A data analysis-based ammonia escape concentration real-time prediction and control system

Abstract

The application relates to the technical field of biological neural networks, in particular to an ammonia escape concentration real-time prediction and control system based on data analysis, which comprises a shared long short-term memory (LSTM) neural network model. The neural network architecture receives multi-source time series enhanced features generated through specific calculation as its input. The neural network not only learns time series dependence to predict future physical quantities, but also quantifies the uncertainty of its own prediction through an integrated Dropout mechanism. The output layer of the neural network is designed as a double-branch structure, which respectively outputs a predicted value and another key system operation state evaluation value. The system deduces technical parameters by using the output information of the neural network model. In addition, a neural network performance monitoring and self-adaptive calibration unit is integrated, which can identify the performance degradation of the neural network model and trigger its retraining and redeployment, thereby exhibiting the self-adaptive learning ability of the neural network.

Description

Technical Field

[0001] This invention relates to the field of biological neural network technology, specifically to a real-time prediction and control system for ammonia escape concentration based on data analysis. Background Technology

[0002] In industrial process optimization, especially in the control of complex systems such as selective catalytic reduction (SCR) denitrification, accurate prediction and control of key parameters (such as ammonia slip concentration) are crucial. The limitations of traditional methods have prompted the industry to seek more advanced computational intelligence solutions. Among them, deep neural networks, as a powerful, biologically inspired nonlinear data modeling tool, have shown great application potential.

[0003] However, directly applying existing deep neural networks to SCR systems still faces many challenges. First, the operating data of SCR systems exhibits characteristics such as multi-source heterogeneity, high-dimensional strong coupling, temporal sequence, and strong noise. This places high demands on the learning ability and architectural design of neural networks. Many traditional neural network models struggle to effectively integrate multi-scale information and, unlike deep neural networks, cannot accurately extract key temporal features from strongly coupled relationships through their deep structures. Furthermore, they are sensitive to noise, resulting in insufficient robustness and generalization ability of neural network models.

[0004] Secondly, the dynamic and time-varying characteristics of industrial processes (such as fluctuations in operating load and catalyst activity decay) pose a severe challenge to the adaptability of deep neural networks. Most deep neural network systems are static after deployment, lacking effective mechanisms to monitor changes in deep neural network performance over time, and even more so, lacking the adaptive capability for automated retraining and optimization of neural network model parameters. This makes it difficult for them to maintain reliability during long-term operation.

[0005] Furthermore, a key challenge lies in the fact that most current deep neural network applications primarily provide deterministic predictions, lacking a quantitative assessment of the uncertainty of these predictions. In industrial control applications requiring risk assessment and robust decision-making, the inability to evaluate the reliability of predictions is a significant drawback.

[0006] To address this, a real-time prediction and control system for ammonia escape concentration based on data analysis is proposed. Summary of the Invention

[0007] The purpose of this invention is to provide a real-time prediction and control system for ammonia slip concentration based on data analysis, overcoming the problems of inaccurate prediction and lag response in existing SCR denitrification systems. By accurately collecting and processing multi-source operating data, it deeply mines key indicators such as the spatial distribution characteristics of ammonia concentration, ABS generation risk, and ammonia injection skewness. An advanced shared neural network model is used to jointly predict ammonia slip and system operating status and assess their uncertainties. Then, an intelligent decision-making algorithm incorporating multiple optimization objectives outputs optimal technical parameters, and the system possesses online monitoring and adaptive calibration capabilities for model performance. This achieves precise control of ammonia slip, ultimately improving the overall economic efficiency and real-time control performance of the system while meeting environmental emission standards.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A data analysis-based real-time prediction and control system for ammonia slip concentration includes:

[0010] The parameter acquisition and processing unit is used to acquire and process multi-source operating data in the SCR denitrification system and output standardized data.

[0011] The characteristic analysis and modeling unit is used to calculate the spatial variance of ammonia concentration and the system operating status evaluation value based on the standardized data, and to deduce the skewness index by combining the sensor position matrix and the local flow velocity field, and output the enhanced feature sequence.

[0012] A multi-parameter prediction unit is used to employ a shared neural network model, based on the enhanced feature sequence, to jointly output the predicted value of ammonia escape concentration and the evaluation value of future system operating status, and to evaluate the prediction uncertainty index.

[0013] An optimized control decision unit is used to construct a multi-objective loss function based on the predicted value, the future system operating state assessment value, and the prediction uncertainty index, and to solve the optimal solution of the multi-objective loss function through a numerical optimization algorithm, and output technical parameters.

