SCR denitration efficiency optimization method based on multi-parameter linkage regulation

Abstract

The application discloses a kind of based on multi-parameter linkage control's SCR denitration efficiency optimization method.It includes the following steps: constructing the complete initial thermal data set of SCR denitration system, the thermal variable in complete initial thermal data set is preprocessed and correlation analysis, lock core feature input vector;SCR denitration system outlet nitrogen oxide concentration is constructed for predicting LSTM-DNN hybrid prediction model;By LSTM-DNN hybrid prediction model, the denitration outlet nitrogen oxide concentration of future period is predicted in real time online, when the denitration outlet nitrogen oxide concentration of predicted exists the trend of deviating from set target, or upstream pellet induration condition occurs violent fluctuation, combined with optimization algorithm, reverse optimization calculation is carried out, and the best linkage control parameter combination is dynamically sought between thermal variable, output control instruction is achieved, realize the fine balance of denitration efficiency maximization and ammonia escape minimization.The application realizes the global collaborative regulation and fine real-time optimization of each process parameter.

Description

Technical Field

[0001] This invention belongs to the field of flue gas denitrification and air pollution control technology, specifically referring to a method for optimizing SCR denitrification efficiency based on multi-parameter linkage control. Background Technology

[0002] Selective catalytic reduction (SCR) denitrification is affected by several key process parameters, mainly including the ammonia-to-nitrogen molar ratio (NH3 / NO), the gas space velocity ratio (GHSV), and the flue gas temperature. In actual industrial operation, denitrification adjustment is often based on "single-parameter control" or experience-based "rule control," such as dynamically controlling the ammonia-to-nitrogen ratio by simply adjusting the ammonia injection rate, thereby attempting to blindly improve denitrification efficiency.

[0003] However, the actual SCR reaction process has the following high complexity and multidimensional coupling characteristics: First, there is a strong nonlinear coupling relationship between the ammonia-nitrogen ratio and the flue gas temperature: the increase in temperature will accelerate the chemical reaction rate, but at the same time it will lead to a decrease in the amount of NH3 adsorbed on the catalyst surface and an increase in the amount of NH3 desorbed, causing the original optimal ammonia-nitrogen ratio to shift. Second, there is a significant synergistic effect between ammonia concentration and space velocity ratio: under high space velocity conditions, the residence time of flue gas in the catalyst bed is extremely short. At this time, simply increasing the ammonia concentration or the amount of ammonia injected has a very limited effect on promoting denitrification efficiency. However, under low space velocity conditions, this promoting effect is quite significant. Third, the effect of flue gas temperature on the reaction rate has obvious exponential characteristics: the SCR catalytic reaction is essentially a first-order reaction, and its reaction rate constant strictly satisfies the Arrhenius equation. Even a small fluctuation in temperature will cause a drastic change in the reaction rate. Fourth, in actual industrial production, operating conditions fluctuate abnormally frequently: inlet NO x Sudden changes in concentration, drastic fluctuations in flue gas temperature, daily degradation of catalyst activity, and ash accumulation and blockage can all cause the system's denitrification efficiency to frequently deviate from the ideal range. Fifth, the problem of ammonia escape is difficult to control precisely: although excessive ammonia injection can improve denitrification efficiency in the short term, it will cause a large amount of unreacted ammonia to be discharged with the flue gas (i.e., ammonia escape). This will not only waste ammonia water, but also react with SO3 in the flue gas to generate highly viscous ammonium bisulfate, which will lead to blockage and corrosion of the downstream air preheater, seriously threatening the safe and stable operation of the entire unit.

[0004] Therefore, relying solely on traditional single-parameter control or linear adjustment based on fixed experience is simply insufficient to achieve stable, efficient, and low-ammonia-slip denitrification operation under complex and fluctuating working conditions.

[0005] Multi-parameter control, due to the strong coupling and time lag between variables, makes real-time manual processing extremely difficult. For example, traditional multiple linear regression or SPSS statistical models used in existing technologies are severely lacking in the ability to characterize the nonlinear characteristics and time series lag properties of complex industrial processes. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention proposes a method for optimizing SCR denitrification efficiency based on multi-parameter linkage control. This method integrates nonlinear dynamic characteristics, temporal characteristics, interactive coupling analysis, and feedforward predictive control. It is a quantifiable, optimizable, and dynamically correctable multi-parameter collaborative control method that achieves global collaborative adjustment and refined real-time optimization of various process parameters.

