Dynamic calibration method for oxygen content in boiler air and flue gas system

Abstract

The invention belongs to the technical field of oxygen content detection and dynamic calibration, and particularly relates to a dynamic calibration method for the oxygen content in a boiler air and flue gas system. The method comprises the steps of multi-working-condition data acquisition and standardization processing, failure feature extraction and feature library establishment, reference oxygen calculation model construction, data driving model construction, anomaly detection and calibration, parameter self-updating and the like, and a calibration effect real-time evaluation link is newly added. The core of the method is that a mechanism + data dual-drive mode is adopted, a reference model is constructed on the basis of a zirconia sensor Nernst equation, an improved bidirectional LSTM network is fused to construct a data drive model, and an adaptive threshold algorithm and a multi-stage calibration strategy are combined to realize accurate recognition and dynamic calibration of abnormal signals. The method is adaptive to a complex flue gas environment and a variable load operation scene, the oxygen content measurement precision is remarkably improved, low-nitrogen combustion and environmental protection compliance are guaranteed, coal consumption and operation cost are reduced, and reliable data support is provided for intelligent power plant digital transformation.

Description

Technical Field

[0001] This invention belongs to the field of oxygen detection and dynamic calibration technology, and particularly relates to a dynamic calibration method for oxygen in boiler flue gas systems. Background Technology

[0002] The accuracy of oxygen measurement in boiler flue gas systems is crucial for ensuring combustion efficiency and controlling pollutant emissions, directly impacting the boiler's economic efficiency, environmental friendliness, and safety. However, existing boiler oxygen calibration technologies have significant limitations: traditional offline and fixed-cycle calibration methods lack real-time capability and cannot adapt to complex operating conditions such as variable load operation, coal type switching, and fluctuating flue gas parameters, leading to sensor drift and inaccuracies, making dynamic correction of measurement deviations difficult. Furthermore, single-mechanism models are limited by their adaptability to operating conditions, while purely data-driven models suffer from insufficient interpretability, neither of which can achieve accurate compensation for dynamic deviations.

[0003] Against the backdrop of the deepening implementation of the "dual-carbon" strategy and increasingly stringent environmental regulations, the "Emission Standard for Air Pollutants from Boilers" explicitly requires coal-fired boilers to undergo ultra-low emission retrofitting. Emissions ≤50mg / m³ 3 Precise oxygen control is a crucial prerequisite for achieving low-NOx combustion and meeting emission limits. Inaccurate oxygen measurement not only leads to decreased efficiency of the denitrification system and excessive ammonia escape, resulting in severe environmental penalties for enterprises, but also causes incomplete combustion, increasing energy consumption and pollutant emissions. Furthermore, it is estimated that a 1% improvement in oxygen measurement accuracy can increase boiler efficiency by 0.3%-0.5%. For a 600MW coal-fired unit, this translates to annual coal savings of thousands of tons and reduced fuel costs by millions of yuan, highlighting the urgent need for energy conservation and emission reduction.

[0004] With the continuous advancement of "smart power plant" and "industrial internet" construction, boiler systems urgently need to transform towards digitalization and intelligence. Reliable underlying data is the foundation for the effective operation of combustion optimization algorithms. The lag and limitations of existing calibration technologies and models can no longer meet the multiple demands of boiler operation under varying conditions, environmental compliance, energy conservation and consumption reduction, and intelligent upgrading. Developing dynamic calibration methods adapted to complex operating conditions is of great practical urgency. Summary of the Invention

[0005] This invention aims to solve the problems of poor real-time performance, insufficient adaptability to operating conditions, and model limitations of traditional boiler oxygen calibration technology. It achieves dynamic and accurate oxygen calibration through a dual-drive mode of "mechanism + data", adapts to complex operating scenarios, ensures environmental compliance and energy conservation, and provides reliable data support for the digital transformation of smart power plants.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] The dynamic calibration method for oxygen content in a boiler flue gas system includes the following steps:

[0008] Multi-condition data acquisition and standardization: Collect zirconium battery output signals, flue gas parameters, and boiler operation data under different loads, coal types, and operating environments; preprocess the collected data to obtain a standardized multi-condition dataset;

[0009] Failure feature extraction and feature library establishment: Based on the standardized multi-condition dataset, static statistical features and dynamic failure features are extracted, and an oxygen sensor failure feature library is constructed that associates operating condition type, failure feature and drift mode.

[0010] Construction of the baseline oxygen content calculation model: Based on the Nernst equation of the zirconia sensor, the flue gas temperature, pressure and zirconium battery operating temperature parameters in the standardized multi-condition dataset are substituted into the temperature correction coefficient and pressure correction coefficient to establish the baseline oxygen content calculation model and output the baseline oxygen content value under static conditions.