[0014] The neural network performance monitoring and adaptive calibration unit is used to execute the technical parameters and identify model degradation in the execution results; if model performance degradation is identified, the adaptive calibration mechanism is initiated to redeploy the shared neural network model.

[0015] Furthermore, the multi-source operational data includes:

[0016] Ammonia and water vapor concentration data from N sensor locations; nitrogen oxide and oxygen content data at the SCR reactor inlet and outlet; flue gas temperature data at the SCR reactor inlet, outlet, and air preheater inlet; flue gas flow rate data; boiler load and total ammonia injection data; and coal sulfur parameters.

[0017] Furthermore, the process of calculating the spatial variance of ammonia concentration and the system operating status assessment value includes:

[0018] Based on the ammonia concentration data in the multi-source operation data, the squared deviation between the concentration value at each point and the average ammonia concentration is calculated, and the data is statistically summarized to obtain the spatial variance of ammonia concentration.

[0019] Based on the ammonia concentration data, water vapor concentration data, coal sulfur content parameter data, oxygen content data, and flue gas temperature data, the flue gas sulfur trioxide concentration under the corresponding operating conditions is evaluated using empirical formulas, and the ABS dew point temperature is calculated.

[0020] A risk function is constructed based on the difference between the ABS dew point temperature and the current actual flue gas temperature to generate a system operating status assessment value.

[0021] Furthermore, the process of reverse-engineering the skewness index includes:

[0022] Using the Kriging interpolation algorithm, a two-dimensional ammonia concentration distribution map is reconstructed based on the ammonia concentration data and sensor location matrix in the standardized data, which is used to reflect the ammonia concentration field in each region under the current operating conditions.

[0023] Combining the local velocity field information and the preset ammonia injection influence area model, a functional mapping relationship between concentration distribution and regional ammonia injection intensity is constructed, which is used to deduce the regional ammonia injection intensity distribution vector from the two-dimensional ammonia concentration distribution map.

[0024] The skewness index is obtained by comparing the ammonia injection intensity distribution vector in the region with the target ammonia injection distribution vector and calculating the skewness index using Euclidean distance.

[0025] Furthermore, the structure of the shared neural network model specifically includes:

[0026] The input layer is used to receive time-series samples of the enhanced feature sequence;

[0027] A shared long short-term memory network layer, connected to the input layer, is used to perform temporal dependency modeling on the time series samples and extract deep temporal features. Furthermore, the shared long short-term memory network layer integrates a Dropout mechanism to generate forward prediction result samples, evaluate confidence intervals, and output prediction uncertainty indicators.

[0028] The output layer, connected to the shared long short-term memory network layer, includes a first branch output path and a second branch output path, wherein:

[0029] The first branch output path, used to output the predicted value, includes at least one fully connected layer and a linear activation function;

[0030] The second branch output path is used to output the future system operating state evaluation value, including at least one fully connected layer and a classification activation function.

[0031] Furthermore, the weighted objective term of the multi-objective loss function includes: a first objective term, the squared deviation between the predicted value and the control target value; a second objective term, the absolute value of the future system operating state assessment value; a third objective term, the skewness index; a fourth objective term, the square of the rate of change of the technical parameters; and a fifth objective term, a linear combination of the prediction uncertainty index.

[0032] Furthermore, the neural network performance monitoring and adaptive calibration unit specifically includes:

[0033] Within a preset sliding time window, the average absolute error between the predicted value and the actual ammonia escape concentration, as well as the average absolute error between the system operating status assessment value and the actual system operating status assessment value, are continuously calculated.

[0034] The mean absolute error is compared with a preset error threshold;

[0035] When the mean absolute error exceeds the error threshold for a preset number of times, the model performance is determined to be degraded, and a model degradation alarm is triggered.

[0036] An adaptive calibration mechanism is initiated, which includes: retraining the shared neural network model using an enhanced feature sequence containing the latest running data, and redeploying the retrained shared neural network model to the multi-parameter prediction unit.

[0037] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0038] 1. In terms of data acquisition and processing, this invention comprehensively collects multi-source operational data and employs deep feature engineering techniques. This not only accurately calculates the spatial variance of ammonia concentration and the system's operational status assessment value, but also, by combining sensor locations and flow velocity fields, skewness indicators are derived in reverse, successfully constructing an enhanced feature sequence. Based on this, a shared neural network model is used for joint prediction, and a prediction uncertainty assessment step is introduced. This provides comprehensive and reliable information support for subsequent optimization work, enhancing the situational awareness of ammonia escape and related risks under complex operating conditions, and laying a solid foundation for achieving precise control.

