Seawater desulfurization energy efficiency optimization and early warning method

Abstract

The invention belongs to the technical field of environmental protection and energy efficiency optimization, particularly relates to a seawater desulfurization energy efficiency optimization and early warning method, and aims at solving the problems that a traditional desulfurization system is high in energy consumption, and hidden faults are difficult to early warn. According to the method, a multi-physical-field coupling dynamic monitoring system is constructed, parameters such as pH, dissolved oxygen and flow velocity are collected in real time, a transient working condition self-adaptive energy efficiency evaluation model is established, the pump frequency, the air volume and alkaline agent adding are dynamically adjusted in combination with a model prediction control strategy, and energy consumption minimization on the premise that the desulfurization efficiency is larger than or equal to 95% is achieved. Wavelet packet-principal component fusion analysis is synchronously implemented, degradation characteristics such as filler pressure drop and aeration uniformity are extracted to construct a degradation index, a three-level early warning mechanism is triggered, and a maintenance decision is pushed. According to the method, the energy efficiency response speed and the control precision are remarkably improved, the unit energy consumption is reduced by 12%-18%, the alkaline agent consumption is reduced by 20%, hidden degradation can be recognized 72 hours in advance, and the early warning accuracy gt is achieved; the false alarm rate is 92%; the non-planned shutdown is reduced by 60%, and the annual energy efficiency stability standard deviation of the system is 1t; and 4%.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of environmental protection and energy efficiency optimization, and particularly relates to a seawater desulfurization energy efficiency optimization and early warning method. BACKGROUND

[0002] With the increasingly stringent requirements for marine environmental protection, seawater desulfurization technology, as an important means of flue gas purification in coal-fired power plants, is widely used due to its renewable resources, low operating cost and other advantages. The traditional seawater desulfurization system relies on fixed parameter control and empirical operation strategy, and is difficult to adapt to dynamic working conditions caused by flue gas load fluctuations and seawater quality changes, resulting in unstable desulfurization efficiency, high energy consumption and increased equipment corrosion risk. Especially in coastal high salinity or seasonal water temperature change areas, the gas-liquid mass transfer efficiency and oxidation reaction rate in the desulfurization tower are easily disturbed by the environment, which puts higher requirements on the energy efficiency regulation accuracy and fault prediction ability of the system.

[0003] However, existing control models are mostly based on steady-state assumptions, lacking dynamic analysis capabilities for the coupling relationship of key parameters under transient conditions, resulting in regulation lag and energy consumption redundancy. At the same time, the desulfurization process involves multi-physical field interaction data (such as pH gradient, dissolved oxygen concentration, flow velocity distribution), and traditional monitoring methods cannot realize the collaborative modeling of multi-dimensional state variables, making it difficult to support energy efficiency optimization decisions. In addition, the early warning mechanism generally relies on threshold triggering, and cannot identify the implicit performance degradation trend (such as the initial stage of filler layer fouling and local blockage of aeration head), resulting in frequent sudden efficiency degradation and unplanned shutdown.

[0004] Therefore, a seawater desulfurization energy efficiency optimization and early warning method is expected. SUMMARY

[0005] The purpose of the present application is to provide a seawater desulfurization energy efficiency optimization and early warning method, which can effectively solve the problems in the background art.

[0006] To achieve the above-mentioned purpose, the technical solution adopted by the present application is: A method for optimizing and early warning of seawater desulfurization energy efficiency includes the following specific steps: Step (1) Constructing a multi-physics coupled dynamic monitoring system: A distributed sensor array is deployed along the gas-liquid flow path in the desulfurization tower to collect real-time data on pH gradient, dissolved oxygen concentration, flow velocity distribution, temperature, and conductivity, forming a multi-dimensional state variable time series covering the entire desulfurization reaction process; Step (2) Establishing a transient operating condition adaptive energy efficiency evaluation model: Based on the multi-dimensional state variable time series, a sliding time window mechanism is used to extract dynamic feature vectors, and combined with the sulfur dioxide concentration and flow rate data at the flue gas inlet, a real-time mapping relationship between desulfurization efficiency and unit energy consumption is constructed through a nonlinear regression algorithm; Step (3) Executing multi-objective collaborative optimization control: With desulfurization efficiency not less than 95% and unit energy consumption minimized as constraints, a multi-objective collaborative optimization control model is used to optimize the desulfurization efficiency and unit energy consumption. The predictive control strategy dynamically adjusts the frequency of the seawater circulation pump, the air volume of the aeration blower, and the acceleration rate of alkalinity compensator to achieve optimal energy efficiency operation; Step (4) Implement the identification of implicit performance degradation trends: continuously monitor the pressure drop of the packing layer, the uniformity of the flow velocity at the outlet of the aeration head, and the fluctuation range of dissolved oxygen in the oxidation reaction zone, and use the wavelet packet decomposition and principal component analysis fusion algorithm to extract performance degradation sensitive features and construct a degradation index time series; Step (5) Trigger graded early warning and decision support: when the degradation index exceeds the preset threshold for three consecutive sampling periods, a first-level early warning is initiated and maintenance suggestions are pushed; if the degradation index growth rate exceeds 0.5 per hour, it is upgraded to a second-level early warning and the operating parameters are automatically adjusted to suppress performance degradation; if the degradation index reaches the critical value of 1.2, a third-level emergency early warning is triggered and a shutdown maintenance is recommended.

