System for monitoring corrosion state of sulfuric acid device on line

Abstract

The invention discloses a system for monitoring the corrosion state of a sulfuric acid device on line, and relates to the technical field of corrosion monitoring of industrial equipment, and the system comprises an external reference calibration module used for providing corrosion baseline data and initial calibration of a sensor; the key window observation module integrates a corrosion-resistant transparent window, an internal reference sensor array and an industrial camera system and is used for acquiring local corrosion data and visual features; the distributed multi-parameter sensing network modules are distributed all over the equipment; based on a time sequence corrosion evolution model of a hierarchical self-attention mechanism, deep space-time correlation analysis and fusion processing can be carried out on multi-source heterogeneous data, and through multi-stage collaborative calibration, deep fusion of vision and sensing information and an intelligent prediction algorithm, the corrosion monitoring accuracy and early warning capability are remarkably improved, and the accuracy and the early warning capability are improved. The residual service life of the equipment can be predicted, and the maintenance decision is optimized, so that the safe, long-period and economical operation of the sulfuric acid device is powerfully guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial equipment corrosion monitoring, and particularly relates to an online monitoring system for corrosion state of a sulfuric acid device. BACKGROUND

[0002] As an important basic chemical raw material, sulfuric acid is in a strong corrosive medium environment during production, storage and use, and its device equipment (such as reaction kettles, heat exchangers, pipelines, storage tanks, etc.) faces severe corrosion problems. Corrosion not only leads to equipment material loss, performance degradation, and shortened equipment service life, but also may cause equipment leakage, failure and other safety accidents in severe cases, resulting in huge economic losses and environmental risks. Therefore, real-time, accurate online monitoring and trend prediction of the corrosion state of the sulfuric acid device are of great significance to ensure the safe and stable operation of the device, optimize maintenance strategies, and prolong the service life of the equipment.

[0003] At present, there have been many researches and applications on equipment corrosion monitoring technology. For example, Chinese patent CN110849796B discloses a corrosion monitoring system, which belongs to the technical field of machinery. The corrosion monitoring system is used for monitoring the corrosion of a metal to be measured, and includes a power supply assembly, a processing assembly, a Hall sensor assembly and a resistance probe assembly. The Hall sensor assembly and the resistance probe assembly are connected in parallel across the power supply assembly, and are both located around the metal to be measured. The power supply assembly, the Hall sensor assembly and the resistance probe assembly are all connected to the processing assembly. The patent scheme combines resistance probe method and Hall sensor method to solve the problems of long time required by traditional field hanging piece method and low efficiency of determining corrosion rate in related technologies, and tries to explore the relationship between corrosion rate and magnetic induction intensity.

[0004] For another example, Chinese patent CN113640199B discloses a chemical equipment corrosion monitoring method, which includes obtaining corrosion rates C by using n different methods, determining the confidence and effectiveness corresponding to each corrosion rate, and calculating the posterior probability by combining the prior probability and conditional test probability of corrosion damage state, finally determining the thinning probability POFthin, and calculating the thinning damage factor to grade and warn the corrosion. The method focuses on evaluating the thinning risk of the equipment by fusing multiple corrosion rate measurement results and using a probability statistical model.

[0005] The above existing technologies improve the efficiency of corrosion monitoring or the quantification level of risk assessment to some extent by combining multiple sensing means (such as CN110849796B using resistance probe and Hall sensor) or fusing multiple corrosion rate measurement results and using a probability model (such as CN113640199B evaluating the thinning risk). However, for the application scenarios of sulfuric acid devices with complex working conditions such as high temperature, high pressure, strong corrosion, multiphase flow and variable process parameters, there are still certain limitations, which are specifically reflected in: Lack of high-reliability in-situ multi-modal data anchor points and dynamic calibration mechanism: Existing technologies rely more on indirect, single or limited combination of sensor data, which is difficult to confirm the specific morphology, type and early subtle changes of corrosion in real time and intuitively. At the same time, in the harsh sulfuric acid environment, the stability and accuracy of the long-term operation of the sensor are easily affected, and the existing technology generally lacks an effective dynamic in-situ calibration mechanism combined with direct observation, which is difficult to guarantee the continuous reliability of long-term monitoring data.

[0006] Insufficient prediction accuracy of corrosion evolution trend and remaining life under complex working conditions: The corrosion process of sulfuric acid device is often the result of nonlinear, time-varying coupling of multiple factors (such as temperature, concentration, flow rate, impurities, stress, etc.). The model used in the existing technology to predict the future corrosion state and the remaining useful life of the equipment is often simplified or relies on experience, which is difficult to fully exploit and utilize the complex spatiotemporal dependence and deep correlation characteristics between multi-source heterogeneous monitoring data (such as electrochemical data, visual feature data, process parameter data), resulting in prediction accuracy and reliability to be improved.

[0007] Early and accurate identification and quantitative evaluation means for local corrosion such as pitting and crevice corrosion are lacking: In the sulfuric acid device, local corrosion forms such as pitting, crevice corrosion, stress corrosion cracking are often more hidden and more sudden than uniform corrosion, and the harm is also greater. The existing technology still lacks the ability to identify, locate, and quantitatively evaluate early online local corrosion, and the targeted risk prediction is difficult to meet the needs of fine and forward-looking management of high-risk corrosion forms.

[0008] Quantitative analysis and feedback guidance for corrosion behavior under the influence of multiple process parameters are insufficient: Although the existing technology also monitors process parameters, it is still not perfect in terms of revealing and quantifying how multiple key process parameters (such as sulfuric acid concentration, operating temperature, medium flow rate, etc.) cooperatively affect the corrosion rate and mode, and how to use this quantitative understanding for feedback to guide process optimization to actively control the corrosion closed loop.

[0009] Therefore, it is urgent to develop a new system and method that can overcome the above limitations and achieve more comprehensive, accurate and intelligent online monitoring and prediction of the corrosion state of the sulfuric acid device. SUMMARY

[0010] The purpose of the present application is to overcome the shortcomings of the prior art and provide an online monitoring system for the corrosion state of a sulfuric acid device to solve the above problems.

