrubbing endpoint judgment system in a polishing resin regeneration process, comprising: a resin separation tower, a negative regeneration tower and a positive regeneration tower, which are connected through a bottom drainage main pipe; A sampling pipeline is arranged on the drainage main pipe, and the sampling pipeline is connected to a resin scrubbing monitor; The resin scrubbing monitor comprises a turbidity sensor, a UV spectrophotometer, a temperature sensor, a constant-flow pump, a compressed air blowing unit and an AI processing module; The AI processing module trains a scrubbing endpoint prediction model based on historical scrubbing data, dynamically predicts the scrubbing endpoint by real-time receiving turbidity, UV absorption spectrum and temperature data, and outputs a scrubbing control instruction.

[0005] Further, the AI processing module further comprises: a multi-modal data fusion unit for aligning and feature extracting turbidity time series data, a UV absorption peak sequence and a temperature sequence; The adaptive learning unit optimizes the parameters of the prediction model in reverse based on the actual pollutant residue data after each scrubbing.

[0006] Furthermore, the system also includes: The resin type identification unit is used to automatically identify the type of resin contamination based on the initial contaminant analysis results and call the corresponding scrubbing endpoint judgment strategy. The abnormal peak detection module is used to identify unexpected absorption peaks in the UV absorption spectrum and trigger alarms and manual intervention processes.

[0007] Furthermore, the turbidity flow cell and the spectral sample cell in the compressed air purging unit and the resin scrubbing monitor are connected; the sampling tube, the compressed air purging unit, the turbidity flow cell and the spectral sample cell are all equipped with a combination of solenoid valve and manual valve, the manual valve is located in front of the solenoid valve, and the manual gate can facilitate the replacement of the solenoid valve and the maintenance of the unit. The constant flow pump is installed in the water inlet pipe inside the resin scrubbing monitor to maintain a stable water sample flow rate when the water pressure in the drain header fluctuates. The temperature sensor is used to monitor the water sample temperature in real time and to provide temperature compensation parameters for the detection process of the turbidity sensor and UV spectrophotometer.

[0008] This specification provides one or more embodiments of a method for determining the endpoint of air scrubbing during the regeneration of fine-treatment resin, including: S1: Obtain the background turbidity of the demineralized water and analyze the pollutant components of the failed resin to determine the characteristic absorption peaks of the main pollutants; S2: Start the scrubbing sequence and collect turbidity data and pollutant characteristic UV absorption spectrum data of the scrubbing drainage in the main drain pipe in real time; S3: Based on the AI ​​prediction model, combined with the turbidity in the drain header, the attenuation trend of the UV absorption peak and temperature compensation, the scrubbing endpoint is dynamically determined. S4: Verify the end point of the scrubbing process. If the verification is successful, perform a UV spot scan analysis. If the absorption peak value is lower than the initial peak value, the scrubbing process ends; otherwise, an abnormal alarm signal is output.

[0009] Furthermore, the step of dynamically determining the endpoint of the scrubbing process includes: Construct a fitting function for the change of turbidity data in scrubbing drainage over time; Based on the changes in the first and second derivatives of the function, candidate time points for the end of the scrubbing process are identified.

[0010] Furthermore, the candidate endpoint time point satisfies the following conditions: ; Where f(t) is the fitting function of turbidity Z versus time t. This refers to the time point when turbidity enters the judgment interval. The time point where the derivative is zero and the second derivative is positive.

[0011] Furthermore, after identifying the candidate endpoint time points, the stability of the endpoint is further verified using a test constant, which is calculated as follows: ; Where ΔZ is the change in turbidity per unit time, and m is the number of sampling points.

[0012] This specification provides one or more embodiments of an electronic device, including: Processor; and, A memory is configured to store computer-executable instructions, which, when executed, cause the processor to implement the steps of the air scrubbing endpoint determination method described above during the regeneration of the fine-treatment resin.

[0013] This specification provides one or more embodiments of a storage medium for storing computer-executable instructions, which, when executed, implement the steps of the air scrubbing endpoint determination method described above during the regeneration of fine-treatment resin.

[0014] By incorporating AI prediction models, multimodal data fusion, and adaptive learning mechanisms, this invention changes the traditional extensive control mode that relies on fixed scrubbing times or single parameter thresholds. It achieves precise and dynamic scrubbing control based on the actual contamination state of the resin, significantly improving the accuracy and reliability of endpoint determination. By integrating turbidity temporal changes, pollutant characteristic ultraviolet absorption spectra, and temperature compensation parameters, and using AI models for intelligent prediction, it avoids misjudgments caused by water quality fluctuations or changes in pollutant composition, fundamentally solving the problems of insufficient or excessive scrubbing. This not only ensures resin regeneration and extends resin lifespan, reducing resin procurement and replacement costs, but also significantly reduces the consumption of demineralized water and compressed air by precisely controlling the scrubbing endpoint, achieving significant water and energy savings. Furthermore, the judgment strategy can be continuously optimized with the accumulation of operational data, possessing the potential for continuous efficiency improvement.

