Deep denitration spraying system with improved urea solution spraying and flue gas mixing uniformity

Abstract

The invention discloses a deep denitration spraying system with improved urea solution spraying and flue gas mixing uniformity, which is characterized in that if the influence degree exceeds a preset threshold range, the urea solution flow is preliminarily adjusted, and the adjusted flow parameter value is obtained for subsequent accurate regulation and control; aiming at the adjusted flow parameter value, combining flue gas flow velocity fluctuation data, calculating a deviation value of injection distribution uniformity, and obtaining an optimization demand index of injection distribution; according to the optimization demand index, adjusting the angle and the speed of a spraying device, generating a corresponding control instruction, and determining a real-time improvement result of spraying distribution uniformity; according to the final flow regulation scheme, a closed-loop feedback signal is generated in combination with reaction process control logic, and whether the mixing uniformity degree reaches the expected standard or not is judged.

Description

Technical Field

[0001] The present invention relates to the technical field of deep denitration spraying, and in particular to a deep denitration spraying system with improved uniformity of urea solution spraying and flue gas mixing. Background Art

[0002] In the field of environmental protection technology, flue gas denitrification, as a key means of controlling nitrogen oxide emissions, is of irreplaceable importance to improving air quality and achieving sustainable development. Especially in industrial production, deep denitrification technology is directly related to whether emission standards are met or not, and has become the focus of industry attention. However, the current mainstream denitrification methods have exposed some deep-seated deficiencies in practical applications, especially in terms of adaptability under complex working conditions. Many systems have difficulty coping with changes in flue gas conditions, resulting in large fluctuations in treatment efficiency and even local failures. These limitations are not simply problems of technical singularity or staticity, but are due to the neglect of dynamic environmental response capabilities and the lack of refined control of key reaction processes.

[0003] Focusing on specific challenges, during the flue gas denitrification process, the injection of urea solution and the mixing uniformity of the flue gas become one of the core factors affecting the effect. Due to the constant changes in flue gas temperature, flow rate and other conditions, the flow control of urea solution is often difficult to accurately match the actual needs. This makes it impossible for urea molecules to form a stable reaction state after injection, which further leads to poor mixing with the flue gas and insufficient reaction. More importantly, this imprecision in flow control will also interfere with the degree of polymerization of urea molecules, threatening the stability of the molecular structure during the reaction process, and ultimately affecting the overall performance of denitrification efficiency. These two factors are closely related. The inaccuracy of the flow directly affects the polymerization state, and the instability of the polymerization state exacerbates the problem of mixing uniformity.

[0004] Therefore, how to achieve precise control of the urea solution flow rate under complex and changeable flue gas conditions, and ensure the stability of the urea molecular polymerization state and uniform mixing with the flue gas through dynamic adjustment, has become a key issue that needs to be urgently solved in this study. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a deep denitrification spray system with improved uniformity of urea solution injection and flue gas mixing in view of the shortcomings of the background technology.

[0006] The present invention adopts the following technical solutions to solve the above technical problems:

[0007] The deep denitrification spray system with improved uniformity of urea solution injection and flue gas mixing includes:

[0008] Real-time data collection of flue gas temperature changes and flue gas flow rate fluctuations is performed through a sensor network, and the collected environmental parameter signals are digitally processed to obtain dynamic change characteristic values ​​of the flue gas working conditions;

[0009] Based on the dynamic change characteristic value, a pre-established analysis model is used to predict the fluctuation trend of the flue gas operating conditions to determine the degree of influence of the current flue gas environment on the injection of urea solution;

[0010] If the impact exceeds a preset threshold, the urea solution flow rate is preliminarily adjusted to obtain an adjusted flow parameter value for subsequent precise control;

[0011] Based on the adjusted flow parameter value and the flue gas velocity fluctuation data, the deviation value of the injection distribution uniformity is calculated to obtain the optimization requirement index of the injection distribution;

[0012] According to the optimization requirement index, the angle and speed of the injection device are adjusted, corresponding control instructions are generated, and the real-time improvement results of the injection distribution uniformity are determined;

[0013] By analyzing the correlation between the improvement results and the molecular polymerization state, the structural stability data of the urea molecules during the reaction process is collected to obtain the fluctuation range of the polymerization state;

[0014] If the fluctuation range exceeds the preset safety interval, the flow precision control strategy is dynamically updated based on the environmental parameters collected by real-time data to obtain the final flow regulation solution;

[0015] Based on the final flow regulation scheme, combined with the reaction process control logic, a closed-loop feedback signal is generated to determine whether the mixing uniformity meets the expected standard;

[0016] Based on the closed-loop feedback signal, the operating status of the entire control system is continuously monitored, the stability data of the system operation is obtained, and the optimization effect of the dynamic environmental response is determined.

