Ammonia escape concentration real-time prediction and control system based on data analysis

Abstract

The invention 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 as its input a multi-source timing enhancement feature generated via a particular calculation. The neural network not only learns time sequence dependence to predict future physical quantities, but also quantifies the uncertainty of self prediction through an integrated Dropout mechanism. An output layer of the neural network is designed to be of a double-branch structure, and a predicted value and another key system operation state evaluation value are output respectively. The system uses output information of the neural network model to derive technical parameters. Besides, a neural network performance monitoring and self-adaptive calibration unit is integrated, performance degradation of the neural network model can be identified, re-training and re-deployment of the neural network model can be triggered, and the self-adaptive learning ability of the neural network is shown.

Description

Technical Field

[0001] The present invention relates to the technical field of biological neural networks, and in particular to a real-time prediction and control system for ammonia escape concentration based on data analysis. Background Art

[0002] Accurately predicting and controlling key parameters, such as ammonia slip concentration, is crucial for industrial process optimization, especially in the control of complex systems like Selective Catalytic Reduction (SCR) denitrification. The limitations of traditional methods have prompted the industry to seek more advanced computational intelligence solutions. Deep neural networks, as a powerful, biologically inspired, nonlinear data modeling tool, have shown great potential for application.

[0003] However, there are still many challenges when directly applying existing deep neural networks to SCR systems. First, the operating data of SCR systems exhibits characteristics such as multi-source heterogeneity, high-dimensional strong coupling, time series, and strong noise. This places high demands on the learning ability and architectural design of neural networks. Many traditional neural network models have difficulty effectively integrating multi-scale information, and it is also difficult to accurately extract key time series features from strongly coupled relationships through their deep structures like deep neural networks. They are also sensitive to noise, resulting in insufficient robustness and generalization capabilities of neural network models.

[0004] Secondly, the dynamic, time-varying nature of industrial processes (such as load fluctuations and catalyst activity decay) poses 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 the adaptive capabilities for automated retraining and optimization of neural network model parameters. This makes them difficult to maintain reliability over the long term.

[0005] Furthermore, a key challenge is that most current deep neural network applications primarily provide deterministic predictions, but lack a quantitative assessment of the uncertainty of these predictions. In industrial control, which requires risk considerations and robust decision-making, the inability to assess the credibility of predictions is a significant drawback.

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

[0007] The purpose of the present invention is to provide a real-time prediction and control system for ammonia slip concentration based on data analysis, so as to overcome the problems of inaccurate prediction and delayed response in the ammonia slip control of the existing SCR denitrification system. By accurately collecting and processing multi-source operating data, deeply exploring key indicators such as the spatial distribution characteristics of ammonia concentration, ABS generation risk and ammonia injection skewness, and using advanced shared neural network models to jointly predict ammonia slip and system operating status and evaluate its uncertainty, the optimal technical parameters are output through an intelligent decision-making algorithm containing multiple optimization objectives, and the model performance has the ability of online monitoring and adaptive calibration, thereby achieving precise control of ammonia slip, and ultimately achieving the overall economy of system operation and real-time control while meeting environmental emission standards.

[0008] To achieve the above object, the present invention provides the following technical solutions:

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

[0010] Parameter acquisition and processing unit, used to collect and process multi-source operating data in the SCR denitrification system and output standardized data;

[0011] a characteristic analysis and modeling unit, configured to calculate the spatial variance of ammonia concentration and the system operation status evaluation value based on the standardized data, and to infer the skewness index by combining the sensor position matrix and the local flow velocity field, and output an enhanced characteristic sequence;

[0012] a multi-parameter prediction unit, configured to employ a shared neural network model to jointly output a predicted value of ammonia escape concentration and an estimated value of future system operation status based on the enhanced feature sequence, and to evaluate a prediction uncertainty index;

[0013] an optimization and control decision-making unit, configured to construct a multi-objective loss function based on the predicted value, the future system operation state evaluation 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 of the execution results; if it is identified as model performance degradation, the adaptive calibration mechanism is started to redeploy the shared neural network model.