[0011] Data-driven model construction: A data-driven model is constructed using a time-series learning algorithm. The standardized multi-condition dataset, failure feature library, and baseline oxygen value are used as model inputs. The model is trained by the time-series features of load fluctuation and flue gas temperature change to capture nonlinear effects and output real-time corrected oxygen values.

[0012] Anomaly detection and calibration: Based on the real-time corrected oxygen value, an adaptive threshold algorithm is used to identify abnormal signals from the oxygen sensor. The drift pattern is matched in combination with the failure feature library, and the corresponding type of calibration signal is triggered according to the boiler operating conditions to perform automatic calibration.

[0013] Parameter self-updating: Automatically adjusts the calibration cycle and correction factor based on real-time operating data.

[0014] Multi-condition data acquisition covers different loads, coal types, and operating environments to avoid insufficient model generalization ability caused by single-condition data. Standardization processing eliminates differences in data dimensions and noise interference, providing high-quality input for subsequent models. The construction of the failure feature library is based on the comprehensive extraction of static and dynamic features. The principle is that static features reflect the overall distribution characteristics of sensor output, while dynamic features capture the changing trend over time. The two are correlated with the operating condition type and drift mode, providing a clear basis for anomaly identification. The benchmark model uses the Nernst equation as the core mechanism, which is consistent with the working nature of zirconia sensors and integrates temperature and pressure correction coefficients to compensate for systematic errors caused by operating condition fluctuations. The data-driven model focuses on time-series feature learning, aiming to capture nonlinear relationships that are difficult for mechanistic models to cover. Anomaly detection adopts an adaptive threshold algorithm, which can dynamically adjust the judgment criteria according to the operating conditions to avoid misjudgment problems caused by fixed thresholds. The parameter self-updating mechanism ensures that the calibration cycle and correction coefficients are optimized in real time with changes in operating conditions, fundamentally solving the lag problem of traditional fixed-cycle calibration.

[0015] It also includes a real-time evaluation step of the calibration effect, which calculates the relative deviation ratio of oxygen values ​​before and after calibration: ,in To calibrate the oxygen level, For the calibrated oxygen value; when If the calibration is abnormal, a secondary calibration process will be triggered.

[0016] The relative deviation ratio can intuitively reflect the correction range of the calibration to the measured value. When the ratio exceeds 5%, it means that the calibration has not achieved the expected effect. This may be due to reasons such as drift mode matching error or improper calibration parameter setting. At this time, triggering the secondary calibration process can correct the calibration deviation in time.

[0017] The static statistical characteristics include mean μ and variance σ. 2 Skewness S and kurtosis K are calculated using the following formulas:

[0018]

[0019]

[0020]

[0021]

[0022] in, For the first The filtered output signal values ​​of the zirconium battery at each time point Calculate the window length for static features;

[0023] The dynamic failure characteristics include drift rate. Fluctuation frequency and autocorrelation coefficient The calculation formula is:

[0024]

[0025]

[0026]

[0027] in, The length of the drift statistics time window. Indicates Fourier transform, It is the static mean. The autocorrelation calculation window length is used; feature extraction employs an adaptive sliding window control algorithm, with the window length... Dynamically adjusted to , This is the initial window length. The attenuation coefficient is... For boiler load change rate, , For boiler load rate, For time intervals.

[0028] In static statistical features, the mean reflects the central trend of the sensor output, the variance reflects the degree of data dispersion, the skewness describes the asymmetry of the distribution, and the kurtosis characterizes the steepness of the distribution. The combination of these four features can capture the overall distribution characteristics of the sensor output signal and identify whether there is a systematic bias. In dynamic failure features, the drift rate quantifies the degree of shift in the sensor output over time, the fluctuation frequency converts the time-domain signal to the frequency domain through Fourier transform to identify the oscillation characteristics of the signal, and the autocorrelation coefficient reflects the correlation between signals at adjacent time points. The three features work together to capture the dynamic failure trend of the sensor. Simultaneously, considering that boiler load changes affect the stability of the sensor signal, an adaptive sliding window algorithm is designed. Its core principle is: the higher the load change rate, the more drastic the signal fluctuation, requiring an exponential function to shorten the window length to capture instantaneous features; when the load is stable, the window length is increased to ensure the accuracy of feature calculation and avoid the feature extraction distortion problem that occurs under complex operating conditions with a fixed window, laying the foundation for building an accurate failure feature library.