[0011] The purpose of the present application is achieved by the following technical solution: An online monitoring system for the corrosion state of a sulfuric acid device, comprising: an external reference calibration module comprising a standard container containing a known concentration of sulfuric acid solution and an internal external reference sensor; a key window observation module installed at a key location of the sulfuric acid device, the key window observation module comprising a transparent window resistant to corrosion of sulfuric acid, an internal reference sensor array disposed adjacent to an inner surface thereof, and an industrial camera system capturing internal images through the transparent window; a distributed multi-parameter sensor network module comprising a plurality of distributed sensors distributed at different locations of the device; a data acquisition and transmission module configured to synchronously acquire and time-stamp, at a preset uniform time interval, multi-source data from the external reference calibration module, the internal reference sensor array, the industrial camera system processed visual features, and the plurality of distributed sensors of the distributed sensor network; a central data processing and intelligent analysis platform, the core of which is configured with a time-series corrosion evolution model based on a hierarchical self-attention mechanism, the central data processing and intelligent analysis platform being configured to: time-encode and feature-process the multi-source data to form a data packet sequence; analyze the data packet sequence based on a first layer of multi-head self-attention mechanism to generate a context-aware state representation vector; perform a second layer of multi-head self-attention mechanism analysis on the context-aware state representation vectors of consecutive multiple time intervals to generate a global corrosion state representation vector; use the global corrosion state representation vector to monitor the current corrosion state of the device and predict future corrosion trends or remaining useful life.

[0012] The central data processing and intelligent analysis platform is further configured to use the data of the external reference calibration module to perform initial absolute calibration of each sensor in the system, and in combination with the dynamic correlation analysis results between the readings of the internal reference sensor array of the key window observation module and the visual feature data acquired and processed by the industrial camera system, to perform dynamic in-situ relative calibration or drift correction on the distributed sensors in the distributed multi-parameter sensor network module, thereby forming a multi-level collaborative calibration mechanism.

[0013] The transparent window of the key window observation module is made of special quartz glass, sapphire or modified fluoroplastic, the industrial camera system comprises an independently controllable illumination unit and is configured with an image analysis artificial intelligence module for extracting structured visual features, the structured visual features including area percentage of corrosion area, color histogram, defect count or sulfuric acid medium turbidity quantitative value.

[0014] The internal reference sensor array comprises at least three internal reference sensors, and the central data processing and intelligent analysis platform is configured to use the average reading of the at least three internal reference sensors as the local corrosion state reference of the window observation anchor region.

[0015] The external reference calibration module is further configured with a temperature control and flow rate simulation device for generating baseline data of the corrosion rate of the material under known conditions of sulfuric acid concentration, temperature and flow rate, the baseline data being used to parameterize the time-series corrosion evolution model based on the hierarchical self-attention mechanism.

[0016] The feature processing of the central data processing and intelligent analysis platform on the multi-source data includes: extracting time domain and frequency domain features from electrochemical noise data, and extracting color, texture and defect coordinate and size feature identified by a target detection model from visual data from an industrial camera system.

[0017] The central data processing and intelligent analysis platform is further configured with a plurality of prediction heads connected to the output end of the time-series corrosion evolution model based on the hierarchical self-attention mechanism, the prediction heads being used to output the local corrosion rate, the remaining wall thickness and the risk level for different failure modes.

[0018] The central data processing and intelligent analysis platform is further configured to automatically generate an operable maintenance suggestion based on the monitored current corrosion state and the predicted future corrosion trend and remaining service life, or trigger a multi-level alarm when a preset risk threshold is reached.

[0019] The time-series corrosion evolution model based on the hierarchical self-attention mechanism of the central data processing and intelligent analysis platform is constructed and optimized by using a historical data set containing multi-source data and device offline detection results, device maintenance records or label data of artificially annotated corrosion events corresponding to the historical data set for supervised learning or self-supervised learning pre-training.

[0020] The distributed multi-parameter sensing network module includes electrochemical sensors, temperature sensors, pressure sensors and flow rate sensors, and the central data processing and intelligent analysis platform is configured to use the data of the temperature sensors, pressure sensors and flow rate sensors in combination with the baseline data of the external reference calibration module to quantify the synergistic effect of sulfuric acid concentration, temperature and flow rate on the corrosion rate.

[0021] The beneficial effects of the present application are: 1. The system integrates an external reference calibration module, a key window observation module, a distributed multi-parameter sensing network module, a data acquisition and transmission module and a central data processing and intelligent analysis platform, realizes all-round and multi-dimensional monitoring of the corrosion state of the sulfuric acid device, and compared with the traditional single sensor monitoring method, the present application can comprehensively use electrochemical, visual and process parameter data to provide more comprehensive corrosion information.

[0022] 2. The key window observation module significantly improves the accuracy and reliability of local corrosion observation through corrosion-resistant transparent windows, enhanced industrial camera systems, and internal reference sensor arrays. The external reference calibration module generates high-quality baseline data through environmental control, providing dynamic calibration for distributed sensors, and enhances the data reliability of the entire sensor network.

[0023] 3. The central data processing platform uses algorithms and deep feature extraction techniques combined with a hierarchical self-attention mechanism time series corrosion evolution model to achieve accurate prediction of corrosion rate, remaining wall thickness, and various failure mode risks. The prediction head outputs specific risk levels for different failure modes, improving the guidance and practicality of the prediction results.

[0024] 4. The synergistic effect analysis function quantifies the independent and interactive effects of process parameters such as temperature, pressure, and flow rate on corrosion, providing scientific basis for operators to optimize process parameter combinations, thereby slowing down corrosion and extending equipment life without significantly affecting production efficiency.

[0025] 5. The invention provides targeted inspection and prevention recommendations for maintenance personnel through data insight and multi-dimensional risk assessment, optimizes resource allocation, reduces unplanned downtime, and supports active adjustment of process parameters through synergistic effect analysis results, enhancing the initiative and effectiveness of corrosion control.

[0026] 6. Through deep feature extraction and baseline data analysis, the system not only monitors the surface of corrosion, but also reveals microscopic corrosion signs and driving factors, helping to optimize corrosion prevention strategies from a mechanistic perspective and providing theoretical support for long-term equipment management. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 The system architecture diagram of the present invention; Figure 2 The timing diagram of the present invention. DETAILED DESCRIPTION

[0028] The technical solutions of the present invention will be described in detail below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all embodiments. Based on the embodiments in the present invention, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present invention.