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

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

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

[0018] Based on the ammonia concentration data in the multi-source operation data, calculating the square of the deviation between the concentration value at each point and the average ammonia concentration, and performing statistical aggregation to obtain the spatial variance of the ammonia concentration;

[0019] Based on the ammonia concentration data, water vapor concentration data, coal sulfur parameter data, oxygen content data, and flue gas temperature data, an empirical formula is used to estimate the flue gas sulfur trioxide concentration under the corresponding operating conditions, 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 operation status evaluation value.

[0021] Furthermore, the process of inferring the skewness index includes:

[0022] Using a Kriging interpolation algorithm, based on the ammonia concentration data in the standardized data and the sensor position matrix, a two-dimensional ammonia concentration distribution map is reconstructed to reflect the ammonia concentration field in each area under the current working conditions;

[0023] Combining the local velocity field information and a preset ammonia injection impact area model, constructing a functional mapping relationship between concentration distribution and regional ammonia injection intensity, for inferring a regional ammonia injection intensity distribution vector from the two-dimensional ammonia concentration distribution map;

[0024] The regional ammonia injection intensity distribution vector is compared with the target ammonia injection distribution vector, and the skewness index is obtained by using Euclidean distance calculation.

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

[0026] An input layer, configured to receive time series samples of the enhanced feature sequence;

[0027] a shared long short-term memory network layer, connected to the input layer, for performing temporal dependency modeling on the time series samples and extracting deep temporal features; and the shared long short-term memory network layer is integrated with a dropout mechanism for generating forward prediction result samples, evaluating confidence intervals, and outputting a prediction uncertainty indicator;

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

[0029] The first branch output path is used to output the predicted value, and 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 operation status evaluation value, and includes at least one fully connected layer and a classification activation function.

[0031] Furthermore, the weighted target items of the multi-objective loss function include: the first target item, the square deviation between the predicted value and the control target value; the second target item, the absolute value of the future system operation status evaluation value; the third target item, the skewness index; the fourth target item: the square of the rate of change of the technical parameters; the fifth target item: the linear combination of the prediction uncertainty index.

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

[0033] Continuously calculating, within a preset sliding time window, the mean absolute error between the predicted value and the actual ammonia slip concentration, and the mean absolute error between the system operating status assessment value and the actual system operating status assessment value;

[0034] Comparing the mean absolute error 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, wherein the adaptive calibration mechanism comprises: retraining the shared neural network model using an enhanced feature sequence including the latest operating data, and redeploying the retrained shared neural network model to the multi-parameter prediction unit.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] 1. In terms of data acquisition and processing, this invention comprehensively collects multi-source operational data and applies deep feature engineering techniques to not only accurately calculate the spatial variance of ammonia concentration and the system operating status assessment value, but also reversely derives the skewness index by combining sensor location and flow velocity fields, successfully constructing an enhanced feature sequence. Furthermore, it uses a shared neural network model to conduct joint predictions and introduces a prediction uncertainty assessment process, providing comprehensive and reliable information support for subsequent optimization work, enhancing situational awareness of ammonia escape and related risks in complex operating environments, and laying a solid foundation for precise control.

[0039] 2. In constructing an optimization strategy, this invention designs a multi-objective optimization function that encompasses forecast bias, future risk, skewness indicators, rate of change of technical parameters, and forecast uncertainty. Leveraging a numerical optimization algorithm, this function accurately finds the optimal balance between numerous objectives, even those that conflict with each other. The resulting decision-making technical parameters effectively control ammonia slip while also fully balancing system safety, ammonia injection uniformity, and control stability, enhancing the intelligence and overall effectiveness of the overall control strategy.

[0040] 3. This invention incorporates a neural network performance monitoring and adaptive calibration unit. This module monitors the deviation between predicted and actual values ​​in real time, automatically identifying model performance degradation caused by factors such as changing operating conditions or equipment aging. Upon detecting any signs of performance degradation, the system rapidly activates an adaptive calibration mechanism, retraining the model using the latest data and promptly redeploying the model. This ensures the long-term effectiveness and robustness of the prediction and control system, safeguarding its accuracy in complex and dynamic environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 The present invention provides a structural schematic diagram of a real-time prediction and control system for ammonia slip concentration based on data analysis;

[0042] Figure 2 Provides a structural schematic diagram of a shared neural network model for the present invention;

[0043] Figure 3 A flowchart illustrating the implementation process of a neural network performance monitoring and adaptive calibration unit is provided for the present invention. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0045] See also Figures 1 to 3 The present invention provides a real-time prediction and control system for ammonia escape concentration based on data analysis. The technical solution is as follows:

