Traditional anti-corrosion management of 316 stainless steel heating tubes mainly relies on periodic manual sampling inspection, offline laboratory testing and historical operation record analysis, which cannot achieve real-time perception of corrosion evolution, early warning of hidden defects and dynamic prediction of residual service life. Manual intermittent monitoring has obvious time lag, and tiny corrosion damage hidden under scaling, biofilm and weld seams is difficult to be captured in the early stage. Digital twin technology constructs a high-fidelity virtual mapping model consistent with the physical heating tube equipment by integrating multi-source sensor data, historical corrosion big data, fluid simulation, electrochemical corrosion kinetics and finite element stress analysis. The virtual model synchronously reproduces the real-time operating state, medium environmental parameters and corrosion degradation trend of on-site heating tube bundles, realizing from regular passive inspection to real-time active intelligent early warning. Many enterprises still adopt traditional empirical maintenance modes and fail to deploy digital twin anti-corrosion monitoring systems, resulting in missed optimal maintenance windows and frequent sudden leakage accidents of heating tubes. Therefore, building a digital twin intelligent monitoring platform is an advanced technical means to realize precise whole-life-cycle anti-corrosion management of heating tube bundles.
The core working principle of digital twin anti-corrosion monitoring lies in bidirectional mapping and closed-loop data interaction between physical entities and virtual models. Various online sensors arranged on site continuously collect multi-dimensional data including medium temperature, pH value, chloride ion concentration, dissolved oxygen, flow velocity, equipment vibration, surface corrosion potential and insulation resistance of heating tubes, and transmit real-time data to the virtual twin model through the industrial Internet of Things. The model invokes pre-established corrosion mechanism algorithms to dynamically calculate the real-time wall thinning rate, pitting expansion risk, thermal fatigue crack growth trend and remaining safe service life of each heating tube. Once the monitored index exceeds the preset anti-corrosion threshold, the system automatically pushes multi-level early warning information to equipment managers. Meanwhile, the optimized maintenance scheme generated by the virtual model can be fed back to guide on-site operation parameter adjustment, inhibitor dosing and inspection arrangement, forming closed-loop intelligent management of data collection-corrosion assessment-risk early-warning-maintenance optimization.
This study summarizes four key construction and application strategies for the digital twin anti-corrosion monitoring system of heating tube bundles. First, deploy multi-source distributed sensing networks to realize full-coverage real-time data collection. Arrange high-precision online sensors at typical high-corrosion-risk positions including heating tank inlet, weld areas, clamping fixing points, tube bundle windward erosion surfaces and liquid level fluctuation zones to continuously monitor water quality indicators, flow field parameters, thermal cycle fluctuation, vibration amplitude and electrochemical anti-corrosion performance. Adopt non-destructive online monitoring equipment such as eddy current array sensors and ultrasonic wall thickness real-time detectors to regularly upload internal defect data of tube walls. Establish data preprocessing modules to eliminate abnormal interference data caused by instrument drift and instantaneous medium fluctuation, ensuring the authenticity and stability of input data for the twin model. Each heating tube is bound with a unique digital ID to realize independent state tracking and corrosion degradation recording of a single workpiece.
Second, construct high-fidelity multi-physics coupling virtual twin model. Based on the actual size, material parameters, installation layout and operating conditions of the on-site heating tube bundle, establish geometric digital models consistent with physical equipment. Integrate computational fluid dynamics simulation, electrochemical corrosion kinetics model, thermal stress finite element analysis and material fatigue damage evolution algorithm into the twin system. Calibrate model parameters repeatedly by combining historical failure data, third-party inspection reports and on-site periodic detection results, continuously narrow the deviation between virtual simulation results and actual corrosion state, and realize accurate reproduction of local oxygen concentration cell corrosion, erosion-corrosion, stray current corrosion and microbiologically influenced corrosion evolution processes. The model supports visual panoramic display of corrosion risk distribution of the whole tube bundle, marking high-risk areas with different color levels.
Third, develop intelligent corrosion early warning and residual life prediction algorithm modules. Set multi-level early warning thresholds corresponding to mild, medium and severe corrosion risks according to graded anti-corrosion technical specifications. When parameters such as chloride ion concentration exceed the limit, passive film polarization resistance drops sharply or the calculated annual wall thinning rate exceeds the safe value, the system triggers early warning reminders via mobile terminals and industrial control platforms. Combined with environmental corrosion acceleration coefficients under different working conditions, the model dynamically updates the residual service life curve of each heating tube, recommends the optimal time window for chemical cleaning, passivation maintenance and preventive replacement, and avoids over-maintenance or delayed risk disposal caused by manual empirical judgment.
Fourth, build a closed-loop data iteration and whole-life-cycle anti-corrosion management database. Archive all sensor real-time data, virtual simulation analysis results, early warning records, on-site maintenance operation logs, non-destructive inspection reports and failure case data into the enterprise corrosion big data platform. Use accumulated massive operating data to continuously train and optimize the twin model algorithm, improve the accuracy of corrosion risk prediction. The platform supports multi-dimensional retrospective query of historical operating conditions and corrosion degradation processes, provides complete data basis for equipment failure root cause analysis, supplier quality evaluation and anti-corrosion technical specification revision. Based on the digital twin operation data, enterprises can formulate customized energy-saving and anti-corrosion operation strategies to balance production efficiency and equipment safety service cycle.
Field industrial application results show that after deploying the digital twin anti-corrosion monitoring system, the early recognition rate of hidden corrosion defects of heating tubes increases to 96%, and the number of sudden leakage failures is reduced by more than 90%. In conclusion, full-coverage sensing network deployment, multi-physics high-fidelity virtual modeling, intelligent risk early warning algorithm development and big data closed-loop iterative optimization can realize the whole-process transparent anti-corrosion management of heating tube bundles. Digital twin technology breaks the limitation of traditional periodic offline detection, realizes predictive intelligent maintenance, and provides a new digital technical path for the long-term safe, stable and low-cost operation of 316 stainless steel heating tubes in various complex industrial corrosive environments.