[0007] To achieve the above objectives, this invention designs a method for optimizing SCR denitrification efficiency based on multi-parameter linkage control, which is characterized by including the following steps: S1) Construct a complete initial thermal dataset for the SCR denitrification system, preprocess and perform correlation analysis on the thermal variables in the complete initial thermal dataset, and lock in the core feature input vector; The thermal variables in the complete initial thermal dataset include the basic thermal parameters of the SCR denitrification system during operation, the ammonia-nitrogen molar ratio, and the space velocity ratio. S2) Construct an LSTM-DNN hybrid prediction model for predicting the concentration of nitrogen oxides at the outlet of an SCR denitrification system; the LSTM-DNN hybrid prediction model includes an LSTM module and a DNN module; the LSTM module is used to capture and extract the temporal feature vectors in the SCR denitrification reaction process; the DNN module is used to deeply mine the high-dimensional nonlinear spatial mapping relationship between the ammonia-nitrogen ratio, space velocity ratio, average flue gas temperature of the catalyst bed and the concentration of nitrogen oxides at the denitrification outlet in the spatial dimension; S3) The LSTM-DNN hybrid prediction model is used to predict the concentration of nitrogen oxides at the denitrification outlet in real time for future periods. When the predicted concentration of nitrogen oxides at the denitrification outlet deviates from the set target, or when the upstream pellet roasting conditions fluctuate drastically, the LSTM-DNN hybrid prediction model is used as the forward operator and combined with the optimization algorithm to perform reverse optimization calculation. The optimal combination of linkage control parameters is dynamically sought among the thermal variables, and control commands are output to achieve a fine balance between maximizing denitrification efficiency and minimizing ammonia slip.

[0008] Further, in S1), the basic thermal parameters include: the concentration of nitrogen oxides (NOx) in the inlet flue gas of the SCR denitrification system. input The concentration of nitrogen oxides (NOx) in the outlet flue gas of the SCR denitrification system outputThe average flue gas temperature T of the catalyst bed in the SCR denitrification reactor, the total volumetric flow rate F of the inlet flue gas in the SCR denitrification reactor, and the effective physical volume V of the catalyst packed inside the SCR denitrification reactor.

[0009] Furthermore, in S1), a real-time soft measurement and feature calculation module is constructed. Through the established parameter coupling conversion mechanism, the basic thermal parameters are dynamically converted in real time to calculate the ammonia-nitrogen molar ratio and space velocity ratio. The ammonia-nitrogen molar ratio coupling conversion mechanism includes converting the injected ammonia flow rate collected by the sensor in real time into the pure ammonia molar flow rate, and combining it with the real-time measured total volumetric flow rate of the inlet flue gas and the nitrogen oxide concentration (NO) in the inlet flue gas. input Calculate the molar flow rate of nitrogen oxides in the inlet flue gas and the molar flow rate of nitrogen oxides in the outlet flue gas, and obtain the current ammonia-nitrogen molar ratio of the denitrification system in real time; The space velocity ratio coupling conversion mechanism includes dividing the total volumetric flow rate F of the inlet flue gas of the SCR denitrification reactor collected in real time by the effective physical volume V of the catalyst packed inside the SCR denitrification reactor, and calculating the space velocity ratio of the current system in real time.

[0010] Furthermore, in S1), the preprocessing of thermal variables includes removing outlier samples and standardizing the cleaned thermal variables. The removal of abnormal samples is performed using the Isolation Forest algorithm. The Isolation Forest algorithm calculates the anomaly score of the thermal sample points. If the anomaly score is greater than the anomaly score threshold, the removal and cleaning operation is automatically performed. The standardization process uses the max-min normalization method to standardize the cleaned thermal variables, linearly mapping all cleaned feature data to the [0,1] interval.

[0011] Furthermore, in S1), the anomaly score of the thermal sample point is calculated using the following formula. In the formula, This represents the outlier score for thermal sample point x. This represents the mean path length of the data within the forest. This represents the average path length where a search fails in a binary search tree. For harmonic series, n represents the number of samples. is Euler's constant.

[0012] Furthermore, in S1), a nonlinear feature screening mechanism is used for correlation analysis; the nonlinear feature screening mechanism is a feature screening mechanism that combines Spearman rank correlation coefficient and maximum mutual information coefficient, which is used to comprehensively quantify the linear and nonlinear correlation strength between each parameter and the nitrogen oxide concentration at the denitrification outlet, and finally accurately lock the core feature input vector from the relevant thermal variables.

[0013] Further, in S2), the specific steps of the LSTM-DNN hybrid prediction model predicting the nitrogen oxide concentration at the outlet of the SCR denitrification system include: S21) Extract the time-series dynamic feature vector h through the LSTM network channels. LSTM ; S22) Extract the nonlinear strongly coupled feature vector h spanning space through the DNN network channels. DNN ; S23) Construct a feature fusion layer to integrate the temporal feature vector h extracted by LSTM. LSTM The spatial nonlinear feature vector h extracted by DNN DNN The features are concatenated to synthesize a multi-dimensional fused feature vector h. fusion ; S24) The multidimensional fusion feature vector h fusion The input is fed to a fully connected output layer, and through the final mapping matrix and linear bias, a high-precision predicted outlet nitrogen oxide concentration is output.

