Heat exchanger sectional type phase change regulation and control method based on steam superheat degree threshold value

Abstract

The invention relates to the technical field of heat exchange control of heat exchangers, in particular to a heat exchanger sectional type phase change regulation and control method based on a steam superheat degree threshold value, which comprises the following steps of: completing virtual segmentation of a heat exchanger based on steam heat transfer characteristics of the heat exchanger, and deploying a whole-section monitoring network in a non-intrusive monitoring mode; measuring steam thermophysical properties and material characteristics; presetting a threshold dynamic correction rule; the preheating section is regulated and controlled according to the superheat degree threshold value; carrying out material flow field adaptive regulation and control on the phase change section; gas-liquid separation of the supercooling section is realized by adopting an external gas-liquid separation assembly; self-adaptive iterative optimization of a regulation and control strategy is realized based on historical operation data; a fault diagnosis and emergency regulation and control mechanism is added; and accurate monitoring of the phase change section without the middle pipe orifice is realized. Virtual segmentation, external monitoring and executive device deployment design are adopted, the transformation construction period is shortened, the transformation cost of a single device is reduced, the method is adaptive to a tube pass heat exchanger in the prior art, and compatibility is high.

Description

Technical Field

[0001] This invention relates to the field of heat exchanger heat exchange control technology, and in particular to a segmented phase change control method for heat exchangers based on steam superheat threshold. Background Technology

[0002] Traditional heat exchanger control methods often involve simply dividing the heat exchange into sections based on changes in medium temperature. The heat exchange process is controlled by adjusting single parameters such as steam flow rate or pressure, often neglecting key phase change parameters like superheat. Furthermore, control thresholds rely heavily on manual experience and lack correlation with material properties. For example, patent application CN202110219690.X discloses a combined sink-and-splitter small temperature difference heat exchanger and its control method, including the following steps: Step 100: Dividing the entire heat exchange process into several heat exchange sections based on the thermophysical properties of the cold and hot media; Step 200: Individually adjusting the mass flow rates of the cold and hot media within each heat exchange section to ensure equal temperature changes in both the cold and hot media within each non-phase-change or phase-change section, and minimizing the temperature changes of the non-phase-change media within heat exchange sections where only the cold or hot media undergo phase change.

[0003] As can be seen from the above description, it has the following drawbacks when used: On the one hand, the above-mentioned scheme takes the equalization of temperature changes of cold and hot media as the control target, ignoring that the superheat of steam will cause the phase change section to shift along the tube side. For example, premature phase change in the preheating section will cause scaling, and insufficient heat release in the phase change section will lead to insufficient heat transfer. Especially in scenarios with precise heat requirements, such as feed processing, it is easy to cause fluctuations in the gelatinization degree of materials.

[0004] On the other hand, focusing solely on the thermophysical properties of cold and hot media can easily lead to problems where heat exchange parameters meet the standards but material quality is substandard. For example, even if steam heat transfer efficiency meets the standards, the material may undergo incomplete reaction due to local overheating and carbonization or insufficient heat transfer. Furthermore, it cannot adapt to working conditions such as batch changes in materials or fluctuations in steam supply pressure.

[0005] Based on this, a segmented phase change control method with steam superheat threshold as the core is designed to solve the problems of insufficient accuracy of phase change control and disconnect between control and quality requirements in traditional technology, which is of great necessity. Summary of the Invention

[0006] To solve one of the aforementioned technical problems, the present invention employs the following technical solution: a segmented phase change control method for heat exchangers based on a steam superheat threshold, comprising the following steps: S1: Based on the heat exchanger steam heat transfer characteristics, the heat exchanger is virtually segmented, and a full-segment monitoring network is deployed through a non-intrusive monitoring method; S2: Determine the thermal properties of steam and the properties of materials, and establish a table showing the coupling relationship between the two; S3: Based on heat transfer requirements, a collaborative algorithm is used to calculate the superheat segment threshold, and a preset threshold dynamic correction rule is implemented. S4: Adjust the preheating section according to the superheat threshold to avoid premature phase change of steam or insufficient preheating; S5: Combining the computational fluid dynamics flow field simulation results with the coupling relationship table, the material flow field of the phase change section is adapted and controlled. S6: An external gas-liquid separation component is used to achieve gas-liquid separation in the subcooling section, combined with closed-loop control of the outlet parameters, and retrospective optimization of the upstream control parameters; S7: Adaptive iterative optimization of control strategies based on historical operating data; S8: Add a fault diagnosis and emergency control mechanism to ensure stable system operation; S9: By monitoring data from external pipes at the original pipe openings at both ends of the heat exchanger, and using interpolation algorithms, accurate monitoring of the phase change section without intermediate pipe openings is achieved.

[0007] Based on any of the above technical solutions, a further optimization is made: Step S1 specifically includes: S11: Along the steam flow path of the heat exchanger, virtual preheating section, virtual phase change section and virtual subcooling section are artificially divided and marked with superheat change rate > 5℃ / m as the definition standard. The existing insulation structure of the heat exchanger is directly used without adjusting the insulation layer thickness. S12: High-precision superheat sensors are fixedly installed on the outer wall of the heat exchanger corresponding to each virtual section using magnetic attraction or detachable fixing methods. Electric flow regulating valves are installed on the external pipes installed at the steam inlet and the corresponding pipe joints of each virtual section. S13: Temperature and pressure sensors are connected in series on the external pipes at each pipe joint, and viscosity online monitoring probes are installed at the pipe joints to form a full-section monitoring network. S14: External pipes should be connected to the various pipe joints on the heat exchanger body as needed, which will greatly reduce the number of new openings.

[0008] Based on any of the above technical solutions, a further optimization is made: Step S12 specifically includes: S121: The superheat sensor is calibrated at three points: T1-5℃, T1, and T1+5℃ to ensure that the measurement error is ≤0.2℃ within the working range; S122: Conduct over-threshold scenario simulation tests on the electric flow control valve to ensure that the response time is ≤0.5s and the flow control deviation is ≤1%; S123: The viscosity probe is calibrated at three points using standard solutions of 2000 cP, 5000 cP, and 8000 cP to ensure that the measurement error is ≤ ±3%; S124: The signal transmission line is shielded with 0.1-0.2mm copper foil, and the grounding resistance is controlled to be ≤4Ω; S125: Superheat sensors are deployed every 0.5m along the outer wall of the heat exchanger, with the spacing increased to 0.3m in the phase change section to ensure parameter coverage throughout the entire section.