[0029] The baseline oxygen content calculation model is constructed by integrating the Nernst equation with real-time operating parameters, and its core formula is:

[0030]

[0031] in, Let be the ideal gas constant. It is Faraday's constant. The temperature correction factor is calculated as follows:

[0032]

[0033] The temperature linearity coefficient is... It is the quadratic coefficient of temperature. The operating temperature (K) of the zirconium battery. For reference temperature; The pressure correction factor is calculated as follows:

[0034]

[0035] This is the pressure correction factor. For sensor to measure pressure; model input parameters Real-time data acquisition via thermocouple array. Measured by a pressure sensor, and Through dynamic correction using an adaptive PID controller, the controller output is:

[0036]

[0037] For error signals, , , For PID parameters, , , .

[0038] The Nernst equation forms the theoretical basis for oxygen measurement using zirconia sensors. It essentially reflects the thermodynamic relationship between oxygen partial pressure and sensor output potential, thus ensuring the physical interpretability of the model. However, in actual boiler operation, fluctuations in the zirconia cell operating temperature and flue gas pressure can cause the ideal assumptions of the Nernst equation to fail, resulting in systematic errors. Therefore, a secondary temperature correction coefficient is introduced to compensate for the nonlinear effects of temperature changes, and a pressure correction coefficient is introduced to correct pressure deviations through a linear compensation term. To further ensure the accuracy of the model input parameters, the zirconia cell operating temperature is acquired in real-time by a thermocouple array, and the flue gas pressure is acquired by a pressure sensor. Both are dynamically calibrated using an adaptive PID controller to ensure accurate and reliable temperature and pressure data input to the model. The final output is a static baseline oxygen value that reflects the actual operating conditions, providing a reliable reference for subsequent dynamic corrections.

[0039] The data-driven model employs an improved bidirectional LSTM network structure, comprising two LSTM layers. The input layer includes a standardized multi-condition dataset, a failure feature library, and baseline oxygen levels. The output layer generates real-time corrected oxygen levels. Model training utilizes the Adam optimization algorithm with a learning rate of [missing information]. The loss function is the weighted mean squared error:

[0040]

[0041] in, These are the sample weight coefficients. The complexity coefficient is the operating condition coefficient. , , For feature weights, For boiler load rate, For maximum load factor, For flue gas temperature, For the maximum flue gas temperature, The attenuation coefficient;

[0042] The model aligns time-series data using a dynamic time warping algorithm. The calculation formula is as follows:

[0043]

[0044] Capture the timing characteristics of load fluctuations.

[0045] The core reason for adopting the improved bidirectional LSTM network is that traditional LSTM can only capture the unidirectional dependency relationship of time series data, while the influence of parameters such as boiler load fluctuation and flue gas temperature change has a bidirectional correlation. Bidirectional LSTM can simultaneously mine time series features from both the forward and backward directions, and the two-layer LSTM unit enhances the model's ability to fit complex nonlinear relationships. The input layer integrates standardized data, failure feature library and benchmark oxygen value to achieve the fusion of "mechanism basis + data supplementation", which not only ensures the interpretability of the model, but also improves the prediction accuracy. The model training employs the Adam optimization algorithm, whose adaptive learning rate balances convergence speed and stability. The learning rate η=0.001 has been optimized through multiple experiments to avoid oscillations caused by an excessively high learning rate or slow convergence caused by an excessively low learning rate. The weighted mean square error loss function assigns weights to different samples based on the complexity coefficient of the operating conditions, with larger weights for more complex conditions, ensuring the calibration accuracy of the model under critical and complex operating conditions. The dynamic time warping algorithm solves the problem of inconsistent time series data lengths under different operating conditions. It achieves time series alignment by calculating the minimum distance between data points, accurately capturing the dynamic characteristics of load fluctuations, and enabling the model to adapt to the dynamic scenario of boiler variable load operation.

[0046] The adaptive threshold algorithm employs a dynamic adjustment mechanism, and the threshold calculation formula is as follows:

[0047]

[0048] in, To correct the exponentially weighted moving average of oxygen levels in real time, , For smoothing coefficients, This is the current baseline oxygen level. To correct the standard deviation of oxygen values ​​in real time; For adaptive coefficients, , As the benchmark coefficient, To adjust the coefficient, Minimum load factor; For drift rate, The maximum allowable drift rate; this algorithm implements anomaly detection through an adaptive threshold controller, and the controller output... , , The threshold is dynamically expanded as an adaptation factor during sudden load changes.