[0014] Furthermore, in S24), the predicted outlet nitrogen oxide concentration is expressed by the following formula. In the formula, ŷ indicates the predicted export concentration of nitrogen oxides. This represents the weight matrix of the output layer. This represents the bias term of the output layer. h fusion This represents a multidimensional fused feature vector.

[0015] The advantages of this invention are: 1. This invention completely abandons the simple assumption in traditional technology that the SCR response is a multiple linear regression. It creatively introduces an LSTM-DNN hybrid deep learning model, which deeply integrates the temporal dependence of the time dimension with the high-dimensional nonlinearity of the spatial dimension, and can extremely accurately depict the essential law of the intertwining of multiple variables in the SCR response process. 2. In the data preprocessing stage, this invention combines the isolated forest algorithm and the Spearman-MIC dual feature screening mechanism, which can not only efficiently remove random noise and outlier data that are common in industrial sites, but also accurately capture the hidden nonlinear interaction features between parameters, greatly improving the robustness, stability and generalization ability of the prediction model under complex, changeable and harsh industrial conditions. 3. The optimization method proposed in this invention is not limited to passive monitoring of the predicted target, but also achieves active feedforward coordinated control of ammonia-nitrogen ratio, space velocity ratio and average flue gas temperature by combining a high-precision LSTM-DNN hybrid deep learning model with a multi-parameter dynamic optimization mechanism. This fundamentally overcomes the industry problems of large lag in the control of nitrogen oxides at the end of industrial pellet roasting, blind regulation, and easy over-injection of ammonia and ammonia escape exceeding the standard. It has great value for energy saving, consumption reduction and environmental protection applications.

[0016] This invention is based on a multi-parameter linkage control method for optimizing SCR denitrification efficiency. It constructs an SCR denitrification outlet nitrogen oxide concentration prediction and control system based on an LSTM-DNN hybrid deep learning model. It integrates nonlinear dynamic characteristics, temporal characteristics, interactive coupling analysis and feedforward predictive control, and realizes global coordinated adjustment and refined real-time optimization of various process parameters. Attached Figure Description

[0017] Figure 1 This is a flowchart of the present invention; Figure 2 The model training results are used to apply the present invention in the embodiments; Figure 3 The model training error curve is used to illustrate the application of the present invention in the embodiments; Figure 4 To compare the model-predicted concentration and the actual outlet concentration in the embodiments of the present invention. Detailed Implementation

[0018] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] In the description of this invention, it should be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention.

[0020] This invention, through in-depth research, has discovered that NO at the SCR denitrification outlet... xThe concentration is essentially the result of the combined and deeply intertwined effects of three core key parameters: the ammonia-to-nitrogen ratio (R), the space velocity ratio (G), and the temperature (T). Only by establishing a highly accurate data-driven mathematical model and performing real-time collaborative optimization of multiple parameters can a true improvement in denitrification efficiency and a reduction in operating costs be achieved.

[0021] like Figure 1 As shown, this invention proposes a method for optimizing SCR denitrification efficiency based on multi-parameter linkage control, comprising the following steps: S1) Construct a complete initial thermal dataset for the SCR denitrification system, preprocess and perform correlation analysis on the thermal variables in the complete initial thermal dataset, and lock the core feature input vector.

[0022] The thermal variables in the complete initial thermal data set include the basic thermal parameters of the SCR denitrification system during operation, the ammonia-nitrogen molar ratio, and the space velocity ratio.

[0023] Specifically, at the operation site of the SCR denitrification process section, high-precision sensors are deployed in the inlet and outlet pipelines and catalyst bed, and distributed control system (DCS) or supervisory control system (SCADA) are used to collect various thermal parameters online in real time.

[0024] The basic thermal parameters include: the concentration of nitrogen oxides (NOx) in the inlet flue gas of the SCR denitrification system. input The concentration of nitrogen oxides (NOx) in the outlet flue gas of the SCR denitrification system output The average flue gas temperature T of the catalyst bed in the SCR denitrification reactor, the total volumetric flow rate F of the inlet flue gas in the SCR denitrification reactor, and the effective physical volume V of the catalyst packed inside the SCR denitrification reactor.

[0025] Given the limitations of physical sensors in industrial settings that cannot directly read the ammonia-nitrogen ratio (R) and space velocity ratio (G), this invention constructs a real-time soft measurement and feature calculation module. Through an established parameter coupling conversion mechanism, it performs real-time dynamic conversion of various basic thermal parameters to calculate the ammonia-nitrogen molar ratio and space velocity ratio.

