Reverse osmosis membrane life cycle prediction method and system for smart factory affairs

Abstract

The invention relates to the technical field of industrial equipment prediction and maintenance, and particularly discloses a reverse osmosis membrane life cycle prediction method and system for smart factory affairs. Aiming at the problem of low prediction precision caused by insufficient multi-source parameter dynamic interaction modeling in the prior art, the core scheme comprises the following steps of: acquiring pressure, flow and water quality parameters in real time by a method side, and constructing multi-modal dynamic characteristics containing time sequence statistics and cross terms after missing value filling and exception filtering treatment; a GRU and XGBoost double-branch hybrid model is adopted, time sequence dependence and static characteristic nonlinear correlation are analyzed respectively, and a residual life prediction value is output through weighted splicing of a fusion layer; a system side is correspondingly provided with a data acquisition, processing, feature engineering and hybrid model prediction module. The method has the advantages that the limitation of traditional single-dimensional modeling is broken through, cross-scale life quantification of multi-parameter dynamic coupling is realized, the prediction precision is improved, and the method is suitable for membrane module maintenance decision of water treatment systems in the fields of semiconductors, pharmacy and the like.

Description

Technical Field

[0001] The present invention belongs to the technical field of big data processing, and in particular relates to a method and system for predicting the life cycle of a reverse osmosis membrane for smart factory operations. Background Art

[0002] In the semiconductor field, reverse osmosis (RO) membranes are key components of water treatment systems, and their performance degradation has a complex nonlinear correlation with the dynamic changes in water quality parameters. Current life prediction methods in the industry have the following technical bottlenecks: fixed-cycle replacement strategies cannot perceive the dynamic impact of water quality fluctuations on membrane materials, resulting in a mismatch between maintenance strategies and actual needs; monitoring technologies that rely on single parameter thresholds lack multi-dimensional parameter coupling analysis, making it difficult to capture the progressive degradation of membrane structures in a timely manner; traditional prediction models are limited by static feature extraction and linear assumptions, making it difficult to analyze the interaction mechanisms of multiple source parameters such as pressure, flow, and water quality components. A more fundamental flaw is that existing methods have not yet established a cross-scale correlation model between dynamic water quality parameters and changes in the microstructure of membrane materials, and lack the ability to systematically model the evolution of characteristics under multiple working conditions, resulting in significant deviations between prediction results and actual lifespan. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to address the deficiencies in the above-mentioned existing technologies and provide a method and system for predicting the life cycle of reverse osmosis membranes for smart plant services. By constructing a multimodal feature space that integrates pressure time series characteristics and operating condition parameters, and combining dynamic feature extraction with nonlinear regression models, the mapping relationship between the dynamic changes of water quality parameters and the attenuation of membrane structure performance is analyzed; this method and system break through the linear assumption limitations of traditional models, realize the coupling analysis of multi-dimensional parameters and cross-scale correlation modeling, significantly improve the accuracy and timeliness of life prediction, and provide technical support for optimizing maintenance strategies and reducing system operation and maintenance costs.

[0004] The first aspect of the present invention discloses a method for predicting the life cycle of a reverse osmosis membrane for smart plant management, comprising the following steps: Data collection steps: Real-time collection of sensor data from the water treatment system, including pressure parameters, flow parameters, and water quality parameters; Data processing steps: fill missing values, filter outliers and standardize the collected data; Feature engineering step: generating multimodal dynamic features based on the sensor data, including time series features and cross features, wherein the time series features include sliding window statistics and frequency domain fluctuation features, and the cross features include pressure gradients and parameter interaction terms; Model building steps: Remaining life prediction is performed using a hybrid model architecture, which includes a GRU branch and an XGBoost branch, wherein the GRU branch is used to process time series data to capture long-term dependencies, and the XGBoost branch is used to process static features to resolve nonlinear interaction effects, and the outputs of the two branches are weighted and spliced ​​through a fusion layer; Model training step: pre-training and joint fine-tuning of the hybrid model, including applying regularization constraints; Prediction step: Output the predicted value of the remaining life of the reverse osmosis membrane based on the trained model.