[0009] Based on any of the above technical solutions, a further optimization is made: Step S2 specifically includes: S21: An external steam testing system is used to adjust the steam pressure from 0.2MPa to 0.6MPa in 0.1MPa increments, covering the commonly used industrial pressure range; S22: Run the system stably for 30 minutes under each pressure gradient, and collect data on steam saturation temperature, enthalpy, and latent heat of condensation. Each parameter is measured 5 times and the average value is taken. S23: Under the conditions of 50-120℃ temperature range and 10%-20% moisture content, measure the gelatinization viscosity of the material. Repeat the measurement 3 times for each working condition and take the average value. S24: Correlate the measured data of steam and materials according to temperature and pressure parameters, establish a coupling relationship table, compare it with industry standard data, and if the deviation exceeds ±1%, recalibrate the equipment and collect data. S25: Import the coupling relationship table into the PLC control system, supporting real-time calling to match monitoring data.

[0010] Based on any of the above technical solutions, a further optimization is made: Step S3 specifically includes: S31: Extract the key parameters corresponding to the target quality of the material from the coupling relationship table, and determine the reference heat transfer power in combination with the heat exchanger outlet temperature requirements; S32: Substitute the reference heat transfer power into the thermodynamic enthalpy balance model, calculate the theoretical steam consumption, combine with the material rheology model, and derive the critical threshold T1 of the preheating section-phase change section and the critical threshold T2 of the phase change section-subcooling section. S33: Preset dual-dimensional dynamic correction rules: When the steam supply pressure fluctuates by ±0.1MPa, T1 is linearly corrected by ±0.8℃; when the initial moisture content of the material fluctuates by ±2%, T2 is linearly corrected by ±0.5℃. S34: Input the threshold and correction rules into the PLC control system, and update the T1 and T2 values ​​in real time by calling the monitoring data.

[0011] Based on any of the above technical solutions, a further optimization is made: Step S5 specifically includes: S51: Use FLUENT software to establish a virtual flow field model of the phase change section, input the thermal property parameters in the coupling relationship table, set the k-ε turbulence model and phase change boundary conditions, and simulate to obtain velocity, temperature and viscosity distribution cloud maps; S52: Extract flow field characteristic parameters, including the proportion of high viscosity regions and the location of regions with temperature gradients >10℃ / m, and establish a mapping table between flow field characteristics and control strategies; S53: Real-time access to monitoring data from the phase change section sensor and viscosity probe, comparing it with simulation results. If the high viscosity percentage is >30%, increase the pressure by 0.05-0.1 MPa; if the temperature gradient is >10℃ / m, decrease the flow rate by 5%-8%. S54: Import the latest monitoring data every 30 minutes to update the flow field model and correct the control strategy to adapt to changes in operating conditions.

[0012] Based on any of the above technical solutions, a further optimization is made: Step S7 specifically includes: S71: Collect historical operating data of the system, including parameters such as superheat, steam pressure, steam flow, material viscosity, and outlet temperature of each section, and label them according to normal operating conditions, fluctuating operating conditions, and fault operating conditions. S72: Data preprocessing: Outliers are removed using the 3σ criterion, missing data are filled in using linear interpolation, and key features with a correlation coefficient > 0.8 with material quality are screened using the Pearson correlation coefficient. S73: Construct a gradient boosting algorithm model, using key features as input and material quality deviation as output, and train the model using 5-fold cross-validation to ensure a prediction accuracy of ≥95%; S74: Embed the trained model into the PLC control system, output control commands in real time, and update the model incrementally with the latest operating data every 12 hours; when the material quality deviation exceeds ±3%, trigger emergency retraining.

[0013] Based on any of the above technical solutions, a further optimization is made: Step S8 specifically includes: S81: Real-time monitoring of the operating status of sensors and valves, setting abnormal judgment thresholds: superheat fluctuation > ±0.5℃, viscosity fluctuation > ±5%, valve response time > 1s; S82: When the sensor malfunctions, it uses interpolation of monitoring data from adjacent sections to estimate and triggers an alarm; when the valve is stuck, it switches to the backup external valve. S83: Under abnormal operating conditions, the control range will be increased by 10%; when the valve is stuck, the flow will be compensated by linkage with other valves. S84: After troubleshooting, use the calibrated data to correct the interpolated estimates and update the model parameters to eliminate the residual effects of emergency control.

[0014] Based on any of the above technical solutions, a further optimization is made: Step S6 specifically includes: S61: The superheat of steam is monitored by a magnetic sensor on the outer wall of the subcooling section. When the monitored value is <0℃, a liquid-carrying signal is sent to the PLC control system. S62: The PLC control system issues a command to reduce the external flow regulating valve of the subcooling section by 10%-15% and at the same time open the three-way valve at the inlet of the external cyclone separator. S63: The inclined spiral guide vanes in the cyclone separator cause the steam to swirl, and the droplets are separated to the inner wall of the separator under the action of centrifugal force and flow into the bottom liquid accumulation chamber. The liquid level sensor monitors the liquid accumulation height in real time. S64: When the liquid level in the accumulating chamber reaches 1 / 2 height, the solenoid valve automatically opens to drain the liquid and maintains the separator pressure stable through the back pressure valve; at the same time, it backtracks and adjusts the phase change section threshold T2 to reduce the risk of liquid carryover; when the liquid level is drained to 1 / 4 height, the solenoid valve closes and the three-way valve is switched to restore the main flow.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention adopts a virtual segmentation and external monitoring and execution device deployment design. All external pipes are connected to the heat exchanger with flange interfaces provided at the factory, which does not require significant changes to the main body structure and insulation layer, avoiding the damage to the equipment strength caused by excessive openings in the traditional method. The modification and construction cycle is shortened, the modification cost of a single unit is reduced, and it is compatible with various existing tube-pass heat exchangers with strong compatibility.

[0016] 2. This invention features segmented and precise control to improve heat exchange stability: It quantitatively divides the heat exchange into three sections—preheating, phase change, and subcooling—based on the rate of change of superheat. It employs targeted strategies such as threshold temperature control in the preheating section, flow field adaptation in the phase change section, and gas-liquid separation in the subcooling section. The superheat in the preheating section is stably controlled at 10℃±0.5℃, the rate of change of superheat in the phase change section is ≤3℃ / m, and the liquid accumulation rate in the subcooling section is reduced. This completely solves problems such as premature phase change of steam, local overheating, and liquid corrosion, thereby improving the material temperature qualification rate.

