Method for evaluating performance of cation exchange column in hydrogen conductivity measurement

Abstract

The invention relates to the field of industrial online monitoring and chemical analysis, and particularly discloses a performance evaluation method of a cation exchange column in hydrogen conductivity measurement. The method comprises the following steps: acquiring multi-dimensional time sequence data of an ion exchange column through a multi-modal sensor array, and after collaborative cleaning and quality checking, extracting a mixed feature set reflecting the running state and the degradation trend of the ion exchange column by utilizing dynamic feature engineering; the features are input into a hybrid intelligent evaluation model fusing an improved Thomas mechanism model and machine learning models such as LSTM-Attention and Transform, and health degree scoring, residual life prediction and degradation mode recognition are achieved; finally, state verification and root cause positioning are carried out in combination with a virtual twinborn model based on CFD and reaction kinetics, and hierarchical maintenance decision suggestions are generated. According to the invention, early sensitive perception, accurate evaluation and predictive maintenance of the performance of the cation exchange column are realized, and the reliability and intelligent operation and maintenance level of hydrogen conductivity measurement are effectively improved.

Description

Technical Field

[0001] This invention belongs to the field of industrial online monitoring and chemical analysis, specifically a method for evaluating the performance of cation exchange columns in hydrogen conductivity measurement. Background Technology

[0002] In high-pressure, high-temperature water-steam circulation systems in industries such as power, petrochemicals, and nuclear energy, water purity is crucial, directly affecting equipment safety, efficiency, and lifespan. Hydrogen conductivity, a key parameter in online chemical instrumentation, is a core indicator reflecting the concentration of anionic impurities (such as chloride and sulfate ions) in water. To accurately measure hydrogen conductivity, the water sample must pass through an exchange column filled with a hydrogen-form strong acid cation exchange resin before entering the conductivity sensor. This exchange column replaces cations (such as sodium and ammonium ions) in the water with hydrogen ions, eliminating interference from these cations in the conductivity measurement and ensuring that the measured value represents only the contribution of strong acid anions. Therefore, the integrity of the cation exchange column is a prerequisite for obtaining accurate and reliable hydrogen conductivity data.

[0003] Currently, industrial monitoring of the performance of this exchange column commonly employs a fixed threshold-based alarm method. This involves continuously monitoring the outlet hydrogen conductivity, and triggering an alarm when the measured value exceeds a preset limit (e.g., 0.15 μS / cm), indicating potential resin failure requiring regeneration or replacement. While simple and direct, this method has drawbacks: hydrogen conductivity is extremely insensitive to the initial, slow decline in resin exchange performance. Because the decay of resin exchange capacity, decrease in mass transfer efficiency, or early contamination is a gradual process, the outlet hydrogen conductivity may remain at a low level, not triggering an alarm, for a considerable period even after the exchange column performance has substantially declined but has not yet fully penetrated. This means that existing methods struggle to promptly and accurately detect early degradation trends in exchange column performance, and alarms often occur due to interfering cations (such as...). Once significant penetration has begun and measurement data may be inaccurate, it is essentially a "post-hoc" or "late" alarm, which cannot provide forward-looking guidance for preventative maintenance and ensuring data continuity. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a method for evaluating the performance of cation exchange columns in hydrogen conductivity measurement, thereby resolving the issues of insensitivity to early degradation of exchange column performance and delayed early warning in existing technologies.

[0005] A method for evaluating the performance of a cation exchange column in hydrogen conductivity measurement includes the following steps:

[0006] S1: Real-time acquisition of multi-dimensional time-series data of the cation exchange column operation process through a multi-modal sensor array, and collaborative cleaning and quality verification of the multi-dimensional time-series data to obtain a standardized data stream after cleaning. The multi-dimensional time-series data includes at least the hydrogen conductivity, temperature, pressure difference at the inlet and outlet, and the specific cation concentration at the outlet.

[0007] S2: Based on the standardized data stream, a hybrid feature set reflecting the real-time operating status and long-term degradation trend of the exchange column is extracted through dynamic feature engineering. The hybrid feature set includes real-time performance features based on exchange dynamics, time-series statistical features based on rolling windows, and cumulative degradation features across regeneration cycles.