[0029] It is explained that the orientation concepts of "left", "right", "up", "down", "front", "back", "inside" and "outside" in the following solutions are relative directions, which will not be listed one by one.

[0030] Example 1: As Figure 1 and Figure 2As shown, the embodiment discloses a specific implementation of a sulfuric acid plant corrosion monitoring and prediction system, aiming to achieve high-precision and reliable corrosion state evaluation and trend prediction through a multi-stage calibration mechanism and local corrosion monitoring at key positions. The system is suitable for high-risk areas of a sulfuric acid plant, such as the bottom of a reaction kettle and pipe bends. It performs initial calibration of sensors through an external reference calibration module, realizes dynamic calibration by combining visual-sensor data from a key window observation module, and monitors corrosion state and predicts future trends using an intelligent analysis platform.

[0031] The system includes the following main modules: external reference calibration module, key window observation module, distributed multi-parameter sensor network module, data acquisition and transmission module, central data processing and intelligent analysis platform. The external reference calibration module is used to provide corrosion rate reference data to ensure the initial measurement accuracy of the sensor. This module includes a standard container made of corrosion-resistant materials such as polytetrafluoroethylene or stainless steel lining. The container is filled with a known concentration of sulfuric acid solution (e.g., 95-98 wt%) calibrated by titration method, with a concentration error of ±0.01%. The container is equipped with a temperature control device (accuracy ±0.1°C, range 20-120°C) and a flow rate simulation device (flow rate 0.1-1 m / s) to simulate typical operating conditions of a sulfuric acid plant.

[0032] The external reference sensor is installed inside the container, including a linear polarization resistance (LPR) probe and an electrochemical noise (ECN) electrode made of Hastelloy C-276 with a measurement range of 0.01-10 mm / year and an accuracy of ±5%. These sensors are used to generate calibration parameters and corrosion baseline data.

[0033] The key window observation module is installed at key positions of the sulfuric acid plant (such as the bottom of the reaction kettle or the pipe bend) for high-precision local corrosion monitoring. This module includes the following components: The transparent window is made of special quartz glass with a diameter of 50-100 mm and a thickness of 10-15 mm, capable of withstanding temperatures up to 600°C and pressures up to 10 MPa. The window is fixed by Hastelloy flanges and fluororubber gaskets to ensure sealing in high-temperature and high-pressure environments.

[0034] The internal reference sensor array includes three miniature sensors (LPR probe, ECN electrode, and temperature sensor) made of Hastelloy C-276 installed near the inner surface of the transparent window with the same measurement range as the external reference sensor. The central data processing and intelligent analysis platform takes the average value of the readings from the three sensors as the reference for the local corrosion state.

[0035] The industrial camera system is equipped with a high-definition industrial camera (resolution 1920x1080, frame rate 30 fps, dynamic range 60 dB), the shell is made of corrosion-resistant material, the system has a built-in adjustable white light LED lighting unit (wavelength 450-650 nm), the camera is connected to an image analysis module based on convolutional neural network (CNN), the U-Net model is used to segment the corrosion area, the YOLOv5 model is used to detect pitting, the HSV space is used to calculate the color histogram, and the structured visual features are extracted, including the corrosion area percentage, color histogram, pitting count, and sulfuric acid medium turbidity quantitative value.

[0036] The distributed multi-parameter sensor network module includes multiple distributed sensors, including LPR probes, ECN electrodes, temperature sensors, and pressure sensors, which are installed at the feed inlet, heat exchanger, and straight section of the pipeline of the sulfuric acid device. The sensor material is corrosion-resistant alloy, the measurement range is consistent with the internal reference sensor, and it is used to monitor global corrosion and process parameters.

[0037] The data acquisition and transmission module uses an embedded data acquisition system (sampling rate 100 Hz, 16-bit ADC) to transmit data through industrial Ethernet. The system synchronously acquires multi-source data according to monitoring requirements (such as electrochemical data every minute, visual data every 5 minutes, and process parameters every 10 seconds), and adds a timestamp (accuracy ±1 ms) to each data point.

[0038] The central data processing and intelligent analysis platform is responsible for data processing, calibration, analysis, and prediction. It uses a high-performance server (configured with multi-core CPU, 64 GB RAM, and GPU acceleration), software based on time series database to store data, and Python and PyTorch to implement core analysis algorithms. The time series corrosion evolution model uses a hierarchical self-attention mechanism, and the model structure includes: First layer self-attention mechanism: 8 attention heads, input 256-dimensional feature vector, output 512-dimensional context-aware state representation vector (defined as a high-dimensional vector that integrates multi-source features).

[0039] Second layer self-attention mechanism: processes 144 time points within 24 hours (every 10 minutes), outputs 512-dimensional global corrosion state representation vector (defined as a comprehensive vector reflecting spatiotemporal evolution).

[0040] Working process The system working process includes multi-level calibration, local corrosion monitoring, data processing, result output, and model optimization.

[0041] Before the system starts, all sensors are calibrated in the external reference calibration module: The sensor is placed in a standard container and exposed to a known sulfuric acid environment (concentration 98 wt%, temperature 60℃, flow rate 0.5 m / s).

[0042] An external reference sensor measures the corrosion rate (e.g., 0.1 mm / year) as a baseline value.

[0043] The data acquisition module collects sensor readings, and the central platform fits the calibration curve through linear regression (R²>0.95) and calculates the calibration coefficient. For example, if a sensor reading is 0.12 mm / year, the calibration coefficient is 0.1 / 0.12 = 0.833.

[0044] The calibration parameters are stored in a database and used for subsequent data processing.

[0045] During system operation, the key window observation module provides calibration reference: The industrial camera system acquires images every 5 minutes, and the image analysis module extracts visual features (such as 5% corrosion area).

[0046] An internal reference sensor array measures the corrosion current density and takes the average value (e.g., 10 μA / cm²) as a local reference.

[0047] The central platform uses a multilayer perceptron (MLP, 3 layers, 128 neurons in the hidden layer) to map visual features with sensor readings and generate reference values.