[0017] 3. This invention establishes a coupling relationship table between steam thermal properties and material properties through orthogonal experiments, and optimizes control parameters by combining FLUENT flow field simulation; the heat transfer coefficient of the heat exchanger is improved and the steam consumption is reduced.

[0018] 4. An intelligent model is built based on the gradient boosting algorithm. The control strategy is dynamically optimized by taking 12 key features as input. The model has a high prediction accuracy. The model is updated incrementally every 12 hours. The control error is reduced when the operating conditions fluctuate, and the steady-state error is reduced. It can adapt to a wide range of operating conditions such as steam pressure (0.2-0.6MPa) and material viscosity (3000-8200cP).

[0019] 5. In this invention, when the sensor drifts, it is estimated by interpolation of adjacent data; when the valve is stuck, the backup device is switched within 2 seconds; and the linkage compensation control ensures continuous operation, reducing the average monthly downtime due to failure. Attached Figure Description

[0020] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or components are generally identified by similar reference numerals. In the drawings, the elements or components are not necessarily drawn to scale.

[0021] Figure 1 This is a graph showing the variation of steam superheat along the steam flow path of the heat exchanger according to the present invention.

[0022] Figure 2 This is a graph showing the relationship between the vapor enthalpy of the present invention and the viscosity and pressure of the material.

[0023] Figure 3 This is a curve comparing the control effects of the adaptive iterative optimization of this invention with those of the traditional fixed threshold strategy.

[0024] Figure 4 A simplified diagram illustrating the deployment of a full-section monitoring network for a heat exchanger.

[0025] In the diagram, 1. Heat exchanger body; 2. Virtual preheating section; 3. Virtual phase change section; 4. Virtual subcooling section; 5. High-precision superheat sensor; 6. External pipe; 7. Electric flow control valve; 8. Temperature sensor; 9. Pressure sensor; 10. Pipe joint; 11. Viscosity online monitoring probe. Detailed Implementation

[0026] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore merely examples and should not be used to limit the scope of protection of the present invention. The specific structure of the present invention is as follows: Figures 1-4 As shown; where, Figure 4 The heat exchanger in this example uses a novel four-pass heat exchanger from existing technology to deploy a full-section monitoring network.

[0027] Example 1: A segmented phase change control method for heat exchangers based on steam superheat threshold, comprising the following steps: S1: Based on the heat transfer characteristics of the heat exchanger steam, the heat exchanger is virtually segmented, and a full-segment monitoring network is deployed through a non-intrusive monitoring method.

[0028] In specific operation, sensors are deployed along the steam flow path of the heat exchanger to collect superheat data. Using a superheat change rate > 5℃ / m as the defining criterion, the system is divided into a virtual preheating section 2, a virtual phase change section 3, and a virtual subcooling section 4. Taking a typical 5m long four-tube heat exchanger as an example, the segmentation is as follows: preheating section 0-1.5m, phase change section 1.5-3.5m, subcooling section 3.5-5m; the results are as follows: Figure 1The curve showing the change in steam superheat along the steam flow path of the heat exchanger.

[0029] Sensors were then deployed in each virtual segment to form a full-segment monitoring network.

[0030] S2: Determine the thermal properties of steam and the properties of materials, and establish a table showing the coupling relationship between the two.

[0031] An external steam experimental system was used, with steam pressure adjusted in 0.1 MPa gradients (0.2-0.6 MPa). Steam enthalpy and other data were stably collected under various operating conditions. Simultaneously, material viscosity data at different temperatures and moisture contents were measured. After repeated measurements and error elimination, a coupling relationship table was established between pressure and temperature-related steam and material parameters, and the results were obtained. Figure 2 The curve showing the correlation between vapor enthalpy and material viscosity and pressure.

[0032] S3: Based on heat transfer requirements, a collaborative algorithm is used to calculate the superheat segment threshold, and a preset threshold dynamic correction rule is implemented. The key parameters are extracted from the coupling relationship table to determine the baseline heat transfer power. The thermodynamic enthalpy balance model and the material rheology model are combined to derive the preheating-phase change critical threshold T1 and the phase change-subcooling critical threshold T2. Dynamic correction rules are preset for fluctuations in steam supply pressure and material moisture content.

[0033] S4: Adjust the preheating section according to the superheat threshold to avoid premature phase change of steam or insufficient preheating; With T1 as the core control target, a strategy of coarse adjustment of steam flow and fine adjustment of material temperature is adopted. Through the synergistic effect of inlet flow regulating valve and temperature monitoring, the superheat at the end of the preheating section is made to stably approach T1.

[0034] S5: Combining the computational fluid dynamics flow field simulation results with the coupling relationship table, the material flow field of the phase change section is adapted and controlled. A virtual flow field model of the phase change section was established using FLUENT software. Thermophysical parameters from the coupling relationship table were input, and simulations yielded velocity, temperature, and viscosity distribution contour maps. A mapping table between flow field characteristics and control strategies was established. Real-time comparison of monitoring data with simulation results allowed for dynamic adjustment of pressure and flow parameters, achieving the desired control effect. Figure 1 As shown.

[0035] S6: An external gas-liquid separation component is used to achieve gas-liquid separation in the subcooling section, combined with closed-loop control of the outlet parameters, and retrospective optimization of the upstream control parameters; The superheat is monitored by a magnetic sensor in the subcooling section. When a liquid signal is detected, the external cyclone separator is activated to separate the gas and liquid. At the same time, T2 is adjusted retrospectively to reduce the generation of condensate from the source.

[0036] S7: Adaptive iterative optimization of control strategies based on historical operating data; The system collects operational data under different working conditions, preprocesses it to select key features, constructs a gradient boosting algorithm model, and embeds it into the PLC system. The model is updated incrementally every 12 hours.

[0037] To quantify the optimization effect, the control error data of the adaptive strategy and the traditional fixed threshold strategy are compared and visualized to obtain... Figure 3 Comparison curves of the control effects of adaptive iterative optimization and traditional fixed threshold strategies.

[0038] S8: Add a fault diagnosis and emergency control mechanism to ensure stable system operation; Set abnormal judgment thresholds for sensors and valves. When a sensor is abnormal, use interpolation of adjacent segment data for estimation. When a valve is stuck, switch to a backup valve. Under abnormal operating conditions, amplify the control range or perform linkage compensation.