[0017] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0018] The present invention discloses an intelligent control method for urea solution injection in a flue gas environment. The method collects flue gas temperature and flow rate data in real time, predicts flue gas operating condition fluctuation trends, and evaluates their impact on urea injection. Based on the degree of impact, the present invention dynamically adjusts the urea solution flow rate and optimizes the angle and rate of the injection device to improve the uniformity of the injection distribution. Simultaneously, the present invention analyzes the polymerization state of urea molecules and updates the flow control strategy in real time to ensure the stability of the reaction process. Through a closed-loop feedback mechanism, the present invention continuously monitors and optimizes the system operating status, achieving precise control of urea solution injection in a flue gas environment and improving denitrification efficiency and system stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The present invention is a flow chart of a deep denitrification spray system with improved uniformity of urea solution injection and flue gas mixing.

[0020] Figure 2 This is a schematic diagram of a deep denitrification spray system with improved uniformity of urea solution injection and flue gas mixing according to the present invention.

[0021] Figure 3 This is another schematic diagram of the deep denitrification spray system with improved uniformity of urea solution injection and flue gas mixing according to the present invention. DETAILED DESCRIPTION

[0022] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 work are within the scope of protection of the present invention. The present invention is described in detail below based on the drawings and preferred embodiments. The purpose and effect of the present invention will become more clear. It should be understood that the specific embodiments described here are only used to explain the present invention and are not used to limit the present invention.

[0024] like Figure 1-3 The deep denitrification spraying system with improved uniformity of urea solution injection and flue gas mixing in this embodiment may specifically include:

[0025] S101 , collecting real-time data on flue gas temperature changes and flue gas flow rate fluctuations through a sensor network, and digitally processing the collected environmental parameter signals to obtain dynamic change characteristic values ​​of flue gas working conditions.

[0026] Raw signal data of flue gas temperature and flow rate is acquired through a sensor network. Analog-to-digital conversion is used to digitize the collected analog signals, generating a digital environmental parameter sequence. This digital environmental parameter sequence is preprocessed using a fast Fourier transform algorithm to decompose the signal's frequency components, extract low-frequency and high-frequency features, and obtain the signal's frequency domain eigenvalues. Based on the frequency domain eigenvalues, if the proportion of the high-frequency component exceeds a preset threshold, significant fluctuations in the flue gas flow rate are determined, generating a flow rate fluctuation feature sequence. For this flow rate fluctuation feature sequence, a sliding window technique is used to calculate the mean and variance within the time window to determine the dynamic trend of the flue gas flow rate. Based on this dynamic trend, a K-means clustering algorithm is used to classify the flue gas operating conditions and determine the current condition's category label. If the category label matches a predefined abnormal condition, a feature description of the abnormal condition is generated, resulting in dynamic monitoring results for the flue gas condition. Time series analysis is performed on the dynamic monitoring results, and an autoregressive model is used to predict the future trend of the operating condition, generating the predicted condition feature values.

[0027] S102: Based on the dynamic change characteristic value, a pre-established analysis model is used to predict the fluctuation trend of the flue gas operating condition to determine the impact of the current flue gas environment on urea solution injection.

[0028] Real-time flue gas operating condition data is acquired through a sensor network, and signal processing techniques are used to extract dynamic change characteristic values ​​to obtain a condition characteristic sequence. A pre-trained regression model is used to analyze the condition characteristic sequence and predict the flue gas operating condition fluctuation trend, generating a fluctuation trend sequence. Based on the fluctuation trend sequence, statistical characteristics within the time window are calculated, and mean and variance analysis methods are used to determine the stability of the fluctuation trend and obtain a stability index. If the stability index falls below a preset threshold, the flue gas operating condition is judged to have abnormal fluctuations, and a characteristic description of the abnormal fluctuation is generated. Based on the abnormal fluctuation characteristic description, a mapping function is used to calculate the adjustment coefficient of the urea solution injection parameter to obtain the injection parameter adjustment value. Based on the injection parameter adjustment value and the operating condition data, a linear interpolation method is used to optimize the urea solution injection amount to obtain the optimized injection parameters. Using the optimized injection parameters, a control signal for the flue gas operating condition is generated, and the final urea solution injection plan is determined.

[0029] S103: If the impact exceeds a preset threshold value, the flow rate of the urea solution is preliminarily adjusted to obtain an adjusted flow parameter value for subsequent precise control.