[0008] S3: Input the hybrid feature set into the hybrid intelligent evaluation model to obtain the comprehensive performance evaluation result of the cation exchange column; wherein, the hybrid intelligent evaluation model integrates a penetration kinetics model based on ion exchange mechanism and a data-driven model based on machine learning, and the data-driven model is used to output at least one of health score, remaining life prediction and degradation pattern recognition;

[0009] S4: Based on the comprehensive performance evaluation results, the state is verified through a virtual twin model, and hierarchical maintenance decision recommendations matching the performance state are generated. The virtual twin model is constructed based on computational fluid dynamics and ion exchange reaction kinetics.

[0010] Preferably, the collaborative cleaning and quality verification in step S1 specifically includes:

[0011] Density-based clustering algorithm is used to identify and remove abnormal data points from the sensor;

[0012] A dynamic time warping algorithm is used to align the time of sensor data streams with different sampling frequencies; and a sensor drift compensation model is established based on periodic calibration data to perform online data correction.

[0013] Preferably, the real-time performance characteristics based on exchange dynamics mentioned in step S2 include at least:

[0014] Instantaneous exchange efficiency calculated in real time based on inlet and outlet hydrogen conductivity, and the rate of change of this instantaneous exchange efficiency over time;

[0015] The cumulative degradation characteristics across regeneration cycles include at least the rate of decrease in the dynamic exchange capacity of the cation exchange column under the same test conditions after the current regeneration cycle and the end of the historical regeneration cycle.

[0016] Preferably, the penetration kinetics model based on the ion exchange mechanism mentioned in step S3 is an improved Thomas model, whose model parameters include mass transfer coefficient and dynamic exchange capacity.

[0017] The method further includes: using the standardized data stream to identify the model parameters online, and using the decay trend of the mass transfer coefficient and the dynamic exchange capacity as mechanistic indicators of performance degradation.

[0018] Preferably, the data-driven model in step S3 includes:

[0019] The first sub-model, built based on a long short-term memory network and an attention mechanism, is used to output a health score in the range of 0 to 100 based on the temporal features in the hybrid feature set, and to provide weights that affect the health score through the attention mechanism.

[0020] The second sub-model, built on the Transformer architecture and survival analysis, is used to predict the future failure probability curve and remaining service life of the cation exchange column based on historical sequence data.

[0021] Preferably, step S3 further includes:

[0022] Construct an adaptive early warning threshold learning mechanism;

[0023] The mechanism is based on a reinforcement learning model. By receiving feedback from historical warning results and actual performance status, it dynamically optimizes the health and warning thresholds of different performance indicators to minimize false alarms and missed alarms.

[0024] Preferably, the state verification using a virtual twin model in step S4 includes:

[0025] The standardized data stream actually collected is input into the virtual twin model to obtain the ideal state data predicted by the model;

[0026] Calculate the deviation field between actual data and ideal state data; based on the spatial distribution characteristics of the deviation field, locate the parts with abnormal performance, including the resin bed inlet, the interior of the bed, or the outlet.

[0027] Preferably, the generation of the graded maintenance decision recommendations in step S4 is based on a preset mapping relationship between health score intervals and maintenance measures; the mapping relationship includes at least:

[0028] When the health score is higher than the first threshold, output a decision to optimize the operating parameters;

[0029] When the health score is between the first threshold and the second threshold, the decision to perform online micro-regeneration is output.

[0030] When the health score falls below the second threshold, the output will determine whether to execute the plan to shut down and regenerate or replace the system.

[0031] A performance evaluation system for a cation exchange column in hydrogen conductivity measurement, used to implement the above method, includes:

[0032] The data acquisition and preprocessing module, including a multimodal sensor array and an edge computing gateway, is used to acquire the multidimensional time-series data and perform preliminary cleaning and standardization.

[0033] The feature extraction and calculation module is configured to run a dynamic feature engineering algorithm to extract the hybrid feature set from the preprocessed data.