[0015] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in one or more embodiments of this specification or in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating a method for determining the endpoint of air scrubbing during the regeneration of fine-treatment resin, provided for one or more embodiments of this specification. Figure 2 This is a schematic diagram of the structure of an electronic device provided for one or more embodiments of this specification. Detailed Implementation

[0018] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this document.

[0019] System Implementation Examples According to an embodiment of the present invention, an air scrubbing endpoint determination system for the regeneration process of fine-treatment resin is provided. The air scrubbing endpoint determination system for the regeneration process of fine-treatment resin in this embodiment of the present invention specifically includes: a resin separation tower, an anion regeneration tower and a cation regeneration tower, used to respectively complete resin separation, anion resin regeneration and cation resin regeneration, and the three are hydraulically connected through a common drain header at the bottom.

[0020] A sampling pipeline is installed on the main drainage pipe, and the sampling pipeline is connected to a resin scrubbing monitor. The resin scrubbing monitor continuously extracts representative samples from the effluent of each tower through the sampling pipeline.

[0021] The resin scrubbing monitoring instrument includes a turbidity sensor, a UV spectrophotometer, a temperature sensor, a constant flow pump, a compressed air purging unit, and an AI processing module. The turbidity sensor is responsible for detecting the turbidity of the water sample in real time, directly reflecting the total amount of suspended solids and fine resin fragments detached from the resin surface. The UV spectrophotometer is used for in-depth water quality analysis, accurately quantifying the concentration of dissolved organic pollutants or specific ions by detecting the absorbance of the water sample at specific ultraviolet-visible wavelengths. The temperature sensor simultaneously monitors the water sample temperature and is input into the analysis algorithm as a temperature compensation parameter during the detection process of the turbidity sensor and the UV spectrophotometer, in order to correct the deviation of turbidity and UV detection values ​​caused by temperature changes.

[0022] The constant flow pump is installed in the inlet pipe inside the resin scrubbing monitor to maintain a stable water sample flow rate when the water pressure in the drain pipe fluctuates, ensuring that the water sample flowing into the turbidity flow cell and the spectral sample cell remains stable. The compressed air purging unit is connected to the turbidity flow cell and the spectral sample cell in the resin scrubbing monitor, and can automatically remove any residues or air bubbles that may be attached to the cell wall by spraying compressed air during monitoring intervals or when necessary, effectively preventing sensor window contamination and clogging. The sampling tube, the compressed air purging unit, the turbidity flow cell, and the spectral sample cell are all equipped with a combination of solenoid valves and manual valves. The solenoid valves receive instructions from the control system to automatically open and close, and the manual valves are located in front of the solenoid valves. The manual gate can be manually closed when the solenoid valve needs to be replaced or a unit needs to be repaired to ensure operational safety.

[0023] The AI ​​processing module trains a scrubbing endpoint prediction model based on historical scrubbing data. By receiving turbidity, UV absorption spectrum and temperature data in real time, it dynamically predicts the scrubbing endpoint and outputs scrubbing control commands.

[0024] The AI ​​processing module further includes a multimodal data fusion unit and an adaptive learning unit. The multimodal data fusion unit is used to collaboratively process monitoring data from different sources and frequencies. It performs time alignment and standardization on continuous turbidity time series curves, discrete UV absorption peak sequences, and temperature change curves, and then extracts key features reflecting the scrubbing process from these synchronized data. The adaptive learning unit is used to verify the accuracy of the prediction model based on the detection results of pollutant residues after each scrubbing, and adjusts the internal parameters of the prediction model in reverse to optimize the prediction model.

[0025] In one embodiment, the system further includes a resin type identification unit and an abnormal peak detection module: The resin type identification unit automatically identifies the resin contamination type based on the initial contaminant analysis results and calls the corresponding scrubbing endpoint determination strategy. At the initial stage of the regeneration process, through rapid contaminant analysis of the initial wastewater sample, especially combined with characteristic absorption spectra obtained by a UV spectrophotometer, the main contamination type of the currently failed resin is automatically identified. This could be primarily iron oxide, organic contamination, or a mixture of oil and metal contamination. Based on the identification results, the system can intelligently call the most suitable scrubbing endpoint determination strategy from a pre-set strategy library.

[0026] The abnormal peak detection module is used to identify unexpected absorption peaks in the UV absorption spectrum and trigger alarm and manual intervention procedures. When any unexpected or abnormal absorption peak is identified outside the characteristic absorption peaks of known pollutants, the abnormal peak detection module will immediately initiate the predetermined alarm and intervention procedures: on the one hand, it sends a high-level alarm to the distributed control system (DCS) to notify the operators; on the other hand, it suggests or directly switches to a conservative operation mode or suspends automatic step skipping, awaiting manual intervention and root cause analysis.