[0039] based on Figure 1 The linear variation characteristics of superheat in adjacent sections are observed, and the interpolation results deviate from the actual values ​​by ≤0.4℃, effectively ensuring the reliability of monitoring under fault conditions.

[0040] S9: By monitoring data from external pipes 6 at the original pipe openings at both ends of the heat exchanger, and using interpolation algorithms, accurate monitoring of the phase change section without intermediate pipe openings is achieved.

[0041] Using the measured data from the inlet monitoring point P1 and the outlet monitoring point P2, the pressure at any position in the phase change section is calculated using the interpolation formula P(x)=P1-(P1-P2) / L×x. After correction by the gradient lifting model, the measurement error can be ≤±0.005MPa.

[0042] In the phase change control process of industrial heat exchangers, traditional invasive monitoring requires at least five large openings on the surface of the heat exchanger body 1 to install sensors inside, which seriously damages the structural strength of the heat exchanger body 1 and increases the risk of leakage. Superheat control relies on empirically fixed thresholds, which cannot adapt to changes in steam-material coupling characteristics, resulting in large fluctuations in heat exchange efficiency. The phase change section lacks intermediate monitoring points, the flow field control is highly blind, and the problem of uneven local heat exchange is prominent.

[0043] To address the problems in existing technologies, this method employs a non-invasive monitoring approach, superheat threshold control, and flow field adaptation.

[0044] A fault diagnosis and interpolation monitoring module has been specially added to make up for the shortcomings of traditional solutions in terms of operational stability and intermediate section monitoring. It can be adapted to various existing heat exchangers such as tube-pass and spiral plate heat exchangers. The upgrade can be completed without major modifications to the structure of the heat exchanger body 1. Traditional heat exchangers only need to be drilled 1-2 times, and the rest can use existing interfaces. In particular, when using the new four-pass heat exchanger in the existing technology, the sensor can be deployed directly without the need for additional drilling, reducing the difficulty and cost of modification.

[0045] It should be noted that the preheating section, phase change section, and subcooling section are divided according to the superheat change rate > 5℃ / m. The original insulation structure of the heat exchanger is directly utilized without changing the thickness of the insulation layer.

[0046] Combining the target heat transfer power with the material rheology model, the preheating-phase change critical threshold T1 and the phase change-subcooling critical threshold T2 are derived. When the steam supply pressure fluctuates by ±0.1MPa, the dynamic rule of T1 corresponding to a correction of ±0.8℃ is preset.

[0047] Non-invasive monitoring refers to monitoring methods that do not require entering the heat exchange chamber of the heat exchanger.

[0048] Using the measured data from the inlet monitoring point P1 and the outlet monitoring point P2, the pressure at any position in the phase change section is calculated using the interpolation formula P(x)=P1-(P1-P2) / L×x. After correction by the gradient lifting model, the measurement error can be ≤±0.005MPa.

[0049] By deploying virtual segmentation and non-intrusive monitoring networks, a spatial positioning and data acquisition foundation is built, providing prerequisites for full-process control. Then, the characteristics of steam and materials are measured and a coupling relationship table is established to form a core parameter correlation benchmark, solving the problem of lack of data support for control.

[0050] Based on heat transfer requirements, segmented thresholds are calculated and correction rules are preset, transforming the data into quantitative control standards and clarifying the control boundaries of each segment. Threshold control is implemented for the preheating segment to avoid abnormal steam phase change from the source and ensure the initial stability of heat exchange. The phase change segment is controlled by integrating flow field simulation and coupled data to adapt to the dynamic characteristics of the core heat exchange region and improve heat exchange efficiency and uniformity.

[0051] The problem of liquid carryover in the subcooled section is solved by using an external separation component, while backtracking and optimizing upstream parameters; adaptive iteration is achieved by using historical data, so that the control strategy can continuously evolve with the accumulation of operating conditions and enhance the system's adaptability; and the monitoring blind spot without intermediate pipe opening is broken through by interpolating data at both ends, so that precise control covers the entire process.

[0052] As can be seen, non-intrusive deployment can quickly shorten the installation cycle, reduce the transformation cost by a lot compared to invasive solutions, and significantly reduce the risk of leakage.

[0053] Among them, the dynamic correction technology for superheat threshold enables high precision control of superheat at the end of the preheating section and improves the accuracy of phase change initiation point judgment.

[0054] Flow field adaptation and control reduces the proportion of high-viscosity regions in the phase change section and lowers the standard deviation of material temperature distribution, effectively eliminating the risk of local carbonization.

[0055] External gas-liquid separation components reduce the liquid accumulation rate in the subcooled section and improve heat exchange efficiency. Interpolation monitoring technology solves the problem of monitoring the intermediate section without pipe openings, resulting in high pressure measurement accuracy.

[0056] Based on any of the above technical solutions, a further optimization is made: Step S1 specifically includes: S11: Along the steam flow path of the heat exchanger, the virtual preheating section 2, virtual phase change section 3 and virtual subcooling section 4 are divided according to the superheat change rate > 5℃ / m. The existing insulation structure of the heat exchanger is directly utilized without adjusting the insulation layer thickness. S12: High-precision superheat sensors 5 are fixedly installed on the outer wall of the tube-type heat exchanger corresponding to each virtual segment using magnetic attraction or detachable fixing. Electric flow regulating valves 7 are installed on the external pipes 6 installed at the steam inlet and the corresponding pipe joints 10 of each virtual segment. S13: Temperature sensor 8 and pressure sensor 9 are connected in series on the external pipes 6 at each pipe joint 10, and viscosity online monitoring probe 11 is installed at each pipe joint 10 to form a full-section monitoring network. S14: External pipe 6 is preferentially connected to each pipe joint 10 on the heat exchanger body 1 as needed, which greatly reduces the number of new openings.

[0057] This invention clearly defines segmentation standards and installation specifications, and specifically addresses the risks of structural modifications and data acquisition distortion.

[0058] Meanwhile, the segmentation standard with a superheat change rate >5℃ / m is adopted to conform to the heat transfer law of steam phase change, ensuring the scientific nature of the segmentation; the external wall installation method is adopted to connect with the original flange interface to achieve a completely non-invasive deployment; and the pipe joint 10 is fully utilized to avoid major modifications to the heat exchanger barrel.