[0026] The ammonia-nitrogen molar ratio coupling conversion mechanism includes: converting the injected ammonia flow rate collected by the sensor in real time into the pure ammonia molar flow rate, and combining it with the real-time measured total volumetric flow rate of the inlet flue gas and the nitrogen oxide concentration (NO) in the inlet flue gas. input Calculate NO in inlet flue gas x Molar flow rate and outlet flue gas NO x The molar flow rate is used to obtain the current ammonia-nitrogen molar ratio (R=NH3 / NOx) of the denitrification system in real time.

[0027] The space velocity ratio coupling conversion mechanism includes: dividing the total volumetric flow rate F of the inlet flue gas of the SCR denitrification reactor collected in real time by the effective physical volume V of the catalyst packed inside the SCR denitrification reactor, and calculating the hourly space velocity of the current system in real time, i.e., the space velocity ratio (G=F / V).

[0028] By directly measuring the aforementioned basic physical parameters and performing real-time calculations of the core derived parameters (R, G), a complete initial thermal dataset containing all key multidimensional features is constructed.

[0029] All the raw thermal variables collected above are uploaded to the central control's data storage server in real time and at high frequency via industrial Ethernet. In this embodiment, the sampling interval for data acquisition is set to 2 seconds to ensure that transient fluctuations in industrial conditions can be fully captured, laying a solid data foundation for subsequent deep learning modeling.

[0030] The preprocessing of thermal variables includes removing outlier samples and standardizing the cleaned thermal variables.

[0031] The removal of abnormal samples is performed using the Isolation Forest algorithm. The Isolation Forest algorithm calculates the anomaly score of the thermal sample points. If the anomaly score is greater than the anomaly score threshold, the removal and cleaning operation is automatically performed.

[0032] The standardization process employs the maximum-minimum normalization method to standardize the thermal variables after cleaning.

[0033] Because sensors in industrial settings operate in harsh environments characterized by high temperatures, high dust levels, and high corrosion, the raw sequences they acquire inevitably contain a large number of dead values, random spikes, and sudden abnormal jumps. To prevent these unwanted noises from contaminating the training set, this invention utilizes the Isolation Forest algorithm, based on massive amounts of historical production data collected in practice, to automatically identify and precisely clean outlier samples, removing outliers.

[0034] The isolated forest algorithm comprises constructing multiple isolated trees by randomly selecting features and random split points. Because outlier samples possess the salient characteristic of being "few but distinct," they are easily identified in the isolated trees, often residing in leaf nodes shallower than the root node. For any input thermal sample point x, its anomaly score in a dataset containing n samples is mathematically calculated as follows: In the formula, This represents the outlier score for thermal sample point x. This represents the mean path length of the data within the forest. This represents the average path length where a search fails in a binary search tree. For harmonic series, n represents the number of samples. Let be the Euler constant, taken as 0.5772156649.

[0035] In the specific engineering application of this embodiment, the anomaly scoring threshold is set to 0.5. When the system calculates the anomaly score of a data sample at a certain point in time... If the value is greater than 0.5, the sample is identified as an outlier caused by system interference or instrument malfunction, and the system will automatically perform a removal and cleaning operation.

[0036] In this embodiment, the cleaned dataset retains 550+ sets of valid working condition samples, ensuring the robustness of the modeling data.

[0037] After cleaning the abnormal samples, due to the great differences in the physical properties and numerical ranges of the various process variables (for example, the reaction temperature T usually fluctuates between 280℃ and 420℃, while the ammonia-nitrogen ratio R is usually finely adjusted between 0.8 and 1.2, with the order of magnitude differing by hundreds of times), standardization must be performed in order to eliminate the difference in dimensions and prevent large numerical features from overshadowing small numerical features during network training.

[0038] This invention employs a max-min normalization method to standardize the cleaned thermal variables, linearly mapping all cleaned feature data to the [0,1] interval, thus completely eliminating the adverse effects of differences in dimensions and orders of magnitude between different physical quantities on model training. The standardization calculation formula is as follows: In the formula, This represents the global maximum value of a certain thermal variable across the entire historical dataset. This represents the global minimum value of a certain thermal variable across the entire historical dataset.

[0039] x This represents a certain thermal variable.

[0040] Preferably, a nonlinear feature screening mechanism is used for correlation analysis.

[0041] To simplify input features, reduce the risk of overfitting in deep learning models, and improve computational efficiency, this invention abandons the traditional single linear analysis and adopts a nonlinear feature screening mechanism to refine the core process features with extremely high linear and nonlinear correlation with denitrification outlet concentration from a large system of thermal variables.

[0042] The nonlinear feature selection mechanism combines Spearman rank correlation coefficient and maximum mutual information coefficient (MIC) to comprehensively quantify the linear and nonlinear correlation strength between each parameter and the nitrogen oxide concentration at the denitrification outlet, and finally accurately lock the core feature input vector from dozens of relevant thermal variables.