[0027] The beneficial effects of this invention are as follows: By incorporating AI prediction models, multimodal data fusion, and adaptive learning mechanisms, this invention changes the traditional extensive control mode that relies on fixed scrubbing times or single parameter thresholds. It achieves precise and dynamic scrubbing control based on the actual contamination state of the resin, significantly improving the accuracy and reliability of endpoint determination. By integrating turbidity temporal changes, pollutant characteristic ultraviolet absorption spectra, and temperature compensation parameters, and using AI models for intelligent prediction, it avoids misjudgments caused by water quality fluctuations or changes in pollutant composition, fundamentally solving the problems of insufficient or excessive scrubbing. This not only ensures resin regeneration and extends resin lifespan, reducing resin procurement and replacement costs, but also significantly reduces the consumption of demineralized water and compressed air by precisely controlling the scrubbing endpoint, achieving significant water and energy savings. Furthermore, the judgment strategy can be continuously optimized with the accumulation of operational data, possessing the potential for continuous efficiency improvement.

[0028] Method Implementation Examples According to an embodiment of the present invention, a method for determining the endpoint of air scrubbing during the regeneration process of fine-treatment resin is provided. Figure 1 A flowchart illustrating a method for determining the endpoint of air scrubbing during the regeneration of fine-treatment resin, provided in one or more embodiments of this specification, is shown below. Figure 1 As shown, the method for determining the endpoint of air scrubbing during the regeneration process of the refined resin according to an embodiment of the present invention specifically includes: S1: Obtain the background turbidity of the demineralized water and analyze the pollutant components of the failed resin to determine the characteristic absorption peaks of the main pollutants; S2: Start the scrubbing sequence and collect turbidity data and pollutant characteristic UV absorption spectrum data of the scrubbing drainage in the main drain pipe in real time; S3: Based on the AI ​​prediction model, combined with the turbidity in the drain header, the attenuation trend of the UV absorption peak and temperature compensation, the scrubbing endpoint is dynamically determined. The steps for dynamically determining the endpoint of the scrubbing process include: Construct a fitting function for the change of turbidity data in scrubbing drainage over time; Based on the changes in the first and second derivatives of the function, candidate time points for the end of the scrubbing process are identified.

[0029] The candidate endpoint time points satisfy the following conditions: ; Where f(t) is the fitting function of turbidity Z versus time t. This refers to the time point when turbidity enters the judgment interval. The time point where the derivative is zero and the second derivative is positive.

[0030] After identifying the candidate endpoint time points, the stability of the endpoint is further verified using a test constant, which is calculated as follows: ; Where ΔZ is the change in turbidity per unit time, and m is the number of sampling points.

[0031] S4: Verify the end point of the scrubbing process. If the verification is successful, perform a UV spot scan analysis. If the absorption peak value is lower than the initial peak value, the scrubbing process ends; otherwise, an abnormal alarm signal is output.

[0032] The method further includes: When the number of scrubbing cycles n ≥ 4 and C still exceeds the limit, an alarm is triggered and abnormal information is recorded; The DCS system receives step skip signals and automatically terminates the scrubbing sequence or proceeds to the next regeneration stage.

[0033] The embodiments of the present invention are method embodiments corresponding to the system embodiments described above. The specific operations of each step can be understood by referring to the description of the system embodiments, and will not be repeated here.

[0034] Device Example 1 This invention provides an electronic structure, such as... Figure 2 As shown, it includes: a memory 20, a processor 22, and a computer program stored in the memory 20 and executable on the processor 22. When the computer program is executed by the processor 22, it performs the following method steps: S1: Obtain the background turbidity of the demineralized water and analyze the pollutant components of the failed resin to determine the characteristic absorption peaks of the main pollutants; S2: Start the scrubbing sequence and collect turbidity data and pollutant characteristic UV absorption spectrum data of the scrubbing drainage in the main drain pipe in real time; S3: Based on the AI ​​prediction model, combined with the turbidity in the drain header, the attenuation trend of the UV absorption peak and temperature compensation, the scrubbing endpoint is dynamically determined. S4: Verify the end point of the scrubbing process. If the verification is successful, perform a UV spot scan analysis. If the absorption peak value is lower than the initial peak value, the scrubbing process ends; otherwise, an abnormal alarm signal is output.

