Boiler water vapor composite type electric adsorption filtration separation method

Abstract

The application discloses a boiler water vapor composite type electric adsorption filtering and separating method, comprising the following steps: obtaining ion migration data and temperature fluctuation signals of water vapor in an electric adsorption area through a monitoring sensor, processing the signals by using a real-time feedback loop, and obtaining a saturation temperature change trend; dynamically adjusting a valve and adjusting heating power according to the saturation temperature change trend, compensating temperature fluctuation for ion migration inhibition, and determining an output power modulation value; applying the output power modulation value to a filtering area by using a temperature control unit integration, obtaining working condition change detection results under external environment monitoring, and judging an adaptive control system activation condition; updating temperature monitoring sensor readings by using a real-time feedback loop according to a filtering and separating cooperative scheme, compensating temperature fluctuation for dynamically adjusting a valve, and determining an ion migration inhibition effect.

Description

Technical Field

[0001] This invention belongs to the field of industrial boiler steam purification technology, specifically a composite electro-adsorption filtration separation method for boiler steam. Background Technology

[0002] In industrial production and environmental purification, electroadsorption technology is widely used for the filtration and separation of ionic liquids, especially for the separation of ions in high-temperature steam at 200-400℃, to achieve efficient purification and resource recovery. However, existing electroadsorption systems face significant technical challenges under these dynamic and complex operating conditions. Specifically, when acquiring temperature fluctuation signals and ion migration data of high-temperature water vapor at 200-400℃, temperature monitoring sensors are easily affected by external environmental interference, leading to signal distortion and making it difficult for the real-time feedback loop to accurately capture the trend of saturation temperature changes. This directly affects the dynamic adjustment accuracy of heating power, making it difficult for valve adjustment and temperature compensation to coordinate effectively, resulting in unstable ion migration suppression effects, and also failing to fully adapt to the filtration requirements of filter cartridges with pore sizes smaller than 100nm.

[0003] Meanwhile, when integrating output power modulation, the temperature control unit lacks a rapid response mechanism to changes in external environment such as high temperature of 200-400℃ and high pressure of 15-20MPa. The interference cancellation algorithm cannot fully integrate the monitoring data under this specific environment, which limits the calibration capability of the adaptive control system and makes it difficult to achieve stable output of the filtration separation synergy scheme that optimizes operating parameters and adapts to specific filter elements.

[0004] Existing technology: A water vapor separation system and method for urea hydrolysis product gas (CN118236826) mainly relies on the physical sieving effect of a hydrophobic microporous filter membrane. Water vapor diffuses and permeates through the micropores based on the pressure difference across the membrane, achieving separation from other gases. However, this technology mainly maintains a stable pressure difference within the water vapor separator based on the product gas flow rate and pressure, ensuring only the continuity of the membrane separation process, and lacks comprehensive monitoring and intelligent control of various complex parameters.

[0005] Therefore, the core technical challenge remains: how to achieve coordinated control of heating power, valve adjustment, and temperature compensation under dynamic and complex operating conditions at high temperatures of 200-400℃ by accurately monitoring the trend of saturation temperature changes and ion migration data, so as to ensure the stability of ion migration suppression effect and the improvement of filtration separation efficiency in the electroadsorption system. Summary of the Invention

[0006] The purpose of this invention is to provide a boiler steam-water composite electro-adsorption filtration separation method to solve the problems in the prior art mentioned in the background, which are difficult to adapt to the dynamic synergistic requirements of saturation temperature monitoring and ion migration suppression in electro-adsorption systems, and lack a real-time control mechanism for temperature fluctuation compensation and environmental interference elimination.

[0007] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0008] A method for boiler steam-water composite electro-adsorption filtration separation, the method comprising the following steps:

[0009] Step S101: Ion migration data and temperature fluctuation signals of water vapor in the electroadsorption region are acquired by monitoring sensors, and these signals are processed by a real-time feedback loop to obtain the trend of saturation temperature change.

[0010] Step S102: Based on the trend of saturation temperature change, dynamically adjust the valve to adjust the heating power, compensate for temperature fluctuations to suppress ion migration, and determine the output power modulation value.

[0011] Step S103: The output power modulation value is applied to the filter area by integrating the temperature control unit, the detection results of the change in working conditions under external environmental monitoring are obtained, and the activation conditions of the adaptive control system are determined.

[0012] Step S104: If the adaptive control system is activated, the temperature and flow rate adjustment signal is processed by the interference cancellation algorithm, and the operating parameter optimization data is obtained from the electroadsorption zone adjustment to obtain the filtration and separation synergistic scheme.

[0013] Step S105: According to the filtration and separation synergy scheme, the temperature monitoring sensor reading is updated in real time by the feedback loop, temperature fluctuation compensation is performed on the dynamic adjustment valve, and the ion migration suppression effect is determined.

[0014] Step S106: Using output power modulation combined with external environment monitoring, the interference elimination algorithm output in the detection of changes in operating conditions is processed to obtain the temperature and flow rate adjustment values ​​after the electroadsorption zone is adjusted, and it is determined that the optimization of operating parameters is completed.

[0015] Step S107: By integrating the ion migration suppression effect and operating parameter optimization through the temperature control unit, the adaptive control system is calibrated for the filtration and separation synergy to obtain the final stable state of the saturation temperature change trend.

[0016] According to the above technical solution, in step S101, ion migration data and temperature fluctuation signals of water vapor in the electroadsorption region are acquired by a temperature monitoring sensor, and these signals are processed by a real-time feedback loop to obtain the trend of saturation temperature change. Specifically, this includes the following steps:

[0017] Data on ion migration and temperature fluctuations of water vapor in the electroadsorption region were collected by monitoring sensors and stored as an initial dataset.

[0018] Signal processing algorithms were used to denoise the initial dataset, resulting in smoothed ion migration data and temperature fluctuation data.

[0019] If the fluctuation amplitude of smoothed ion migration data and temperature fluctuation data exceeds the preset threshold, the frequency characteristics of the signal are extracted by Fourier transform to determine the frequency distribution characteristics.

[0020] Based on the frequency distribution characteristics, the Kalman filter algorithm is used to predict the trend of saturation temperature change and obtain the predicted saturation temperature data.

[0021] The predicted saturation temperature data is compared with the sensor signal through a real-time feedback loop to calculate the deviation value.

[0022] If the deviation exceeds the preset threshold, the control parameters of the real-time feedback loop are adjusted and the predicted saturation temperature data is updated.

[0023] Based on the updated predicted saturation temperature data, a dynamic sequence of saturation temperature change trends is generated and stored as the final output data.

[0024] According to the above technical solution, in step S102, the heating power is dynamically adjusted by regulating the valve based on the saturation temperature change trend, temperature fluctuation compensation is performed to suppress ion migration, and the output power modulation value is determined. Specifically, this includes the following steps:

[0025] The system acquires real-time monitoring data on the trend of saturation temperature changes and obtains temperature fluctuation values ​​through sensors to determine the current temperature fluctuation status.

[0026] If the temperature fluctuation exceeds the preset threshold, the required temperature compensation for ion migration suppression is calculated based on the trend of saturation temperature change, and the compensation target value is determined.

[0027] The valve opening is adjusted by temperature compensation, and the control signal is generated by proportional-integral-derivative algorithm to obtain the valve adjustment parameters.

[0028] The heating power is dynamically controlled by adjusting valve parameters, and the output power modulation value is generated by pulse width modulation technology to obtain a power distribution scheme.

[0029] Real-time heating power data is extracted from the power allocation scheme, and iterative optimization is performed in combination with the saturation temperature change trend to obtain the optimized modulation value.