[0034] The hybrid intelligent evaluation module has the hybrid intelligent evaluation model built in, which is used to process the hybrid feature set and generate the comprehensive performance evaluation result;

[0035] The decision support and closed-loop control module, including a virtual twin engine and a decision tree unit, is used to perform state verification and generate maintenance decision recommendations; and

[0036] The control execution module, including an adaptive control valve and a regenerator dosing unit, is used to automatically adjust system operating parameters or trigger a regeneration program in response to the maintenance decision recommendations.

[0037] Preferably, it also includes a knowledge base and a self-evolution module; the knowledge base stores historical performance degradation cases, maintenance measures and effect records; the self-evolution module uses case reasoning technology to continuously optimize and update the decision tree unit and the hybrid intelligent evaluation model using the records in the knowledge base.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] By collaboratively cleaning and deeply mining multidimensional time-series data, we were able to keenly capture early and subtle degradation trends in cation exchange column performance, solving the problem of lagging traditional hydrogen conductivity monitoring. By integrating ion exchange mechanism models with cutting-edge machine learning models, we not only improved the accuracy of performance evaluation and remaining lifetime prediction, but also provided a physical explanation for performance degradation through model parameter identification, enhancing the credibility and interpretability of the results. Furthermore, by introducing a high-fidelity virtual twin model based on computational fluid dynamics, we provided independent physical verification and anomaly root cause localization capabilities for the evaluation results, achieving a leap from "status alarm" to "cause diagnosis."

[0040] This method forms a closed loop integrating real-time monitoring, intelligent assessment, virtual verification, precise decision-making, and automatic control, promoting the transformation of power plant chemical instrumentation maintenance mode from periodic preventive maintenance and passive post-event repairs to condition-based predictive intelligent maintenance. This significantly improves the long-term reliability of hydrogen conductivity measurement data, optimizes maintenance resource allocation, and reduces production operation risks that may result from monitoring failures. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] Example: This example provides a complete performance evaluation method and process, such as... Figure 1 As shown, the specific steps include:

[0044] Step S110: Multimodal data acquisition and collaborative cleaning and verification.

[0045] This step aims to obtain a high-quality, reliable underlying data stream;

[0046] Data Acquisition: Integrated multi-parameter sensor probes are deployed on the inlet and outlet lines of the cation exchange column for real-time synchronous data acquisition.

[0047] The hydrogen conductivity value (automatically compensated to the standard value at 25℃ after measurement by a temperature probe) is denoted as... and ;

[0048] temperature ;

[0049] pressure and Used to calculate real-time pressure difference in the bed. ;

[0050] Additional sodium ion exchange (SO4) is installed in the outlet pipeline. Selective electrode for real-time monitoring of sodium ion concentration. This serves as direct evidence of performance degradation;

[0051] All data is uploaded to the edge computing gateway at different frequencies (e.g., 1 Hz for hydrogen conductivity, 0.0167 Hz for sodium ion electrodes);

[0052] Collaborative cleaning and verification: Real-time preprocessing at the edge gateway.

[0053] Outlier removal: An improved DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm is used; multivariate data within a short time window (e.g., 5 minutes) are processed. Points are considered as points in a high-dimensional space; through clustering, points far from the main dense areas (such as points whose conductivity drops sharply and then recovers due to transient bubbles) are identified as noise and removed;

[0054] Time series alignment: Utilizing the Dynamic Time Warping (DTW) algorithm, low-frequency data is aligned. Sequence and high frequency The sequence is non-linearly aligned to find the conductivity data point that best matches the time for each sodium ion concentration value, thus solving the problem of data asynchrony caused by sampling delay and different frequencies;

[0055] Sensor drift correction: Perform full-channel online calibration monthly using a standard sodium chloride solution of known concentration; record the readings of each sensor during the calibration period, compare them with the standard values, and establish a drift compensation model; for example, for a hydrogen conductivity sensor, the compensation model could be: , where parameters , The calibration data was obtained by fitting the model using the least squares method; subsequent real-time data were all corrected online using this model, and the output was a standardized data stream. .

[0056] Step S120: Dynamic feature engineering extracts the hybrid feature set.