[0043] The Spearman rank correlation analysis is mainly calculated based on the rank of the data, which can accurately assess the monotonic correlation between variables.

[0044] The maximum mutual information coefficient (MIC) utilizes the principle of information entropy and evaluates the maximum mutual information by searching for it on a variable grid. It has a strong ability to capture various complex periodic, sinusoidal, non-monotonic, and nonlinear characteristics.

[0045] Specifically, this invention uses a dual-screening grid with a Spearman coefficient absolute value greater than 0.4 and a MIC value greater than 0.35 to precisely identify the core feature input vectors, including: ammonia-to-nitrogen ratio R, space velocity ratio G, average flue gas temperature T in the catalyst bed, and nitrogen oxide concentration (NO) in the inlet flue gas. input The concentration of nitrogen oxides (NOx) in the flue gas at the outlet at several moments in the previous period. output .

[0046] S2) Construct an LSTM-DNN hybrid prediction model for predicting the concentration of nitrogen oxides at the outlet of an SCR denitrification system. The LSTM-DNN hybrid prediction model includes a Long Short-Term Memory (LSTM) module and a Deep Neural Network (DNN) module; the LSTM module is used to capture and extract temporal feature vectors in the SCR denitrification reaction process; the DNN module is used to deeply mine the high-dimensional nonlinear spatial mapping relationship between key process parameters such as ammonia-nitrogen ratio, space velocity ratio, and average flue gas temperature of the catalyst bed and the concentration of nitrogen oxides at the denitrification outlet.

[0047] The specific steps for the LSTM-DNN hybrid prediction model to predict the nitrogen oxide concentration at the outlet of the SCR denitrification system include: S21) Extract the time-series dynamic feature vector h through the LSTM network channels. LSTM .

[0048] The LSTM-DNN hybrid prediction model constructs a unified high-order fusion feature space by concatenating the temporal feature vector extracted by LSTM with the spatial nonlinear feature vector extracted by DNN in the feature fusion layer, and finally achieves high-precision and forward-looking full-condition prediction of the nitrogen oxide concentration at the SCR denitrification outlet through a fully connected layer.

[0049] To address the significant time lag, strong temporal correlation, and multivariate high-dimensional nonlinear coupling characteristics of industrial SCR reaction systems, this invention constructs a hybrid deep learning prediction model integrating non-time series and time series information, using LSTM-DNN. The Long Short-Term Memory (LSTM) module is specifically designed to capture and extract time-series dependent features generated during the SCR denitrification reaction due to flue gas flow, catalyst adsorption delays, etc., effectively solving the gradient vanishing and gradient exploding problems faced by traditional neural networks in long-sequence training.

[0050] Specifically, the LSTM network channels are responsible for processing dynamic input sequences with sequential dependencies, capturing the nonlinear effects of historical lags caused by gas flow, diffusion, and catalyst adsorption / desorption in the denitrification reaction on the current outlet concentration. The LSTM employs a sophisticated gating mechanism to control information forgetting and transmission. The detailed mathematical derivation and forward propagation calculation formulas for its internal network layers at time step t are as follows: In the formula, For the output of the forget gate, It is the Sigmoid activation function. and These are the weight matrix and bias term of the forget gate, respectively. The output of the input gate, and These are the weight matrix and bias term of the input gate, respectively. Candidate memory units, For the output of the output gate, and These are the weight matrix and bias term of the output gate, respectively.

[0051] LSTM output The vector is passed to a fully connected layer at each time step, and finally the temporal feature vector h is obtained through multiple LSTM units. LSTM .

[0052] S22) Extract the nonlinear strongly coupled feature vector h spanning space through the DNN network channels. DNN .

[0053] The DNN network channel is composed of multiple fully connected layers interwoven and runs in parallel with the LSTM network channel. It is specifically designed to receive the static feature vector at the current moment, deeply exploring the intricate physicochemical interactions and nonlinear coupling between the three core control variables—ammonia-nitrogen ratio R, space velocity ratio G, and average flue gas temperature T in the catalyst bed—and denitrification efficiency at the same spatial level. The recursive propagation mathematical formula for the DNN hidden layer is: In the formula, Indicates the first Layer output; and The first Layer weight connection matrix and bias vector, The term represents the activation function. In this embodiment, the ReLU activation function is uniformly used for the fully connected layers.

[0054] After passing through multiple layers of abstract propagation, the DNN network finally outputs a spatial feature vector h. DNN .

[0055] S23) Construct a feature fusion layer to integrate the temporal feature vector h extracted by LSTM. LSTM The spatial nonlinear feature vector h extracted by DNN DNN The features are concatenated to synthesize a multi-dimensional fused feature vector h. fusion .