[0030] If the deviation between the optimized modulation value and the target compensation value is less than the preset threshold, the valve state is updated by the control signal to obtain a stable temperature fluctuation control result.

[0031] Based on the stable temperature fluctuation control results, the ion migration suppression effect is monitored in real time to obtain the final output power modulation value.

[0032] According to the above technical solution, in step S103, the output power modulation value is applied to the filter area using a temperature control unit to obtain the detection results of changes in operating conditions under external environmental monitoring, and to determine the activation conditions of the adaptive control system. Specifically, this includes the following steps:

[0033] External environmental monitoring data is acquired through a temperature control unit to generate a set of environmental parameters;

[0034] Extract the characteristics of operating condition changes from the set of environmental parameters to obtain the changing trends;

[0035] The support vector machine algorithm is used to classify the changing trends and determine the operating conditions.

[0036] If the operating condition exceeds the preset threshold, a modulation value is generated and a control signal is output.

[0037] The output power of the temperature control unit is adjusted by the control signal to obtain system response data;

[0038] Extract activation condition features from system response data to determine whether the adaptive control system is activated;

[0039] If the activation conditions are met, the control signal is updated through the data processing module to obtain the optimized system response.

[0040] According to the above technical solution, in step S104, if the adaptive control system is activated, the temperature and flow rate adjustment signal is processed by the interference cancellation algorithm, and the operating parameter optimization data is obtained from the electroadsorption zone adjustment to obtain the filtration and separation synergistic scheme, which specifically includes the following steps:

[0041] The adaptive control system detects the environmental conditions, acquires temperature and flow rate signals, and determines the initial operating parameters.

[0042] If the initial operating parameters exceed the preset threshold, an interference suppression algorithm is used to denoise the temperature and flow rate signals to obtain an optimized signal.

[0043] Based on the optimization signal, real-time operating parameters are extracted from the electroadsorption region to generate a parameter dataset.

[0044] If the parameter dataset meets the requirements of the collaborative scheme, the parameter dataset is classified using the support vector machine algorithm to determine the feasibility of the filtering and separation scheme.

[0045] Based on the judgment results, the gradient descent algorithm is used to optimize the operating parameters of the filtering and separation scheme, and the adjusted parameter set is obtained.

[0046] By adjusting the parameter set, a filtering and separation coordination scheme is generated, and the final running configuration is determined.

[0047] If the final running configuration meets the system startup conditions, the configuration will be applied to the electroadsorption zone to obtain real-time feedback data.

[0048] According to the above technical solution, step S104 further includes processing the temperature adjustment and flow rate control signals through an interference cancellation algorithm, obtaining optimized operating parameter data of the electroadsorption zone, and obtaining a synergistic filtration and separation scheme, specifically including:

[0049] The temperature adjustment signal and flow rate control signal are processed by an interference cancellation algorithm to obtain the denoised signal data;

[0050] Based on the denoised signal data, the support vector machine algorithm is used to analyze the correlation between the temperature adjustment signal and the flow rate control signal, and to determine the signal correlation model.

[0051] If the output value of the signal correlation model exceeds the preset threshold, the operating parameters of the electroadsorption zone are predicted by the linear regression algorithm to obtain the optimized parameter values.

[0052] Based on the optimized parameter values, the gradient descent algorithm is used to adjust the configuration of temperature control and flow rate regulation to obtain the optimized operating parameters;

[0053] By analyzing the adsorption efficiency of the electro-adsorption zone through optimized operating parameters, a synergistic control strategy for filtration and separation is determined.

[0054] If the adsorption efficiency of the collaborative control strategy reaches the preset threshold, a filtration and separation collaborative scheme is generated, and the final operating parameter configuration is output.

[0055] Based on the final operating parameter configuration, the temperature control and flow rate regulation of the electroadsorption zone are adjusted to obtain a stable filtration and separation synergy scheme.

[0056] According to the above technical solution, in step S105, based on the filtration and separation synergy scheme, the temperature monitoring sensor reading is updated in real time via the feedback loop, temperature fluctuation compensation is performed on the dynamic adjustment valve, and the ion migration suppression effect is determined. Specifically, the step also includes the following steps:

[0057] Saturation temperature monitoring data is collected by sensors, and the data is processed using a collaborative filtering algorithm to obtain the temperature change trend.

[0058] If the temperature change trend exceeds the preset threshold, the sensor data will be updated in real time through the feedback loop to determine the adjustment requirements.

[0059] Based on the adjustment requirements, the valve control parameters are dynamically adjusted to obtain the execution status of the compensation mechanism;

[0060] Temperature fluctuations are corrected through a compensation mechanism to obtain stable temperature data;

[0061] The data processing module was used to analyze the stabilized temperature data and ion migration data to determine the ion migration inhibition effect.

[0062] If the suppression effect does not meet the preset standard, the valve control parameters are readjusted through the feedback loop to obtain new temperature data;

[0063] Based on the new temperature data, repeated data processing and suppression effect assessment are performed to obtain the final suppression effect.

[0064] According to the above technical solution, in step S106, output power modulation combined with external environment monitoring is used to process the interference elimination algorithm output in the detection of changes in operating conditions, obtain the temperature and flow rate adjustment values ​​after the electroadsorption zone is adjusted, and determine that the optimization of operating parameters is completed. Specifically, this includes the following steps:

[0065] By acquiring monitoring signals of changes in operating conditions through environmental monitoring, and by using a sensor network to collect temperature and flow rate data, a real-time operating condition dataset is obtained.

[0066] The real-time operating condition dataset is processed by data fusion, and the monitoring signal is smoothed by combining the Kalman filter algorithm to eliminate noise interference and obtain smooth operating condition data.

[0067] If the smoothing condition data exceeds the preset threshold range, power modulation is triggered to adjust the output power of the electro-adsorption region and obtain the modulation power value.

[0068] Based on the modulation power value, the temperature adjustment value of the electroadsorption region is calculated, and the temperature change trend is predicted by the linear regression algorithm to obtain the temperature adjustment parameters.

[0069] Based on the temperature adjustment parameters and combined with the fluid dynamics model, the flow velocity adjustment value is calculated to obtain the flow velocity adjustment parameters;

[0070] If both the temperature adjustment parameter and the flow rate adjustment parameter meet the preset optimization threshold, then the optimization of the operating parameters is considered complete, and the optimization result is obtained.

[0071] The control strategy for the electroadsorption region is updated by optimizing the results, and the power modulation is adjusted by using a feedback control mechanism to obtain the updated operating parameters.

[0072] According to the above technical solution, step S106 further includes real-time adjustment of the temperature and flow rate in the electroadsorption zone, integration of ambient humidity and temperature data, and power modulation to process changes in operating conditions, to obtain optimized operating parameter values ​​after interference elimination, specifically including:

[0073] Temperature and flow rate data of the electroadsorption zone are acquired by sensors, and combined with ambient humidity and ambient temperature data to determine the first fusion dataset.

[0074] If the temperature data or flow rate data of the first fused dataset exceeds the preset threshold, the Kalman filter algorithm is used to process the temperature data and flow rate data to obtain the first filtered dataset.

[0075] Based on the first filtered dataset, the operating power of the electroadsorption region is adjusted using a power modulation algorithm to obtain the first power adjustment value;

[0076] By comparing the first power adjustment value with the operating condition change data, if the deviation between the operating condition change data and the first power adjustment value exceeds a preset threshold, the least squares method is used to optimize the power adjustment to obtain the second power adjustment value.

[0077] Based on the second power adjustment value, adjust the operating parameters of the electroadsorption zone to obtain the first set of operating parameters;

[0078] By fusing the first set of operating parameters with the ambient humidity and ambient temperature data, if the deviation between the fused parameter set and the preset operating target is less than the preset threshold, then the optimized set of operating parameters is determined.