[0039] 2. In terms of optimizing strategy construction, this invention designs a multi-objective optimization function that encompasses prediction bias, future risk, skewness indicators, rate of change of technical parameters, and prediction uncertainty. Solving this function using numerical optimization algorithms allows for the precise identification of the optimal balance point among numerous objectives, even those that conflict with each other. The resulting decision-making technical parameters effectively control ammonia escape while also fully considering system safety, uniformity of ammonia injection, and control stability, thereby enhancing the overall intelligence and comprehensive benefits of the control strategy.

[0040] 3. This invention designs a neural network performance monitoring and adaptive calibration unit. This module can monitor the deviation between the prediction error and the actual value in real time and automatically identify model performance degradation problems caused by factors such as changes in operating conditions or equipment aging. Once signs of performance degradation are detected, the system will quickly activate the adaptive calibration mechanism, retrain the model using the latest data, and promptly redeploy the model, ensuring the long-term effectiveness and robustness of the prediction and control system and guaranteeing the accuracy of system control in complex and ever-changing dynamic environments. Attached Figure Description

[0041] Figure 1 This invention provides a schematic diagram of the structure of a real-time prediction and control system for ammonia escape concentration based on data analysis.

[0042] Figure 2 A schematic diagram of the structure of the shared neural network model is provided for this invention;

[0043] Figure 3 This is a flowchart illustrating the implementation process of the neural network performance monitoring and adaptive calibration unit provided in this invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] Please see Figures 1 to 3 This invention provides a real-time prediction and control system for ammonia escape concentration based on data analysis, the technical solution of which is as follows:

[0046] Example 1:

[0047] This embodiment uses the SCR (Selective Catalytic Reduction) denitrification system of a typical 600MW coal-fired power generating unit as an example. The company needs to meet strict emission standards during operation, such as controlling the emission concentration to 50mg / Nm³. 3 Within a certain range, the ammonia injection rate must be controlled to ensure that the ammonia slip concentration does not exceed 2 ppm, in order to prevent the formation of ammonium bisulfate (ABS) in downstream equipment such as the air preheater and the resulting blockage. Traditional control methods mainly rely on experience-based adjustments and hysteresis feedback, which often leads to excessive or insufficient ammonia injection when facing complex operating conditions such as boiler load changes and coal type fluctuations. To solve this problem, a real-time ammonia slip concentration prediction and control system based on data analysis is introduced, such as... Figure 1 As shown, it includes:

[0048] refer to Figure 1 The parameter acquisition and processing unit is used to collect and process multi-source operating data in the SCR denitrification system and output standardized data.

[0049] Furthermore, the multi-source operational data includes:

[0050] Ammonia concentration and water vapor concentration data are collected from N sensor locations. Four sets of online ammonia / water analyzer optical monitoring terminals are evenly installed in a 2×2 matrix on the key monitoring section of the SCR reactor outlet flue (cross-sectional dimensions 8m×10m in this embodiment). Each monitoring terminal can independently collect ammonia concentration and water vapor volume fraction data at its location in real time. All monitoring terminals are connected to the data acquisition host via optical fiber to achieve multi-point synchronous monitoring and data aggregation.

[0051] Nitrogen oxide concentration data and oxygen content data. Online flue gas analyzers are installed at the inlet and outlet flues of the SCR reactor to acquire real-time nitrogen oxide concentration and oxygen content data.

[0052] Flue gas temperature data. Temperature sensors are installed at key locations such as the SCR reactor inlet, SCR reactor outlet, and air preheater inlet to monitor and acquire flue gas temperature in real time.

[0053] Flue gas flow data. A matrix flow meter is installed at the SCR outlet flue (or a representative location within the main flue). This flow meter calculates and outputs the average velocity and volumetric flow rate of the flue gas using a multi-point differential pressure sampling principle combined with preset flue cross-sectional area parameters.

[0054] Boiler load data and total ammonia injection data. The actual operating load of the boiler and the total ammonia or urea solution injection volume of the total ammonia injection system are read in real time from the power plant's distributed control system.

[0055] Coal sulfur content parameter data. The sulfur content of currently used or most recently fed coal is obtained from the power plant's fuel management system via an interface.

[0056] All collected data is accompanied by precise timestamps to ensure time synchronization between different data sources. This multi-source data forms the foundation of the system's input, providing comprehensive and crucial real-time data support for subsequent prediction and optimization algorithms. It helps to accurately characterize the spatial distribution of parameters such as ammonia concentration and flue gas velocity, thereby achieving the synergistic optimization goal of ammonia injection cost control and equipment operational safety while meeting environmental protection standards.