[0049] The threshold is jointly determined by the exponentially weighted moving average, standard deviation, adaptive coefficient, and drift rate of the real-time corrected oxygen value. The exponentially weighted moving average balances the weights of historical and current data through a smoothing coefficient, reflecting the dynamic trend of the oxygen value. The standard deviation reflects the dispersion of the data, and the drift rate reflects the degree of sensor failure. The combination of these three factors allows the threshold to be dynamically adjusted according to changes in sensor status and operating conditions. The adaptive coefficient changes with the load rate, increasing as the load rate increases, appropriately expanding the threshold to avoid misjudgments caused by data fluctuations during sudden load changes. The adaptive coefficient further adjusts the threshold according to the drift rate, with a larger threshold expansion range as the drift rate increases, ensuring sensitive identification of severe drift. The maximum allowable drift rate and minimum load rate are both set based on the actual operating conditions of the boiler, ensuring that the threshold adjustment meets actual engineering needs. This algorithm outputs the final judgment threshold through an adaptive threshold controller, which not only ensures the sensitivity of abnormal signal identification but also effectively suppresses false triggers caused by operating condition fluctuations, improving the accuracy and reliability of anomaly detection.

[0050] In the parameter self-update step, the calibration period The dynamic adjustment formula is:

[0051]

[0052] in, For the current calibration cycle, For the initial calibration period, For drift rate, Given the current boiler load rate, For maximum load factor, This is the attenuation coefficient, with a value range of 0.05-0.2.

[0053] The calibration cycle is negatively correlated with the drift rate and boiler load rate, and is dynamically adjusted through an exponential function. When the sensor drift rate increases, it indicates that the sensor is becoming inaccurate faster, requiring a shorter calibration cycle to correct the deviation in a timely manner. When the boiler load rate increases, the complexity of the operating conditions increases, and the risk of sensor drift increases, also requiring a shorter calibration cycle. The initial calibration cycle is the base value, and the attenuation coefficient can be flexibly adjusted according to the sensor characteristics and operating conditions of different units, ensuring the flexibility and adaptability of the cycle adjustment. This design breaks away from the traditional one-size-fits-all calibration model, enabling the calibration cycle to accurately match the actual failure state of the sensor and the operating conditions of the boiler. While ensuring calibration accuracy, it reduces unnecessary calibration operations, lowers system operating losses, and improves the economy and practicality of the calibration method.

[0054] The calibration signal triggering condition is as follows:

[0055]

[0056] in, The sensor measures real-time values. As the baseline oxygen level, For adaptive threshold, For drift rate, The drift rate threshold, Given the current boiler load rate, Minimum load factor For maximum load factor, This is the load factor triggering coefficient, with a value range of 0.7-0.9.

[0057] Traditional calibration triggering often relies on a single deviation condition, making it susceptible to false triggers due to operating condition fluctuations or transient interference. This design employs a triple-condition approach: the first condition ensures the relative deviation between the sensor's measured value and the reference value exceeds a dynamic threshold, serving as the core basis for calibration; the second condition identifies instances of sensor drift failure, eliminating deviations caused by transient operating condition fluctuations; and the third condition ensures calibration is triggered under high-load, stable conditions, avoiding invalid calibration under low-load, unstable conditions. The calibration signal is triggered only when all three conditions are met simultaneously. Its core logic balances the necessity of calibration with the applicability of the operating conditions, ensuring timely calibration when the sensor is inaccurate while avoiding false triggers due to operating condition fluctuations or interference. This improves the relevance and effectiveness of calibration operations and reduces system operating costs.

[0058] After the calibration signal is triggered, a multi-level calibration strategy is executed, including two modes: fast response calibration and high precision calibration. Fast response calibration is used for scenarios with frequent load fluctuations, and instantaneous correction is achieved based on historical data interpolation through a sliding window. High precision calibration is used for steady-state operation scenarios, and iterative least squares method is used to optimize sensor output.

[0059] Boiler operation typically involves two scenarios: frequent load fluctuations and steady-state operation. These scenarios present different calibration requirements. During frequent load fluctuations, operating conditions change rapidly, and sensor signals fluctuate dynamically. Complex, high-precision calibration can lead to calibration lag, failing to promptly match changes in operating conditions. Therefore, a rapid response calibration mode, based on sliding window historical data interpolation, quickly generates correction values ​​through linear or nonlinear interpolation of adjacent data points, achieving instantaneous correction and meeting the real-time requirements of dynamic scenarios. During steady-state operation, operating conditions are stable, and sensor signal fluctuations are small. In this case, higher calibration accuracy is required. The iterative least squares method optimizes the sum of squared errors between the sensor output and the actual oxygen value through multiple iterations, gradually approaching the optimal solution and improving calibration accuracy. This multi-level calibration strategy automatically switches modes based on scenario recognition, solving the problem of insufficient real-time calibration in dynamic scenarios while meeting the high-precision calibration requirements in steady-state scenarios. It adapts to diverse boiler operating states, further enhancing the practicality and adaptability of the calibration method.