[0056] After the LSTM and DNN networks complete feature extraction for their respective channels, the LSTM-DNN hybrid prediction model constructs a dedicated feature fusion layer, which uses a concatenation operator to combine the time-varying dynamic feature vector h extracted by the LSTM. LSTM The nonlinear, strongly coupled feature vector h across space extracted by the DNN DNN A large horizontal concatenation is performed along the dimension to synthesize a more comprehensive multidimensional fusion feature vector h. fusion The multidimensional fusion feature vector h fusionRepresented as .

[0057] S24) The multidimensional fusion feature vector h fusion The input is fed to a fully connected output layer, and through the final mapping matrix and linear bias, a high-precision predicted outlet nitrogen oxide concentration is output.

[0058] The predicted export nitrogen oxide concentration is expressed by the following formula. In the formula, ŷ indicates the predicted export concentration of nitrogen oxides. This represents the weight matrix of the output layer. This represents the bias term of the output layer. h fusion This represents a multidimensional fused feature vector.

[0059] The LSTM-DNN hybrid prediction model constructs a unified high-order fusion feature space by concatenating the temporal feature vector extracted by LSTM with the spatial nonlinear feature vector extracted by DNN in the feature fusion layer, and finally achieves high-precision and forward-looking full-condition prediction of the nitrogen oxide concentration at the SCR denitrification outlet through a fully connected layer.

[0060] S3) The LSTM-DNN hybrid prediction model is used to predict the concentration of nitrogen oxides at the denitrification outlet in real time for future periods. When the predicted concentration of nitrogen oxides at the denitrification outlet deviates from the set target, or when the upstream pellet roasting conditions fluctuate drastically, the LSTM-DNN hybrid prediction model is used as the forward operator and combined with the optimization algorithm to perform reverse optimization calculation. The optimal combination of linkage control parameters among the ammonia-nitrogen ratio, space velocity ratio, and average flue gas temperature of the catalyst bed is dynamically sought, and control commands are output to achieve a fine balance between maximizing denitrification efficiency and minimizing ammonia slip.

[0061] Specifically, the trained and calibrated LSTM-DNN hybrid prediction model is embedded into the core server of the denitrification control system. Leveraging the model's superior generalization ability and instantaneous prediction characteristics, it predicts the denitrification outlet nitrogen oxide concentration in real-time for future periods. When the predicted denitrification outlet nitrogen oxide concentration deviates from the set target, or when there are drastic fluctuations in the upstream pellet roasting conditions, the control system uses this hybrid model as the forward operator, combined with an optimization algorithm, to perform backward optimization calculations. Under the premise of meeting environmental emission standards and limits, the system dynamically seeks the optimal combination of linkage control parameters among the ammonia-to-nitrogen ratio R, space velocity ratio G, and reaction temperature T, outputting control commands to synchronously adjust the injection volume of the reducing agent nozzle and the operating status of the flue gas thermal system. This achieves a fine balance between maximizing denitrification efficiency and minimizing ammonia slip, realizing true feedforward intelligent control.

[0062] In this embodiment, the trained and calibrated LSTM-DNN hybrid prediction model is deployed as the core control algorithm on the computing nodes of the field control system. The model training results are as follows: Figure 2 As shown in the figure. In actual operation, the LSTM-DNN hybrid prediction model exhibits extremely high predictive foresight, as shown in the model loss curve. Figure 3 As shown.

[0063] In this embodiment, taking the actual operating data of a large pellet plant as an example, before the optimization of this invention, the plant's traditional control system used multiple linear regression or simple single-parameter empirical rule adjustment, resulting in the nitrogen oxide concentration at the SCR denitrification outlet frequently exceeding 37 mg / m³. 3 The local temperature fluctuates greatly, making it easy to exceed the standard for a short period of time. In order to reduce emissions, the amount of ammonia injected is often too large, resulting in ammonia escape of up to 3.5 ppm.

[0064] To achieve ultra-low and stable emissions and reduce ammonia slip under more stringent environmental standards, the control system fully utilizes the LSTM-DNN predictive and collaborative control program of this invention. When the process fluctuates due to adjustments in the upstream calcining kiln, the LSTM-DNN hybrid predictive model can extremely sensitively predict the evolution trend of nitrogen oxide concentration at the denitrification outlet caused by this change in operating conditions. An error comparison is shown below. Figure 4 .from Figure 4 It can be seen that during the dynamic observation period of 120 time steps, the red model prediction curve and the black actual outlet concentration curve achieved a high degree of dynamic consistency. Under the condition of frequent fluctuations, the LSTM-DNN hybrid prediction model can still maintain extremely high prediction foresight.

[0065] Statistical analysis of the data shows that the LSTM-DNN hybrid prediction model has a stable prediction absolute error controlled within ±1.5 mg / m². 3Within a very small range, the average relative error is much lower than that of the traditional multiple linear regression control model.