[0059] The established monitoring network covers key parameters on both sides of steam and materials, and across all segments, providing a precise data foundation for subsequent coupled measurements and threshold calculations, thus avoiding the limitations of traditional single-parameter monitoring.

[0060] In this process, an infrared thermometer is used to scan the steam flow path and record the continuous change curve of superheat. The inflection point with a change rate > 5℃ / m is identified as the segment boundary. For a typical 5m tube-type heat exchanger, the segmentation results are: preheating section 0-1.5m, phase change section 1.5-3.5m, and subcooling section 3.5-5m.

[0061] The superheat sensor is magnetically fixed and can withstand temperatures up to 120℃; the electric flow regulating valve 7 is connected to the shell-side inlet external pipe 6 of the heat exchanger via a flange. The external pipe 6 uses standard fittings, and the flange is fixed to the original interface of the heat exchanger with bolts.

[0062] The segmented standard for superheat change rate improves the accuracy of segment boundary judgment, enhancing precision compared to the traditional length-based equal division method. The monitoring network data sampling frequency of 10Hz ensures good synchronization of steam and material parameters, eliminating the time lag problem caused by traditional misaligned installations.

[0063] The original flange interface design allows for a short downtime during the installation of external piping 6, effectively adapting to existing heat exchangers in use. The comprehensive monitoring solution enhances the integrity of parameter acquisition, providing robust data support for subsequent coupling relationship tables.

[0064] Based on any of the above technical solutions, a further optimization is made: Step S12 specifically includes: S121: The superheat sensor is calibrated at three points: T1-5℃, T1, and T1+5℃ to ensure that the measurement error is ≤0.2℃ within the working range; S122: Conduct an over-threshold scenario simulation test on the electric flow regulating valve 7 to ensure that the response time is ≤0.5s and the flow control deviation is ≤1%; S123: The viscosity probe is calibrated at three points using standard solutions of 2000 cP, 5000 cP, and 8000 cP to ensure that the measurement error is ≤ ±3%; S124: The signal transmission line is shielded with 0.1-0.2mm copper foil, and the grounding resistance is controlled to be ≤4Ω; S125: Superheat sensors are deployed every 0.5m along the outer wall of the heat exchanger, with the spacing increased to 0.3m in the phase change section to ensure parameter coverage throughout the entire section.

[0065] The three-point calibration in this method can cover key nodes in the working range and ensure measurement accuracy across the entire range; the dense deployment of the phase transition section solves the monitoring blind spots in areas of parameter abrupt change; and the shielding measures are adapted to strong electromagnetic environments in industry, building a reliable data transmission chain for precise control.

[0066] A dedicated superheat calibrator was used, and the preheating-phase change critical threshold T1 was set to 10℃. Calibration was performed in constant temperature environments of 5℃, 10℃, and 15℃. Each calibration point was kept at the temperature for 30 minutes, and the deviation between the sensor reading and the standard value was recorded.

[0067] A valve performance test bench was set up to simulate a superheat of 12℃ (2℃ above the threshold) and send adjustment commands. A high-speed camera was used to record the valve opening change process. The actual response time of the timer was ≤0.5s. The flow rate deviation from the set value was verified to be ≤1% through the flow meter.

[0068] Under a constant temperature environment of 25℃, the probes were calibrated using standard viscosity solutions of 2000 cP, 5000 cP, and 8000 cP. Each concentration was measured five times, and the average value was used to establish a correction curve to ensure that the measurement error was ≤ ±3%. The probes were deployed at a density of 0.5 m / unit for the preheating section, 0.3 m / unit for the phase change section, and 0.5 m / unit for the subcooling section.

[0069] The three-point calibration designed in this invention reduces the error of the superheat sensor across its entire operating range, improving accuracy compared to traditional single-point calibration, and reducing the error in determining the phase change initiation point. When the steam pressure changes abruptly by ±0.05 MPa, the time for the superheat to recover to the target value is shortened from 5 seconds to 1.5 seconds, improving the system's dynamic control performance.

[0070] After the viscosity probe is calibrated, the measurement error is ≤±3%, which reduces the error in the calculation of the heat transfer coefficient and provides a reliable basis for the accurate calculation of the threshold.

[0071] The dense deployment of phase change sections increases the parameter sampling density, which can capture local overheating abrupt changes that are missed by traditional sparse deployment (such as an anomaly where the temperature drops to 8°C instantaneously at 1.8m), thus improving the targeted control.

[0072] Based on any of the above technical solutions, a further optimization is made: Step S2 specifically includes: S21: An external steam testing system is used to adjust the steam pressure from 0.2MPa to 0.6MPa in 0.1MPa increments, covering the commonly used industrial pressure range; S22: Run the system stably for 30 minutes under each pressure gradient, and collect data on steam saturation temperature, enthalpy, and latent heat of condensation. Each parameter is measured 5 times and the average value is taken. S23: Under the conditions of 50-120℃ temperature range and 10%-20% moisture content, measure the gelatinization viscosity of the material. Repeat the measurement 3 times for each working condition and take the average value. S24: Correlate the measured data of steam and materials according to temperature and pressure parameters, establish a coupling relationship table, compare it with industry standard data, and if the deviation exceeds ±1%, recalibrate the equipment and collect data. S25: Import the coupling relationship table into the PLC control system, supporting real-time calling to match monitoring data.

[0073] Changes in steam pressure lead to changes in saturation temperature, which directly affects the gelatinization viscosity of materials. However, traditional methods do not establish a correlation between the two, resulting in large fluctuations in heat exchange efficiency.

[0074] This invention clarifies the data acquisition specifications for multiple operating conditions and high repeatability, and specifically addresses the problem of parameter disconnect and low data reliability.

[0075] The set pressure range of 0.2-0.6MPa covers 90% of industrial steam operating conditions, and the temperature range of 50-120℃ covers the critical range of material gelatinization. Multiple repeated data collections reduce random errors, and comparison with industry standards ensures data accuracy, providing a core data foundation for threshold calculation and flow field simulation.

[0076] The existing external steam test system consists of a steam generator, a precision pressure regulating valve, and a flow meter. The pressure regulation accuracy is ±0.001MPa, and five gradients are set at 0.2, 0.3, 0.4, 0.5, and 0.6MPa.