[0005] In the above method, the sensor data in the data acquisition step includes inlet pressure, outlet pressure, concentrate pressure, outlet flow rate, concentrate flow rate, resistivity and total organic carbon.

[0006] In the above method, the missing value filling in the data processing step is based on time series correlation interpolation completion, and the outlier filtering is achieved through statistical distribution analysis and dynamic threshold recognition.

[0007] In the above method, the feature engineering step also includes feature screening, which retains key features based on feature importance ranking.

[0008] In the above method, in the model construction step, the fusion layer adopts an attention mechanism or weighted averaging as an alternative to the weighted splicing.

[0009] In the above method, the joint fine-tuning in the model training step includes fixing the parameters of the XGBoost branch and optimizing the fusion layer weights.

[0010] The above method also includes a model evaluation step: calculating indicators of prediction accuracy, including mean square error and R2 value, and performing robustness testing.

[0011] The above method also includes a model optimization step: adapting to the new data distribution through hyperparameter tuning and an online update mechanism, wherein the online update includes incremental training.

[0012] The above method is applied to water treatment systems in the fields of semiconductor manufacturing, pharmaceuticals or chemical industry, and is used to predict the life cycle of ultrafiltration membranes or nanofiltration membranes.

[0013] A second aspect of the present invention discloses a reverse osmosis membrane life cycle prediction system for smart plant management, comprising: A data acquisition module configured to acquire sensor data in real time, wherein the sensor data includes pressure parameters, flow parameters, and water quality parameters; A data processing module is configured to fill missing values, filter outliers and perform standardization on the collected data; A feature engineering module configured to generate multimodal dynamic features, including time series features and cross features; The model module includes a hybrid model architecture of a GRU branch and an XGBoost branch, wherein the GRU branch is used for time series data processing, the XGBoost branch is used for static feature analysis, and the fusion result is output through the fusion layer; The prediction module is configured to output a predicted value of the remaining life of the reverse osmosis membrane based on the model module.

[0014] Compared with the prior art, the present invention has the following advantages: 1. Through multimodal dynamic feature fusion and dynamic weighting, the complex interactive relationship between multi-dimensional parameters such as pressure, water quality, and flow is captured, breaking through the limitations of traditional single-dimensional modeling, achieving accurate analysis of nonlinear correlations, and significantly improving the accuracy of remaining life prediction.

[0015] 2. Adopting the XGBoost-GRU hybrid architecture, combining time series modeling and static feature analysis, embedding the transfer learning mechanism, adapting to different water quality conditions and equipment types, and solving the performance degradation problem of traditional models caused by scene switching.

[0016] 3. Through regression prediction and life cycle risk analysis, the remaining service life of the reverse osmosis membrane (RO membrane) is quantified, and maintenance plans can be accurately predicted and prioritized, reducing resource waste caused by unplanned replacement and excessive maintenance.

[0017] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of the method of Example 1 of the present invention.

[0019] Figure 2 This is a system architecture diagram of Example 2 of the present invention. DETAILED DESCRIPTION