[0046] Example 1:

[0047] This example uses the SCR (Selective Catalytic Reduction) denitrification system of a typical 600MW coal-fired power generation unit as the application object. The company needs to meet strict emission standards during operation, such as controlling the emission concentration to 50mg / Nm 3 At the same time, the amount of ammonia injection must be controlled to ensure that the ammonia escape concentration does not exceed 2ppm to avoid the generation of ammonium bisulfate (ABS) in downstream equipment such as air preheaters and causing blockage. Traditional control methods mainly rely on experience adjustment and hysteresis feedback. When faced with complex operating conditions such as boiler load changes and coal type fluctuations, excessive or insufficient ammonia injection often occurs. To solve this problem, a real-time prediction and control system for ammonia escape concentration based on data analysis is introduced, such as Figure 1 Shown, including:

[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 operation data includes:

[0050] Ammonia concentration and water vapor concentration data from N sensor points. Four optical monitoring terminals from online ammonia / water analyzers are evenly distributed in a 2×2 matrix across the key monitoring section of the SCR reactor outlet flue (8m×10m in this example). Each terminal independently collects real-time ammonia concentration and water vapor volume fraction data at its location. All terminals are connected to a data acquisition host via optical cables, enabling multi-point synchronous monitoring and data aggregation.

[0051] Nitrogen oxide concentration data and oxygen content data. By placing online flue gas analyzers at the inlet and outlet flues of the SCR reactor, real-time nitrogen oxide concentration data and oxygen content data can be obtained.

[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 obtain flue gas temperature in real time.

[0053] Flue gas flow data. Install a matrix flow meter at the SCR outlet flue (or at a representative location within the main flue). This flow meter uses the principle of multi-point differential pressure sampling, combined with preset flue cross-sectional area parameters, to calculate and output the average flow rate and volume flow of the flue gas.

[0054] Boiler load data and total ammonia injection data. From the power plant's distributed control system, read the actual operating load of the boiler and the total amount of ammonia or urea solution injected by the total ammonia injection system in real time.

[0055] Coal sulfur parameter data: Obtain the sulfur content of the currently used or most recently charged coal from the power plant fuel management system through an interface.

[0056] All collected data is accurately timestamped to ensure time synchronization across different data sources. Multi-source data forms the fundamental input for this system, providing comprehensive and critical real-time data support for subsequent prediction and optimization algorithms. This helps accurately characterize the spatial distribution characteristics of parameters such as ammonia concentration and flue gas flow rate, thereby achieving the coordinated optimization goals of ammonia injection cost control and equipment operational safety while meeting environmental protection targets.

[0057] Furthermore, to ensure the quality of data input to the subsequent analysis and modeling modules, the following preprocessing is performed on the collected and initially summarized data: the original second-level or higher frequency sampling data is aggregated into a 1-minute period (such as calculating the average value), and different data sources are aligned to a unified minute-level timestamp. For individual minute data missing due to communication interruption, sensor maintenance, etc., a linear interpolation method is used to fill in the missing data based on the previous and next valid data points to ensure data continuity and integrity. All numerical feature data used for modeling are Z-score standardized, that is, each feature is normalized to zero mean and unit variance to eliminate dimensional differences. After the preprocessing process, the standardized data is finally output as the input basis for the 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 operation status evaluation value based on the standardized data, and to infer the skewness index by combining the sensor position matrix and the local flow velocity field, and output an enhanced characteristic sequence.

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

[0060] Based on the ammonia concentration data in the multi-source operational data, the square of the deviation between the concentration value at each point 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 points at the same minute are extracted, and the ammonia concentration values ​​at the four points are arithmetic averaged to obtain the average ammonia concentration of the cross section at that moment. The deviation between the ammonia concentration value at each point and the average ammonia concentration is then calculated and squared. The squared deviation values ​​of the four points are added and divided by 4 to obtain the spatial variance of the ammonia concentration at that moment. A higher value indicates a more uneven spatial distribution of the ammonia concentration.