[0079] Based on the optimized set of operating parameters, the temperature and flow rate of the electroadsorption zone are adjusted to obtain the final optimized values ​​of the operating parameters.

[0080] According to the above technical solution, in step S107, by integrating the ion migration suppression effect and operating parameter optimization through the temperature control unit, and calibrating the adaptive control system for filtration and separation synergy, the final stable state of the saturation temperature change trend is obtained, specifically including the following steps:

[0081] The initial operating parameters are determined by acquiring ambient temperature data and ion migration rate through the temperature control unit.

[0082] If the initial operating parameters exceed the preset threshold, the output power of the temperature control unit is adjusted through a parameter optimization algorithm to obtain the optimized operating parameters;

[0083] The optimized operating parameters are used to drive the filtration and separation module to obtain the separation efficiency and ion migration inhibition effect, and to determine the trend of saturation temperature change.

[0084] If the saturation temperature change trend has not reached a stable state, the operating frequency of the filter separation module is adjusted through an adaptive control algorithm to obtain updated control parameters;

[0085] Based on the updated control parameters, obtain real-time feedback data from the temperature control unit to determine the dynamic trend of saturation temperature change;

[0086] By using dynamic trend analysis, the support vector machine algorithm is employed to predict the change in saturation temperature and obtain the predicted steady state.

[0087] If the predicted steady state meets the preset threshold, the coordination parameters of the temperature control unit and the filter separation module are adjusted by the control calibration module to obtain the final steady state.

[0088] Compared with the prior art, the present invention has the following beneficial effects:

[0089] This invention discloses a synergistic optimization system for ion migration suppression and filtration separation in the electroadsorption zone based on saturated temperature monitoring and adaptive control. Addressing the problems of unstable saturated temperature control and low filtration efficiency caused by ion migration and temperature fluctuations in traditional steam treatment, this invention proposes a solution integrating real-time feedback and dynamic adjustment. The invention acquires ion migration data and temperature fluctuation signals of steam in the electroadsorption zone using a temperature monitoring sensor. A real-time feedback loop processes the signals to generate a saturated temperature change trend, and dynamically adjusts the valve to control the heating power, compensating for temperature fluctuations and suppressing ion migration. Simultaneously, the invention integrates a temperature control unit, applying the output power modulation value to the filtration zone. Combined with external environmental monitoring, it detects changes in operating conditions and activates the adaptive control system. An interference cancellation algorithm optimizes the temperature and flow rate adjustment signals, acquiring optimized operating parameter data to form a synergistic filtration separation scheme. Through real-time updating of sensor readings and multiple iterative optimizations, this invention ultimately achieves stable control of the saturated temperature change trend. Its technical effect is a significant improvement in ion migration suppression and filtration separation efficiency in the electroadsorption zone, ensuring stable operation and high-efficiency synergy of the system under complex operating conditions. Attached Figure Description

[0090] Figure 1 This is a flowchart of a boiler steam-water composite electro-adsorption filtration separation method according to the present invention. Detailed Implementation

[0091] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0092] Example 1

[0093] like Figure 1 As shown, a boiler steam-water composite electro-adsorption filtration separation method specifically includes:

[0094] Step S101: Ion migration data and temperature fluctuation signals of water vapor in the electroadsorption region are acquired by monitoring sensors, and these signals are processed by a real-time feedback loop to obtain the trend of saturation temperature change.

[0095] Step S102: Based on the trend of saturation temperature change, dynamically adjust the valve to adjust the heating power, compensate for temperature fluctuations to suppress ion migration, and determine the output power modulation value.

[0096] Step S103: The output power modulation value is applied to the filter area by integrating the temperature control unit, the detection results of the change in operating conditions under external environmental monitoring are obtained, and the activation conditions of the adaptive control system are determined.

[0097] In step S104, if the adaptive control system is activated, the temperature and flow rate adjustment signals are processed by the interference cancellation algorithm, and the operating parameter optimization data are obtained from the electroadsorption zone adjustment to obtain the filtration and separation synergistic scheme.

[0098] Step S105: Based on the filtration and separation synergy scheme, the temperature monitoring sensor reading is updated in real time via the feedback loop, temperature fluctuation compensation is performed on the dynamic adjustment valve, and the ion migration suppression effect is determined.

[0099] Step S106: Using output power modulation combined with external environment monitoring, the interference elimination algorithm output in the processing of operating condition change detection is used to obtain the temperature and flow rate adjustment values ​​after the electroadsorption zone is adjusted, and it is determined that the operation parameter optimization is completed.

[0100] Step S107: By integrating the ion migration suppression effect and operating parameter optimization through the temperature control unit, the adaptive control system is calibrated for the filtration and separation synergy to obtain the final stable state of the saturation temperature change trend.

[0101] It should be noted that the high-temperature steam processed in this invention is saturated water vapor at 200-400℃ and 15-20MPa. Its saturation temperature is strictly positively correlated with pressure. The specific correspondence is shown in the industrial steam property table: saturation temperature is approximately 345℃ at 15MPa, approximately 348℃ at 16MPa, approximately 351℃ at 17MPa, approximately 357℃ at 18MPa, approximately 361℃ at 19MPa, and approximately 366℃ at 20MPa. All saturation temperature monitoring and control in this invention are designed based on the above correspondence to ensure that the high-temperature steam maintains a stable saturated state at the target pressure, thereby suppressing ion migration.

[0102] This embodiment provides a specific implementation of step S101: The following low-temperature example is only used to verify the feasibility of the algorithm logic. When actually applied to high temperature and high pressure conditions of 200-400℃ and 15-20MPa, it is necessary to replace the high temperature and high pressure sensor (such as the ion migration sensor IMS-100H-HP, which can withstand temperatures of -50~500℃ and pressures of ≤25MPa).

[0103] Ion migration data and temperature fluctuation signals of water vapor in the electroadsorption zone are collected by a temperature monitoring sensor and stored as an initial dataset. Signal processing algorithms are used to denoise the initial dataset, resulting in smoothed ion migration and temperature fluctuation data. If the fluctuation amplitude of the smoothed ion migration and temperature fluctuation data exceeds a preset threshold, Fourier transform is used to extract the signal frequency features and determine the frequency distribution characteristics. Based on the frequency distribution characteristics, a Kalman filter algorithm is used to predict the saturation temperature change trend, obtaining predicted saturation temperature data. A real-time feedback loop compares the predicted saturation temperature data with the sensor signal, calculating the deviation. If the deviation exceeds a preset threshold, the control parameters of the real-time feedback loop are adjusted to update the predicted saturation temperature data. Based on the updated predicted saturation temperature data, a dynamic sequence of the saturation temperature change trend is generated and stored as the final output data.

[0104] For example, the ion migration data and temperature fluctuation signal of water vapor in the electroadsorption zone are obtained by a temperature monitoring sensor. The specific implementation method is as follows: First, a high-precision ion migration sensor and a temperature sensor are deployed. The sensors are installed in the electroadsorption zone. The ion migration sensor obtains the ion migration rate at a sampling frequency of 100 times per second, and the temperature sensor records the temperature at an interval of 0.1 seconds with an accuracy of 0.01°C.

[0105] Specifically, the temperature sensor uses a high-temperature and high-pressure resistant sapphire fiber optic temperature sensor (model: OS3600), and the ion migration sensor uses a high-frequency ion migration sensor (model: IMS-100H).