[0007] Preferably, in step (1), the distributed sensor array includes a monitoring section set every 2 meters along the height of the desulfurization tower, with 8 composite sensing units evenly arranged in each section. Each composite sensing unit integrates a pH electrode, a dissolved oxygen probe, an ultrasonic flow meter, a platinum resistance temperature sensor, and a conductivity electrode. The sampling frequency is 1 Hz, and the data synchronization accuracy is better than 10 milliseconds.

[0008] Preferably, in step (2), the sliding time window length is set to 300 seconds, the window sliding step size is 30 seconds, the dynamic feature vector includes the pH gradient change rate, dissolved oxygen concentration standard deviation, flow velocity distribution skewness coefficient and temperature-conductivity coupling factor, the nonlinear regression algorithm adopts the Gaussian process regression model, its kernel function is a linear combination of radial basis function and periodic function, and the hyperparameters are automatically tuned through Bayesian optimization.

[0009] Preferably, in step (3), the prediction time domain of the model prediction control strategy is 600 seconds, the control time domain is 300 seconds, the optimization objective function is the weighted sum of energy consumption per unit of desulfurization and alkalinity compensator consumption, and the weight coefficient is dynamically adjusted according to the measured value of seawater alkalinity. When the seawater alkalinity is lower than 2.0 mmol / L, the weight of alkalinity compensator consumption is increased to 0.7.

[0010] Preferably, in step (4), the wavelet packet decomposition uses the db4 wavelet basis, the decomposition layer is 5 layers, the detail coefficients of the 3rd to 5th layers are selected as the feature subspace, the principal component analysis retains the principal components with a cumulative contribution rate greater than 90%, the degradation index is defined as the weighted Euclidean norm of the scores of each principal component, and the weights are determined by logistic regression inversion from historical fault data.

[0011] Preferably, in step (5), the first-level warning threshold is set to 0.8, the second-level warning growth rate threshold corresponds to a degradation index slope greater than 0.5 per hour, and the third-level critical value of 1.2 is calibrated based on the correlation data between the maximum allowable pressure drop of the packing layer and the 90% blockage rate of the aerator provided by the equipment manufacturer. The warning information is pushed to the human-machine interface in the central control room via industrial Ethernet and is simultaneously recorded in the operation log database.

[0012] Preferably, it also includes: establishing an energy efficiency benchmark library containing no less than 5,000 sets of historical operating condition data. The benchmark library is classified and stored according to season, seawater temperature range and flue gas load level. It is used to dynamically calibrate the baseline of the energy efficiency assessment model. The calibration cycle is once every 24 hours, and the calibration error tolerance is ±3%.

[0013] Preferably, it also includes: installing an online flue gas composition analyzer in the flue gas inlet of the desulfurization tower to monitor the concentration of sulfur dioxide, oxygen and dust in real time. When the dust concentration exceeds 50 milligrams per cubic meter, the pre-spraying pre-cleaning program is automatically started. The spraying intensity is linearly adjusted according to the dust concentration, and the adjustment coefficient is 0.02 liters per second per milligram per cubic meter.

[0014] Preferably, it also includes: implementing independent zone control of the aeration system, dividing the aeration zone into 4 concentric annular areas along the radial direction of the tower, with each area equipped with an independent air valve, and dynamically adjusting the air volume distribution ratio of each area according to the dissolved oxygen distribution non-uniformity index, where the non-uniformity index is defined as the root mean square of the deviation between the dissolved oxygen concentration of each area and the average value of the entire tower.

[0015] Preferably, it also includes: installing an online corrosion rate monitoring probe in the seawater circulation pipeline, using electrochemical impedance spectroscopy to assess the corrosion status of metal components in real time, and automatically increasing the amount of corrosion inhibitor added when the corrosion rate exceeds 0.1 mm per year, with the addition increment being proportional to the extent of corrosion rate exceeding the limit, and the proportionality coefficient being 0.5 kg per millimeter per year.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The ability to optimize dynamic energy efficiency has been significantly improved. Breaking through the limitations of steady-state control: By constructing a multi-physics field coupled dynamic monitoring system and a transient operating condition adaptive energy efficiency evaluation model, a rapid response to sudden changes in flue gas load and fluctuations in seawater quality is achieved, reducing the adjustment delay time to less than 60 seconds and reducing energy consumption redundancy by more than 15% compared with traditional fixed parameter control. High precision of multi-objective collaborative control: The model predictive control strategy reduces energy consumption per unit of desulfurization by 12% to 18% and alkalinity compensator consumption by 20% while ensuring desulfurization efficiency of no less than 95%, especially under low load conditions.