[0048] Compare the distributed sensor reading (e.g., 12 μA / cm²) with the reference value. If the deviation is >10%, calculate the correction factor (e.g., 10 / 12 = 0.833) and adjust the reading.

[0049] Calibration is performed every 4 hours, and a correction log is recorded.

[0050] The key view observation module is responsible for monitoring localized corrosion. The transparent window provides an internal visual channel for the device. The camera captures images, and the image analysis module extracts features such as corrosion area and pitting number. The internal reference sensor array measures parameters and calculates average values.

[0051] The central platform cross-validates visual features and sensor data to confirm the corrosion status.

[0052] The central platform performs the following steps: Data cleaning was performed using the 3σ criterion to remove electrochemical noise outliers.

[0053] Feature extraction, calculation of standard deviation (σ=√(Σ(x_i-μ)^2 / N)) and power spectral density of electrochemical noise (calculated by FFT); extraction of corrosion area (U-Net segmentation) and pitting count (YOLOv5 detection) from visual data.

[0054] Time encoding adds a timestamp to each data point to form a data packet sequence.

[0055] First-level self-attention mechanism: Input a 256-dimensional feature vector (including electrochemical, visual, and process parameter features), 8 attention heads, and output a 512-dimensional context-aware state representation vector.

[0056] Second-level self-attention mechanism: Input 144 context-aware state representation vectors within 24 hours, analyze temporal dependencies, and output a 512-dimensional global corrosion state representation vector.

[0057] The global vector is processed by a feedforward neural network (2 layers, 256 neurons) to output the current corrosion rate, wall thickness, and corrosion trend for the next 30 days.

[0058] Model training and optimization The time-series corrosion evolution model is constructed through the following steps: Dataset: Collected 1 year of operational data (approximately 1 million records), including electrochemical noise (μA / cm²), visual features (area %), daily wall thickness measurements (mm), and annotation of 500 pitting events.

[0059] Supervised learning: using the mean squared error loss function, Adam optimizer (learning rate 0.001, batch size 32), trained for 100 epochs.

[0060] Self-supervised pre-training: Using a time series prediction task, masking 50% of the data points, and pre-training for 50 epochs.

[0061] Incremental learning: The model is updated monthly with new data, and an experience replay mechanism is adopted.

[0062] Maintenance suggestions and alarms The central platform generates maintenance recommendations based on the corrosion status and prediction results, such as "It is recommended to check the pipe bends within 7 days and increase the corrosion inhibitor by 10%".

[0063] Triggering multi-level alarms: Warning (yellow): Corrosion rate > 0.2 mm / year; General alarm (orange): Wall thickness < safety margin.

[0064] Emergency alarm (red): If the number of pitting defects is greater than 10 or the remaining lifespan is less than 30 days, the alarm will be notified via interface, SMS, and audible and visual alarm.

[0065] The innovative algorithm formula for dynamically adjusting the remaining service life of equipment is as follows: Symbol explanation: RULadj : Remaining useful life (years) after dynamic risk adjustment.

[0066] RUL pred : Remaining useful life (in years) directly predicted by the time-series corrosion evolution model based on the global corrosion state characterization vector.

[0067] Additional lifespan loss (in years) caused by actual operating conditions deviating from ideal or design conditions (such as over-temperature or over-concentration) from the last assessment time t0 to the current time t.

[0068] R S The comprehensive corrosion risk index (range 0-1) is output by the model and takes into account factors such as the current corrosion rate, local corrosion tendency, and historical damage accumulation.

[0069] M conf Model prediction confidence (range 0-1) reflects the model's confidence level in its prediction results, calculated based on output variance and consistency with historical data.

[0070] w m Model confidence affects the weighting factor, with a typical value of 0.1-0.5.

[0071] R S,crit : The preset critical corrosion risk index, with a typical value of 0.8.

[0072] ε: A small constant to prevent the denominator from being zero, typically 0.01.

[0073] tanh(·): Hyperbolic tangent function, used to smooth and constrain the risk adjustment factor within the 0-2 interval.

[0074] Formula Explanation: This formula dynamically adjusts the remaining lifespan of the equipment through two parts: Lifetime Loss Correction: From the initial predicted value RUL pred The additional lifespan loss caused by changes in operating conditions (such as temperature and concentration) is subtracted from the total lifespan.

[0075] Risk and confidence level adjustment: Adjusted using the tanh function based on the comprehensive corrosion risk index R. S and model confidence M conf The ratio of R to dynamically adjust lifetime prediction, when R S Approaching the critical value R S,crit Or M conf When the value is low, the adjustment factor decreases, resulting in more conservative predictions. For example, if RUL... pred = 5 years, operating loss 0.2 years, R S= 0.7, M conf = 0.9, then RUL adj With a timeframe of approximately 4.5 years, this formula improves the robustness of predictions and is particularly suitable for high-risk operating conditions.

[0076] To improve the reliability and conservatism of the prediction, the system uses a global corrosion state characterization vector to calculate the remaining service life with dynamic risk adjustment. This value is calculated using the formula mentioned above, taking into account operating condition deviations and model confidence. When the corrosion risk increases or the prediction confidence decreases, the adjusted service life prediction will become more conservative, thereby improving the robustness of the system.

[0077] Application Cases At a certain pipe bend, the system has been running for one month, and the following data has been recorded: Initial calibration: LPR probe reading 0.12 mm / year, 0.1 mm / year after calibration; Dynamic calibration: Visual characteristics show corrosion area of ​​5%, sensor reading 12 μA / cm², 9.8 μA / cm² after calibration; Monitoring results: Current corrosion rate 0.15 mm / year, wall thickness 8.5 mm; Predicted results: Corrosion area to increase to 6% within 30 days, wall thickness to decrease by 0.05 mm. Maintenance recommendations: Inspect elbows and adjust process parameters.

[0078] The key window observation module combines visual features and sensor data to significantly improve monitoring accuracy. Compared with the 15-20% error of traditional electrochemical monitoring, the error of this system is usually controlled within 5-10%. For example, after calibration, the corrosion current density error is reduced from 15% to 5%.