[0106] For example, under experimental conditions, the initial ion mobility was 50 × 10⁻⁶. -8 m 2 / (V·s), temperature 95.2°C. The sensor transmits data to the central processing unit in real time via a wireless communication module. The data stream is encapsulated in JSON format, containing timestamps, ion migration rates, and temperature values. Next, the real-time feedback loop processes these signals, using a Kalman filter algorithm to reduce noise in the ion migration and temperature data. The filter parameters are set to process noise covariance Q=0.01 and measurement noise covariance R=0.1 to ensure signal smoothness while preserving key fluctuation characteristics. The processed data enters the trend analysis module, using a sliding window algorithm (window size 60 seconds) to calculate the saturation temperature change trend. A linear regression model is used to fit the relationship between temperature and ion migration rate; the regression equation is:

[0107]

[0108] Where T is temperature, M is ion migration rate, and a and b are fitting parameters.

[0109] For example, after analyzing 60 seconds of data, a=0.15, b=90.3, and R²=0.92, indicating a positive correlation between ion migration rate and temperature. The trend analysis module further calculates the rate of change of saturation temperature through time series differencing.

[0110] For example, ΔT / Δt = 0.05°C / s, predicting the saturation temperature may rise to 100.5°C within the next 300 seconds. The analysis process incorporates business scenarios, such as industrial distillation systems, where the saturation temperature trend is used to dynamically adjust the heating power. The power adjustment formula is:

[0111]

[0112] Where P is the adjusted heating power, P0 is the base power of 1000W, K is the adjustment coefficient of 0.02 to ensure stable system operation, and ΔT is the deviation between the actual temperature and the target saturation temperature. All steps are automated through an embedded processor and cloud computing platform, requiring no manual intervention. Data is stored in a cloud database, supporting subsequent optimization and analysis.

[0113] This embodiment provides a specific implementation of step S102: Real-time monitoring of saturated temperature change trend data is acquired, and temperature fluctuation values ​​are collected through sensors to obtain the current temperature fluctuation state. If the temperature fluctuation value exceeds a preset threshold, the temperature compensation amount required for ion migration suppression is calculated based on the saturated temperature change trend, and the compensation target value is determined. The valve opening is adjusted using the temperature compensation amount, and a control signal is generated using a proportional-integral-differential algorithm to obtain valve adjustment parameters. The heating power is dynamically controlled using the valve adjustment parameters, and an output power modulation value is generated using pulse width modulation technology to obtain a power allocation scheme. Real-time heating power data is extracted from the power allocation scheme, and iterative optimization is performed in conjunction with the saturated temperature change trend to obtain an optimized modulation value. If the deviation between the optimized modulation value and the target compensation value is less than a preset threshold, the valve state is updated through a control signal to obtain a stable temperature fluctuation control result. Based on the stable temperature fluctuation control result, the ion migration suppression effect is monitored in real time to obtain the final output power modulation value.

[0114] For example, by monitoring the saturation temperature change trend in real time, the system uses a temperature sensor to collect temperature data of water vapor inside the reactor. Assuming the initial temperature is 80°C, the target saturation temperature is 100°C, and the sampling frequency is 1 second / sampling. Based on historical data analysis, the saturation temperature change rate is calculated as ΔT / Δt, where ΔT is the temperature difference between two adjacent samplings, and Δt is the time interval (1 second). If ΔT / Δt > 0.5°C / s is detected, it indicates that the temperature is rapidly approaching the saturation temperature, and the system automatically triggers the valve adjustment algorithm. The valve opening adjustment uses a PID control algorithm, with the following formula:

[0115]

[0116] Where u(t) is the controller output, Kp is the proportional coefficient, and Ki is the integral coefficient. Let Kd be the integral term of the error, e(t) be the differential coefficient, and Kp = 0.8, Ki = 0.1, Kd = 0.05. Assuming the current e(t) = 100 - 95 = 5°C, the cumulative error of the integral term is 10°C·s, and the differential term de(t) / dt = 0.4°C / s, we calculate u(t) = 4.25, and adjust the valve opening to 42.5%. To suppress ion migration, the system performs power compensation based on temperature fluctuation characteristics, controlling the fluctuation range within ±0.3°C. The temperature fluctuation is analyzed by Fourier transform to extract the fluctuation signal with a dominant frequency of 0.1Hz, and the compensation power is calculated as shown in the following formula:

[0117]

[0118] Among them, P comp To compensate for the power, |ΔT| is the absolute value of the temperature difference, and f is the frequency. |ΔT| = 0.3°C, f = 0.1Hz, therefore P... comp =0.0006kW. The final output power modulation value is the sum of the base power (set to 10kW) and the compensation power, i.e., 10.0006kW. The system sends this value to the heating controller to ensure temperature stability and suppress ion migration. Logically, saturation temperature monitoring, valve adjustment, and fluctuation compensation form a closed-loop control. First, the heating demand is determined by the saturation temperature trend, then the valve is precisely adjusted using PID control, and finally, the power is compensated through fluctuation analysis to ensure the stability of the reaction process.

[0119] This embodiment provides a specific implementation of step S103: External environmental monitoring data is acquired through the temperature control unit to generate an environmental parameter set. Operating condition change characteristics are extracted from the environmental parameter set to obtain the change trend. The change trend is classified using a support vector machine algorithm to determine the operating condition state. If the operating condition state exceeds a preset threshold, a modulation value is generated, and a control signal is output. The output power of the temperature control unit is adjusted using the control signal to obtain system response data. Activation condition features are extracted from the system response data to determine whether the adaptive control system is activated. If the activation condition is met, the control signal is updated through the data processing module to obtain an optimized system response.

[0120] For example, in the process of using a temperature control unit integrated system to apply the output power modulation value to the filter area to obtain the detection results of changes in operating conditions under external environmental monitoring and to determine the activation conditions of the adaptive control system, environmental data is first collected through the temperature control unit integrated system. Assuming a temperature sensor array is used to monitor the external ambient temperature at a frequency of once per second, the temperature sequence T(t) = {25.5, 26.0, 26.3, 25.8} degrees Celsius is obtained. The humidity sensor collects the relative humidity sequence H(t) = {60.2, 61.0, 60.8, 61.5}%. The sliding window algorithm (window size 4) is used to calculate the rate of change of temperature and humidity, as shown in the formula:

[0121]

[0122]

[0123] in, Let T(t) be the temperature rate of change, T(t) be the temperature sample value at time t, and T(t-1) be the temperature sample value at time t-1; Δt be the sampling time interval. Let H(t) be the humidity rate of change, H(t) be the humidity sample value at time t, and H(t-1) be the humidity sample value at time t-1. We calculate ΔT = [0.5, 0.3, -0.5] degrees Celsius / second and ΔH = [0.8, -0.2, 0.7]% / second. Next, the output power modulation value P(t) is set as a linear function based on temperature change: P(t) = 50 + 10 × ΔT, in watts. We calculate P(t) = [55.0, 53.0, 45.0] watts. The power value is applied in real-time to the servo motor in the filter area by a digital signal processor to control the filtration efficiency. Assuming the wind speed V(t) in the filter area is proportional to the power, V(t) = 0.02 × P(t), we obtain V(t) = [1.1, 1.06, 0.9] meters / second. Then, based on environmental monitoring data, the changes in operating conditions are analyzed, and the standard deviation algorithm is used to assess the fluctuations in temperature and humidity. The standard deviation of the temperature series is calculated as shown in the following formula:

[0124]

[0125] in, Let T(t) be the temperature standard deviation, and T(t) be the temperature sample value at time t. Here, N represents the average temperature, and N is the number of samples. The calculation yields... The temperature is 0.33 degrees Celsius, and the standard deviation of humidity is σ. H =0.51%, if σ T >0.3℃ or σ HIf the temperature fluctuation is greater than 0.5%, the operating condition is considered to have changed significantly, triggering the activation condition of the adaptive control system. The system determines whether to activate based on preset thresholds (temperature fluctuation > 0.3 degrees Celsius, humidity fluctuation > 0.5%). T =0.33, σ H =0.51 satisfies the condition, therefore adaptive control is activated, automatically adjusting the filtration zone power to P(t)+5 watts, i.e., [60.0, 58.0, 50.0] watts, to optimize filtration efficiency. This process is implemented through an embedded controller, and the data is stored in a cloud database for subsequent analysis and optimization, forming a closed-loop control logic.