[0057] Furthermore, to ensure the quality of the data input to the subsequent analysis and modeling modules, the collected and initially summarized data undergoes the following preprocessing: Raw second-level or higher frequency sampling data is aggregated in 1-minute intervals (e.g., averaging), and different data sources are aligned to a unified minute-level timestamp. For individual minutes of missing data due to communication interruptions, sensor maintenance, etc., linear interpolation is used to fill in the missing data based on valid data points before and after, ensuring data continuity and integrity. All numerical feature data used for modeling undergoes Z-score standardization, i.e., zero-mean, unit-variance normalization is applied to each feature to eliminate dimensional differences. After the preprocessing process, the standardized data is finally output as the input basis for subsequent feature engineering and predictive modeling modules.

[0058] refer to Figure 1 The characteristic analysis and modeling unit is used to calculate the spatial variance of ammonia concentration and the system operating status evaluation value based on the standardized data, and to deduce the skewness index by combining the sensor position matrix and the local flow velocity field, and output the enhanced feature sequence.

[0059] Furthermore, the process of calculating the spatial variance of ammonia concentration and the system operating status assessment value includes:

[0060] Based on the ammonia concentration data from the multi-source operational data, the squared deviation between the concentration value at each location and the average ammonia concentration is calculated and statistically summarized to obtain the spatial variance of the ammonia concentration. Specifically, in this embodiment, four sensor locations are extracted at the same minute, and the ammonia concentration values ​​at the four locations are arithmetically averaged to obtain the cross-sectional average ammonia concentration at that moment. Then, the deviation between the ammonia concentration value at each location and the average ammonia concentration is calculated, and its square is taken. The squared deviation values ​​of the four locations are added together and divided by 4 to obtain the spatial variance of the ammonia concentration at that moment. The higher the value, the more uneven the spatial distribution of the ammonia concentration.

[0061] To assess the risk of ammonium bisulfate (ABS) generation in downstream equipment such as air preheaters, based on the ammonia concentration data, water vapor concentration data, coal sulfur content parameter data, oxygen content data, and flue gas temperature data, the flue gas sulfur trioxide concentration under corresponding operating conditions is evaluated using empirical formulas, and the ABS dew point temperature is calculated. A risk function is constructed based on the difference between the ABS dew point temperature and the current actual flue gas temperature to generate a system operating status assessment value.

[0062] The empirical formula can be expressed as:

[0063] Among them, SO 3,Est S represents the concentration of sulfur trioxide in flue gas. fuel For coal sulfur content parameter data, L actual For the current boiler load, L rated For rated load, The excess air coefficient correction factors are calculated based on actual oxygen content. A, B, and C are empirical coefficients obtained from historical data or relevant literature. In this embodiment, coefficients A, B, and C are obtained through multiple linear regression analysis of operational data from the past year.

[0064] Then, based on the calculated SO 3,Est The current theoretical dew point temperature of ABS is calculated based on the empirical formula for ABS dew point optimized for the operating conditions of this power plant, and is expressed as: T dp,ABS =K1 + K2 × log 10 (NH 3,AVG +∈)+K3×log 10 (SO 3,Est +∈)+K3×log 10 (H2O AVG +∈); where T dp,ABS NH is the theoretical dew point temperature of ABS. 3,AVG The average ammonia concentration, H2O AVG The average water vapor concentration is represented by K1, K2, K3, and K4, which are empirical constants that can also be obtained through multiple linear regression analysis. ∈ is a very small positive number used to prevent the parameters of the logarithmic function from being zero or negative. 10 () is a logarithmic function.

[0065] The calculated T dp,ABS The actual flue gas temperature T at the air preheater inlet APH,inlet Compare and set a safe temperature margin T. margin For example, at 5℃, based on the risk function, the system operating status assessment value R is... ABS It can be represented as:

[0066]

[0067] Among them, R ABS =1 indicates a high-risk state, R ABS =0 indicates a low-risk state.

[0068] By quantifying the spatial unevenness of ammonia slip and the risk of ABS formation, these key factors are transformed into specific numerical indicators. This not only deepens the system's understanding of the complexity of the SCR process but also improves the accuracy of ammonia slip prediction and the reliability of ABS risk warning. Furthermore, it provides a more refined and targeted decision-making basis for optimized control.

[0069] Furthermore, the process of reverse-engineering the skewness index includes:

[0070] Using the Kriging interpolation algorithm, a two-dimensional ammonia concentration distribution map is reconstructed based on the ammonia concentration data and sensor location matrix in the standardized data. This map reflects the ammonia concentration field in each region under the current operating conditions. Kriging interpolation is an optimal linear unbiased estimation method based on spatial statistics theory, suitable for reconstructing a continuous spatial distribution field from a small number of discrete sampling points. In this embodiment, four ammonia concentration sampling points are arranged on the outlet section of the SCR reactor. Using the Kriging interpolation algorithm, the data from these four points can be spatially interpolated into a 16×20 grid two-dimensional ammonia concentration distribution map, which is used to visualize the ammonia concentration field in each region within the flue gas duct section.