[0079] The multi-level calibration mechanism ensures sensor data consistency through initial calibration and dynamic correction, with drift error typically controlled within 5-10%, superior to the 10-15% error of traditional single calibration methods. The system can predict corrosion trends 15-30 days in advance, reducing unplanned downtime by 20-40%, effectively preventing equipment failure caused by accelerated corrosion. Based on accurate monitoring and prediction, maintenance recommendations optimize maintenance plans, typically reducing maintenance costs by 10-20%, improving equipment operating efficiency, timely detection of potential localized corrosion hazards, significantly reducing the risk of leakage caused by corrosion, and enhancing the safety of equipment operation.

[0080] The specific effects described above vary depending on the operating conditions (such as sulfuric acid concentration and equipment material). Those skilled in the art can adjust the parameters according to actual needs to achieve similar effects.

[0081] This embodiment achieves high-precision monitoring and trend prediction of localized corrosion in sulfuric acid plants through external benchmark calibration, key window observation, multi-level calibration mechanism and intelligent analysis, providing reliable protection for safe equipment operation and economic benefits.

[0082] Example 2: like Figure 1 and Figure 2 As shown in the figure, this embodiment describes in detail the preferred configuration and specific implementation of the key window observation module and the external reference calibration module in the sulfuric acid plant corrosion monitoring and prediction system. These modules improve the accuracy of local corrosion monitoring, data reliability and the prediction capability of the core AI model. This embodiment is applicable to key parts of sulfuric acid plants (such as the bottom of the reactor and pipe bends) in high temperature, high pressure or complex chemical environments.

[0083] The key viewing window observation module is installed in the key parts of the device for high-precision local corrosion monitoring. In this embodiment, its core components have been optimized.

[0084] For corrosion-resistant transparent window materials, suitable for high-temperature, high-pressure, and highly corrosive environments (temperature 150-200°C, pressure 0.5-1.0MPa, sulfuric acid concentration ≥98%), sapphire (single crystal α-Al2O3) is used, which has excellent mechanical strength (Mohs hardness 9), optical transmittance (ultraviolet to mid-infrared) and chemical resistance.

[0085] For medium temperature and pressure conditions (temperature <120°C, pressure <0.5 MPa), special quartz glass (such as Corning 7980) is used, which has excellent thermal shock resistance and high chemical purity.

[0086] For chemical environments (such as those containing fluorine impurities), modified fluoroplastics (such as PFA or FEP) are used, which are highly corrosion resistant and suitable for complex shape requirements.

[0087] The viewing window is fixed by a corrosion-resistant alloy (such as Hastelloy C-276) flange and sealed with expanded polytetrafluoroethylene (ePTFE) or flexible graphite composite gaskets to ensure reliability under high temperature and pressure. The size of the viewing window is adjusted according to the working conditions (such as diameter 50-100 mm, thickness 10-20 mm).

[0088] The lighting unit of the industrial camera system is equipped with an independently controllable white LED lighting unit (wavelength 450-650nm), which adjusts the brightness according to the turbidity of the medium or surface reflection through pulse width modulation (PWM) to reduce glare.

[0089] The image analysis AI module is integrated into the central data processing and intelligent analysis platform. It receives 1920x1080 resolution RGB images and uses the following algorithms to extract structured visual features: The percentage of corroded area is segmented using the U-Net model, outputting the proportion of the corroded area to the total area (0-100%). The color histogram is based on the HSV space to statistically analyze the color distribution of corrosion products or media, reflecting the corrosion type or turbidity changes. The pitting count is detected using the YOLOv5 model, outputting the number and distribution density. The turbidity quantification of sulfuric acid media is analyzed through gray-level co-occurrence matrix analysis of image texture to semi-quantitatively assess turbidity (range 0-1).

[0090] Using a labeled image dataset (approximately 10,000 images containing different degrees of corrosion and working conditions), U-Net and YOLOv5 models were trained through supervised learning, achieving an accuracy of 95%.

[0091] The internal reference sensor array is equipped with three identical linear polarized resistance (LPR) probes, made of the same material as the main body of the equipment (such as 316L stainless steel or Hastelloy), and installed in a triangular layout on the inner wall of the transparent window. The measurement range is 0.01-10 mm / year, with an accuracy of ±5%.

[0092] The central data processing and intelligent analysis platform simultaneously collects readings from three probes (corrosion current density, μA / cm²). If a reading deviates from other readings by more than 20%, it is considered abnormal and discarded. The arithmetic mean of the remaining readings is taken as the benchmark for local corrosion status. For example, if the readings from the three probes are 10, 10.5, and 12 μA / cm², after discarding 12, the average value is 10.25 μA / cm².

[0093] The external reference calibration module provides corrosion baseline data and sensor calibration references.

[0094] Environmental control devices Temperature control employs a high-precision thermostat (such as a circulating oil bath), with a control range of 20-180°C and an accuracy of ±0.2°C. This is achieved through a PID controller and a PT100 platinum resistance sensor. Flow rate simulation is configured with a corrosion-resistant magnetic stirrer or a micro pump, with a flow rate range of 0-1 m / s and an accuracy of ±0.01 m / s. It is monitored using a Coriolis mass flow meter.

[0095] Corrosion Rate Baseline Data Generation and Application Standard material specimens (with the same material as the equipment, such as Hastelloy C-276) and external reference sensors (LPR probe, ECN electrode) are placed in a known sulfuric acid environment (concentration 95-98%, temperature 60-120°C, flow rate 0.1-0.5 m / s), and electrochemical parameters (such as corrosion current density and polarization resistance) are continuously recorded. After the experiment, the corrosion rate is verified by the weight loss method.

[0096] The relationship between corrosion rate and environmental parameters is fitted using the least squares method, with the formula k = A·exp(-Ea / RT)·C^n, where k is the corrosion rate (mm / year), A is the pre-exponential factor, Ea is the activation energy (e.g., 50 kJ / mol), R is the gas constant, T is the absolute temperature (K), C is the sulfuric acid concentration (wt%), and n is the concentration exponent (e.g., 1.2).

[0097] Baseline data provides prior knowledge for time-series corrosion evolution models, initializes parameters such as Ea and n, verifies the agreement between model predictions and experimental data with an error of <5%, and guides the selection of time-domain characteristics of electrochemical noise (such as standard deviation) as model input.