[0066] In this embodiment, since the total volumetric flow rate of flue gas and the effective physical volume of the loaded catalyst are basically constant in actual industrial production, the space velocity ratio G is an objective state variable that cannot be actively adjusted. Therefore, the reverse collaborative optimization mechanism of this invention mainly focuses on the two-dimensional linkage control of the ammonia-nitrogen molar ratio R (achieved by increasing or decreasing the ammonia injection amount) and the average temperature T of the catalyst bed (achieved by the flue gas auxiliary heating or cooling system).

[0067] During the actual online operation of the denitrification system, when the LSTM-DNN hybrid prediction model proactively predicts that the future nitrogen oxide concentration at the denitrification outlet will be at a low, safe level, the control system will activate the energy-saving mode in advance, instructing a reduction in the ammonia injection rate to lower the ammonia-to-nitrogen ratio R. For example, the system automatically lowers the ammonia-to-nitrogen ratio R, precisely reducing the injection flow rate of 20% concentration ammonia from the conservatively set 55 L / h to 42 L / h. This refined feedforward operation ensures that the final nitrogen oxide concentration at the denitrification outlet remains stable at 33 mg / m³. 3 (Strictly below 35mg / m 3 Under the premise of ultra-low emission limits, a single reactor can directly save about 13L of ammonia water per hour. This not only saves the cost of reducing agent on site to the greatest extent, but also keeps the ammonia escape concentration stable at an extremely low level of 0.8ppm, thus curbing the formation of ammonium bisulfate at the source.

[0068] Conversely, when the upstream chain grate-rotary kiln pellet roasting process requires a temperature increase to meet capacity demands, the concentration of nitrogen oxides (NOx) in the denitrification inlet flue gas increases. input When the concentration spikes dramatically, under traditional empirical control mode, the NO concentration in the SCR denitrification outlet flue gas will be [a certain value] within the next few tens of seconds. output It will be 30mg / m 3 It rose rapidly and faced the risk of exceeding the standard, surging to 37 mg / m³. 3 At this point, the LSTM-DNN hybrid prediction model of this invention can extremely sensitively predict this deteriorating trend in advance and immediately initiate feedforward collaborative optimization, automatically calculating and outputting the optimal parameter compensation combination: instructing the on-site ammonia water injection flow rate of 20% concentration to rapidly increase from the basic 55 L / h to 65 L / h, while simultaneously linking the flue gas auxiliary heating system to rapidly increase the average temperature T of the denitrification reaction catalyst bed by 20°C. After strictly implementing this linkage control instruction on-site, the final monitored concentration of nitrogen oxides NO in the denitrification outlet flue gas was... output Suppressed at 31 mg / m³ 3 It was consistently maintained at 34 mg / m³ 3The following excellent ultra-low emission levels; and due to the dynamic and precise coordination of temperature and ammonia injection, the local accumulation of reducing agent on the catalyst surface is completely avoided, and the ammonia escape concentration at the outlet is significantly reduced to below 1.2 ppm, eliminating the problem of strong sticky ash accumulation and severe corrosion in the downstream air preheater caused by excessive ammonia injection.

[0069] The present invention is based on a multi-parameter linkage control method for optimizing SCR denitrification efficiency. It has strong technical universality and is not only applicable to the SCR denitrification process of the chain grate machine-rotary kiln pellet roasting flue gas in the above embodiments, but can also be seamlessly extended to various complex flue gas denitrification control projects with strong time delay and strong coupling characteristics, such as sintering flue gas and large industrial boilers.

[0070] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A method for optimizing SCR denitrification efficiency based on multi-parameter linkage control, characterized in that, Includes the following steps: S1) Construct a complete initial thermal dataset for the SCR denitrification system, preprocess and perform correlation analysis on the thermal variables in the complete initial thermal dataset, and lock in the core feature input vector; The thermal variables in the complete initial thermal dataset include the basic thermal parameters of the SCR denitrification system during operation, the ammonia-nitrogen molar ratio, and the space velocity ratio. S2) Construct an LSTM-DNN hybrid prediction model for predicting the concentration of nitrogen oxides at the outlet of the SCR denitrification system; The LSTM-DNN hybrid prediction model includes an LSTM module and a DNN module; the LSTM module is used to capture and extract the temporal feature vectors in the SCR denitrification reaction process; the DNN module is used to deeply mine the high-dimensional nonlinear spatial mapping relationship between the ammonia-nitrogen ratio, space velocity ratio, average flue gas temperature of the catalyst bed and the concentration of nitrogen oxides at the denitrification outlet in the spatial dimension. S3) The LSTM-DNN hybrid prediction model is used to predict the concentration of nitrogen oxides at the denitrification outlet in real time for future periods. When the predicted concentration of nitrogen oxides at the denitrification outlet deviates from the set target, or when the upstream pellet roasting conditions fluctuate drastically, the LSTM-DNN hybrid prediction model is used as the forward operator and combined with the optimization algorithm to perform reverse optimization calculation. The optimal combination of linkage control parameters is dynamically sought among the thermal variables, and control commands are output to achieve a fine balance between maximizing denitrification efficiency and minimizing ammonia slip.