[0071] By combining the local velocity field information and the preset ammonia injection influence area model, a functional mapping relationship between concentration distribution and regional ammonia injection intensity is constructed, which is used to deduce the regional ammonia injection intensity distribution vector from the two-dimensional ammonia concentration distribution map.

[0072] The ammonia injection influence area model assumes that the ammonia injection grid (AIG) is divided into several virtual ammonia injection zones. For example, four sensor locations are divided into four independent virtual zones, each contributing independently to the downstream ammonia concentration distribution. The model predefines the response weights or functional relationships between the ammonia injection intensity of each virtual injection zone and the concentration at different locations on the two-dimensional ammonia concentration distribution map. These functional relationships can be constructed based on a simplified fluid dynamics model and regression analysis of historical operating data.

[0073] After constructing the forward mapping model, a functional mapping relationship is established from the regional ammonia injection intensity distribution vector (e.g., a 4-dimensional vector) to the 2D ammonia concentration distribution map. Then, using the least squares method or other feasible optimization algorithms, the reconstructed actual 2D ammonia concentration distribution map is used as the fitting target, and the above functional mapping is solved in reverse to obtain the optimal matching regional ammonia injection intensity distribution vector.

[0074] Finally, the ammonia injection intensity distribution vector obtained by reverse calculation is compared with a set of target ammonia injection distribution vectors, and the difference between the two is calculated using Euclidean distance to obtain the ammonia injection skewness index. The larger the value of the ammonia injection skewness index, the greater the deviation between the current equivalent ammonia injection distribution and the ideal ammonia injection distribution, reflecting potential problems such as uneven distribution, injection bias, or blockage in some areas of the ammonia injection system.

[0075] The target ammonia injection distribution vector is a preset ideal ammonia injection configuration scheme, which can be generated by expert experience or SCR system design specifications based on current boiler load and other operating parameters. It represents the regional ammonia injection intensity ratio required to achieve the best mixing and denitrification efficiency under this operating condition.

[0076] Skewness indicators are not limited to alarms and diagnoses; they can indicate potential problems such as blockages or improper adjustments in the ammonia injection system. More importantly, they can serve as a key input or optimization target for the control decision-making module. When adjusting the total ammonia injection rate, the control system can refer to this skewness indicator to reduce ammonia slip, mitigate ABS risks, and strive to make the ammonia injection distribution as close to ideal as possible. Even without directly controlling specific zones, it can influence the conservatism of the total injection decision, thereby indirectly improving the overall denitrification efficiency and the uniformity of catalyst utilization.

[0077] refer to Figure 1 The multi-parameter prediction unit is used to employ a shared neural network model, based on the enhanced feature sequence, to jointly output the predicted value of ammonia escape concentration and the evaluation value of future system operating status, and to evaluate the prediction uncertainty index.

[0078] Furthermore, such as Figure 2 As shown, the structure of the shared neural network model specifically includes:

[0079] The input layer is used to receive time series samples of the enhanced feature sequence; specifically, the enhanced feature data of the most recent 60 minutes is used as an input sample, with a sample dimension of [60, M], where M is the dimension of the enhanced features, including features such as the mean ammonia concentration and spatial variance.

[0080] A shared long short-term memory (LSTM) network layer, connected to the input layer, is used to model the temporal dependencies of the time series samples, extract deep temporal features, and is configured with 32 hidden units and an activation function of tanh. Furthermore, the LSM network layer integrates a Dropout mechanism with a Dropout ratio set to 0.2. During the inference phase, it enables multiple forward propagations by enabling Dropout, generating forward prediction result samples, evaluating confidence intervals, and outputting a prediction uncertainty index.

[0081] The output layer, connected to the shared long short-term memory network layer, includes a first branch output path and a second branch output path, wherein:

[0082] The first branch output path, used to output the predicted value, includes a fully connected layer with 32 neurons and ReLU activation function. It is then connected to an output neuron using a linear activation function to directly output the predicted average ammonia escape concentration for the next 15 minutes.

[0083] The second branch output path is used to output the future system operating state assessment value. The structure includes: a fully connected layer with 16 neurons and ReLU activation function. Then, an output neuron is connected, using the Sigmoid classification activation function to output the ABS risk index for the next 15 minutes. This output is a continuous value between 0 and 1, used to quantify the probability level of ABS crystallization risk.