[0126] This embodiment provides a specific implementation of step S104: An adaptive control system detects the environmental state, acquires temperature and flow rate signals, and determines initial operating parameters. If the initial operating parameters exceed a preset threshold, an interference suppression algorithm is used to denoise the temperature and flow rate signals to obtain optimized signals. Based on the optimized signals, real-time operating parameters are extracted from the electroadsorption zone to generate a parameter dataset. If the parameter dataset meets the requirements of the collaborative scheme, a support vector machine algorithm is used to classify the parameter dataset and determine the feasibility of the filtering and separation scheme. Based on the judgment result, a gradient descent algorithm is used to optimize the operating parameters of the filtering and separation scheme to obtain an adjusted parameter set. Using the adjusted parameter set, a filtering and separation collaborative scheme is generated, and the final operating configuration is determined. If the final operating configuration meets the system startup conditions, the configuration is applied to the electroadsorption zone to acquire real-time feedback data.

[0127] By processing temperature adjustment and flow rate control signals through interference cancellation algorithms, optimized operating parameter data of the electroadsorption zone are obtained, leading to a synergistic filtration and separation scheme.

[0128] The temperature adjustment signal and flow rate control signal are processed using an interference cancellation algorithm to obtain denoised signal data. Based on the denoised signal data, a support vector machine algorithm is used to analyze the correlation between the temperature adjustment signal and the flow rate control signal to determine a signal correlation model. If the output value of the signal correlation model exceeds a preset threshold, a linear regression algorithm is used to predict the operating parameters of the electroadsorption zone to obtain optimized parameter values. Based on the optimized parameter values, a gradient descent algorithm is used to adjust the configuration of temperature control and flow rate regulation to obtain optimized operating parameters. The adsorption efficiency of the electroadsorption zone is analyzed using the optimized operating parameters to determine a synergistic control strategy for filtration and separation. If the adsorption efficiency of the synergistic control strategy reaches a preset threshold, a synergistic filtration and separation scheme is generated, and the final operating parameter configuration is output. Based on the final operating parameter configuration, the temperature control and flow rate regulation of the electroadsorption zone are adjusted to obtain a stable synergistic filtration and separation scheme.

[0129] For example, in the optimization of operating parameters in the electroadsorption zone, the temperature adjustment and flow rate control signals are first processed using an interference cancellation algorithm. Assuming the input temperature signal is 25.5°C and the flow rate signal is 2.0 m / s, there is random noise of ±0.5°C and ±0.1 m / s. A Kalman filter algorithm is then used to denoise the signals. The state transition matrix A = [10; 01], the measurement matrix H = [10; 01], the process noise covariance Q = 0.01, and the measurement noise covariance R = 0.1. Through iterative calculation of the state estimate, the smoothed temperature signal is obtained as 25.48°C, the flow rate as 2.02 m / s, and the noise variance is reduced to 0.02, improving signal stability. Next, the processed signal was input into the optimization model. Based on a genetic algorithm, the operating parameters of the electroadsorption zone were optimized. The objective function was to minimize energy consumption and maximize adsorption efficiency. The electrode voltage range was set to 1.0-2.0V, the current density to 0.5-1.5mA / cm², the population size to 50, and 100 iterations with a crossover probability of 0.8 and a mutation probability of 0.01. The optimal parameters were obtained: voltage 1.8V, current density 1.2mA / cm², resulting in a 15% reduction in energy consumption and an increase in adsorption efficiency to 92%. Subsequently, the effects of temperature and flow rate on the adsorption effect were analyzed using response surface methodology (RSM). Temperature (20-30°C) and flow rate (1.5-2.5 m / s) were used as independent variables, and adsorption rate (80-95%) was used as the dependent variable. A quadratic regression model was fitted, yielding R² = 0.95, indicating strong explanatory power. Under the optimized parameters, the adsorption rate reached 93.5%. Finally, based on the filtration-separation synergy scheme and combined with the particle swarm optimization (PSO) algorithm, with a particle number of 30, an inertia weight of 0.7, and learning factors c1=c2=2, the filter membrane pore size was optimized to 0.1μm, and the flow rate was adjusted to 2.1m / s, achieving a separation efficiency of 98% and a synergistic adsorption rate of 94%. Through signal denoising, parameter optimization, and synergistic analysis, a closed-loop control logic was formed to ensure efficient operation of the electroadsorption zone.

[0130] This embodiment provides a specific implementation of step S105: Saturation temperature monitoring data is collected by a sensor, and the data is processed using a collaborative filtering algorithm to obtain the temperature change trend. If the temperature change trend exceeds a preset threshold, the sensor data is updated in real time through a feedback loop to determine the adjustment requirement. Based on the adjustment requirement, the valve control parameters are dynamically adjusted to obtain the execution status of the compensation mechanism. The temperature fluctuation is corrected through the compensation mechanism to obtain stable temperature data. The stable temperature data and ion migration data are analyzed using a data processing module to determine the ion migration suppression effect. If the suppression effect does not meet the preset standard, the valve control parameters are readjusted through the feedback loop to obtain new temperature data. Based on the new temperature data, the data processing and suppression effect judgment are repeated to obtain the final suppression effect.

[0131] For example, based on the filtration and separation collaborative scheme, the real-time feedback loop acquires temperature data through a temperature monitoring sensor. Assuming the sensor collects temperature data once per second with an accuracy of ±0.1℃, the current reading is 95.5℃, and the standard saturation temperature is 100℃, the system updates the valve opening using a PID (Proportional-Integral-Derivative) algorithm. The proportional coefficient Kp is set to 0.5, the integral time Ti to 10 seconds, and the derivative time Td to 2 seconds. The calculated error e = 100 - 95.5 = 4.5℃, and the output valve opening adjustment is shown in the following formula:

[0132]

[0133] Where Ki=Kp / Ti=0.05, Kd=Kp*Td=1.0, we get u(t)=2.25+0.05*45+1.0*0.1=4.6%. The valve opening is adjusted from 50% to 54.6% via a servo motor, with a response time of 0.2 seconds. Temperature fluctuation compensation is achieved by analyzing historical data, detecting a temperature fluctuation cycle of 30 seconds and an amplitude of ±0.3℃. The system uses Fourier transform to extract the main frequency of 0.033Hz, dynamically adjusts the valve opening, and superimposes a sinusoidal compensation signal with an amplitude of 0.15% and a frequency of 0.033Hz to ensure the temperature remains stable at 100±0.1℃. Ion migration suppression is monitored by a conductivity sensor. The initial conductivity is 10mS / cm, and the target is 8mS / cm. The system uses the migration rate formula, as follows:

[0134]

[0135] Where J is the current density, σ is the conductivity, and E is the electric field strength. Applying a reverse electric field E = 100 V / m reduces the conductivity to 8.2 mS / cm. Analysis shows that the conductivity decreases by 20% within 60 seconds, indicating a significant suppression effect. All steps are integrated through a PLC controller, sensor data is transmitted via the Modbus protocol, and the algorithm runs in real time within the embedded system, ensuring a closed-loop logic.