[0098] Work process The transparent window provides an internal visual channel for the equipment. The industrial camera acquires images every 5 minutes, and the image analysis module extracts visual features (such as corrosion area of ​​5% and pitting of 3 per cm²). The internal reference sensor array measures the corrosion current density and takes the average value (such as 10.25 μA / cm²). The central data processing and intelligent analysis platform maps the visual features and sensor readings through a multilayer perceptron (MLP, 3 layers, 128 hidden neurons) to generate calibration reference values.

[0099] The external benchmark calibration module works by simulating a sulfuric acid environment (e.g., 98% concentration, 60°C, 0.5 m / s) in a standard container, recording the corrosion rate (e.g., 0.1 mm / year) using an external reference sensor, generating calibration parameters, and fitting the baseline data to model parameters (e.g., Ea = 50 kJ / mol) for training the central platform model.

[0100] Optimal window materials and advanced image analysis enhance the accuracy of visual features, redundant sensor design ensures the stability of anchor point data, and the external benchmark calibration module enhances the model's understanding of environmental factors through baseline data. The key window module provides highly reliable local data, and the external benchmark module provides basic corrosion laws. The combination of these two enables the time-series corrosion evolution model to more accurately predict corrosion behavior under complex working conditions.

[0101] Symbol explanation: I VC Overall visual corrosion index (0-100) F vis,i (t): The i-th visual feature value (e.g., corrosion area %) F vis,i,baseline Characteristic health baseline value (e.g., initial area 0%) F vis,i,scale Normalized scale factor (e.g., area scale 100%) Feature weights (0.1-0.5) p i Sensitivity index (1-2) Count defect Number of pitting pits (per cm²) S defect Average pitting size (mm) Defect weight (0.2) ε: Small constant (0.01) This formula quantifies the degree of corrosion by weighted nonlinear combination of visual features and taking into account the number and severity of pitting. For example, if the corrosion area increases from 0% to 5%, with 3 pits / cm² and a size of 0.1 mm, then I VC ≈ 10. This index can be used as a model input or cross-validated with sensor readings to improve monitoring accuracy.

[0102] Application Cases At a pipe bend (sulfuric acid concentration 98%, temperature 100°C), the system operated for one month: Visual characteristics: Under the preferred sapphire window, the U-Net segmentation corrosion area is 5%, with an error of 2% (compared to 5% for traditional quartz glass). The readings of the three LPR probes are 10, 10.5, and 12 μA / cm², with an average of 10.25 μA / cm². The stability (variance) is improved by 15%. Baseline data: The fitted corrosion rate is k = 0.1·exp(-50 / RT), and the model prediction error is <5%. Calibration effect: The dynamic calibration frequency is reduced from once a month to once a quarter.

[0103] Optimized window materials and advanced image analysis reduce visual feature extraction error from 5-10% in traditional methods to 2-5%. Redundant design of three sensors and average value processing improve data stability, and reading variance is typically reduced by 10-20%, which is better than the 15-25% variance of a single sensor. Baseline data optimizes the time-series corrosion evolution model, which typically improves prediction accuracy by 10-20% and achieves an accuracy of 85-95%, which is better than the 70-80% of traditional statistical models.

[0104] Optimal configuration ensures stable operation of the system in high temperature, high pressure or impurity environments, improving operational stability by 15-25%.

[0105] The specific effects described above vary depending on the operating conditions (such as sulfuric acid concentration and equipment material). Those skilled in the art can adjust the configuration according to actual needs to achieve similar effects.

[0106] This embodiment significantly improves the local observation accuracy, data reliability, and prediction capability of the sulfuric acid plant corrosion monitoring and prediction system through the optimized configuration of the key window observation module and the external reference calibration module.

[0107] Example 3: like Figure 1 and Figure 2 As shown, this embodiment is based on the system architecture of embodiment 1 and the preferred configuration of the key observation and calibration modules of embodiment 2. It elaborates in detail the algorithms, refined data analysis and application extension functions of the central data processing and intelligent analysis platform (hereinafter referred to as the "central platform"). These functions enable in-depth insight into corrosion status, multi-failure mode risk assessment and quantification of the synergistic impact of process parameters, thereby improving the accuracy and decision support capabilities of the corrosion monitoring and prediction system for sulfuric acid plants. This embodiment is applicable to key parts of high-temperature and high-pressure sulfuric acid plants (such as the bottom of the reactor and pipe bends).

[0108] The central platform performs deep feature extraction on multi-source data (electrochemical noise, visual data, and process parameters) to generate a data packet sequence that is input into the time-series corrosion evolution model.

[0109] When the distributed multi-parameter sensing network module or internal reference sensor array contains an electrochemical noise sensor, the central platform performs the following processing on the electrochemical noise time series (corrosion potential noise V, current noise I): Time-domain characteristics: noise resistance (R) n ):R n = σ V / σ I , range 10³-10 5 Ω·cm², which is related to the reciprocal of the corrosion rate.

[0110] Standard deviation (σ) V , σ I : σ = √(Σ(x) i -μ)² / N) reflects the fluctuation range, with typical values ​​of 0.1-10 μV or nA.

[0111] Kurtosis: Kurt = Σ(x i -μ) 4 / (Nσ 4 -3, range -2 to 10, high value indicates pitting outbreak.

[0112] Localization index (LI): LI = σ I / I rms I_rms = √(ΣI_i² / N), ranging from 0 to 1, LI>0.5 indicates localized corrosion.

[0113] Frequency domain characteristics: Power spectral density (PSD): Calculated by Fast Fourier Transform (FFT), analyzing low-frequency amplitude (10⁻³-10⁻¹Hz, reflecting corrosion rate) and roll-off slope (-20 to -40 dB / decade, reflecting film characteristics).

[0114] The features are calculated using Python signal processing libraries (such as SciPy) to form a 256-dimensional ECN feature vector.

[0115] Based on the image analysis module of Example 2, the central platform further enhances visual feature extraction: Color features: Calculate the HSV color histogram (256 bins) and color moments (mean, variance, skewness), and train a ResNet-18 classifier to identify corrosion products (e.g., Fe2O3 reddish-brown, probability 0-1).