[0020] Example 1: like Figure 1 As shown, a method for predicting the life cycle of a reverse osmosis membrane for smart plant management is characterized by comprising the following steps: Data collection steps: Real-time collection of sensor data from the water treatment system, including pressure parameters, flow parameters, and water quality parameters. During implementation, sensors (such as pressure transmitters and flow meters) are installed on the reverse osmosis membrane system to collect sensor data every second or every minute in real time. Technicians need to configure the data collection frequency (for example, 1Hz) and store the data in a central database. These technicians can then read pressure parameters (such as membrane inlet pressure), flow parameters (such as concentrate flow), and water quality parameters (such as resistivity) from the water treatment control system. Data processing steps: Missing value filling, outlier filtering, and normalization are performed on the collected data. Missing value filling is based on time series correlation interpolation. For example, if a pressure value is missing at time point t, it is linearly calculated using the values ​​at time points t-1 and t+1. Outlier filtering is achieved through dynamic threshold identification. For example, the mean and standard deviation of the flow data over the past 24 hours are calculated, and points exceeding ±2 standard deviations are identified as outliers and deleted. Normalization uses the Z-score method to convert the data into a format with a mean of 0 and a standard deviation of 1 for ease of subsequent processing. Implementation details are simple: technicians can use Python's Pandas library or similar tools to implement it. Feature engineering step: Generate multimodal dynamic features based on the sensor data, including time series features and cross features, wherein the time series features include sliding window statistics and frequency domain fluctuation features, and the cross features include pressure gradient and parameter interaction terms; time series features include sliding window statistics (for example, using a 10-minute window size to calculate the mean and maximum value of the flow parameter within the window) and frequency domain fluctuation features (for example, extracting the frequency domain components of the flow signal through fast Fourier transform and identifying the main fluctuation frequency); cross features include pressure gradient (for example, calculating the difference between the inlet pressure and the outlet pressure divided by the membrane length value to simulate the pressure drop rate) and parameter interaction terms (for example, multiplying the outlet flow rate by the resistivity to simulate the comprehensive impact of water quality); technicians manually program to calculate these features without the need for complex models; Model building steps: Use a hybrid model architecture to predict remaining life, the hybrid model architecture includes a GRU branch and an XGBoost branch, wherein the GRU branch is used to process time series data to capture long-term dependencies, and the XGBoost branch is used to process static features to resolve nonlinear interaction effects, and the outputs of the two branches are weighted and spliced ​​through the fusion layer; the GRU branch constructs a GRU network (gated recurrent unit), inputs a time series feature sequence, and processes the time series to capture long-term changes (such as pressure trends in the past week); the XGBoost branch inputs static features (such as water quality parameters) and trains a tree model to resolve nonlinear relationships (such as how temperature interacts with pressure to affect life); the fusion layer merges the results through weighted splicing (for example: the GRU output weight is 0.6, and the XGBoost output weight is 0.4); technicians can call the TensorFlow or XGBoost library to build the model.

[0021] Model training steps: Pre-train and jointly fine-tune the hybrid model, including applying regularization constraints; first pre-train (for example, training the GRU and XGBoost branches on historical data), then jointly fine-tune, including applying regularization constraints (such as L2 regularization to reduce overfitting); during implementation, use an optimizer (such as Adam) to adjust parameters; split the dataset into 70% for training and 30% for validation; Prediction Steps: Based on the trained model, the remaining lifespan of the reverse osmosis membrane is predicted. The trained model inputs real-time data and outputs a predicted remaining lifespan (e.g., predicting the membrane has 100 days of life remaining). Technicians deploy the model to the server and retrieve the prediction results through the API.

[0022] In a semiconductor fab, 10 minutes of sensor data (e.g., inlet pressure = 1.5 MPa, concentrate flow rate = 5 L / min, resistivity = 0.5 μS / cm) is collected. After data processing, anomalous flow values ​​are filtered out. Feature engineering is used to calculate the sliding window mean (average flow rate = 4.8 L / min) and frequency domain fluctuation characteristics (dominant frequency = 0.1 Hz). These features are then used in model construction: GRU processes the time series and outputs probability values; XGBoost inputs water quality parameters and outputs lifetime contributions; and a fusion layer performs weighted calculations to predict lifetime. This solution, through integrated data collection, processing, feature engineering, and hybrid model prediction, significantly improves the accuracy and robustness of reverse osmosis membrane remaining life predictions, reducing production interruptions caused by false alarms. It also supports real-time monitoring, meeting the high-precision water quality requirements of semiconductor manufacturing (e.g., resistivity below 0.1 μS / cm), and reducing the cost of reverse osmosis membrane replacement and the risk of downtime.

[0023] In this embodiment, the sensor data in the data collection step includes inlet pressure, outlet pressure, concentrate pressure, outlet flow rate, concentrate flow rate, resistivity and total organic carbon.

[0024] During implementation, technicians installed specific sensors at key points in the water treatment pipeline: an inlet pressure sensor at the membrane inlet, an outlet pressure sensor at the outlet, a brine pressure sensor at the brine outlet, effluent and brine flowmeters to monitor water flow, a resistivity meter to measure water purity, and a total organic carbon analyzer to detect contaminants. Data collection was set at once per second, and the data was stored in the SCADA system. For example, during reverse osmosis membrane operation, real-time readings of brine flow were 4.2 L / min and resistivity was 0.2 μS / cm.