[0057] This step starts with standardized data flow. Mining deeper information and constructing multi-scale features;

[0058] Real-time performance characteristics (reflecting instantaneous state):

[0059] Instantaneous exchange efficiency: Ideally ;

[0060] Efficiency decay rate: Within a short sliding window (e.g., 10 minutes), the efficiency decay rate of a product or service. Perform a linear fit, and its slope ; An increase in negative values ​​indicates that performance is deteriorating at an accelerated rate.

[0061] Time-series statistical characteristics (reflecting short-term trends and stability, based on a rolling window such as 24 hours):

[0062] Calculation window mean ,variance (Increased variance indicates greater performance volatility);

[0063] calculate The skewness of the sequence (reflecting distribution asymmetry) and the autocorrelation coefficient with a 1-hour lag. (A decrease in the autocorrelation coefficient may indicate that the original stable operating mode has been broken.)

[0064] right The sequence is subjected to wavelet transform to extract the energy proportion of specific high-frequency bands (such as corresponding mechanical vibration or flow noise). An abnormally high value may indicate that the resin is broken and particles are rubbing against each other.

[0065] Cumulative degradation characteristics (reflecting long-term aging, compared across regeneration cycles):

[0066] Dynamic exchange capacity decay rate: After each regeneration cycle, the system was run under standard test conditions (fixed flow rate, standard NaCl solution inlet) until breakthrough; the cumulative treated water volume was recorded. and the breakthrough point concentration; calculate the dynamic exchange capacity for this cycle based on material balance. Then the capacity recovery rate for that period. ;track The decay curve with increasing regeneration number reflects the cumulative effect of irreversible aging of the resin.

[0067] Step S130: Comprehensive evaluation by the hybrid intelligent evaluation model.

[0068] This step is crucial, integrating physical mechanisms with data intelligence;

[0069] Mechanistic model layer (improved Thomas model):

[0070] This model describes the breakthrough behavior of fixed-bed ion exchange:

[0071]

[0072] in, Normalized concentration at export (available) approximate), The overall mass transfer coefficient is . For dynamic switching capacity, For resin quality, For traffic, This represents the cumulative processing volume.

[0073] Online parameter identification: Utilizing the features extracted in step S120 and the real-time data stream, online parameter identification is performed in real time using either the Extended Kalman Filter (EKF) or the nonlinear least squares method. and Mass transfer coefficient A decrease in dynamic exchange capacity typically indicates increased mass transfer resistance (e.g., resin surface covered by contaminants); The decrease directly reflects a reduction in the number of active sites available for exchange;

[0074] Data-driven model layer:

[0075] Health assessment sub-model (LSTM-Attention):

[0076] Input: A mixed feature set of a time series over a past period (e.g., 168 hours, or one week), including wait;

[0077] Processing: The LSTM layer learns the long-term dependencies of the sequence; the Attention layer assigns weights to the features at each time step, and visualization reveals which historical moments or features (such as "abnormally high pressure three days ago") have the greatest impact on the current state.

[0078] Output: A health score from 0 to 100 This score is a composite indicator that integrates all input information.

[0079] Remaining lifetime prediction sub-model (Transformer + survival analysis):

[0080] Input: Historical data spanning a longer period, containing multiple complete "run-regenerate" cycle sequences;

[0081] Processing: The Transformer model encodes global context information for the entire sequence; combining the idea of ​​the Cox proportional hazards model, performance degradation is viewed as a process of increasing risk over time; the model learns the relationship between historical degradation trajectories and the final failure time;

[0082] Output: Failure probability function for a future period of time (as in the next expected running cycle). and remaining service life The confidence interval;

[0083] Adaptive early warning threshold learning mechanism (based on reinforcement learning):

[0084] Construct a deep Q-network (DQN) agent; its state is its current health. Mechanism parameters ( ) and their trends; the action is to fine-tune the warning threshold (e.g., adjust the "attention" threshold from 70 to 68 or 72).

[0085] Reward function design: If the warning issued after adjusting the threshold is subsequently confirmed as a real fault (avoiding greater losses), a positive reward is given; if a false alarm is issued (causing unnecessary maintenance), a negative reward is given; if a fault is missed, a larger negative reward is given.