[0017] A qualitative leap has been achieved in the ability to predict latent faults. High sensitivity for early degradation identification: Based on the degradation index fusion algorithm of wavelet packet decomposition and principal component analysis, it can identify the hidden performance degradation trend such as the initial scaling of the packing layer and local blockage of the aeration head 72 hours in advance, with an identification accuracy of more than 92%, which is far better than the traditional threshold triggering mechanism. The tiered early warning mechanism is highly practical: the three-level early warning system combines the absolute value of the degradation index and the growth rate as dual criteria to effectively distinguish between normal fluctuations and real performance degradation. The false alarm rate is less than 5%, providing a reliable basis for operation and maintenance decisions and reducing the number of unplanned downtimes by more than 60%.

[0018] System stability and security have been comprehensively enhanced. Multidimensional state variable collaborative modeling: Distributed sensor arrays achieve full coverage monitoring of key areas within the desulfurization tower, with high spatiotemporal resolution of data, supporting the implementation of refined control strategies and reducing equipment corrosion risk by 30%; Adaptive benchmark calibration mechanism: The energy efficiency benchmark library is dynamically updated according to the environment and operating conditions to ensure the long-term effectiveness of the evaluation model and avoid control deviations caused by seasonal changes in seawater. The system's annual energy efficiency stability standard deviation is less than 4%. Attached Figure Description

[0019] Figure 1 This is a flowchart of the overall technical solution of the present invention. Detailed Implementation

[0020] Please refer to Figure 1 To make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to specific embodiments.

[0021] Currently, seawater desulfurization systems commonly suffer from problems such as lagging energy efficiency assessment, static control strategies, and slow fault warnings, leading to high operating energy consumption, increased maintenance costs, and frequent unplanned shutdowns. To address these technical issues, this invention proposes a seawater desulfurization energy efficiency optimization and early warning method based on multi-physics field dynamic monitoring and intelligent optimization control, and applies it to a seawater desulfurization energy efficiency optimization and early warning system.

[0022] In the above-mentioned method for optimizing and early warning of seawater desulfurization energy efficiency, step (1) involves constructing a multi-physics coupled dynamic monitoring system: a distributed sensor array is deployed along the gas-liquid flow path within the desulfurization tower to collect real-time data on pH gradient, dissolved oxygen concentration, flow velocity distribution, temperature, and conductivity, forming a multi-dimensional state variable time series covering the entire desulfurization reaction process. Specifically, the distributed sensor array includes a monitoring section set every 2 meters along the height of the desulfurization tower, with 8 composite sensing units evenly arranged in each section. Each composite sensing unit integrates a pH electrode, a dissolved oxygen probe, an ultrasonic flow meter, a platinum resistance temperature sensor, and a conductivity electrode. The sampling frequency is 1 Hz, and the data synchronization accuracy is better than 10 milliseconds. This deployment method ensures continuous coverage of key parameters across the entire height range from the bottom to the top of the tower, avoiding misjudgments of the state due to local monitoring blind spots. Each composite sensing unit is connected to the central data acquisition system through an industrial-grade Ethernet switch, and a timestamp synchronization mechanism is used to achieve time alignment of multi-source heterogeneous data, with the synchronization error controlled within 10 milliseconds, meeting the requirements for data consistency in subsequent dynamic modeling. The pH electrode uses a glass membrane electrode structure, with a measurement range of 3.0 to 11.0 and an accuracy of ±0.05 pH units. The dissolved oxygen probe is a fluorescence sensor with a response time of less than 10 seconds, a measurement range of 0 to 20 mg / L, and an accuracy of ±0.1 mg / L. The ultrasonic flow meter operates at a frequency of 1 MHz, with a measurement range of 0.1 to 5 m / s and an accuracy of ±0.01 m / s. The platinum resistance temperature sensor is model Pt100, with a temperature measurement range of 0 to 100 degrees Celsius and an accuracy of ±0.1 degrees Celsius. The conductivity electrode uses a four-electrode design, with a measurement range of 0.1 to 10,000 microSiemens per centimeter and an accuracy of ±0.5%. All sensors have self-calibration capabilities, with the system automatically performing zero-point drift compensation and full-scale calibration periodically every 72 hours. After calibration, the data deviation is less than 0.5%. The collected data undergoes preliminary filtering at edge computing nodes to remove transient noise interference. The filtering algorithm employs a low-pass filter with a moving average window length of 5 sampling points, effectively suppressing signal jitter caused by power frequency interference and mechanical vibration. The processed data is encapsulated in JSON format and uploaded to the central database via the Modbus TCP protocol. The storage structure includes fields such as timestamp, sensor ID, physical quantity name, value, unit, and quality flag, supporting efficient querying and indexing by time series, spatial location, and variable type. This monitoring system achieves comprehensive perception of the internal chemical reaction environment, mass transfer process, and thermodynamic conditions of the desulfurization tower, providing a high spatiotemporal resolution data foundation for subsequent energy efficiency assessment and control decisions.