[0060] The failure feature library includes an online update mechanism, which updates the feature weights based on newly collected data using an incremental learning algorithm. The update formula is as follows:

[0061]

[0062] in, These are the original feature weights. For the updated weights, The change in features calculated for the new data. It is a forgetting factor.

[0063] During long-term boiler operation, factors such as coal type switching, equipment aging, and adjustments to operating parameters can cause changes in the failure characteristics of sensors. Traditional fixed feature libraries cannot reflect new failure modes, leading to decreased drift mode matching accuracy. This design employs an incremental learning algorithm, calculating feature changes based on newly acquired data. A forgetting factor balances the contributions of the original feature weights with the new feature changes—ensuring that validated historical feature weights are retained while assigning appropriate weights to new feature changes, allowing the feature library to promptly incorporate new failure feature information. This online update mechanism enables the failure feature library to continuously adapt to changes in boiler operating conditions and sensor characteristics, ensuring long-term stability of drift mode matching accuracy. It avoids calibration accuracy degradation due to an outdated feature library, extending the lifespan and applicability of the entire calibration method.

[0064] The present invention has the following beneficial effects:

[0065] Improving Measurement Accuracy and Adaptability to Operating Conditions: This invention adopts a dual-drive model of "mechanism + data," integrating the Nernst equation of the zirconia sensor with an improved bidirectional LSTM network. By combining real-time operating data and sensor failure characteristics, it achieves dynamic correction of oxygen measurement values. Through an adaptive threshold algorithm and parameter self-updating mechanism, it can accurately capture deviations caused by sensor drift and operating condition fluctuations. The calibration accuracy is significantly better than traditional offline and fixed-cycle calibration methods, making it suitable for diverse operating scenarios such as boiler load changes, coal type switching, and complex flue gas environments.

[0066] Ensuring environmental compliance and reducing emission risks: Precise oxygen calibration provides reliable data support for low-NOx combustion control, effectively optimizing the combustion process and reducing the emission concentrations of pollutants such as CO, NOx, and SO2, ensuring that NOx emissions meet the requirement of ≤50mg / m³. Simultaneously, it avoids the decrease in denitrification system efficiency and ammonia escape exceeding standards caused by inaccurate oxygen levels, mitigating environmental penalties at the source and helping enterprises achieve their "dual carbon" goals.

[0067] Significant energy savings and reduced consumption, improving economic efficiency: Improved oxygen measurement accuracy directly drives boiler combustion efficiency optimization and reduces fuel costs. Furthermore, by reducing heat loss from incomplete combustion, boiler energy utilization efficiency is further enhanced, creating significant economic benefits for enterprises.

[0068] Supporting the digital transformation of smart power plants: The high-quality and high-reliability oxygen data provided by this invention lays a solid foundation for the application of intelligent technologies such as combustion optimization algorithms, AI power adjustment, and adaptive control. It promotes the transformation of boiler systems from "experience-based control" to "data-driven control," meets the construction needs of "smart power plants" and "industrial internet," and helps the digital and intelligent upgrading of boiler systems. Attached Figure Description

[0069] Figure 1 This is a flowchart of the dynamic calibration method for oxygen content in the boiler flue gas system proposed in this invention. Detailed Implementation

[0070] The present invention will be further described in detail below with reference to specific embodiments. 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.

[0071] The dynamic calibration method for oxygen content in a boiler flue gas system includes the following steps:

[0072] I. Multi-condition data acquisition and standardized processing

[0073] (a) Data collection

[0074] Data collection targets: Three core data categories covering boiler load ranges of 30% to 100% (including minimum load of 30% and maximum load of 100%), three or more typical coal types, and different environmental temperature and humidity conditions.

[0075] Zirconium battery output signal (filtered raw data);

[0076] Flue gas parameters: flue gas temperature (0~1500℃), flue gas pressure (actual sensor measurement);

[0077] Boiler operating data: boiler load rate, load change rate, burner operating status, etc.

[0078] Data acquisition frequency: Continuous acquisition at intervals of Δt=1s, with a single-condition acquisition duration of no less than 1 hour to ensure the integrity of time-series data.

[0079] (ii) Standardization Processing

[0080] Preprocessing: Moving average filtering is used to remove noise from the zirconium battery signal, and Z-score normalization is used to eliminate differences in data dimensions. The formula is: ( The sample mean. (sample standard deviation);

[0081] Data filtering: Outliers (data exceeding 3 times the standard deviation) are removed, resulting in a standardized multi-condition dataset for subsequent feature extraction and model training.