[0061] To assess the risk of ammonium bisulfate (ABS) generation in downstream equipment such as air preheaters, an empirical formula is used to estimate the flue gas sulfur trioxide concentration under corresponding operating conditions based on ammonia concentration data, water vapor concentration data, coal sulfur parameter data, oxygen content data, and flue gas temperature data, 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] Among them, the empirical formula can be expressed as:

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

[0064] Then according to the calculated SO 3,Est , calculate the current ABS theoretical dew point temperature according to the ABS dew point empirical formula optimized for the power plant operating conditions, 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 is the theoretical dew point temperature of ABS, NH 3,AVG is the average ammonia concentration, H2O AVG is the average water vapor concentration, K1, K2, K3 and K4 are empirical constants, which can also be obtained through multiple linear regression analysis. ∈ is a very small positive number used to prevent the parameter 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 For comparison, set a safety temperature margin T margin , for example, 5℃, according to the risk function, the system operation status assessment value R ABS It can be expressed as:

[0066]

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

[0068] By quantifying the spatially uneven distribution of ammonia slip and the risk of ABS formation, these key factors are converted 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 predictions and the reliability of ABS risk warnings. Furthermore, it provides a more refined and targeted basis for decision-making for optimized control.

[0069] Furthermore, the process of inferring the skewness index includes:

[0070] A two-dimensional ammonia concentration distribution map is reconstructed using the Kriging interpolation algorithm based on the ammonia concentration data and sensor location matrix in the standardized data to reflect 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 restoring a continuous spatial distribution field using a small number of discrete sampling points. In this embodiment, a total of four ammonia concentration sampling points are arranged on the SCR reactor outlet cross-section. Using the Kriging interpolation algorithm, the data from these four points can be spatially interpolated into a two-dimensional ammonia concentration distribution map with a 16×20 grid, which is used to visualize the ammonia concentration field in each region within the flue cross-section.

[0071] Combining the local velocity field information and the preset ammonia injection impact area model, a functional mapping relationship between concentration distribution and regional ammonia injection intensity is constructed to reversely derive the regional ammonia injection intensity distribution vector from the two-dimensional ammonia concentration distribution map.

[0072] The ammonia injection impact area model assumes that the ammonia injection grid (AIG) is divided into several virtual ammonia injection zones. For example, four independent virtual zones are divided corresponding to four sensor locations. Each zone has an independent regulatory contribution to the downstream ammonia concentration distribution. In the model, a predefined weight or functional relationship is established between the ammonia injection intensity of each virtual ammonia injection zone and the concentration at different locations in the two-dimensional ammonia concentration distribution map. This functional relationship 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 algorithm, the actual 2D ammonia concentration distribution map reconstructed is used as the fitting target. The functional mapping is then reversed to obtain the optimal matching regional ammonia injection intensity distribution vector.

[0074] Finally, the regional ammonia injection intensity distribution vector obtained by reverse engineering 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. A larger value of the ammonia injection skewness index indicates a greater deviation between the current equivalent ammonia injection distribution and the ideal ammonia injection distribution, indicating potential problems in the ammonia injection system, such as uneven distribution, biased injection, or blockage in some areas.

[0075] Among them, 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 operating parameters such as boiler load, and represents the regional ammonia injection intensity ratio required to achieve optimal mixing and denitrification efficiency under the operating conditions.

[0076] The skewness indicator is not limited to alarms and diagnostics; it can also indicate potential blockages or improper adjustments in the ammonia injection system. More importantly, it serves as a key input or optimization target for the optimization control decision module. The control system uses this skewness indicator when adjusting the total ammonia injection rate. This helps minimize ammonia slip, mitigate ABS risks, and maintain an ideal ammonia injection distribution. Even without directly controlling the zones, it can influence the conservatism of total injection decisions, thereby indirectly improving overall denitration efficiency and catalyst utilization uniformity.

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

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

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

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

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

[0082] The first branch output path is used to output the predicted value. Its structure includes a fully connected layer with 32 neurons and a Reluctant Unit (ReLU) activation function. It is then connected to an output neuron with 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 status assessment value. The structure includes a fully connected layer with 16 neurons and a Reluctant Unit (ReLU) activation function. This layer is then connected to an output neuron with a Sigmoid classification activation function. This output is a continuous value between 0 and 1, quantifying the probability level of ABS crystallization risk for the next 15 minutes.