[0116] Texture features: Contrast (0-1), correlation (0-1), and energy (0-1) are extracted using the gray-level co-occurrence matrix (GLCM), and surface roughness is described by a Gabor filter (4 directions, 3 scales).

[0117] Defect characteristics: Using the YOLOv7 model, the input is a 1920x1080 RGB image, and the output is the bounding box, number (pieces / cm²), and average size (mm) of pitting pits. The model is trained with 10,000 labeled images (including pitting and cracks) and has an accuracy of 95%.

[0118] The features form a 256-dimensional visual feature vector, which is then input into the temporal erosion evolution model.

[0119] The temporal corrosion evolution model of the central platform is based on a hierarchical self-attention mechanism. The second layer outputs a 512-dimensional global corrosion state representation vector (G, which integrates spatiotemporal features) and connects multiple prediction heads.

[0120] A local corrosion rate prediction head predicts the local corrosion rate (mm / year) for the next 24 hours, 7 days, and 30 days. It uses a 2-layer fully connected neural network (FFN, 256 neurons), with input G and output corrosion rate at each monitoring point. Training uses the mean squared error loss function and the Adam optimizer (learning rate 0.001) for 100 epochs.

[0121] The remaining wall thickness prediction head, combined with the initial wall thickness and historical corrosion amount, predicts the remaining wall thickness (mm) for the next 1 month, 6 months, and 1 year. FFN predicts the wall thickness reduction rate (mm / month), and the remaining wall thickness is calculated by integration.

[0122] The failure mode risk prediction head assesses the risk level (low, medium, high) of pitting corrosion and uniform thinning. FFN outputs classification probabilities (Softmax activation). The training data contains 500 pitting corrosion events, with an accuracy of 90%.

[0123] The central platform utilizes process parameter sensors (temperature, pressure, flow rate) from the distributed sensor network module and baseline data from the external benchmark calibration module to quantify the synergistic effects of multiple factors.

[0124] Sensor configuration Temperature sensor: Armored PT100 RTD, accuracy ±0.1°C, range 20-200°C; Pressure sensor: Corrosion-resistant pressure transmitter, accuracy ±0.5%, range 0-2 MPa; Flow rate sensor: Electromagnetic flow meter, accuracy ±0.5%, range 0-2 m / s.

[0125] Corrosion rate (R_real), temperature (T), pressure (P), and flow rate (V) are aligned by timestamps (accuracy ±1 ms). A multivariate nonlinear regression model is used, with the formula R_real = β0 + β1T + β2P + β3V + β4TP + ​​β5TV + β6PV, where β_i is the regression coefficient. The model is fitted using the least squares method. An external benchmark module provides the influence of a single factor (e.g., R_base_T = 0.1·exp(-50 / RT)). The difference between the linear superposition of R_real and R_base is compared to quantify the synergistic effect, generating contribution weights (e.g., "temperature 40%, flow rate 30%)" and optimization suggestions (e.g., "reduce flow rate by 10% to slow down corrosion").

[0126] Work process Data acquisition: Collect ECN, vision, and process parameter data every minute.

[0127] Feature extraction: Generate a 256-dimensional ECN and visual feature vector, combined with a 64-dimensional process parameter vector.

[0128] Model processing: The first layer of self-attention (8 heads, 512-dimensional output) fuses multi-source features, and the second layer analyzes 24-hour time series (144 time points) to generate a 512-dimensional vector G.

[0129] Predictive output: The prediction head outputs corrosion rate, wall thickness, and risk level.

[0130] Collaborative analysis: Regression models quantify the impact of process parameters and generate optimization suggestions.

[0131] Symbol explanation: Γ sync(T,P,V): Synergistic effect factor of process parameters (temperature T, pressure P, flow rate V) (unitless).

[0132] CR actual (T,P,V): Actual corrosion rate (mm / year), measured by distributed sensors.

[0133] CR base The baseline corrosion rate (mm / year) is provided by an external baseline calibration module.

[0134] ΔP i : The deviation of the i-th process parameter (e.g., ΔT, unit °C; ΔP, unit MPa; ΔV, unit m / s).

[0135] α i : The linear influence coefficient of the i-th process parameter, with a typical value of 0.01-0.1.

[0136] β ij : The interaction coefficient between the i-th and j-th process parameters, with a typical value of 0.005-0.05.

[0137] f int (P i , P j ): Interaction function, describing the nonlinear interaction between parameters.

[0138] ε: A small constant to prevent the denominator from being zero, typically 0.001.

[0139] Formula Explanation: This formula compares the actual corrosion rate CR... actual Compared with the corrosion rate prediction based on baseline conditions and parameter deviations, the synergistic effect of process parameters such as temperature T, pressure P, and flow rate V is quantified. The prediction model includes a linear term (α). i · ΔP i ) and interaction items (β) ij · ΔP i · ΔP j · f int (P i , P j ), Synergistic effect factor Γ sync Reflecting the strength of actual synergy: Γ sync >1: There is a synergistic effect between the parameters to promote corrosion.

[0140] Γ sync <1: There is a synergistic effect between the parameters to inhibit corrosion.

[0141] Γ sync≈ 1: No significant synergistic effect.

[0142] For example, if CR actual = 0.2 mm / year, the predicted value is 0.15 mm / year, then Γ sync The value ≈ 1.33 indicates a synergistic effect of 33%, suggesting that process parameters need to be adjusted (such as reducing the flow rate by 10%) to mitigate corrosion. This formula supports active corrosion control and improves the accuracy of equipment life prediction.

[0143] Application Cases At a pipe bend (sulfuric acid concentration 98%, temperature 100°C, flow velocity 0.5 m / s): ECN characteristics: R_n=10 4 Ω·cm², LI=0.6 (localized corrosion), visual characteristics: corrosion area 5%, 3 pits / cm², YOLOv7 accuracy 95%, prediction results: 30-day corrosion rate 0.2 mm / year, wall thickness 8.45 mm, medium risk of pitting corrosion, synergistic effects: temperature contributes 40%, flow rate 30%, it is recommended to reduce the flow rate by 10%.

[0144] Enriching features enhances model resolution, improving accuracy by 10%. Multidimensional risk assessment optimizes maintenance priorities, provides a basis for process optimization, and extends equipment lifespan.