[0082] II. Failure Feature Extraction and Feature Library Establishment

[0083] (I) Feature Extraction Preparation

[0084] Enable adaptive sliding window control algorithm to dynamically adjust window length:

[0085] 1. Core Formula: ,in This is the initial window length (default is 60s). This is the attenuation coefficient (default 0.1).

[0086] 2. Calculation of load change rate: ( (where Δt = 1s is the boiler load rate).

[0087] 3. Window adjustment logic: The higher the load change rate, the shorter the window length to ensure the capture of instantaneous features; when the load is stable, the window length is extended to improve the accuracy of feature calculation.

[0088] (II) Calculation of Static Statistical Characteristics

[0089] Based on the filtered zirconium battery output signal Calculate the four types of features using the following formula:

[0090] 1. Mean: ( (Calculate the window length for static features, output by the adaptive window algorithm).

[0091] 2. Variance: ;

[0092] 3. Skewness: ;

[0093] 4. Kurtosis: .

[0094] (III) Calculation of dynamic failure characteristics

[0095] Drift rate: ( (This is the length of the drift statistics time window, set to 300 seconds by default).

[0096] Fluctuation frequency: ( (This represents the Fourier transform, which converts a time-domain signal into a frequency-domain signal for computation.)

[0097] Autocorrelation coefficient: ( (This is the length of the autocorrelation calculation window, consistent with the static feature window).

[0098] (iv) Construction of the failure feature library

[0099] Establish a three-dimensional correlation table: operating condition type (load range, coal type, operating environment) → combination of static and dynamic characteristics → drift mode (such as linear drift, fluctuation drift, sudden drift);

[0100] Enable online update mechanism: Based on newly collected data, update feature weights through incremental learning algorithm, as shown in the formula. ( Forgetting factor, The original weights, (The change in new data features).

[0101] III. Construction of the Baseline Oxygen Calculation Model

[0102] (I) Acquisition and calibration of core parameters

[0103] Input parameter acquisition: Zirconium battery operating temperature (K) Flue gas pressure is collected in real time by a thermocouple array. Measured by a pressure sensor;

[0104] Adaptive PID correction: for and For dynamic correction, the controller output formula is: ,in For error signals, , , Fixed parameters for PID.

[0105] (II) Calculation of Correction Factors

[0106] Temperature correction factor: ( The temperature linearity coefficient is... It is the quadratic coefficient of temperature. (This is a reference temperature, defaulted to 800K, and needs to be calibrated according to the sensor model).

[0107] Pressure correction factor: ( For standard reference pressure, This is the pressure correction factor, default 0.05.

[0108] (III) Calculation of baseline oxygen content

[0109] Substituting into the core formula of the Nernst equation, the static baseline oxygen content is output: ,in It is the ideal gas constant (8.314 J / (mol·K)). It is the Faraday constant (96485 C / mol). For reference oxygen partial pressure, The oxygen partial pressure is measured by the sensor.

[0110] IV. Data-Driven Model Construction

[0111] (I) Model Structure Construction

[0112] An improved bidirectional LSTM network is used, with the following structure:

[0113] 1. Input layer: Standardized multi-condition dataset, failure feature library feature values, and baseline oxygen content values ​​(dimensions are adjusted according to actual data; the default input dimension is 12).

[0114] 2. Hidden layers: Two LSTM units, the first layer has 64 neurons and the second layer has 32 neurons, using ReLU as the activation function;

[0115] 3. Output layer: 1 neuron, outputting real-time corrected oxygen value.

[0116] (ii) Model training configuration

[0117] Sample weight calculation: ,in Operating condition complexity coefficient ( , , , );

[0118] Time series data alignment: A dynamic time warping algorithm is used, the formula is as follows: Eliminate the difference in timing length under different operating conditions;

[0119] Training parameters: The optimization algorithm is Adam, and the learning rate is... The loss function is the weighted mean square error. ( For the true value, (Predicted value);

[0120] Training termination conditions: The number of iterations reaches 1000 or the loss function value is less than 0.001.

[0121] (III) Model Output

[0122] After training is completed, input real-time operating condition data and output real-time corrected oxygen values. .

[0123] V. Anomaly Detection and Calibration

[0124] (a) Adaptive threshold calculation

[0125] Dynamically generate anomaly detection thresholds using the following steps:

[0126] 1. Exponentially weighted moving average: ( (smoothing coefficient);

[0127] 2. Standard deviation calculation: ( The threshold is used to calculate the window length (default 100s).