[0084] The system uses historical operational data for offline training, and it is recommended that the data period be no less than 3 to 6 months of minute-level operational data. During training, a joint loss function is used, which weights and sums the loss terms of the two tasks. Specifically, the ammonia escape concentration prediction task uses the mean squared error loss function, and the ABS risk prediction task uses the binary cross-entropy loss function.

[0085] By sharing an LSTM network and a multi-task output structure, the system can simultaneously predict the ammonia slip concentration and ABS generation risk for the next 15 minutes based on the same set of input features. This allows the system to more comprehensively assess the overall operational risk over a period of time, providing a longer time window and more complete decision-making information for subsequent optimized control, thereby improving the uniformity of ammonia injection and the real-time performance of control.

[0086] refer to Figure 1 The optimization and control decision unit is used to construct a multi-objective loss function based on the predicted value, the future system operating status assessment value and the prediction uncertainty index, and solve the optimal solution of the multi-objective loss function through a numerical optimization algorithm, and output the technical parameters, namely the total ammonia injection setpoint.

[0087] Among them, the numerical optimization algorithm is selected from the Sequential Quadratic Programming (SQP) algorithm or similar constraint optimization algorithm. Within the physical upper and lower limits of the total ammonia injection amount, the optimal technical parameters are obtained by minimizing the multi-objective loss function.

[0088] Furthermore, the weighted objective terms of the multi-objective loss function include: a first objective term, the squared deviation between the predicted value and the control target value, used to minimize the deviation between the future ammonia escape concentration and the desired control target (e.g., 2 ppm); a second objective term, the absolute value of the future system operating state assessment value, used to control the crystallization risk level of the equipment; a third objective term, the skewness index, used to penalize the non-uniformity of the spatial ammonia injection distribution and improve the spatial consistency of the ammonia injection system; a fourth objective term, the square of the rate of change of the technical parameters, used to limit the variation range of the ammonia injection setpoint and ensure the stability of system regulation; and a fifth objective term, a linear combination of the prediction uncertainty indices, used to weight the confidence level of the prediction results during the decision-making process, thereby reducing the risk of control deviation caused by prediction fluctuations.

[0089] By constructing and solving a multi-objective loss function encompassing ammonia slip prediction, ABS risk prediction, prediction uncertainty, ammonia injection skewness, and spatial distribution uniformity, the system can intelligently and in real-time weigh and optimize multiple interrelated and even conflicting control objectives. This allows the system to move beyond controlling a single indicator and proactively mitigate potential risks, address the impact of uneven spatial distribution, and fully consider the reliability of prediction results. Consequently, the overall intelligence and real-time performance of the system's control are improved.

[0090] refer to Figure 1 The neural network performance monitoring and adaptive calibration unit is used to execute the technical parameters and identify model degradation in the execution results; if model performance degradation is identified, the adaptive calibration mechanism is initiated to redeploy the shared neural network model.

[0091] Furthermore, such as Figure 3 As shown, the neural network performance monitoring and adaptive calibration unit specifically includes:

[0092] The system executes technical parameters and acquires real-time operating data from the data acquisition and preprocessing unit. Within a preset sliding time window, such as the past 24 hours, it continuously calculates the average absolute error between the predicted value and the actual ammonia slip concentration, as well as the average absolute error between the system operating status assessment value and the actual system operating status assessment value.

[0093] The mean absolute error is compared with a preset error threshold;

[0094] When the mean absolute error exceeds the error threshold for a preset number of times (e.g., 3 times), the model performance is determined to have degraded, and a model degradation alarm is triggered. For example, the ammonia escape prediction error threshold can be set to 0.5 ppm, and the ABS risk prediction error threshold can be set to 0.1 ppm. An adaptive calibration mechanism is initiated, which includes: retraining the shared neural network model using an enhanced feature sequence containing the latest running data, and redeploying the retrained shared neural network model to the multi-parameter prediction unit.

[0095] By calculating prediction errors in real time and comparing them with preset thresholds, the system quickly identifies model performance degradation caused by factors such as operating condition drift, coal type changes, or catalyst aging. Once performance degradation is detected, the system automatically initiates an adaptive calibration mechanism, retraining and redeploying the neural network model using the latest operating data. This not only ensures that the ammonia slip prediction and control system maintains high accuracy and stability during long-term operation, but also enables the system to dynamically adapt to changes in process characteristics, continuously optimize prediction accuracy, and ensure the effectiveness of upper-level optimization control decisions, thereby improving the real-time performance of the overall system control.