[0077] Based on the correlation logic of pressure-temperature-enthalpy-viscosity, a coupling relationship table of a 200×200 data matrix is ​​established. The data is compared with the ASME steam data table and standard data. If the deviation exceeds ±1%, the sensor is recalibrated and the data is collected again.

[0078] The coupling relationship table is imported into the PLC in XML format, supporting real-time query and matching based on pressure and temperature parameters.

[0079] Multi-pressure gradient acquisition enables steam thermophysical property data to cover most industrial application scenarios, improving adaptability compared to single-pressure acquisition schemes. Five repeated acquisitions significantly reduce random errors in steam parameters, and three repeated measurements further reduce material viscosity errors.

[0080] The coupling relationship table enables steam-material parameter matching response time to be ≤0.01s, significantly improving efficiency compared to traditional manual queries. Comparison with industry standards ensures high data accuracy, preventing control failures caused by erroneous data.

[0081] In practical applications, when the steam pressure increases from 0.3MPa to 0.4MPa, the system quickly matches the relationship table and corrects the superheat threshold T1 from 10℃ to 10.8℃ to ensure heat exchange stability.

[0082] Based on any of the above technical solutions, a further optimization is made: Step S3 specifically includes: S31: Extract the key parameters corresponding to the target quality of the material from the coupling relationship table, and determine the reference heat transfer power in combination with the heat exchanger outlet temperature requirements; S32: Substitute the reference heat transfer power into the thermodynamic enthalpy balance model, calculate the theoretical steam consumption, combine with the material rheology model, and derive the critical threshold T1 of the preheating section-phase change section and the critical threshold T2 of the phase change section-subcooling section. S33: Preset dual-dimensional dynamic correction rules: When the steam supply pressure fluctuates by ±0.1MPa, T1 is linearly corrected by ±0.8℃; when the initial moisture content of the material fluctuates by ±2%, T2 is linearly corrected by ±0.5℃. S34: Input the threshold and correction rules into the PLC control system, and update the T1 and T2 values ​​in real time by calling the monitoring data.

[0083] Traditional heat exchanger control uses a fixed threshold (e.g., T1=10℃), ignoring dynamic interference factors such as steam supply pressure and material moisture content. When the pressure fluctuates by 0.1MPa, the actual deviation of superheat reaches ±0.8℃, resulting in a fluctuation of ±0.5m in the phase change section length. The threshold calculation only considers the heat transfer power, making it impossible for control to match the fluctuation of the heat transfer coefficient caused by viscosity changes.

[0084] The coupled calculation and dynamic correction technical solution of this invention specifically addresses the technical problems of threshold rigidity and one-sided calculation. The dual-dimensional correction rules adapt to core disturbance factors; the real-time update mechanism enables the threshold to be dynamically adjusted according to the operating conditions, providing a scientific basis for segmented and precise control.

[0085] Coupled calculations improve the matching degree between T1, T2 and material quality, increasing the calculation accuracy compared to a single model, while reducing the accuracy of superheat control at the end of the preheating section. The dual-dimensional correction rule improves threshold adaptability under varying operating conditions: when the steam supply pressure suddenly increases from 0.3MPa to 0.4MPa, T1 is corrected to 10.8℃ in real time, and the phase change section length remains stable at 2m±0.1m, avoiding length fluctuations caused by traditional fixed thresholds.

[0086] Compared with traditional methods, the steam phase change completion rate is improved, the steam latent heat utilization rate is improved, the material quality qualification rate is improved, and the scientific nature and robustness of threshold control are significantly improved.

[0087] By combining thermodynamic and rheological models, the reference power is converted into specific superheat thresholds T1 and T2, realizing the transformation of process requirements into quantitative thresholds and clarifying the control boundaries of each stage. S33 targets two key interference factors, namely steam supply pressure and material moisture content, and presets dynamic correction rules to quantify the correspondence between fluctuation amplitude and threshold correction amount, thus solving the problem of threshold failure caused by operating condition fluctuations.

[0088] Based on any of the above technical solutions, a further optimization is made: Step S5 specifically includes: S51: Use FLUENT software to establish a virtual flow field model of the phase change section, input the thermal property parameters in the coupling relationship table, set the k-ε turbulence model and phase change boundary conditions, and simulate to obtain velocity, temperature and viscosity distribution cloud maps; S52: Extract flow field characteristic parameters, including the proportion of high viscosity regions and the location of regions with temperature gradients >10℃ / m, and establish a mapping table between flow field characteristics and control strategies; S53: Real-time access to monitoring data from the phase change section sensor and viscosity probe, comparing it with simulation results. If the high viscosity percentage is >30%, increase the pressure by 0.05-0.1 MPa; if the temperature gradient is >10℃ / m, decrease the flow rate by 5%-8%. S54: Import the latest monitoring data every 30 minutes to update the flow field model and correct the control strategy to adapt to changes in operating conditions.

[0089] The phase change section is the core heat exchange area of ​​the heat exchanger, but the internal flow field is complex and invisible. Traditional control relies only on inlet and outlet parameters, which cannot detect problems such as high viscosity accumulation and abnormal temperature gradients, resulting in local heat exchange temperature differences exceeding 10°C. The lack of simulation support makes the control strategy highly blind. For example, flow regulation relies on experience, which is prone to over-regulation or under-regulation.

[0090] This invention combines simulation prediction with real-time monitoring. FLUENT simulation enables visualization of the internal flow field, a mapping table establishes a standardized control strategy library, real-time comparison enables closed-loop correction, and the model is updated every 30 minutes to adapt to changes in operating conditions, thus achieving precise local control.

[0091] Flow field simulation visualizes the internal temperature distribution, and the accuracy of identifying areas with temperature gradients > 10℃ is high, enabling the detection of anomalies earlier than traditional inlet and outlet monitoring.

[0092] Pressure regulation slows down the phase change process, allowing for more complete release of latent heat from steam and improved heat exchange efficiency; flow rate regulation reduces the temperature gradient, significantly reducing or eliminating the risk of localized carbonization.

[0093] Even under conditions of continuous fluctuations in steam pressure and material viscosity, the phase change section parameters can still remain stable.