[0084] The system uses historical operating data for offline training. The recommended data range is at least three to six consecutive months of minute-level operating data. A joint loss function is used during training, performing a weighted summation of the loss terms for both tasks. The ammonia escape concentration prediction task uses the mean squared error loss function, while the ABS risk prediction task uses the binary cross-entropy loss function.

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

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

[0087] Among them, the numerical optimization algorithm selects the sequential quadratic programming (SQP) algorithm or a similar constrained optimization algorithm, and within the physical upper and lower limit constraints of the total ammonia injection amount, aims to minimize the multi-objective loss function and obtain the optimal technical parameters.

[0088] Furthermore, the weighted target items of the multi-objective loss function include: the first target item, the square deviation between the predicted value and the control target value, which is used to minimize the deviation between the future ammonia escape concentration and the desired control target (such as 2ppm); the second target item, the absolute value of the future system operation status evaluation value, which is used to control the equipment crystallization risk level; the third target item, the skewness index, which is used to punish the unevenness of the spatial ammonia injection distribution and improve the spatial consistency of the ammonia injection system; the fourth target item: the square of the rate of change of the technical parameters, which is used to limit the change range of the ammonia injection set value and ensure the smoothness of the system regulation; the fifth target item: the linear combination of the prediction uncertainty indicators, which is used to weightedly consider the confidence of the prediction results in the decision-making process, thereby reducing the control deviation risk 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 achieves intelligent, real-time trade-offs and optimization among multiple, even conflicting, control objectives. This enables the system to move beyond single-target control and proactively mitigate potential risks, proactively address the impact of spatially uneven distribution, and fully consider the reliability of prediction results. This improves the overall intelligence and real-time nature of the system's control performance.

[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 of the execution results; if it is identified as model performance degradation, the adaptive calibration mechanism is started to redeploy the shared neural network model.

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

[0092] Execute technical parameters and obtain real-time operating data from the data acquisition and preprocessing unit. Within a preset sliding time window, such as the past 24 hours, continuously calculate the mean absolute error between the predicted value and the actual ammonia slip concentration, and the mean absolute error between the system operating status assessment value and the actual system operating status assessment value;

[0093] Comparing the mean absolute error with a preset error threshold;

[0094] When the mean absolute error exceeds the error threshold for a preset number of times (e.g., three times), model performance is determined to be degraded, and a model degradation alarm is triggered. For example, the ammonia slip prediction error threshold can be set to 0.5 ppm, and the ABS risk prediction error threshold can be set to 0.1. An adaptive calibration mechanism is initiated, which includes retraining the shared neural network model using an enhanced feature sequence containing the latest operating 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 against 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 the high accuracy and stability of the ammonia slip prediction and control system over 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] The present invention covers 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 predict the ammonia escape concentration and related operational risks (such as ABS generation and spatial uneven distribution) in the SCR denitrification process in a forward-looking and high-precision manner, and realize 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 actively avoid equipment safety risks caused by problems such as ABS generation, but also improves the robustness and pertinence of control strategies by quantifying spatial distribution characteristics and model uncertainties. In addition, the adaptive model calibration mechanism ensures the long-term stability and efficiency of the system, and ultimately improves the automation level and real-time control 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 the first embodiment, uses a real-time prediction and control system for ammonia slip concentration based on data analysis, including:

[0099] 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.

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

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

[0102] As shown in Table 1, a baseline approach uses a standard LSTM model trained separately, without shared structure or uncertainty assessment (i.e., one LSTM is trained for ammonia escape and another independent LSTM is trained for ABS risk). Compared to independent LSTM models, the shared LSTM network structure and multi-task learning achieve lower prediction errors for ammonia escape concentration and future system operating state assessments. Uncertainty indicators can also be assessed and output for the prediction results, thereby improving the robustness and safety of the entire control system.

[0103] Table 1 Comparison of ammonia escape concentration prediction performance

[0104] Performance indicators unit Benchmark Method The present invention Mean absolute error (ammonia escape concentration) ppm 0.65 0.35 Root mean square error (ammonia escape concentration) ppm 0.90 0.50 Correlation coefficient between predicted value and actual value (ammonia escape concentration) (none) 0.85 0.92 Accuracy (future system operation status evaluation value) ％ 88.0 94.5 Accuracy (future system operation status evaluation value) ％ 78.0 85.0 Recall rate (evaluation value of future system operation status) ％ 80.0 88.0 F1 score (future system operation status evaluation value) (none) 0.79 0.86 Correlation between uncertainty index and actual error (none) not applicable 0.75 Average uncertainty calibration error ％ not applicable <5％

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

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

[0107] To verify the performance and advantages of the present invention in actual industrial applications, a typical 30-day continuous operation cycle was selected to conduct comparative experiments using both a traditional control strategy and the present invention system. The traditional control strategy used a simple PID feedback control loop, with the primary goal of ensuring that ammonia slip met the standard.