[0136] This embodiment provides a specific implementation of step S106: Monitoring signals indicating changes in operating conditions are acquired through environmental monitoring; temperature and flow rate data are collected using a sensor network to obtain a real-time operating condition dataset. The real-time operating condition dataset is processed using data fusion, and the monitoring signals are smoothed using a Kalman filter algorithm to eliminate noise interference, resulting in smoothed operating condition data. If the smoothed operating condition data exceeds a preset threshold range, power modulation is triggered to adjust the output power of the electroadsorption zone, obtaining a modulation power value. Based on the modulation power value, the temperature adjustment value of the electroadsorption zone is calculated, and a linear regression algorithm is used to predict the temperature change trend, obtaining temperature adjustment parameters. Based on the temperature adjustment parameters and a fluid dynamics model, the flow rate adjustment value is calculated, obtaining flow rate adjustment parameters. If both the temperature adjustment parameters and the flow rate adjustment parameters meet preset optimization thresholds, the operating parameters are considered optimized, and the optimization result is obtained. The control strategy of the electroadsorption zone is updated based on the optimization result, and a feedback control mechanism is used to adjust the power modulation, obtaining updated operating parameters.

[0137] For example, in optimizing the operating parameters of the electroadsorption zone, initial data is first obtained by combining output power modulation with external environmental monitoring. Assuming a power modulator is used with a base of 10kW, dynamically adjusted to 8-12kW, combined with environmental sensors to collect temperature (25°C), humidity (60%), and airflow velocity (2m / s). This data is transmitted in real-time to the central processing unit at a frequency of 1Hz via a data acquisition system. Next, interference elimination in the detection of changes in operating conditions is performed, using a Kalman filter algorithm to process the temperature and flow velocity signals. The algorithm assumes a noise covariance matrix Q=0.01 and measurement noise R=0.05. Through iterative calculation of the state estimate, interference caused by external wind speed fluctuations (±0.5m / s) is filtered out, resulting in a smoothed temperature value of 26.2°C and a flow velocity value of 2.1m / s. Based on this, the electroadsorption zone regulator calculates the adjustment value according to a preset model (the linear relationship between temperature and flow velocity: T=25+0.5v, where T is the target temperature and v is the flow velocity). If the target temperature is 27°C, substituting into the formula yields the required flow rate v = 4 m / s. The regulator uses PID control (proportional coefficient Kp = 0.8, integral time Ti = 10 s, derivative time Td = 2 s) to increase the flow rate from 2.1 m / s to 4 m / s, resulting in an adjusted output power of 11 kW. Finally, the system determines that the operating parameters have been optimized by comparing the deviation between the actual temperature (27.1°C) and the target value (27°C) (0.1°C < threshold 0.5°C). If the deviation exceeds the limit, the system automatically iterates and adjusts until the threshold is met. This process is automated using industrial control software (such as a PLC), and the data is stored in a cloud database to ensure parameter traceability. The entire process forms a closed-loop control, ensuring efficient operation of the electroadsorption zone.

[0138] By adjusting the temperature and flow rate in the electroadsorption zone in real time, integrating environmental humidity and temperature data, and using power modulation to process changes in operating conditions, optimized values ​​of operating parameters after interference elimination are obtained.

[0139] Temperature and flow rate data of the electroadsorption zone are acquired by sensors and combined with ambient humidity and temperature data to determine a first fused dataset. If the temperature or flow rate data in the first fused dataset exceeds a preset threshold, a Kalman filter algorithm is used to process the temperature and flow rate data to obtain a first filtered dataset. Based on the first filtered dataset, a power modulation algorithm is used to adjust the operating power of the electroadsorption zone to obtain a first power adjustment value. By comparing the first power adjustment value with the operating condition change data, if the deviation between the operating condition change data and the first power adjustment value exceeds a preset threshold, the least squares method is used to optimize the power adjustment to obtain a second power adjustment value. Based on the second power adjustment value, the operating parameters of the electroadsorption zone are adjusted to obtain a first operating parameter set. By fusing the first operating parameter set with ambient humidity and temperature data, if the deviation of the fused parameter set from the preset operating target is less than a preset threshold, an optimized operating parameter set is determined. Based on the optimized operating parameter set, the temperature and flow rate of the electroadsorption zone are adjusted to obtain the final optimized operating parameter values.

[0140] For example, by adjusting the temperature and flow rate in the electroadsorption zone in real time, integrating ambient humidity and temperature data, and using power modulation to handle changes in operating conditions, optimized operating parameters after interference elimination can be obtained. This can be implemented using the following techniques. First, the real-time adjustment of the temperature and flow rate in the electroadsorption zone can employ a PID control algorithm. Assuming the initial temperature setpoint is 25.0°C and the flow rate is 2.0 m / s, the sensor collects temperature data T and flow rate data V every second. If T deviates from the setpoint by ±0.5°C, the PID algorithm calculates the control input:

[0141]

[0142] Where Kp=5.0, Ki=0.1, Kd=0.5, and e(t) is the temperature error, the heater power or fan speed is adjusted to stabilize the temperature and flow rate within the set range, with the error converging to ±0.2°C and the flow rate fluctuation less than 0.1 m / s. Next, ambient humidity and temperature data are fused using a multi-sensor data fusion algorithm, such as Kalman filtering, assuming the ambient temperature sensor reading is T. env =20.0°C, humidity RH=60.0%, noise variances are 0.1°C and 2.0%, respectively. Kalman filtering fuses the state equation and observation equation T env And RH, as shown in the following formula:

[0143]

[0144]

[0145] Fusion T env The smoothed temperature estimate T is obtained from RH. fused =20.2°C, estimated humidity RH fused =59.8%, improving data accuracy and reducing noise interference. Then, power modulation is used to handle changes in operating conditions. Using pulse width modulation (PWM) technology, assuming the change in operating conditions causes the load power demand to increase from 1000W to 1200W, the PWM duty cycle is dynamically adjusted according to the load demand. The calculation formula is:

[0146]

[0147] Where D is the duty cycle of PWM (Pulse Width Modulation), P demand P represents the power demand under the current operating conditions. max P represents the maximum output power of the temperature control unit. max =1500W, after adjustment the duty cycle D=80% to ensure stable power output with fluctuations less than ±5W. Finally, the optimized operating parameters after interference elimination are optimized using the least squares method. Assuming the objective function is E, iterative calculations minimize E to obtain the optimized temperature T. opt =25.1°C, flow velocity V opt =2.02m / s, the iterative calculation formula is as follows:

[0148]

[0149] Where E is the error function for parameter optimization, and T fused T represents the actual fusion temperature. set To set the target temperature; V is the actual variable value, V set The value of the target variable.

[0150] Analysis shows that the fused data improves system stability, power modulation reduces energy consumption by about 10%, optimized parameters improve system efficiency by 15%, and closed-loop control logic ensures data consistency and real-time performance at each step.

[0151] This embodiment provides a specific implementation of step S107: The ambient temperature data and ion migration rate are acquired through the temperature control unit to determine initial operating parameters. If the initial operating parameters exceed a preset threshold, the output power of the temperature control unit is adjusted using a parameter optimization algorithm to obtain optimized operating parameters. The optimized operating parameters are used to drive the filtration and separation module to obtain separation efficiency and ion migration suppression effect, and to determine the saturation temperature change trend. If the saturation temperature change trend has not reached a stable state, the operating frequency of the filtration and separation module is adjusted using an adaptive control algorithm to obtain updated control parameters. Based on the updated control parameters, real-time feedback data from the temperature control unit is acquired to determine the dynamic trend of saturation temperature change. Through dynamic trend analysis, a support vector machine algorithm is used to predict the saturation temperature change to obtain the predicted stable state. If the predicted stable state meets a preset threshold, the coordination parameters of the temperature control unit and the filtration and separation module are adjusted through a control calibration module to obtain the final stable state.