[0025] During implementation, technicians need to calibrate the sensors to ensure accuracy (for example, flow meter error <1%); sensor data is transmitted to the central platform via the Modbus protocol, and technicians can directly configure the data table to store these parameters.

[0026] By clearly specifying the sensor data type, we ensure comprehensive and targeted data collection (for example, resistivity and total organic carbon reflect changes in water quality), improve the comprehensiveness of predictions, and reduce mispredictions due to missing data. This is particularly suitable for semiconductor high-purity water treatment requirements (for example, reducing membrane degradation caused by organic contamination).

[0027] In this embodiment, the missing value filling in the data processing step is based on time series correlation interpolation completion, and the outlier filtering is achieved through statistical distribution analysis and dynamic threshold recognition.

[0028] It should be noted that missing value filling is based on time series correlation interpolation: for example, if pressure data is missing at time point t, the average value of the values ​​at time points t-1 and t+1 is used to fill the missing value (such as (t-1 pressure value + t+1 pressure value) / 2); if the data is missing for multiple points in a row, linear or spline interpolation is used to simulate the trend.

[0029] Outlier filtering is performed through statistical distribution analysis: the mean and standard deviation of the data over a certain period (such as 1 day) are calculated, and a dynamic threshold (such as mean ± 3 times the standard deviation) is set. For example, the normal range of concentrate flow is 3-5 L / min. Points outside this range are identified as outliers and removed.

[0030] Those skilled in the art use simple software tools (such as Excel or Python scripts) to implement the following: read the data file, traverse each parameter sequence to perform interpolation and filtering operations; for example: in semiconductor fabs, the outlet water flow sequence has one missing point (time 10:00 is missing), and the interpolated value 4.4 L / min is calculated using the values ​​at 9:59 and 10:01 (such as 4.5 L / min and 4.3 L / min); outlier filtering identifies a point where the flow rate = 0 (hardware failure) and deletes it.

[0031] By providing targeted data preprocessing methods, we solve the problem of data collection interruption or noise, reduce the preprocessing error rate, and ensure the stability of model input data.

[0032] In this embodiment, the feature engineering step also includes feature screening, which retains key features based on feature importance ranking.

[0033] When implementing feature screening: After feature generation, use XGBoost or a similar model to train a temporary model and calculate feature importance (such as sorting by information gain value); retain the top-N features (such as the top 10 important features); for example, from all generated features, sort the pressure gradient to be the most important.

[0034] Those skilled in the art may perform the following operations: Before model training, historical datasets are used to evaluate feature importance and low-ranking features (e.g., importance < 0.1) are deleted; implementation details include using Python's scikit-learn library to calculate feature scores and deriving a list of retained key features; for example, for a set of 20 features, after calculating importance, the sliding window average flow rate and pressure gradient are retained and the rest are discarded.

[0035] By screening and optimizing the feature set, computational redundancy is reduced, model efficiency is improved, and noise interference (such as irrelevant sensor parameters) is avoided, thereby improving prediction robustness.

[0036] In this embodiment, in the model construction step, the fusion layer adopts an attention mechanism or weighted averaging as an alternative to the weighted splicing.

[0037] In actual implementation, if a weighted concatenation alternative is used: weighted averaging directly sets fixed weights (such as GRU output weight 0.5, XGBoost output 0.5); if the attention mechanism is used, the weights are calculated dynamically: input feature sequences (such as past time points) and calculate the weights at each moment (such as using the softmax function).

[0038] During implementation, technicians choose a solution: for example, weighted averaging is simple and efficient; using the attention mechanism requires building an additional small network; during operation, they call a deep learning library (such as Keras) to define the fusion layer. For example, on the GRU output sequence, the attention mechanism identifies the importance of pressure data at a certain moment, assigns a high weight (0.8), and fuses the results.

[0039] By providing a flexible fusion solution, the attention mechanism can adaptively allocate weights (such as assigning high weights to key time points), improve model adaptability (improve prediction accuracy), and is particularly suitable for water treatment environments with large fluctuations (such as a surge in flow during startup).

[0040] In this embodiment, the joint fine-tuning in the model training step includes fixing the parameters of the XGBoost branch and optimizing the fusion layer weights.