[0094] Based on any of the above technical solutions, a further optimization is made: Step S7 specifically includes: S71: Collect historical operating data of the system, including parameters such as superheat, steam pressure, steam flow, material viscosity, and outlet temperature of each section, and label them according to normal operating conditions, fluctuating operating conditions, and fault operating conditions. S72: Data preprocessing: Outliers are removed using the 3σ criterion, missing data are filled in using linear interpolation, and key features with a correlation coefficient > 0.8 with material quality are screened using the Pearson correlation coefficient. S73: Construct a gradient boosting algorithm model, using key features as input and material quality deviation as output, and train the model using 5-fold cross-validation to ensure a prediction accuracy of ≥95%; S74: Embed the trained model into the PLC control system, output control commands in real time, and update the model incrementally with the latest operating data every 12 hours; when the material quality deviation exceeds ±3%, trigger emergency retraining.

[0095] This invention proposes a data-driven, adaptive iterative technical solution to specifically address the problem of poor model generalization ability.

[0096] Multi-condition data annotation enhances model robustness, feature selection simplifies input dimensions, 5-fold cross-validation avoids model overfitting, and 12-hour incremental updates and emergency retraining mechanisms ensure continuous model adaptation, enabling control strategies to evolve with equipment and operating conditions.

[0097] The solution of this invention can improve the generalization ability of the model by annotating data under multiple operating conditions, improve the prediction accuracy under fault conditions, and reduce the abnormal control rate. Feature selection reduces the model calculation time from 0.1s to 0.03s, meeting the requirements of real-time control.

[0098] Example 2: Compared with Example 1, this example also includes the following technical features: Based on any of the above technical solutions, a further optimization is made: Step S8 specifically includes: S81: Real-time monitoring of the operating status of sensors and valves, setting abnormal judgment thresholds: superheat fluctuation > ±0.5℃, viscosity fluctuation > ±5%, valve response time > 1s; S82: When the sensor malfunctions, it uses interpolation of monitoring data from adjacent sections to estimate and triggers an alarm; when the valve is stuck, it switches to the backup external valve. S83: Under abnormal operating conditions, the control range will be increased by 10%; when the valve is stuck, the flow will be compensated by linkage with other valves. S84: After troubleshooting, use the calibrated data to correct the interpolated estimates and update the model parameters to eliminate the residual effects of emergency control.

[0099] Sensor drift and valve jamming are common faults in industrial settings. Traditional control solutions lack real-time monitoring and emergency mechanisms, resulting in delayed fault detection. This can easily lead to overheating (>±2℃), flow loss, equipment overpressure, or material scrapping. Furthermore, the lack of data calibration and model updates after fault resolution means that residual errors will continue to affect the accuracy of subsequent control.

[0100] This invention establishes a full-process mechanism of monitoring-emergency response-recovery, uses precise abnormal thresholds to achieve rapid fault identification, interpolation estimation and backup valves to ensure continuous operation, amplification control and linkage compensation to ensure stable heat transfer, and calibration updates to eliminate residual effects, thus comprehensively improving system reliability.

[0101] Interpolation estimation reduces the overheating monitoring error when the sensor malfunctions, and switching to a backup valve shortens the flow interruption time, ensuring continuous heat exchange and reducing material scrap rate.

[0102] The increased emergency control range improved heat transfer stability under abnormal operating conditions, and the material temperature deviation was still controlled within a reasonable range, without triggering the safety shutdown threshold.

[0103] Data calibration after fault recovery reduces the deviation of model parameters, and the accuracy of subsequent regulation is not affected.

[0104] S81 sets anomaly detection thresholds for sensors and valves, quantifies anomaly standards, enables early fault detection, and prevents anomalies from escalating and causing system downtime.

[0105] For two typical faults, sensor malfunction and valve jamming, the S82 has developed a rapid handling solution for interpolation estimation alarm and backup valve switching, which can respond quickly when a fault occurs and reduce the impact of the fault on the control.

[0106] Under abnormal operating conditions, the S83 adopts an emergency strategy of amplifying the control range and compensating for valve linkage to ensure that the system can still maintain basic operation under fault conditions and avoid production interruption caused by the failure of a single component.

[0107] Based on any of the above technical solutions, a further optimization is made: Step S6 specifically includes: S61: The superheat of steam is monitored by a magnetic sensor on the outer wall of the subcooling section. When the monitored value is <0℃, a liquid-carrying signal is sent to the PLC control system. S62: The PLC control system issues a command to reduce the external flow regulating valve of the subcooling section by 10%-15% and at the same time open the three-way valve at the inlet of the external cyclone separator. S63: The inclined spiral guide vanes in the cyclone separator cause the steam to swirl, and the droplets are separated to the inner wall of the separator under the action of centrifugal force and flow into the bottom liquid accumulation chamber. The liquid level sensor monitors the liquid accumulation height in real time. S64: When the liquid level in the accumulating chamber reaches 1 / 2 height, the solenoid valve automatically opens to drain the liquid and maintains the separator pressure stable through the back pressure valve; at the same time, it backtracks and adjusts the phase change section threshold T2 to reduce the risk of liquid carryover; when the liquid level is drained to 1 / 4 height, the solenoid valve closes and the three-way valve is switched to restore the main flow.

[0108] After the steam in the subcooled section condenses, it is easy to form liquid carryover, resulting in a high liquid accumulation rate, which seriously affects the steam flow and heat exchange efficiency. Relying solely on end-point regulation cannot solve the liquid carryover problem at the source, causing the liquid carryover phenomenon to recur. During the drainage process, the pressure fluctuates greatly, affecting the overall stability of the system.

[0109] The present invention proposes a technical solution of magnetic attraction monitoring + external separation + backtracking control, which specifically solves the problems of severe liquid accumulation, repeated liquid carrying, and unstable drainage.

[0110] The magnetic sensor enables non-invasive monitoring of liquid-laden signals, the cyclone separator has high separation efficiency, the retrospective adjustment of T2 reduces condensate generation from the source, and the pressure-stabilized drainage avoids system fluctuations, forming a closed-loop control system for the entire subcooling section.

[0111] In addition, pressure stabilization drainage technology reduces system pressure fluctuations and avoids large fluctuations in overheating caused by traditional drainage. Retrospective adjustment of T2 reduces the risk of liquid carryover and decreases the frequency of repeated liquid carryover.

[0112] The overall technology improves the heat exchange stability of the subcooling section, reduces steam loss during drainage, saves a large amount of steam annually, and has significant economic benefits.

[0113] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention. For those skilled in the art, any alternative improvements or transformations made to the implementation of the present invention fall within the protection scope of the present invention.

[0114] Any aspects of this invention not described in detail are well-known to those skilled in the art.