[0023] In the above-mentioned method for optimizing and early warning the energy efficiency of seawater desulfurization, step (2) establishes an adaptive energy efficiency evaluation model for transient operating conditions: based on the time series of the multidimensional state variables, a sliding time window mechanism is used to extract dynamic feature vectors, and combined with the sulfur dioxide concentration and flow rate data at the flue gas inlet, a real-time mapping relationship between desulfurization efficiency and unit energy consumption is constructed through a nonlinear regression algorithm. Specifically, the sliding time window length is set to 300 seconds, the window sliding step is 30 seconds, the dynamic feature vector includes the pH gradient change rate, the standard deviation of dissolved oxygen concentration, the skewness coefficient of flow velocity distribution, and the temperature-conductivity coupling factor. The nonlinear regression algorithm adopts a Gaussian process regression model, whose kernel function is a linear combination of radial basis functions and periodic functions, and the hyperparameters are automatically tuned through Bayesian optimization. The sliding time window mechanism divides continuous time series data into multiple overlapping time segments, each segment corresponding to an independent dynamic state snapshot. For each time window, the pH gradient change rate is first calculated, which is the absolute value of the difference between the pH values ​​of two adjacent sampling points divided by the sampling interval. Then, the average value of this value is calculated for all sampling points within the entire window to obtain an index reflecting the intensity of dynamic disturbances in acid-base balance. The standard deviation of dissolved oxygen concentration is directly calculated by calculating the standard deviation of all dissolved oxygen readings within the window, characterizing the degree of fluctuation in the redox environment. The skewness coefficient of velocity distribution is obtained by calculating the ratio of the third central moment of the velocity value at each monitoring section to the cube of the standard deviation, and is used to quantify the non-uniformity of the flow field. A skewness coefficient greater than 0 indicates the existence of a high-velocity flow region, and less than 0 indicates the existence of a low-velocity stagnation region. The temperature-conductivity coupling factor is defined as the normalized value of the product of temperature and conductivity, and its trend can indirectly reflect the synergistic effect of seawater salinity and ion activity. These features together constitute a 4-dimensional dynamic feature vector, which serves as the model input. At the same time, sulfur dioxide concentration and flow rate data obtained from the online flue gas composition analyzer are interpolated and aligned with the sensor data on the time axis, serving as additional input variables. The Gaussian process regression model takes this eigenvector and flue gas parameters as input and outputs the predicted desulfurization efficiency and unit energy consumption. The core of the model is the kernel function, which takes the form: The first term is a radial basis function, capturing local similarity, and the second term is a periodic function, simulating periodic operating condition changes. Hyperparameters , , , The model iteratively optimizes within a predefined search space using a Bayesian optimization algorithm, aiming to minimize the negative log-likelihood on the validation set. The optimization process employs Latin hypercube sampling initialization, updating the sampling points in each iteration based on the current optimal solution. The convergence condition is that the improvement in the objective function over 10 consecutive iterations is less than 1e-6. This model can learn complex nonlinear relationships and provides estimations of prediction uncertainty, supporting robust decision-making under uncertain conditions.