[0041] In actual implementation, the specific operation of joint fine-tuning is to first fix the parameters of XGBoost (without updating), and then use the backpropagation algorithm to optimize only the weights of the fusion layer (such as adjusting the connection parameters from the GRU output to the fusion layer); technicians set XGBoost to a frozen state during the fine-tuning phase and only update the fusion layer; for specific operations, use a deep learning framework (such as PyTorch) to specify the parameter update range in the training loop. For example: after training for 1000 epochs, fix XGBoost and fine-tune the fusion layer for an additional 100 rounds.

[0042] By optimizing the training strategy, reducing model complexity and overfitting risk, training time can be reduced while improving fusion consistency.

[0043] This embodiment also includes a model evaluation step: calculating indicators of prediction accuracy, including mean square error and R2 value, and performing robustness testing.

[0044] In actual implementation, mean squared error (MSE) and R² values ​​are calculated. After prediction, the actual remaining life and predicted values ​​are used to calculate the MSE (mean squared error) and R² (coefficient of determination), using the formulas MSE = mean((actual - predicted)^²) and R² = 1- (prediction error variance / total variance). Robustness testing is performed by introducing noise data (such as randomly adding a ±1% flow disturbance) to test the stability of the model output. Specifically, this is achieved by partitioning the test data set and calling library functions to calculate the metrics. For example, in semiconductor system testing, the actual life is 150 days and the predicted value is 145 days. The calculated MSE = 25 and R² = 0.92 indicate a high degree of fit.

[0045] This embodiment also includes a model optimization step: adapting to the new data distribution through hyperparameter tuning and an online update mechanism, wherein the online update includes incremental training; the model can be adaptively updated, and when the water treatment environment changes (such as new water quality standards), it can be optimized in real time, reducing offline maintenance and continuously improving prediction performance.

[0046] In practice, hyperparameter tuning uses grid search or random search to optimize model parameters (such as the GRU learning rate), for example, by testing different values ​​within a specific range. Online update mechanisms include incremental training, which periodically (e.g., daily) updates model weights with newly collected data (e.g., training only the last 100 data points), eliminating the need for full retraining. Those skilled in the art can schedule incremental training after model deployment, setting up scheduled tasks (e.g., cron jobs) to perform incremental training. For example, when a new batch of sensor data arrives, only that data is used to fine-tune model parameters.

[0047] In this embodiment, the method is applied to water treatment systems in the fields of semiconductor manufacturing, pharmaceuticals, or chemical industry, and is used to predict the life cycle of ultrafiltration membranes or nanofiltration membranes.

[0048] During implementation, parameters are adjusted according to different fields. For example, in pharmaceutical factories, the same model architecture is used, but the acquisition parameters are changed to pH value and ion concentration; for ultrafiltration membranes, feature engineering is modified (such as adding flux parameters).

[0049] Those skilled in the art can reuse the code base and only reconfigure the input sensor type. For example, in chemical water treatment, the same system is used to predict the life of nanofiltration membranes, and the sensor data includes different pH parameters.

[0050] Example 2: like Figure 2 As shown, a reverse osmosis membrane life cycle prediction system for smart plant management includes: A data acquisition module configured to acquire sensor data in real time, wherein the sensor data includes pressure parameters, flow parameters, and water quality parameters; A data processing module is configured to fill missing values, filter outliers and perform standardization on the collected data; A feature engineering module configured to generate multimodal dynamic features, including time series features and cross features; The model module includes a hybrid model architecture of a GRU branch and an XGBoost branch, wherein the GRU branch is used for time series data processing, the XGBoost branch is used for static feature analysis, and the fusion result is output through the fusion layer; The prediction module is configured to output a predicted value of the remaining life of the reverse osmosis membrane based on the model module.

[0051] It should be noted that the technical essence of Example 2 is the same as that of Example 1. If there are any unclear points, please refer to Example 1.

[0052] It should be noted that the system is deployed on an edge server or cloud platform, and modules communicate through a message queue (such as Kafka). After installing the modules, technicians can input real-time data for testing. For example, in a semiconductor fab, the system automatically reads sensor values ​​and outputs a warning about the remaining membrane life.