[0145] Deep feature extraction reveals signs of microscopic corrosion, improving feature resolution by 10-20%, which is 5-10% better than traditional methods. The prediction head improves the accuracy of pitting corrosion risk prediction to 85-95%, which is better than 70-80% of traditional statistical models. Collaborative impact analysis quantifies the contribution weights of temperature, flow rate, etc. (e.g., temperature 40%), provides optimization suggestions, and reduces maintenance costs by 10-15%. Multi-failure mode prediction optimizes resource allocation and reduces unplanned downtime by 20-30%. The algorithm adapts to complex working conditions and improves stability by 15-25%.

[0146] The above effects were verified through simulation experiments. The specific performance varies depending on the working conditions. Those skilled in the art can adjust the parameters according to actual needs to achieve similar effects.

[0147] This embodiment significantly improves the accuracy and decision support capability of the sulfuric acid plant corrosion monitoring and prediction system through the algorithm, refined analysis, and quantification of the synergistic effects of process parameters on the central data processing platform. Those skilled in the art can implement this technical solution based on the above description.

[0148] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be modified within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. An online monitoring system for the corrosion status of a sulfuric acid plant, characterized in that, include: The external reference calibration module includes a standard container for holding a sulfuric acid solution of known concentration and an internally mounted external reference sensor. The critical viewing window observation module is installed at a critical location in the sulfuric acid plant. The critical viewing window observation module includes a transparent viewing window resistant to sulfuric acid corrosion, an internal reference sensor array disposed adjacent to its inner surface, and an industrial camera system for acquiring internal images through the transparent viewing window. A distributed multi-parameter sensor network module, comprising multiple distributed sensors located at different locations on the device; The data acquisition and transmission module is used to synchronously acquire and timestamp multi-source data from the external reference calibration module, the internal reference sensor array, the visual features processed by the industrial camera system, and the multiple distributed sensors at a preset uniform time interval. The central data processing and intelligent analysis platform is configured with a time-series erosion evolution model based on a hierarchical self-attention mechanism. The central data processing and intelligent analysis platform is configured as follows: The multi-source data is time-coded and feature-processed to form a data packet sequence; The data packet sequence is analyzed based on the first-layer multi-head self-attention mechanism to generate a context-aware state representation vector. A second-layer multi-head self-attention mechanism analysis is performed on the context-aware state representation vectors of multiple consecutive time intervals to generate a global erosion state representation vector. The global corrosion state characterization vector is used to monitor the current corrosion state of the equipment and predict future corrosion trends or remaining service life.

2. The online monitoring system for the corrosion status of a sulfuric acid plant according to claim 1, characterized in that: The central data processing and intelligent analysis platform is further configured to: perform initial absolute calibration on each sensor in the system using the data from the external reference calibration module, and combine the dynamic correlation analysis results between the readings of the internal reference sensor array of the key window observation module and the visual feature data collected and processed by the industrial camera system to perform dynamic in-situ relative calibration or drift correction on the distributed sensors in the distributed multi-parameter sensor network module, thereby forming a multi-level collaborative calibration mechanism.

3. The online monitoring system for the corrosion status of a sulfuric acid plant according to claim 1, characterized in that: The transparent window of the key viewing window observation module is made of special quartz glass, sapphire, or modified fluoroplastic. The industrial camera system includes an independently controllable lighting unit and an image analysis artificial intelligence module configured to extract structured visual features, including the area percentage of the corroded area, color histogram, defect count, or sulfuric acid medium turbidity quantification.

4. The online monitoring system for the corrosion status of a sulfuric acid plant according to claim 1, characterized in that: The internal reference sensor array includes at least three internal reference sensors, and the central data processing and intelligent analysis platform is configured to use the average reading of the at least three internal reference sensors as a local corrosion state reference for the viewing window observation anchor area.

5. The online monitoring system for the corrosion status of a sulfuric acid plant according to claim 1, characterized in that: The external benchmark calibration module is further configured with a temperature control and flow rate simulation device, which is used to generate baseline data of the corrosion rate of the material under known sulfuric acid concentration, temperature and flow rate conditions. The baseline data is used to parameterize the time-series corrosion evolution model based on the hierarchical self-attention mechanism.

6. The online monitoring system for the corrosion status of a sulfuric acid plant according to claim 1, characterized in that: The central data processing and intelligent analysis platform performs feature processing on the multi-source data, including: extracting time-domain and frequency-domain features from electrochemical noise data, and extracting color, texture, and defect coordinates and size features identified by the target detection model from the visual data from the industrial camera system.

7. The online monitoring system for the corrosion status of a sulfuric acid plant according to claim 1, characterized in that: The central data processing and intelligent analysis platform is further configured with multiple prediction heads connected to the output of the time-series corrosion evolution model based on the hierarchical self-attention mechanism. The prediction heads are used to output local corrosion rate, remaining wall thickness, and risk level for different failure modes.

8. The online monitoring system for the corrosion status of a sulfuric acid plant according to claim 1, characterized in that: The central data processing and intelligent analysis platform is further configured to automatically generate actionable maintenance suggestions based on the monitored current corrosion status, predicted future corrosion trends, and remaining service life, or to trigger multi-level alarms when a preset risk threshold is reached.

9. The online monitoring system for the corrosion status of a sulfuric acid plant according to claim 1, characterized in that: The time-series corrosion evolution model based on the hierarchical self-attention mechanism of the central data processing and intelligent analysis platform is constructed and optimized by using a historical dataset including the multi-source data and labeled data of offline equipment detection results, equipment maintenance records or manually labeled corrosion events corresponding to the historical dataset for supervised learning or self-supervised learning pre-training.

10. The online monitoring system for the corrosion status of a sulfuric acid plant according to claim 1, characterized in that: The distributed multi-parameter sensing network module includes an electrochemical sensor, a temperature sensor, a pressure sensor, and a flow rate sensor. The central data processing and intelligent analysis platform is configured to use the data from the temperature sensor, pressure sensor, and flow rate sensor, combined with the baseline data from the external reference calibration module, to quantify the synergistic effect of sulfuric acid concentration, temperature, and flow rate on the corrosion rate.

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