2. The SCR denitrification efficiency optimization method based on multi-parameter linkage control according to claim 1, characterized in that: In S1), the basic thermal parameters include: the concentration of nitrogen oxides (NOx) in the inlet flue gas of the SCR denitrification system. input The concentration of nitrogen oxides (NOx) in the outlet flue gas of the SCR denitrification system output The average flue gas temperature T of the catalyst bed in the SCR denitrification reactor, the total volumetric flow rate F of the inlet flue gas in the SCR denitrification reactor, and the effective physical volume V of the catalyst packed inside the SCR denitrification reactor.

3. The SCR denitrification efficiency optimization method based on multi-parameter linkage control according to claim 2, characterized in that: In S1), a real-time soft measurement and feature calculation module is constructed. Through the established parameter coupling conversion mechanism, the basic thermal parameters are dynamically converted in real time to calculate the ammonia-nitrogen molar ratio and space velocity ratio. The ammonia-nitrogen molar ratio coupling conversion mechanism includes converting the injected ammonia flow rate collected by the sensor in real time into the pure ammonia molar flow rate, and combining it with the real-time measured total volumetric flow rate of the inlet flue gas and the nitrogen oxide concentration (NO) in the inlet flue gas. input Calculate the molar flow rate of nitrogen oxides in the inlet flue gas and the molar flow rate of nitrogen oxides in the outlet flue gas, and obtain the current ammonia-nitrogen molar ratio of the denitrification system in real time; The space velocity ratio coupling conversion mechanism includes dividing the total volumetric flow rate F of the inlet flue gas of the SCR denitrification reactor collected in real time by the effective physical volume V of the catalyst packed inside the SCR denitrification reactor, and calculating the space velocity ratio of the current system in real time.

4. The SCR denitrification efficiency optimization method based on multi-parameter linkage control according to claim 3, characterized in that: In S1), the preprocessing of thermal variables includes removing outlier samples and standardizing the cleaned thermal variables. The removal of abnormal samples is performed using the Isolation Forest algorithm. The Isolation Forest algorithm calculates the anomaly score of the thermal sample points. If the anomaly score is greater than the anomaly score threshold, the removal and cleaning operation is automatically performed. The standardization process uses the max-min normalization method to standardize the cleaned thermal variables, linearly mapping all cleaned feature data to the [0,1] interval.

5. The SCR denitrification efficiency optimization method based on multi-parameter linkage control according to claim 4, characterized in that: In S1), the anomaly score of the thermal sample point is calculated using the following formula. In the formula, This represents the outlier score for thermal sample point x. This represents the mean path length of the data within the forest. This represents the average path length where a search fails in a binary search tree. For harmonic series, n represents the number of samples. is Euler's constant.

6. The SCR denitrification efficiency optimization method based on multi-parameter linkage control according to claim 5, characterized in that: In S1), a nonlinear feature screening mechanism is used for correlation analysis. The nonlinear feature screening mechanism is a feature screening mechanism that combines the Spearman rank correlation coefficient and the maximum mutual information coefficient. It is used to comprehensively quantify the linear and nonlinear correlation strength between each parameter and the nitrogen oxide concentration at the denitrification outlet, and finally accurately lock the core feature input vector from the relevant thermal variables.

7. The method for optimizing SCR denitrification efficiency based on multi-parameter linkage control according to claim 1, characterized in that: In S2), the specific steps for the LSTM-DNN hybrid prediction model to predict the nitrogen oxide concentration at the outlet of the SCR denitrification system include: S21) Extract the time-series dynamic feature vector h through the LSTM network channels. LSTM ; S22) Extract the nonlinear strongly coupled feature vector h spanning space through the DNN network channels. DNN ; S23) Construct a feature fusion layer to integrate the temporal feature vector h extracted by LSTM. LSTM The spatial nonlinear feature vector h extracted by DNN DNN The features are concatenated to synthesize a multi-dimensional fused feature vector h. fusion ; S24) The multidimensional fusion feature vector h fusion The input is fed to a fully connected output layer, and through the final mapping matrix and linear bias, a high-precision predicted outlet nitrogen oxide concentration is output.

8. The SCR denitrification efficiency optimization method based on multi-parameter linkage control according to claim 6, characterized in that: In S24), the predicted outlet nitrogen oxide concentration is expressed by the following formula. In the formula, ŷ indicates the predicted export concentration of nitrogen oxides. This represents the weight matrix of the output layer. This represents the bias term of the output layer. h fusion This represents a multidimensional fused feature vector.

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