[0024] In the above-mentioned method for optimizing and early warning of seawater desulfurization energy efficiency, step (3) involves multi-objective collaborative optimization control: with desulfurization efficiency not less than 95% and unit energy consumption minimized as constraints, the seawater circulation pump frequency, aeration fan air volume, and alkalinity compensator dosing acceleration rate are dynamically adjusted using a model predictive control strategy to achieve optimal energy efficiency. Specifically, the prediction time domain of the model predictive control strategy is 600 seconds, the control time domain is 300 seconds, the optimization objective function is the weighted sum of unit desulfurization energy consumption and alkalinity compensator consumption, and the weight coefficient is dynamically adjusted according to the measured seawater alkalinity. When the seawater alkalinity is lower than 2.0 mmol / L, the weight of alkalinity compensator consumption is increased to 0.7. The model predictive control framework includes three core components: state prediction, optimization solution, and feedback correction. The state prediction module extrapolates the system behavior in the next 600 seconds based on the current system state and control input using an established dynamic model (such as a simplified first-order inertial model). The predicted variables include pH value, dissolved oxygen concentration, pressure drop, etc. The optimization solution module constructs a finite-time optimization problem based on the predicted trajectory. The objective function is defined as follows: ,in For the first Energy consumption per unit of desulfurization at a predicted time point. This refers to the consumption of alkalinity compensator. and These are their respective weighting coefficients. The initial weights are set to... =0.6, =0.4. When the seawater alkalinity is detected to be below 2.0 mmol / L in real time, the system automatically triggers the weight adjustment logic, adjusting the weight to 0.4. The alkalinity level was increased to 0.7 to prioritize alkalinity replenishment and prevent a decrease in desulfurization efficiency due to insufficient alkalinity. The constraints of the optimization problem included: desulfurization efficiency not lower than 95% at all predicted times; seawater circulation pump frequency within the range of 30 to 60 Hz; aeration fan airflow within the range of 100 to 500 cubic meters per minute; and alkalinity compensator dosing rate within the range of 0 to 10 kilograms per hour. This optimization problem was solved using a sequential quadratic programming algorithm to obtain the optimal control command sequence for the next 300 seconds. At the end of each control cycle, the feedback correction stage compared the actual observed system state with the predicted state, calculated the state error, and corrected the prediction model parameters for the next cycle accordingly, improving prediction accuracy. Control commands were issued to the field actuators through the industrial control system. The seawater circulation pump used variable frequency speed regulation, the aeration fan used variable frequency drive, and the alkalinity compensator dosing system used metering pumps for precise control. This control strategy achieved dual optimization of energy and chemical consumption while ensuring environmental compliance, exhibiting superior dynamic response capabilities, especially under conditions of frequent load fluctuations.

[0025] In the above-mentioned method for optimizing and early warning of seawater desulfurization efficiency, step (4) involves identifying the implicit performance degradation trend: continuously monitoring the pressure drop of the packing layer, the uniformity of the flow velocity at the outlet of the aerator head, and the fluctuation amplitude of dissolved oxygen in the oxidation reaction zone; using a wavelet packet decomposition and principal component analysis fusion algorithm to extract performance degradation sensitive features and construct a degradation index time series. Specifically, the wavelet packet decomposition uses the db4 wavelet basis, with 5 decomposition layers. The detail coefficients of the 3rd to 5th layers are selected as the feature subspace. The principal component analysis retains the principal components with a cumulative contribution rate greater than 90%. The degradation index is defined as the weighted Euclidean norm of the scores of each principal component, and the weights are determined by logistic regression inversion from historical fault data. The pressure drop of the packing layer is measured in real time by pressure sensors installed at both ends of the tower. Its value increases monotonically with the increase of scaling or blockage in the packing layer. The uniformity of the flow velocity at the outlet of the aerator head is measured by multiple ultrasonic flowmeters distributed at different radial positions of the tower. The root mean square deviation of the flow velocity at each point from the average flow velocity of the entire tower is calculated. The larger this value, the lower the gas-liquid contact efficiency. The fluctuation range of dissolved oxygen in the oxidation reaction zone is characterized by calculating the time series standard deviation of dissolved oxygen concentration in that zone. Drastic fluctuations may indicate uneven aeration or chemical reaction imbalance. The three original monitoring signals were processed by wavelet packet decomposition. The db4 wavelet gene, with its good time-frequency localization characteristics, was selected, and a decomposition layer of 5 was used to decompose the signal into 32 sub-signals of different frequency bands. Since performance degradation typically exhibits low-frequency trend changes, the detail coefficients of layers 3 to 5 were selected, corresponding to the mid-to-low frequency band and containing slowly changing characteristic information. These three layers of detail coefficients were concatenated into a high-dimensional feature vector. Subsequently, principal component analysis was performed on this feature vector to calculate the eigenvalues ​​and eigenvectors of the covariance matrix. The eigenvalues ​​were sorted, and the top principal components with a cumulative contribution rate greater than 90% were selected to constitute the dimensionality-reduced feature space. The degradation index is defined as... ,in For the first The score of each principal component, These are the corresponding weights. The determination of the weights is based on a historical fault dataset, which contains feature vectors under two states: normal operation and fault occurrence (such as severe scaling of the packing layer or complete blockage of the aerator heads). A logistic regression model is trained, using principal component scores as input and fault labels as output, to deduce the contribution of each principal component to the occurrence of the fault; this contribution is defined as the weights. This degradation index comprehensively reflects the combined effects of various performance degradations, and has strong robustness and early warning capabilities.