[0152] For example, by integrating ion migration suppression and operating parameter optimization through a temperature control unit, a mathematical model of the temperature control unit must first be constructed. A PID control algorithm is then used, setting the initial proportional gain Kp = 5.0, integral time Ti = 2.0 seconds, and derivative time Td = 0.5 seconds, based on the thermodynamic conduction equation:

[0153]

[0154] Where k is the thermal conductivity (0.6 W / (m·K)), A is the heat transfer area (0.1 m²), ΔT is the temperature difference, and the target is to stabilize the temperature at 80°C ± 0.5°C. Temperature data is collected in real time, sampled every 0.1 seconds by a sensor, and input into the control system for deviation calculation. If the deviation e = |T| 目标 -T 实际 If the temperature exceeds 0.5°C, the heating power P = 1000W × (1 + e / 10) is adjusted to achieve dynamic temperature control. Next, for ion migration suppression, an electric field control algorithm is used. A voltage U = 50V is applied, and the electric field strength E = U / d (d is the electrode spacing of 0.01m). E = 5000V / m is calculated, suppressing ion mobility to 10. -4 cm 2 / (V·s). Finite element analysis was used to simulate the ion concentration distribution and verify the suppression effect, reducing the ion concentration to 10% of the initial value. Parameter optimization was performed using a genetic algorithm with a population size of 50 and 100 iterations. The optimization objective was to minimize energy consumption W, as shown in the following equation:

[0155]

[0156] Where W represents total power and P(t) represents instantaneous power, with a temperature fluctuation constraint of <0.5°C, the optimized energy consumption is reduced by 15%, and the stable operating power is approximately 850W. The filtration and separation collaborative calibration employs Kalman filtering, fusing multi-sensor data from temperature, ion concentration, and flow rate. The state equation is:

[0157]

[0158] Observation equation:

[0159]

[0160] Where x(k+1) is the state vector at time k+1, x(k) is the state vector at time k, A, B, and H are the system matrices, u(k) is the input vector at time k, and w(k) and v(k) are the noise covariances. The filtered temperature estimation error is <0.2°C, and the ion concentration error is <5%. The saturation temperature variation trend is predicted by a thermodynamic model, combined with the Antoine equation, as shown in the following equation:

[0161]

[0162] in, Let T be the common logarithm of the saturated vapor pressure, T be the saturation temperature, and A, B, and C be Antoine constants. A = 8.07131, B = 1730.63, and C = 233.426. The saturation temperature was calculated as a function of pressure. As the pressure increased from 1 atm to 1.5 atm, the saturation temperature increased from 100°C to 108°C. Under steady-state conditions, the saturation temperature fluctuation was <0.1°C. Analysis showed that the system reached stability within 30 minutes, with the saturation temperature tending towards 108°C, verifying the effectiveness of the adaptive control.

[0163] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0164] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for boiler steam-water composite electro-adsorption filtration separation, characterized in that: The method includes the following steps: Step S101: Ion migration data and temperature fluctuation signals of water vapor in the electroadsorption region are acquired by monitoring sensors, and the ion migration data and temperature fluctuation signals are processed by a real-time feedback loop to obtain the trend of saturation temperature change. Step S102: Based on the trend of saturation temperature change, dynamically adjust the valve to adjust the heating power, compensate for temperature fluctuations to suppress ion migration, and determine the output power modulation value. Step S103: The output power modulation value is applied to the filter area by integrating the temperature control unit, the detection results of the change in working conditions under external environmental monitoring are obtained, and the activation conditions of the adaptive control system are determined. Step S104: If the adaptive control system is activated, the temperature and flow rate adjustment signals are processed by the interference cancellation algorithm, and the operating parameter optimization data is obtained from the electroadsorption zone adjustment to obtain the filtration and separation synergistic scheme; specifically, the following steps are included: The adaptive control system detects the environmental conditions, acquires temperature and flow rate signals, and determines the initial operating parameters. If the initial operating parameters exceed the preset threshold, an interference suppression algorithm is used to denoise the temperature and flow rate signals to obtain an optimized signal. Based on the optimization signal, real-time operating parameters are extracted from the electroadsorption region to generate a parameter dataset. If the parameter dataset meets the requirements of the collaborative scheme, the parameter dataset is classified using the support vector machine algorithm to determine the feasibility of the filtering and separation scheme. Based on the judgment results, the gradient descent algorithm is used to optimize the operating parameters of the filtering and separation scheme, and the adjusted parameter set is obtained. By adjusting the parameter set, a filtering and separation coordination scheme is generated, and the final running configuration is determined. If the final running configuration meets the system startup conditions, the configuration will be applied to the electroadsorption zone to obtain real-time feedback data. Step S105: According to the filtration and separation synergy scheme, the temperature monitoring sensor reading is updated in real time by the feedback loop, temperature fluctuation compensation is performed on the dynamic adjustment valve, and the ion migration suppression effect is determined. Step S106: Using output power modulation combined with external environment monitoring, the interference elimination algorithm output in the detection of changes in operating conditions is processed to obtain the temperature and flow rate adjustment values ​​after the electroadsorption zone is adjusted, and it is determined that the optimization of operating parameters is completed. Step S107: By integrating the ion migration suppression effect and operating parameter optimization through the temperature control unit, the adaptive control system is calibrated for the filtration and separation synergy to obtain the final stable state of the saturation temperature change trend.

2. The boiler steam-water composite electro-adsorption filtration separation method according to claim 1, characterized in that: In step S101, ion migration data and temperature fluctuation signals of water vapor in the electroadsorption region are acquired by monitoring sensors, and these signals are processed using a real-time feedback loop to obtain the trend of saturation temperature change. This specifically includes the following steps: Data on ion migration and temperature fluctuations of water vapor in the electroadsorption region were collected by monitoring sensors and stored as an initial dataset. Signal processing algorithms were used to denoise the initial dataset, resulting in smoothed ion migration data and temperature fluctuation data. If the fluctuation amplitude of smoothed ion migration data and temperature fluctuation data exceeds the preset threshold, the frequency characteristics of the signal are extracted by Fourier transform to determine the frequency distribution characteristics. Based on the frequency distribution characteristics, the Kalman filter algorithm is used to predict the trend of saturation temperature change and obtain the predicted saturation temperature data. The predicted saturation temperature data is compared with the sensor signal through a real-time feedback loop to calculate the deviation value. If the deviation exceeds the preset threshold, the control parameters of the real-time feedback loop are adjusted and the predicted saturation temperature data is updated. Based on the updated predicted saturation temperature data, a dynamic sequence of saturation temperature change trends is generated and stored as the final output data.

3. The boiler steam-water composite electro-adsorption filtration separation method according to claim 1, characterized in that: In step S102, the heating power is dynamically adjusted by regulating the valve according to the trend of saturation temperature change, temperature fluctuation compensation is performed to suppress ion migration, and the output power modulation value is determined. This specifically includes the following steps: The system acquires real-time monitoring data on the trend of saturation temperature changes and obtains temperature fluctuation values ​​through sensors to determine the current temperature fluctuation status. If the temperature fluctuation exceeds the preset threshold, the required temperature compensation for ion migration suppression is calculated based on the trend of saturation temperature change, and the compensation target value is determined. The valve opening is adjusted by temperature compensation, and the control signal is generated by proportional-integral-derivative algorithm to obtain the valve adjustment parameters. The heating power is dynamically controlled by adjusting valve parameters, and the output power modulation value is generated by pulse width modulation technology to obtain a power distribution scheme. Real-time heating power data is extracted from the power allocation scheme, and iterative optimization is performed in combination with the saturation temperature change trend to obtain the optimized modulation value. If the deviation between the optimized modulation value and the target compensation value is less than the preset threshold, the valve state is updated by the control signal to obtain a stable temperature fluctuation control result. Based on the stable temperature fluctuation control results, the ion migration suppression effect is monitored in real time to obtain the final output power modulation value.