[0053] In actual implementation: Data Collection Module: This module reads data from sensors in real time via an Industrial IoT interface (e.g., OPC UA) at a configurable frequency (e.g., once per second). Technicians configure the collection logic (e.g., collecting only key data points).

[0054] Data processing module: The software layer performs missing value filling (as described in Example 1), outlier filtering, and normalization.

[0055] Feature Engineering Module: Programmatically generates time series features and cross features (such as sliding window calculations), which can be implemented using standard libraries.

[0056] Model module: Build GRU and XGBoost branches, and the fusion layer outputs the fusion results (such as through API calls).

[0057] Prediction module: Outputs the remaining life value based on the fusion results (such as displayed on the HMI interface).

[0058] The function of predicting the life cycle of reverse osmosis membranes is realized through a modular system, providing end-to-end prediction capabilities, and is easy to integrate into the plant management platform to achieve real-time online prediction, which can reduce manual intervention.

[0059] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any way. Any simple modification, change and equivalent structural change made to the above embodiment based on the technical essence of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for predicting the life cycle of a reverse osmosis membrane for smart plant management, characterized in that: The following steps are involved: Data collection steps: Real-time collection of sensor data from the water treatment system, including pressure parameters, flow parameters, and water quality parameters; Data processing steps: fill missing values, filter outliers and standardize the collected data; Feature engineering step: generating multimodal dynamic features based on the sensor data, including time series features and cross features, wherein the time series features include sliding window statistics and frequency domain fluctuation features, and the cross features include pressure gradients and parameter interaction terms; Model building steps: Remaining life prediction is performed using a hybrid model architecture, which includes a GRU branch and an XGBoost branch, wherein the GRU branch is used to process time series data to capture long-term dependencies, and the XGBoost branch is used to process static features to resolve nonlinear interaction effects, and the outputs of the two branches are weighted and spliced ​​through a fusion layer; Model training step: pre-training and joint fine-tuning of the hybrid model, including applying regularization constraints; Prediction step: Output the predicted value of the remaining life of the reverse osmosis membrane based on the trained model.

2. The method according to claim 1, characterized in that The sensor data in the data acquisition step include inlet pressure, outlet pressure, concentrate pressure, outlet flow rate, concentrate flow rate, resistivity and total organic carbon.

3. The method according to claim 1, characterized in that The missing value filling in the data processing step is based on time series correlation interpolation completion, and the outlier filtering is achieved through statistical distribution analysis and dynamic threshold recognition.

4. The method according to claim 1, wherein The feature engineering step also includes feature screening, which involves retaining key features based on feature importance ranking.

5. The method according to claim 1, wherein In the model building step, the fusion layer adopts an attention mechanism or weighted averaging as an alternative to the weighted splicing.

6. The method according to claim 1, wherein The joint fine-tuning in the model training step includes fixing the parameters of the XGBoost branch and optimizing the fusion layer weights.

7. The method according to claim 1, characterized in that It also includes a model evaluation step: calculating indicators of prediction accuracy, including mean squared error and R2 value, and performing robustness testing.

8. The method according to claim 1, characterized in that It also includes a model optimization step: adapting to new data distributions through hyperparameter tuning and online update mechanisms, where the online update includes incremental training.

9. The method according to claim 1, characterized in that The method is applied to water treatment systems in semiconductor manufacturing, pharmaceutical or chemical fields, and is used to predict the life cycle of ultrafiltration membranes or nanofiltration membranes.

10. A reverse osmosis membrane life cycle prediction system for smart plant management, characterized in that: include: A data acquisition module configured to acquire sensor data in real time, wherein the sensor data includes pressure parameters, flow parameters, and water quality parameters; A data processing module is configured to fill missing values, filter outliers and perform standardization on the collected data; A feature engineering module configured to generate multimodal dynamic features, including time series features and cross features; The model module includes a hybrid model architecture of a GRU branch and an XGBoost branch, wherein the GRU branch is used for time series data processing, the XGBoost branch is used for static feature analysis, and the fusion result is output through the fusion layer; The prediction module is configured to output a predicted value of the remaining life of the reverse osmosis membrane based on the model module.

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