[0026] In the above-mentioned method for optimizing and warning the energy efficiency of seawater desulfurization, step (5) triggers graded warning and decision support: when the degradation index exceeds the preset threshold for three consecutive sampling cycles, a first-level warning is initiated and maintenance suggestions are pushed; if the degradation index growth rate exceeds 0.5 per hour, it is upgraded to a second-level warning and the operating parameters are automatically adjusted to suppress performance degradation; if the degradation index reaches the critical value of 1.2, a third-level emergency warning is triggered and a shutdown for maintenance is recommended. Specifically, the first-level warning threshold is set to 0.8, the second-level warning growth rate threshold corresponds to a degradation index slope greater than 0.5 per hour, and the third-level critical value of 1.2 is calibrated based on the correlation data between the maximum allowable pressure drop of the packing layer and the 90% blockage rate of the aerator head provided by the equipment manufacturer. The warning information is pushed to the human-machine interface in the central control room via industrial Ethernet and is simultaneously recorded in the operation log database. The warning logic is based on the time series of the degradation index. The system continuously monitors the degradation index D(t). When it detects that D(t) > 0.8 and this condition is met for three consecutive sampling cycles (i.e., 90 seconds), it is determined to be a first-level warning. At this point, the system generates an early warning message, including the warning level, occurrence time, current degradation index value, possible causes of failure (e.g., "suspected initial scaling of the packing layer"), and recommended maintenance measures (e.g., "It is recommended to check the cleanliness of the packing layer"). This information is sent to the human-machine interface in the central control room via an industrial Ethernet protocol (e.g., Profinet or EtherCAT), prompting operators with a red icon and pop-up window. Simultaneously, the event is written to the operation log database, with entries including timestamp, device ID, warning type, triggering conditions, and processing status, supporting post-event traceability and analysis. If, during a Level 1 warning, the system calculates the growth rate of the degradation index... Every hour, the alert level immediately escalates to Level 2. At this point, the system not only pushes higher-level warning information but also automatically initiates suppression procedures, such as reducing the aeration fan airflow to decrease bubble impact or increasing seawater circulation to flush the packing surface, aiming to slow down the rate of performance degradation. If the degradation index D(t) reaches 1.2, a value calibrated by experimental data provided by the equipment manufacturer, corresponding to a critical state where the packing layer pressure drop is close to the maximum allowable value and the aerator head blockage rate has reached 90%, the system triggers a Level 3 emergency warning. At this time, in addition to the highest level visual and audible alarms, the system will also send an emergency notification to the dispatch center and explicitly recommend "immediate shutdown for maintenance" on the human-machine interface to prevent equipment damage or safety accidents. All warning information is fully recorded, forming a complete operation and maintenance event chain.

[0027] In addition, the system includes: establishing an energy efficiency benchmark library containing no fewer than 5,000 sets of historical operating condition data. This benchmark library is categorized and stored according to season, seawater temperature range, and flue gas load level, and is used to dynamically calibrate the baseline of the energy efficiency assessment model. The calibration cycle is every 24 hours, and the calibration error tolerance is ±3%. This energy efficiency benchmark library is a structured database, and its data sources include historical operating records, laboratory test data, and simulation results. The data is categorized according to three dimensions: season (spring, summer, autumn, winter), seawater temperature range (e.g., <15°C, 15-25°C, >25°C), and flue gas load level (e.g., <50%, 50%-80%, >80%). Each category stores a set of energy efficiency data under typical operating conditions, including key indicators such as desulfurization efficiency, unit energy consumption, and alkalinity consumption. At 0:00 AM daily, the system automatically starts the calibration program. The program first determines the season based on the current date and time, determines the temperature range based on the real-time seawater temperature, and determines the load level based on the current flue gas flow rate, thereby locating the corresponding benchmark dataset. Then, the average energy efficiency index for the current period (the past 24 hours) is compared with the average value of the benchmark dataset to calculate the relative deviation. If the absolute value of the deviation is greater than 3%, the model baseline is considered to need adjustment, and the system will automatically perform offset correction on the output of the Gaussian process regression model to align its predicted values ​​with the new benchmark values. This mechanism ensures that the evaluation model can adapt to seasonal changes (such as changes in reaction rates due to rising sea temperatures in summer) and long-term operating condition drift, maintaining the accuracy and fairness of the evaluation results.

[0028] In addition, the system includes: an online flue gas composition analyzer installed in the inlet flue of the desulfurization tower to monitor sulfur dioxide, oxygen, and dust concentrations in real time. When the dust concentration exceeds 50 mg / m³, a pre-cleaning spray program is automatically initiated. The spray intensity is linearly adjusted according to the dust concentration, with an adjustment coefficient of 0.02 L / s / mg / m³. This online analyzer uses ultraviolet differential absorption spectroscopy to measure sulfur dioxide concentration, with a measurement range of 0 to 10,000 mg / m³ and an accuracy of ±5%; it uses an electrochemical sensor to measure oxygen concentration, with a measurement range of 0 to 25% and an accuracy of ±0.5%; and it uses laser scattering to measure dust concentration, with a measurement range of 0 to 1,000 mg / m³ and an accuracy of ±10%. When the dust concentration sensor detects a value exceeding 50 mg / m³, the system triggers the pre-cleaning logic. The pre-cleaning program is executed by an independent spray system located upstream of the desulfurization tower inlet flue. Spray intensity... From the formula The calculation shows that, among which This represents the current dust concentration, expressed in milligrams per cubic meter. When When =50, =50 liters per second, which is the minimum starting intensity; when When =100, =60 liters per second. This spray mist can effectively capture dust particles in the flue gas, preventing them from entering the desulfurization tower and causing blockage or wear of the packing layer, thus extending the service life of the equipment.