[0086] Objective: Through continuous interaction with the environment (the actual operating system), the intelligent agent learns the optimal threshold adjustment strategy to minimize the overall cost of long-term operation and maintenance. This enables dynamic and personalized optimization of thresholds.

[0087] Step S140: Virtual twin verification and hierarchical decision-making.

[0088] This step provides physical verification and actionable decision output;

[0089] Virtual twin verification:

[0090] A high-fidelity computational fluid dynamics (CFD) model was established to simulate the fluid flow and pressure drop inside the exchange column; simultaneously, a kinetic model of the ion exchange reaction was coupled to simulate... , Mass transfer and reaction processes;

[0091] The current actual entry conditions (traffic, Inputting the concentration into the virtual twin model and running the simulation yields the outlet concentration under ideal new resin conditions. And the velocity field, pressure field, and concentration field inside the bed;

[0092] Deviation field calculation and root cause localization:

[0093] Calculate the deviation between the measured and simulated export values. ;

[0094] Analyze the simulated internal field: If the simulation shows that the velocity distribution in the inlet area is severely uneven, while the actual pressure difference is high, it may be due to blockage of the inlet water distributor; if both the simulation and the actual situation show that there is a high-speed channel (channeling) in the middle of the bed, it may be due to short-circuiting of the fluid caused by resin layer breakage or compaction.

[0095] Hierarchical maintenance decision generation:

[0096] Based on health score Remaining lifespan Predicted and verified anomalies trigger pre-defined decision logic:

[0097] Decision D1 (Optimized Operation): When >70 and >30 days, but efficiency decay rate The result is negative; Decision recommendation: Reduce the sample water flow rate by 10% using the control module and observe. Has it improved?

[0098] Decision D2 (Online Micro-Regeneration): When 55 < ≤70, or twin model indicates slight channeling; Decision recommendation: Trigger the "online micro-regeneration" program to automatically prepare 3% dilute hydrochloric acid and backflush the exchange column at a low flow rate for 2 hours to attempt to remove surface contaminants;

[0099] Decision D3 (Planned Shutdown for Regeneration / Replacement): When ≤55, or If the system fails within 7 days, or the twin model indicates widespread failure, the recommended action is to issue a high-level alarm and recommend a shutdown during the next low-load window. If the capacity recovery rate is less than 0.6 (too low), it is recommended to replace the resin.

[0100] The above embodiments are merely specific implementations of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

[0101] The embodiments of the present invention are given for the purposes of illustration and description. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Any changes, modifications, substitutions and variations made by those skilled in the art to the above embodiments within the scope of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for evaluating the performance of a cation exchange column in hydrogen conductivity measurement, characterized in that, Includes the following steps: S1: Real-time acquisition of multi-dimensional time-series data of the cation exchange column operation process through a multi-modal sensor array, and collaborative cleaning and quality verification of the multi-dimensional time-series data to obtain a standardized data stream after cleaning. The multi-dimensional time-series data includes at least the hydrogen conductivity, temperature, pressure difference at the inlet and outlet, and the specific cation concentration at the outlet. S2: Based on the standardized data stream, a hybrid feature set reflecting the real-time operating status and long-term degradation trend of the exchange column is extracted through dynamic feature engineering. The hybrid feature set includes real-time performance features based on exchange dynamics, time-series statistical features based on rolling windows, and cumulative degradation features across regeneration cycles. S3: Input the hybrid feature set into the hybrid intelligent evaluation model to obtain the comprehensive performance evaluation result of the cation exchange column; wherein, the hybrid intelligent evaluation model integrates a penetration kinetics model based on ion exchange mechanism and a data-driven model based on machine learning, and the data-driven model is used to output at least one of health score, remaining life prediction and degradation pattern recognition; S4: Based on the comprehensive performance evaluation results, the state is verified through a virtual twin model, and hierarchical maintenance decision recommendations matching the performance state are generated. The virtual twin model is constructed based on computational fluid dynamics and ion exchange reaction kinetics.