[0096] This invention encompasses data acquisition and preprocessing, characteristic analysis and spatial modeling, multi-parameter joint prediction (including uncertainty assessment), multi-objective optimization and control decision-making, and neural network performance monitoring and adaptive calibration. It can proactively and accurately predict ammonia slip concentration and related operational risks (such as ABS generation and uneven spatial distribution) during SCR denitrification, and achieves intelligent and refined closed-loop control. This not only helps enterprises reduce operating costs such as ammonia consumption while strictly meeting environmental emission standards and proactively avoid equipment safety risks caused by problems such as ABS generation, but also improves the robustness and specificity of control strategies by quantifying spatial distribution characteristics and model uncertainties. Furthermore, the adaptive model calibration mechanism ensures the long-term stability and efficiency of the system, ultimately improving the automation level and real-time control performance of the SCR denitrification system.

[0097] Example 2:

[0098] To further explore and verify the flexibility and real-time performance of the present invention, this embodiment, based on Embodiment 1, uses a data analysis-based real-time prediction and control system for ammonia escape concentration, including:

[0099] The parameter acquisition and processing unit is used to acquire and process multi-source operating data in the SCR denitrification system and output standardized data.

[0100] The characteristic analysis and modeling unit is used to calculate the spatial variance of ammonia concentration and the system operating status evaluation value based on the standardized data, and to deduce the skewness index by combining the sensor position matrix and the local flow velocity field, and output the enhanced feature sequence.

[0101] The multi-parameter prediction unit is used to employ a shared neural network model to jointly output the predicted value of ammonia escape concentration and the evaluation value of future system operating status based on the enhanced feature sequence, and to evaluate the prediction uncertainty index.

[0102] As shown in Table 1, a baseline method is set up: a standard LSTM model trained independently without sharing the structure or evaluating uncertainty (i.e., one LSTM is trained for ammonia escape, and another independent LSTM is trained for ABS risk). Compared to the independent LSTM model, by sharing the LSTM network structure and multi-task learning, the prediction error of ammonia escape concentration and the prediction error of future system operating state evaluation values ​​are lower. It can also evaluate and output the uncertainty index of the prediction results, thereby improving the robustness and safety of the entire control system.

[0103] Table 1 Comparison of Ammonia Slip Concentration Prediction Performance

[0104] Performance indicators unit Benchmark Method This invention Mean absolute error (ammonia slip concentration) ppm 0.65 0.35 Root mean square error (ammonia slip concentration) ppm 0.90 0.50 Correlation coefficient between predicted and actual values ​​(ammonia slip concentration) (none) 0.85 0.92 Accuracy (an assessment of the future operating status of the system) ％ 88.0 94.5 Accuracy (an assessment of future system performance) ％ 78.0 85.0 Recall rate (an assessment of the future operational status of the system) ％ 80.0 88.0 F1 score (Future system performance assessment value) (none) 0.79 0.86 Correlation between uncertainty indicators and actual error (none) not applicable 0.75 Average uncertainty calibration error ％ not applicable <5％

[0105] The optimized control decision unit is used to construct a multi-objective loss function based on the predicted value, the future system operating state assessment value, and the prediction uncertainty index, and to solve the optimal solution of the multi-objective loss function through a numerical optimization algorithm, and output technical parameters.

[0106] The neural network performance monitoring and adaptive calibration unit is used to execute the technical parameters and identify model degradation in the execution results; if model performance degradation is identified, the adaptive calibration mechanism is initiated to redeploy the shared neural network model.

[0107] To verify the performance and advantages of this invention in practical industrial applications, a typical 30-day continuous operating cycle was selected, and comparative experiments were conducted using both traditional control strategies and the system of this invention. The traditional control strategy employs a simple PID feedback control loop, with the primary objective of ensuring that ammonia slip meets the standards.

[0108] As shown in Table 2, the present invention can more effectively control secondary pollution. In addition, it can quantify and utilize information such as the spatial distribution of ammonia concentration and the ammonia injection skewness for optimized control, and has the ability to predict uncertainty assessment and model adaptive calibration, demonstrating a high degree of intelligence and robustness.