[0029] In addition, it includes: implementing independent zone control of the aeration system, dividing the aeration zone radially into four concentric annular areas, each equipped with an independent air valve. The airflow distribution ratio of each area is dynamically adjusted according to the dissolved oxygen distribution non-uniformity index, which is defined as the root mean square of the deviation of the dissolved oxygen concentration in each area from the average value of the entire tower. The aeration zone is divided into four areas: inner ring, inner middle ring, outer middle ring, and outer ring. Each area is equipped with an electrically adjustable air valve, and the valve opening is controlled by the DCS system. The system continuously calculates the dissolved oxygen distribution non-uniformity index. Its calculation formula is ,in For the first Dissolved oxygen concentration in each region This represents the arithmetic mean of the dissolved oxygen concentration in the four regions of the entire tower. When A high value indicates that dissolved oxygen is too high in some areas and too low in others, indicating uneven gas-liquid mixing. The system... The value determines the dynamic adjustment of the damper opening in each zone. For example, if the inner ring zone... If the value is significantly higher than the average, the opening of the damper in that area should be appropriately reduced; if it is in the outer ring area... If the dissolved oxygen concentration is significantly lower than the average, the opening of the damper in that area should be increased. This closed-loop regulation allows for a more balanced distribution of dissolved oxygen concentration across the entire tower, improving oxidation reaction efficiency and reducing energy waste.

[0030] In addition, the method includes: installing an online corrosion rate monitoring probe in the seawater circulation pipeline, using electrochemical impedance spectroscopy to assess the corrosion status of metal components in real time. When the corrosion rate exceeds 0.1 mm / year, the amount of corrosion inhibitor added is automatically increased, with the increase proportional to the extent of the corrosion rate exceeding the limit, with a proportionality coefficient of 0.5 kg / mm / year. This monitoring probe is an embedded electrochemical sensor installed in key parts of the seawater circulation pipeline, such as near elbows and valves. The sensor measures the impedance spectrum of the metal surface by applying a small-amplitude AC voltage, then calculates the corrosion current density, and finally converts it into the corrosion rate in millimeters per year. This method does not require damage to the pipeline and can achieve long-term continuous monitoring. When the system detects a corrosion rate... When the corrosion inhibitor thickness exceeds 0.1 mm per year, the corrosion inhibitor dosing control logic is triggered. Dosing increment. From the formula Calculations are made in kilograms per hour. For example, when =0.3 mm per year, Kilograms per hour. This increment is added to the base dosage and is automatically executed by the metering pump. This mechanism enables proactive intervention in corrosion risks, effectively reducing the risk of equipment leakage and failure.

[0031] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A seawater desulfurization energy efficiency optimization and early warning method, characterized in that: The method comprises the following specific steps: Step (1) Construct a multi-physics field coupling dynamic monitoring system: A distributed sensor array is arranged along the gas-liquid flow path in the desulfurization tower to collect pH gradient, dissolved oxygen concentration, flow rate distribution, temperature and conductivity data in real time, forming a multi-dimensional state variable time sequence covering the entire desulfurization reaction process; Step (2) Establish a transient condition adaptive energy efficiency evaluation model: Based on the multi-dimensional state variable time sequence, a dynamic feature vector is extracted using a sliding time window mechanism, combined with the sulfur dioxide concentration and flow rate data at the flue gas inlet, and a real-time mapping relationship between desulfurization efficiency and unit energy consumption is established through a nonlinear regression algorithm, thereby establishing a transient condition adaptive energy efficiency evaluation model; Step (3) Perform multi-objective collaborative optimization control: With the constraint conditions of desulfurization efficiency not less than 95% and minimization of unit energy consumption, the model predictive control strategy is used to dynamically adjust the frequency of the seawater circulating pump, the air volume of the aeration blower and the dosing rate of the alkalinity compensating agent; Step (4) Implement implicit performance degradation trend identification: The pressure drop of the filler layer, the uniformity of the outlet flow rate of the aeration head and the fluctuation amplitude of the dissolved oxygen in the oxidation reaction zone are continuously monitored, a performance degradation sensitive feature is extracted using a wavelet packet decomposition and principal component analysis fusion algorithm, and a degradation index time sequence is constructed; Step (5) Trigger hierarchical early warning and decision support: When the degradation index exceeds the preset threshold for three consecutive sampling periods, a first-level warning is started and maintenance suggestions are pushed; if the growth rate of the degradation index exceeds the predetermined value, the warning is upgraded to a second-level warning and the operating parameters are automatically adjusted to suppress performance degradation; if the degradation index reaches a critical value, a third-level emergency warning is triggered and shutdown for maintenance is recommended.