4. The boiler steam-water composite electro-adsorption filtration separation method according to claim 1, characterized in that: In step S103, the output power modulation value is applied to the filter area using an integrated temperature control unit to obtain the detection results of changes in operating conditions under external environmental monitoring, and to determine the activation conditions of the adaptive control system. This specifically includes the following steps: External environmental monitoring data is acquired through a temperature control unit to generate a set of environmental parameters; Extract the characteristics of operating condition changes from the set of environmental parameters to obtain the changing trends; The support vector machine algorithm is used to classify the changing trends and determine the operating conditions. If the operating condition exceeds the preset threshold, a modulation value is generated and a control signal is output. The output power of the temperature control unit is adjusted by the control signal to obtain system response data; Extract activation condition features from system response data to determine whether the adaptive control system is activated; If the activation conditions are met, the control signal is updated through the data processing module to obtain the optimized system response.

5. The boiler steam-water composite electro-adsorption filtration separation method according to claim 1, characterized in that: Step S104 also includes processing the temperature adjustment and flow rate control signals through an interference cancellation algorithm, obtaining optimized operating parameter data for the electroadsorption zone, and obtaining a synergistic filtration and separation scheme, specifically including: The temperature adjustment signal and flow rate control signal are processed by an interference cancellation algorithm to obtain the denoised signal data; Based on the denoised signal data, the support vector machine algorithm is used to analyze the correlation between the temperature adjustment signal and the flow rate control signal, and to determine the signal correlation model. If the output value of the signal correlation model exceeds the preset threshold, the operating parameters of the electroadsorption zone are predicted by the linear regression algorithm to obtain the optimized parameter values. Based on the optimized parameter values, the gradient descent algorithm is used to adjust the configuration of temperature control and flow rate regulation to obtain the optimized operating parameters; By analyzing the adsorption efficiency of the electro-adsorption zone through optimized operating parameters, a synergistic control strategy for filtration and separation is determined. If the adsorption efficiency of the collaborative control strategy reaches the preset threshold, a filtration and separation collaborative scheme is generated, and the final operating parameter configuration is output. Based on the final operating parameter configuration, the temperature control and flow rate regulation of the electroadsorption zone are adjusted to obtain a stable filtration and separation synergy scheme.

6. The boiler steam-water composite electro-adsorption filtration separation method according to claim 1, characterized in that: In step S105, according to the filtration and separation synergy scheme, the temperature monitoring sensor reading is updated in real time via the feedback loop, temperature fluctuation compensation is performed on the dynamic adjustment valve, and the ion migration suppression effect is determined. Specifically, the steps include: Saturation temperature monitoring data is collected by sensors, and the data is processed using a collaborative filtering algorithm to obtain the temperature change trend. If the temperature change trend exceeds the preset threshold, the sensor data will be updated in real time through the feedback loop to determine the adjustment requirements. Based on the adjustment requirements, the valve control parameters are dynamically adjusted to obtain the execution status of the compensation mechanism; Temperature fluctuations are corrected through a compensation mechanism to obtain stable temperature data; The data processing module was used to analyze the stabilized temperature data and ion migration data to determine the ion migration inhibition effect. If the suppression effect does not meet the preset standard, the valve control parameters are readjusted through the feedback loop to obtain new temperature data; Based on the new temperature data, repeated data processing and suppression effect assessment are performed to obtain the final suppression effect.

7. The boiler steam-water composite electro-adsorption filtration separation method according to claim 1, characterized in that: In step S106, output power modulation combined with external environment monitoring is used to process the interference elimination algorithm output in the detection of changes in operating conditions, obtain the temperature and flow rate adjustment values ​​after the electroadsorption zone is adjusted, and determine that the optimization of operating parameters is completed. Specifically, this includes the following steps: By acquiring monitoring signals of changes in operating conditions through environmental monitoring, and by using a sensor network to collect temperature and flow rate data, a real-time operating condition dataset is obtained. The real-time operating condition dataset is processed by data fusion, and the monitoring signal is smoothed by combining the Kalman filter algorithm to eliminate noise interference and obtain smooth operating condition data. If the smoothing condition data exceeds the preset threshold range, power modulation is triggered to adjust the output power of the electro-adsorption region and obtain the modulation power value. Based on the modulation power value, the temperature adjustment value of the electroadsorption region is calculated, and the temperature change trend is predicted by the linear regression algorithm to obtain the temperature adjustment parameters. Based on the temperature adjustment parameters and combined with the fluid dynamics model, the flow velocity adjustment value is calculated to obtain the flow velocity adjustment parameters; If both the temperature adjustment parameter and the flow rate adjustment parameter meet the preset optimization threshold, then the optimization of the operating parameters is considered complete, and the optimization result is obtained. The control strategy for the electroadsorption region is updated by optimizing the results, and the power modulation is adjusted by using a feedback control mechanism to obtain the updated operating parameters.

8. The boiler steam-water composite electro-adsorption filtration separation method according to claim 7, characterized in that: Step S106 also includes real-time adjustment of the temperature and flow rate in the electroadsorption zone, integration of ambient humidity and temperature data, and power modulation to process changes in operating conditions, resulting in optimized operating parameter values ​​after interference elimination. Specifically, this includes: Temperature and flow rate data of the electroadsorption zone are acquired by sensors, and combined with ambient humidity and ambient temperature data to determine the first fusion dataset. If the temperature data or flow rate data of the first fused dataset exceeds the preset threshold, the Kalman filter algorithm is used to process the temperature data and flow rate data to obtain the first filtered dataset. Based on the first filtered dataset, the operating power of the electroadsorption region is adjusted using a power modulation algorithm to obtain the first power adjustment value; By comparing the first power adjustment value with the operating condition change data, if the deviation between the operating condition change data and the first power adjustment value exceeds a preset threshold, the least squares method is used to optimize the power adjustment to obtain the second power adjustment value. Based on the second power adjustment value, adjust the operating parameters of the electroadsorption zone to obtain the first set of operating parameters; By fusing the first set of operating parameters with the ambient humidity and ambient temperature data, if the deviation between the fused parameter set and the preset operating target is less than the preset threshold, then the optimized set of operating parameters is determined. Based on the optimized set of operating parameters, the temperature and flow rate of the electroadsorption zone are adjusted to obtain the final optimized values ​​of the operating parameters.

9. The boiler steam-water composite electro-adsorption filtration separation method according to claim 1, characterized in that: In step S107, by integrating the ion migration suppression effect and operating parameter optimization through the temperature control unit, an adaptive control system calibration is performed for the filtration and separation synergy to obtain the final stable state of the saturation temperature change trend. This specifically includes the following steps: The initial operating parameters are determined by acquiring ambient temperature data and ion migration rate through the temperature control unit. If the initial operating parameters exceed the preset threshold, the output power of the temperature control unit is adjusted through a parameter optimization algorithm to obtain the optimized operating parameters; The optimized operating parameters are used to drive the filtration and separation module to obtain the separation efficiency and ion migration inhibition effect, and to determine the trend of saturation temperature change. If the saturation temperature change trend has not reached a stable state, the operating frequency of the filter separation module is adjusted through an adaptive control algorithm to obtain updated control parameters; Based on the updated control parameters, obtain real-time feedback data from the temperature control unit to determine the dynamic trend of saturation temperature change; By using dynamic trend analysis, the support vector machine algorithm is employed to predict the change in saturation temperature and obtain the predicted steady state. If the predicted steady state meets the preset threshold, the coordination parameters of the temperature control unit and the filter separation module are adjusted by the control calibration module to obtain the final steady state.

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