[0109] Table 2 Comparison of Economic Efficiency and Model Performance

[0110]

[0111] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A real-time prediction and control system for ammonia slip concentration based on data analysis, characterized in that, include: The parameter acquisition and processing unit is used to acquire and process multi-source operating data in the SCR denitrification system and output standardized data. The characteristic analysis and modeling unit is used to calculate the spatial variance of ammonia concentration and the system operating status evaluation value based on the standardized data, and to deduce the skewness index by combining the sensor position matrix and the local flow velocity field, and output the enhanced feature sequence. A multi-parameter prediction unit is used to employ a shared neural network model, based on the enhanced feature sequence, to jointly output the predicted value of ammonia escape concentration and the evaluation value of future system operating status, and to evaluate the prediction uncertainty index. An optimized control decision unit is used to construct a multi-objective loss function based on the predicted value, the future system operating state assessment value, and the prediction uncertainty index, and to solve the optimal solution of the multi-objective loss function through a numerical optimization algorithm, outputting technical parameters. The weighted objective terms of the multi-objective loss function include: a first objective term, the squared deviation between the predicted value and the control target value; a second objective term, the absolute value of the future system operating state assessment value; a third objective term, the skewness index; a fourth objective term, the square of the rate of change of the technical parameters; and a fifth objective term, a linear combination of the prediction uncertainty index. The neural network performance monitoring and adaptive calibration unit is used to execute the technical parameters and identify model degradation in the execution results; if model performance degradation is identified, the adaptive calibration mechanism is initiated to redeploy the shared neural network model.

2. The ammonia slip concentration real-time prediction and control system based on data analysis according to claim 1, characterized in that, The multi-source operational data includes: Ammonia and water vapor concentration data from N sensor locations; nitrogen oxide and oxygen content data at the SCR reactor inlet and outlet; flue gas temperature data at the SCR reactor inlet, outlet, and air preheater inlet; flue gas flow rate data; boiler load and total ammonia injection data; and coal sulfur parameters.

3. The real-time prediction and control system for ammonia slip concentration based on data analysis according to claim 1, characterized in that, The process of calculating the spatial variance of ammonia concentration and the system operating status assessment value includes: Based on the ammonia concentration data in the multi-source operation data, the squared deviation between the concentration value at each point and the average ammonia concentration is calculated, and the data is statistically summarized to obtain the spatial variance of ammonia concentration. Based on the ammonia concentration data, water vapor concentration data, coal sulfur content parameter data, oxygen content data, and flue gas temperature data, the flue gas sulfur trioxide concentration under the corresponding operating conditions is evaluated using empirical formulas, and the ABS dew point temperature is calculated. A risk function is constructed based on the difference between the ABS dew point temperature and the current actual flue gas temperature to generate a system operating status assessment value.

4. The ammonia slip concentration real-time prediction and control system based on data analysis according to claim 1, characterized in that, The process of reverse-engineering the skewness index includes: Using the Kriging interpolation algorithm, a two-dimensional ammonia concentration distribution map is reconstructed based on the ammonia concentration data and sensor location matrix in the standardized data, which is used to reflect the ammonia concentration field in each region under the current operating conditions. Combining the local velocity field information and the preset ammonia injection influence area model, a functional mapping relationship between concentration distribution and regional ammonia injection intensity is constructed, which is used to deduce the regional ammonia injection intensity distribution vector from the two-dimensional ammonia concentration distribution map. The skewness index is obtained by comparing the ammonia injection intensity distribution vector in the region with the target ammonia injection distribution vector and calculating the skewness index using Euclidean distance.

5. The real-time prediction and control system for ammonia escape concentration based on data analysis according to claim 1, characterized in that, The specific structure of the shared neural network model includes: The input layer is used to receive time-series samples of the enhanced feature sequence; A shared long short-term memory network layer, connected to the input layer, is used to perform temporal dependency modeling on the time series samples and extract deep temporal features. Furthermore, the shared long short-term memory network layer integrates a Dropout mechanism to generate forward prediction result samples, evaluate confidence intervals, and output prediction uncertainty indicators. The output layer, connected to the shared long short-term memory network layer, includes a first branch output path and a second branch output path, wherein: The first branch output path is used to output the predicted value, including a fully connected layer and a linear activation function; The second branch output path is used to output the future system operating state evaluation value, including the fully connected layer and the classification activation function.

6. The real-time prediction and control system for ammonia slip concentration based on data analysis according to claim 1, characterized in that, The neural network performance monitoring and adaptive calibration unit specifically includes: Within a preset sliding time window, the average absolute error between the predicted value and the actual ammonia escape concentration, as well as the average absolute error between the system operating status assessment value and the actual system operating status assessment value, are continuously calculated. The mean absolute error is compared with a preset error threshold; When the mean absolute error exceeds the error threshold for a preset number of times, the model performance is determined to be degraded, and a model degradation alarm is triggered. An adaptive calibration mechanism is initiated, which includes: retraining the shared neural network model using an enhanced feature sequence containing the latest running data, and redeploying the retrained shared neural network model to the multi-parameter prediction unit.

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