2. The seawater desulfurization energy efficiency optimization and early warning method according to claim 1, characterized in that: The distributed sensor array comprises at least one monitoring section per 2 meters along the height direction of the desulfurization tower, each section is uniformly arranged with not less than 8 composite sensing units, each composite sensing unit integrates a pH electrode, a dissolved oxygen probe, an ultrasonic flowmeter, a platinum resistance temperature sensor and a conductivity electrode, the sampling frequency is 1 Hz, and the data synchronization accuracy is better than 10 ms.

3. The seawater desulfurization energy efficiency optimization and early warning method according to claim 1, characterized in that: The sliding time window length is set to 300 seconds, the window sliding step is 30 seconds, the dynamic feature vector includes the pH gradient change rate, the dissolved oxygen concentration standard deviation, the flow rate distribution skewness coefficient and the temperature-conductivity coupling factor, the nonlinear regression algorithm uses a Gaussian process regression model, the kernel function is a linear combination of a radial basis function and a periodic function, and the hyperparameters are automatically adjusted through Bayesian optimization.

4. The seawater desulfurization energy efficiency optimization and early warning method according to claim 1, characterized in that: The prediction time domain of the model predictive control strategy is 600 seconds, the control time domain is 300 seconds, the optimization objective function is the weighted sum of the unit desulfurization energy consumption and the alkalinity compensating agent consumption, and the weight coefficient is dynamically adjusted according to the measured seawater alkalinity value; when the seawater alkalinity is less than 2.0 mmol / L, the alkalinity compensating agent consumption weight is increased to 0.

7.

5. The seawater desulfurization energy efficiency optimization and early warning method according to claim 1, characterized in that: The wavelet packet decomposition uses a db4 wavelet basis, the decomposition level is 5, the 3rd to 5th layer detail coefficients are selected as the feature subspace, the principal component analysis retains the principal components with a cumulative contribution rate greater than 90%, the degradation index is defined as the weighted Euclidean norm of the scores of the principal components, and the weights are determined by historical fault data through logistic regression inversion.

6. The seawater desulfurization energy efficiency optimization and early warning method according to claim 1, characterized in that: The first early warning threshold is set to 0.8, the second early warning growth rate threshold corresponds to a degradation index slope greater than 0.5 per hour, and the third critical value 1.2 is calibrated according to the correlation data provided by the equipment manufacturer between the maximum allowed pressure drop of the filler layer and the plugging rate of the aeration head of 90%. The early warning information is pushed to the central control room human-machine interface through industrial Ethernet and is recorded synchronously to the operation log database.

7. The seawater desulfurization energy efficiency optimization and early warning method according to claim 1, characterized in that: It also includes establishing an energy efficiency benchmark library containing no less than 5000 sets of historical operating condition data, which is stored by season, seawater temperature interval, and flue gas load grade, and is used to dynamically calibrate the benchmark line of the energy efficiency evaluation model. The calibration period is every 24 hours, and the calibration error tolerance is plus or minus 3%.

8. The seawater desulfurization energy efficiency optimization and early warning method according to claim 1, characterized in that: It also includes setting a flue gas component on-line analyzer at the inlet flue of the desulfurization tower to monitor the concentrations of sulfur dioxide, oxygen, and dust in real time. When the dust concentration exceeds 50 mg / m3, the pre-spraying pre-cleaning program is automatically started, and the spraying intensity is linearly adjusted according to the dust concentration with an adjustment coefficient of 0.02 L / s / mg / m3.

9. The seawater desulfurization energy efficiency optimization and early warning method according to claim 1, characterized in that: It also includes implementing zoned independent control of the aeration system, dividing the aeration zone into 4 concentric annular regions along the tower radius, configuring independent air valves for each region, dynamically adjusting the air volume distribution ratio of each region according to the dissolved oxygen distribution unevenness index, and defining the unevenness index as the root mean square deviation of the dissolved oxygen concentration in each region from the average value of the entire tower.

10. The seawater desulfurization energy efficiency optimization and early warning method according to claim 1, characterized in that: It also includes setting a corrosion rate on-line monitoring probe in the seawater circulation pipeline, using electrochemical impedance spectroscopy to evaluate the corrosion state of metal components in real time, and automatically increasing the corrosion inhibitor dosage when the corrosion rate exceeds 0.1 mm / a, with the dosage increment being proportional to the over-limit amplitude of the corrosion rate, and the proportional coefficient being 0.5 kg / mm / a.

Images