[0128] 3. Adaptive coefficient: ( , , );

[0129] 4. Base threshold value: ( (Maximum allowable drift rate);

[0130] 5. Final threshold: ,in ( (For fitness coefficient).

[0131] (ii) Calibration signal triggering conditions

[0132] The calibration signal is triggered when all three of the following conditions are met simultaneously:

[0133] 1. Relative deviation condition: ( The sensor measures real-time values. (Based on baseline oxygen levels).

[0134] 2. Drift rate condition: ( (This is the drift rate threshold, default 0.1%).

[0135] 3. Load factor condition: ( This is the load factor trigger coefficient, with a value ranging from 0.7 to 0.9, and a default value of 0.8.

[0136] (III) Implementation of Multi-level Calibration Strategy

[0137] Scene recognition: Determine the boiler operating status and load change rate. This is for scenarios with frequent load fluctuations; otherwise, it is for steady-state operation scenarios.

[0138] Fast response calibration (fluctuation scenario): Based on historical sliding window data (window length 30s), the instantaneous correction value is calculated using linear interpolation, with the following formula: ;

[0139] High-precision calibration (steady-state scenario): Iterative least squares method is used to optimize sensor output, with the objective function being... The iteration count is no less than 10 times, and the optimal calibration value is output.

[0140] VI. Real-time evaluation of calibration results

[0141] Calculation of relative deviation ratio: ,in To calibrate the oxygen level before, This is the calibrated oxygen level value;

[0142] Result determination:

[0143] like Calibration successful; proceed to parameter self-update step.

[0144] like If a calibration anomaly is detected, the anomaly detection process is retried to perform a second calibration (the second calibration uses high-precision mode by default).

[0145] VII. Parameter Self-Update

[0146] (a) Dynamic adjustment of calibration cycle

[0147] Update the calibration cycle according to the formula: ,in This is the initial calibration period (default 3600s). This is the attenuation coefficient (values ​​range from 0.05 to 0.2, default is 0.1). The current drift rate, This represents the current load factor.

[0148] (ii) Update of correction coefficients

[0149] The temperature correction factor is dynamically adjusted based on the calibration results and real-time operating data. , and pressure correction factor If the relative deviation after calibration Between 3% and 5%, the coefficient adjustment range is ±0.01; if The coefficients remain unchanged; if they still remain unchanged after a second calibration. The coefficient adjustment range is ±0.03, and the operating condition information is recorded and fed back to the failure feature database.

[0150] (III) Feature Library Weight Update

[0151] Invoke the incremental learning algorithm in step two (four) to calculate based on the calibration data. Update the feature weights of the corresponding working conditions in the failure feature library to ensure the accuracy of drift mode matching.

[0152] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is 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 dynamic calibration method for oxygen content in a boiler flue gas system, characterized in that: Includes the following steps: Multi-condition data acquisition and standardized processing: Acquire zirconium battery output signals, flue gas parameters, and boiler operation data under different loads, coal types, and operating environments; The collected data is preprocessed to obtain a standardized multi-condition dataset; Failure feature extraction and feature library establishment: Based on the standardized multi-condition dataset, static statistical features and dynamic failure features are extracted, and an oxygen sensor failure feature library is constructed that associates operating condition type, failure feature and drift mode. Construction of the baseline oxygen content calculation model: Based on the Nernst equation of the zirconia sensor, the flue gas temperature, pressure and zirconium battery operating temperature parameters in the standardized multi-condition dataset are substituted into the temperature correction coefficient and pressure correction coefficient to establish the baseline oxygen content calculation model and output the baseline oxygen content value under static conditions. Data-driven model construction: A data-driven model is constructed using a time-series learning algorithm. The standardized multi-condition dataset, failure feature library, and baseline oxygen value are used as model inputs. The model is trained by the time-series features of load fluctuation and flue gas temperature change to capture nonlinear effects and output real-time corrected oxygen values. Anomaly detection and calibration: Based on the real-time corrected oxygen value, an adaptive threshold algorithm is used to identify abnormal signals from the oxygen sensor. The drift pattern is matched in combination with the failure feature library, and the corresponding type of calibration signal is triggered according to the boiler operating conditions to perform automatic calibration. Parameter self-updating: Automatically adjusts the calibration cycle and correction factor based on real-time operating data.

2. The dynamic calibration method for oxygen content in a boiler flue gas system according to claim 1, characterized in that: It also includes a real-time evaluation step of the calibration effect, which calculates the relative deviation ratio of oxygen values ​​before and after calibration: ,in To calibrate the oxygen level, For the calibrated oxygen value; when If the calibration is abnormal, a secondary calibration process will be triggered.