[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 uneven spatial distribution of ammonia concentration and the skewed state of ammonia injection for optimized control. It also has the ability to evaluate prediction uncertainty and perform model adaptive calibration, demonstrating a high degree of intelligence and robustness.

[0109] Table 2 Comparison of economic efficiency and model performance

[0110]

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

Claims

1. A real-time prediction and control system for ammonia escape concentration based on data analysis, characterized in that: include: Parameter acquisition and processing unit, used to collect and process multi-source operating data in the SCR denitrification system and output standardized data; a characteristic analysis and modeling unit, configured to calculate the spatial variance of ammonia concentration and the system operation status evaluation value based on the standardized data, and to infer the skewness index by combining the sensor position matrix and the local flow velocity field, and output an enhanced characteristic sequence; a multi-parameter prediction unit, configured to employ a shared neural network model to jointly output a predicted value of ammonia escape concentration and an estimated value of future system operation status based on the enhanced feature sequence, and to evaluate a prediction uncertainty index; an optimization and control decision-making unit, configured to construct a multi-objective loss function based on the predicted value, the future system operation state evaluation 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; The neural network performance monitoring and adaptive calibration unit is used to execute the technical parameters and identify model degradation of the execution results; if it is identified as model performance degradation, the adaptive calibration mechanism is started to redeploy the shared neural network model.

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

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, calculating the square of the deviation between the concentration value at each point and the average ammonia concentration, and performing statistical aggregation to obtain the spatial variance of the ammonia concentration; Based on the ammonia concentration data, water vapor concentration data, coal sulfur parameter data, oxygen content data, and flue gas temperature data, an empirical formula is used to estimate the flue gas sulfur trioxide concentration under the corresponding operating conditions, 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 operation status evaluation value.

4. 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 inferring the skewness indicator includes: Using a Kriging interpolation algorithm, based on the ammonia concentration data in the standardized data and the sensor position matrix, a two-dimensional ammonia concentration distribution map is reconstructed to reflect the ammonia concentration field in each area under the current working conditions; Combining the local velocity field information and a preset ammonia injection impact area model, constructing a functional mapping relationship between concentration distribution and regional ammonia injection intensity, for inferring a regional ammonia injection intensity distribution vector from the two-dimensional ammonia concentration distribution map; The regional ammonia injection intensity distribution vector is compared with the target ammonia injection distribution vector, and the skewness index is obtained by using Euclidean distance calculation.

5. The real-time prediction and control system for ammonia slip concentration based on data analysis according to claim 1, characterized in that: The structure of the shared neural network model specifically includes: An input layer, configured to receive time series samples of the enhanced feature sequence; a shared long short-term memory network layer, connected to the input layer, for performing temporal dependency modeling on the time series samples and extracting deep temporal features; and the shared long short-term memory network layer is integrated with a dropout mechanism for generating forward prediction result samples, evaluating confidence intervals, and outputting a prediction uncertainty indicator; The output layer is connected to the shared long short-term memory network layer, and 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 operation status evaluation value, including a fully connected layer and a 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 weighted target items of the multi-objective loss function include: the first target item, the square deviation between the predicted value and the control target value; the second target item, the absolute value of the future system operation status evaluation value; the third target item, the skewness index; the fourth target item: the square of the rate of change of the technical parameters; the fifth target item: the linear combination of the prediction uncertainty index.

7. 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: Continuously calculating, within a preset sliding time window, the mean absolute error between the predicted value and the actual ammonia slip concentration, and the mean absolute error between the system operating status assessment value and the actual system operating status assessment value; Comparing the mean absolute error 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, wherein the adaptive calibration mechanism comprises: retraining the shared neural network model using an enhanced feature sequence including the latest operating data, and redeploying the retrained shared neural network model to the multi-parameter prediction unit.

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