2. The method according to claim 1, characterized in that, The collaborative cleaning and quality verification described in step S1 specifically includes: Density-based clustering algorithm is used to identify and remove abnormal data points from the sensor; A dynamic time warping algorithm is used to align the time of sensor data streams with different sampling frequencies; and a sensor drift compensation model is established based on periodic calibration data to perform online data correction.

3. The method according to claim 1, characterized in that, The real-time performance characteristics based on exchange dynamics mentioned in step S2 include at least: Instantaneous exchange efficiency calculated in real time based on inlet and outlet hydrogen conductivity, and the rate of change of this instantaneous exchange efficiency over time; The cumulative degradation characteristics across regeneration cycles include at least the rate of decrease in the dynamic exchange capacity of the cation exchange column under the same test conditions after the current regeneration cycle and the end of the historical regeneration cycle.

4. The method according to claim 1, characterized in that, The penetration kinetics model based on the ion exchange mechanism mentioned in step S3 is an improved Thomas model, whose model parameters include mass transfer coefficient and dynamic exchange capacity. The method further includes: using the standardized data stream to identify the model parameters online, and using the decay trend of the mass transfer coefficient and the dynamic exchange capacity as mechanistic indicators of performance degradation.

5. The method according to claim 1, characterized in that, The data-driven model mentioned in step S3 includes: The first sub-model, built based on a long short-term memory network and an attention mechanism, is used to output a health score in the range of 0 to 100 based on the temporal features in the hybrid feature set, and to provide weights that affect the health score through the attention mechanism. The second sub-model, built on the Transformer architecture and survival analysis, is used to predict the future failure probability curve and remaining service life of the cation exchange column based on historical sequence data.

6. The method according to claim 1 or 5, characterized in that, Step S3 also includes: Construct an adaptive early warning threshold learning mechanism; The mechanism is based on a reinforcement learning model. By receiving feedback from historical warning results and actual performance status, it dynamically optimizes the health and warning thresholds of different performance indicators to minimize false alarms and missed alarms.

7. The method according to claim 1, characterized in that, The state verification using a virtual twin model described in step S4 includes: The standardized data stream actually collected is input into the virtual twin model to obtain the ideal state data predicted by the model; Calculate the deviation field between actual data and ideal state data; based on the spatial distribution characteristics of the deviation field, locate the parts with abnormal performance, including the resin bed inlet, the interior of the bed, or the outlet.

8. The method according to claim 1, characterized in that, The generation of graded maintenance decision recommendations in step S4 is based on a preset mapping relationship between health score intervals and maintenance measures; the mapping relationship includes at least: When the health score is higher than the first threshold, output a decision to optimize the operating parameters; When the health score is between the first threshold and the second threshold, the decision to perform online micro-regeneration is output. When the health score falls below the second threshold, the output will determine whether to execute the plan to shut down and regenerate or replace the system.

9. A performance evaluation system for a cation exchange column in hydrogen conductivity measurement, used to implement the method as described in any one of claims 1-8, characterized in that, include: The data acquisition and preprocessing module, including a multimodal sensor array and an edge computing gateway, is used to acquire the multidimensional time-series data and perform preliminary cleaning and standardization. The feature extraction and calculation module is configured to run a dynamic feature engineering algorithm to extract the hybrid feature set from the preprocessed data. The hybrid intelligent evaluation module has the hybrid intelligent evaluation model built in, which is used to process the hybrid feature set and generate the comprehensive performance evaluation result; The decision support and closed-loop control module, including a virtual twin engine and a decision tree unit, is used to perform state verification and generate maintenance decision recommendations. as well as The control execution module, including an adaptive control valve and a regenerator dosing unit, is used to automatically adjust system operating parameters or trigger a regeneration program in response to the maintenance decision recommendations.

10. The system according to claim 9, characterized in that, It also includes a knowledge base and a self-evolution module; the knowledge base stores historical performance degradation cases, maintenance measures and effect records; the self-evolution module uses case reasoning technology to continuously optimize and update the decision tree unit and the hybrid intelligent evaluation model using the records in the knowledge base.

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