3. The dynamic calibration method for oxygen content in a boiler flue gas system according to claim 1, characterized in that: The static statistical characteristics include mean μ, variance σ², skewness S, and kurtosis K, calculated using the following formula: in, For the first The filtered output signal values ​​of the zirconium battery at each time point Calculate the window length for static features; The dynamic failure characteristics include drift rate. Fluctuation frequency and autocorrelation coefficient The calculation formula is: in, The length of the drift statistics time window. Indicates Fourier transform, It is the static mean. The autocorrelation calculation window length is used; feature extraction employs an adaptive sliding window control algorithm, with the window length... Dynamically adjusted to , This is the initial window length. The attenuation coefficient is... For boiler load change rate, , For boiler load rate, For time intervals.

4. The dynamic calibration method for oxygen content in a boiler flue gas system according to claim 1, characterized in that: The baseline oxygen content calculation model is constructed by integrating the Nernst equation with real-time operating parameters, and its core formula is: in, Let be the ideal gas constant. It is Faraday's constant. The temperature correction factor is calculated as follows: The temperature linearity coefficient is... It is the quadratic coefficient of temperature. The operating temperature (K) of the zirconium battery. For reference temperature; The pressure correction factor is calculated as follows: This is the pressure correction factor. For sensor to measure pressure; model input parameters Real-time data acquisition via thermocouple array. Measured by a pressure sensor, and Through dynamic correction using an adaptive PID controller, the controller output is: For error signals, , , For PID parameters, , , .

5. The dynamic calibration method for oxygen content in a boiler flue gas system according to claim 1, characterized in that: The data-driven model adopts an improved bidirectional LSTM network structure, which includes two layers of LSTM units. The input layer contains a standardized multi-condition dataset, a failure feature library, and a baseline oxygen value. The output layer generates a real-time corrected oxygen value. The model training uses the Adam optimization algorithm with a learning rate of [missing information]. The loss function is the weighted mean squared error: in, Let be the true oxygen content label value of the i-th training sample. These are the sample weight coefficients. The complexity coefficient is the operating condition coefficient. , , For feature weights, For boiler load rate, For maximum load factor, For flue gas temperature, For the maximum flue gas temperature, This is the attenuation coefficient.

6. The dynamic calibration method for oxygen content in a boiler flue gas system according to claim 1, characterized in that: The adaptive threshold algorithm employs a dynamic adjustment mechanism, and the threshold calculation formula is as follows: in, To correct the exponentially weighted moving average of oxygen levels in real time, , For smoothing coefficients, This is the current baseline oxygen level. To correct the standard deviation of oxygen values ​​in real time; For adaptive coefficients, , As the benchmark coefficient, To adjust the coefficient, Minimum load factor; For drift rate, The maximum allowable drift rate; this algorithm implements anomaly detection through an adaptive threshold controller, and the controller output... , , The threshold is dynamically expanded as an adaptation factor during sudden load changes.

7. The dynamic calibration method for oxygen content in a boiler flue gas system according to claim 1, characterized in that: In the parameter self-update step, the calibration period The dynamic adjustment formula is: in, For the current calibration cycle, For the initial calibration period, For drift rate, Given the current boiler load rate, For maximum load factor, This is the attenuation coefficient, with a value range of 0.05-0.

2.

8. The dynamic calibration method for oxygen content in a boiler flue gas system according to claim 1, characterized in that: The calibration signal triggering condition is as follows: in, The sensor measures real-time values. As the baseline oxygen level, For adaptive threshold, For drift rate, The drift rate threshold, Given the current boiler load rate, Minimum load factor For maximum load factor, This is the load factor triggering coefficient, with a value range of 0.7-0.

9.

9. The dynamic calibration method for oxygen content in a boiler flue gas system according to claim 1, characterized in that: After the calibration signal is triggered, a multi-level calibration strategy is executed, including two modes: fast response calibration and high precision calibration. Fast response calibration is used for scenarios with frequent load fluctuations, and instantaneous correction is achieved based on historical data interpolation through a sliding window. High precision calibration is used for steady-state operation scenarios, and iterative least squares method is used to optimize sensor output.

10. The dynamic calibration method for oxygen content in a boiler flue gas system according to claim 1, characterized in that: The failure feature library includes an online update mechanism, which updates the feature weights based on newly collected data using an incremental learning algorithm. The update formula is as follows: in, These are the original feature weights. For the updated weights, The change in features calculated for the new data. It is a forgetting factor.

Images