Claims

1. A segmented phase change control method for heat exchangers based on steam superheat threshold, characterized in that, Includes the following steps: S1: Based on the heat transfer characteristics of the heat exchanger steam, the heat exchanger is virtually segmented, and a full-segment monitoring network is deployed through a non-intrusive monitoring method; S2: Determine the thermal properties of steam and the properties of materials, and establish a table showing the coupling relationship between the two; S3: Based on heat transfer requirements, a collaborative algorithm is used to calculate the superheat segment threshold, and a preset threshold dynamic correction rule is implemented. S4: Adjust the preheating section according to the superheat threshold to avoid premature phase change of steam or insufficient preheating; S5: Combining the computational fluid dynamics flow field simulation results with the coupling relationship table, the material flow field of the phase change section is adapted and controlled. S6: An external gas-liquid separation component is used to achieve gas-liquid separation in the subcooling section, combined with closed-loop control of the outlet parameters, and retrospective optimization of the upstream control parameters; S7: Adaptive iterative optimization of control strategies based on historical operating data; S8: Add a fault diagnosis and emergency control mechanism to ensure stable system operation; S9: By monitoring data from external pipes at the original pipe openings at both ends of the heat exchanger, and using interpolation algorithms, accurate monitoring of the phase change section without intermediate pipe openings is achieved.

2. The method according to claim 1, characterized in that, Step S1 specifically includes: S11: Along the steam flow path of the heat exchanger, the virtual preheating section, virtual phase change section and virtual subcooling section are divided according to the superheat change rate > 5℃ / m. The existing insulation structure of the heat exchanger is directly utilized without adjusting the insulation layer thickness. S12: High-precision superheat sensors are fixedly installed on the outer wall of the heat exchanger corresponding to each virtual section using magnetic attraction or detachable fixing methods. Electric flow regulating valves are installed on the external pipes installed at the steam inlet and the corresponding pipe joints of each virtual section. S13: Temperature and pressure sensors are connected in series on the external pipes at each pipe joint, and viscosity online monitoring probes are installed at the pipe joints to form a full-section monitoring network. S14: External pipes should be connected to the various pipe joints on the heat exchanger body as needed.

3. The method according to claim 2, characterized in that, Step S12 specifically includes: S121: The superheat sensor is calibrated at three points: T1-5℃, T1, and T1+5℃. S122: Perform over-threshold scenario simulation test on the electric flow control valve; S123: The viscosity probe is calibrated at three points using standard solutions of 2000 cP, 5000 cP, and 8000 cP. S124: The signal transmission line is shielded with 0.1-0.2mm copper foil, and the grounding resistance is controlled to be ≤4Ω; S125: Superheat sensors are deployed every 0.5m along the outer wall of the heat exchanger, with the spacing increased to 0.3m in the phase change section to ensure parameter coverage throughout the entire section.

4. The method according to claim 3, characterized in that, Step S2 specifically includes: S21: An external steam testing system is used to adjust the steam pressure from 0.2MPa to 0.6MPa in 0.1MPa increments, covering the commonly used industrial pressure range; S22: Run the system stably for 30 minutes under each pressure gradient, and collect data on steam saturation temperature, enthalpy, and latent heat of condensation. Each parameter is measured 5 times and the average value is taken. S23: Under the conditions of 50-120℃ temperature range and 10%-20% moisture content, measure the gelatinization viscosity of the material. Repeat the measurement 3 times for each working condition and take the average value. S24: Correlate the measured data of steam and materials according to temperature and pressure parameters, establish a coupling relationship table, compare it with industry standard data, and if the deviation exceeds ±1%, recalibrate the equipment and collect data. S25: Import the coupling relationship table into the PLC control system, supporting real-time calling to match monitoring data.

5. The method according to claim 4, characterized in that, Step S3 specifically includes: S31: Extract the key parameters corresponding to the target quality of the material from the coupling relationship table, and determine the reference heat transfer power in combination with the heat exchanger outlet temperature requirements; S32: Substitute the reference heat transfer power into the thermodynamic enthalpy balance model, calculate the theoretical steam consumption, combine with the material rheology model, and derive the critical threshold T1 of the preheating section-phase change section and the critical threshold T2 of the phase change section-subcooling section. S33: Preset dual-dimensional dynamic correction rules: When the steam supply pressure fluctuates by ±0.1MPa, T1 is linearly corrected by ±0.8℃; when the initial moisture content of the material fluctuates by ±2%, T2 is linearly corrected by ±0.5℃. S34: Input the threshold and correction rules into the PLC control system, and update the T1 and T2 values ​​in real time by calling the monitoring data.

6. The method according to claim 5, characterized in that, Step S5 specifically includes: S51: Use FLUENT software to establish a virtual flow field model of the phase change section, input the thermophysical parameters in the coupling relationship table, set the k-ε turbulence model and phase change boundary conditions, and simulate to obtain velocity, temperature and viscosity distribution cloud maps; S52: Extract flow field characteristic parameters, including the proportion of high viscosity regions and the location of regions with temperature gradients >10℃ / m, and establish a mapping table between flow field characteristics and control strategies; S53: Real-time access to monitoring data from the phase change section sensor and viscosity probe, comparing it with simulation results. If the high viscosity percentage is >30%, increase the pressure by 0.05-0.1 MPa; if the temperature gradient is >10℃ / m, decrease the flow rate by 5%-8%. S54: Import the latest monitoring data every 30 minutes to update the flow field model and correct the control strategy to adapt to changes in operating conditions.

7. The method according to claim 6, characterized in that, Step S7 specifically includes: S71: Collect historical operating data of the system, including parameters such as superheat, steam pressure, steam flow, material viscosity, and outlet temperature of each section, and label them according to normal operating conditions, fluctuating operating conditions, and fault operating conditions. S72: Data preprocessing: Outliers are removed using the 3σ criterion, missing data are filled in using linear interpolation, and key features with a correlation coefficient > 0.8 with material quality are screened using the Pearson correlation coefficient. S73: Construct a gradient boosting algorithm model, using key features as input and material quality deviation as output, and train the model using 5-fold cross-validation to ensure a prediction accuracy of ≥95%; S74: Embed the trained model into the PLC control system, output control commands in real time, and update the model incrementally with the latest operating data every 12 hours; when the material quality deviation exceeds ±3%, trigger emergency retraining.

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