[0035] Device Example 2 This invention provides a computer-readable storage medium storing an information transmission implementation program. When executed by a processor 22, the program performs the following method steps: S1: Obtain the background turbidity of the demineralized water and analyze the pollutant components of the failed resin to determine the characteristic absorption peaks of the main pollutants; S2: Start the scrubbing sequence and collect turbidity data and pollutant characteristic UV absorption spectrum data of the scrubbing drainage in the main drain pipe in real time; S3: Based on the AI ​​prediction model, combined with the turbidity in the drain header, the attenuation trend of the UV absorption peak and temperature compensation, the scrubbing endpoint is dynamically determined. S4: Verify the end point of the scrubbing process. If the verification is successful, perform a UV spot scan analysis. If the absorption peak value is lower than the initial peak value, the scrubbing process ends; otherwise, an abnormal alarm signal is output.

[0036] The computer-readable storage media described in this embodiment include, but are not limited to, ROM, RAM, disk, or optical disk.

[0037] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A system for determining the endpoint of air scrubbing during the regeneration process of refined resin, characterized in that, include: The resin separation tower, anion regeneration tower, and cation regeneration tower are connected by a bottom drain header. A sampling pipeline is installed on the drainage main pipe, and the sampling pipeline is connected to a resin scrubbing monitoring instrument. The resin scrubbing monitoring instrument includes a turbidity sensor, a UV spectrophotometer, a temperature sensor, a constant flow pump, a compressed air purging unit, and an AI processing module. The AI ​​processing module trains a scrubbing endpoint prediction model based on historical scrubbing data. By receiving turbidity, UV absorption spectrum and temperature data in real time, it dynamically predicts the scrubbing endpoint and outputs scrubbing control commands.

2. The system according to claim 1, characterized in that, The AI ​​processing module further includes: A multimodal data fusion unit is used to align and extract features from turbidity time-series data, UV absorption peak sequences, and temperature sequences; The adaptive learning unit optimizes the parameters of the prediction model in reverse based on the actual pollutant residue data after each scrubbing.

3. The system according to claim 2, characterized in that, The system also includes: The resin type identification unit is used to automatically identify the type of resin contamination based on the initial contaminant analysis results and call the corresponding scrubbing endpoint judgment strategy. The abnormal peak detection module is used to identify unexpected absorption peaks in the UV absorption spectrum and trigger alarms and manual intervention processes.

4. The system according to claim 1, characterized in that, The turbidity flow cell and the spectral sample cell in the compressed air purging unit and the resin scrubbing monitor are connected; the sampling tube, the compressed air purging unit, the turbidity flow cell and the spectral sample cell are all equipped with a combination of solenoid valve and manual valve, the manual valve is located in front of the solenoid valve, and the manual gate can facilitate the replacement of the solenoid valve and the maintenance of the unit. The constant flow pump is installed in the water inlet pipe inside the resin scrubbing monitor to maintain a stable water sample flow rate when the water pressure in the drain header fluctuates. The temperature sensor is used to monitor the water sample temperature in real time and to provide temperature compensation parameters for the detection process of the turbidity sensor and UV spectrophotometer.

5. A method for determining the endpoint of air scrubbing during the regeneration process of refined resin, characterized in that, include: S1: Obtain the background turbidity of the demineralized water and analyze the pollutant components of the failed resin to determine the characteristic absorption peaks of the main pollutants; S2: Start the scrubbing sequence and collect turbidity data and pollutant characteristic UV absorption spectrum data of the scrubbing drainage in the main drain pipe in real time; S3: Based on the AI ​​prediction model, combined with the turbidity in the drain header, the attenuation trend of the UV absorption peak and temperature compensation, the scrubbing endpoint is dynamically determined. S4: Verify the end point of the scrubbing process. If the verification is successful, perform a UV spot scan analysis. If the absorption peak value is lower than the initial peak value, the scrubbing process ends; otherwise, an abnormal alarm signal is output.

6. The method according to claim 1, characterized in that, The steps for dynamically determining the endpoint of the scrubbing process include: Construct a fitting function for the change of turbidity data in scrubbing drainage over time; Based on the changes in the first and second derivatives of the function, candidate time points for the end of the scrubbing process are identified.

7. The method according to claim 1, characterized in that, The candidate endpoint time points satisfy the following conditions: ； Where f(t) is the fitting function of turbidity Z versus time t. This refers to the time point when turbidity enters the judgment interval. The time point where the derivative is zero and the second derivative is positive.

8. The method according to claim 1, characterized in that, After identifying the candidate endpoint time points, the stability of the endpoint is further verified using a test constant, which is calculated as follows: ； Where ΔZ is the change in turbidity per unit time, and m is the number of sampling points.

9. An electronic device, characterized in that, include: processor; as well as, A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the steps of the air scrubbing endpoint determination method during the regeneration of fine-processed resin as described in any one of claims 5 to 8.

10. A storage medium, characterized in that, Used to store computer-executable instructions, which, when executed, implement the steps of the air scrubbing endpoint determination method during the regeneration of fine-treatment resin as described in any one of claims 5 to 8.

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