[0030] Multi-dimensional sensor data is collected in real time from flue gas operating conditions and processed using data fusion technology to obtain a standardized operating condition dataset. If the fluctuation amplitude of the standardized operating condition dataset exceeds a preset threshold, the dataset is analyzed using a pre-trained random forest model to predict the dynamic adjustment requirements for the flow parameters and obtain an adjustment requirement sequence. Based on the adjustment requirement sequence, the statistical characteristic values ​​within the time window are calculated, and the preliminary adjustment coefficients for the flow parameters are determined using mean analysis. Using these preliminary adjustment coefficients and the operating condition dataset, the urea solution flow rate is optimized using linear interpolation to obtain the optimized flow parameters. If the deviation between the optimized flow parameters and the real-time operating condition data exceeds the preset range, the parameters are iteratively adjusted using a gradient descent algorithm to obtain the fine-tuned flow parameters. Based on the fine-tuned flow parameters, a control signal for urea solution injection is generated to determine the final injection parameter values. Based on the final injection parameter values, the injection equipment is adjusted using a closed-loop control method to achieve a stable operating state.

[0031] S104 , calculating the deviation value of injection distribution uniformity based on the adjusted flow parameter value and combining it with the flue gas flow velocity fluctuation data, and obtaining an optimization requirement index for injection distribution.

[0032] By combining the adjusted flow parameters with flue gas flow rate fluctuation data and using data integration technology to process multi-source information, a preliminary uniformity value for the injection distribution is obtained. If the preliminary uniformity value deviates from a preset threshold, a deviation analysis method is used to calculate the specific deviation data for the injection distribution and determine the deviation distribution characteristics. Based on the deviation distribution characteristics, the distribution adjustment requirements under the influence of flue gas flow rate are determined. A linear regression model is used to analyze the correlation between flow rate and distribution, resulting in a reference coefficient for distribution adjustment. Using the reference coefficient for distribution adjustment and combined with the flow parameter correlation data, a mean smoothing method is used to optimize the control parameters for the injection distribution and determine the optimized distribution parameter values. If the optimized distribution parameter values ​​match the real-time flue gas flow rate fluctuation data below a preset threshold, the distribution parameters are adjusted through iterative calculation to obtain the final distribution adjustment solution. Based on the final distribution adjustment solution, control instructions for the injection equipment are generated in conjunction with the flow rate impact data to determine whether the injection distribution has reached a stable state. The execution results of the control instructions are used to obtain real-time injection distribution indicators. Data comparison methods are used to analyze the consistency of the distribution indicators with the optimization requirements and determine the final distribution optimization results.

[0033] S105 , adjusting the angle and speed of the injection device according to the optimization requirement index, generating corresponding control instructions, and determining a real-time improvement result of the injection distribution uniformity.

[0034] Based on the optimization requirements and indicator data, the current parameter configuration of the injection device is obtained. Preliminary control instructions are generated based on the requirements for angle adjustment and rate control. Using these preliminary control instructions, the operating status of the injection device is adjusted, real-time distribution uniformity data is obtained, and a determination is made as to whether the distribution status meets the preset threshold range. If the distribution status does not meet the preset threshold range, a linear regression model is used to analyze the compatibility between angle adjustment and rate control, combining real-time improvement data and parameter correlation information, to obtain improved device parameters. Based on the improved device parameters, new control instructions are generated, and a secondary adjustment is made to the injection device to obtain updated distribution uniformity data. Using this updated distribution uniformity data, combined with the indicator data and optimization requirements, the stability of the distribution status is analyzed to determine whether further parameter adjustment is necessary. If deviations in the distribution status still exist, fluctuation information is obtained from the improved data, and the parameter correlation data is processed using a mean smoothing method to obtain the final control instructions. Based on the final control instructions, the operating parameters of the injection device are adjusted, real-time distribution status data is obtained, and a determination is made as to whether the distribution uniformity meets the optimization requirements.

[0035] S106. By analyzing the correlation between the improvement result and the molecular polymerization state, the structural stability data of the urea molecule during the reaction process is collected to obtain the fluctuation range of the polymerization state.

[0036] By collecting data during the reaction process, the structural stability information of the urea molecules is recorded to obtain a preliminary stability data set. Based on this preliminary stability data set, a correlation analysis method is used to extract the state change characteristics of the urea molecules in the polymerization state and identify key change nodes. If the key change node exceeds the preset threshold range, the state change data is calibrated using a fluctuation range calculation tool to obtain calibrated fluctuation data. Based on the calibrated fluctuation data, the changing trends of the molecular characteristics are continuously monitored to obtain dynamic adjustment information during the reaction process to determine whether the stability standard is met. If the dynamic adjustment information indicates deviations in structural stability, the process monitoring data is processed using a mean smoothing algorithm to obtain smoothed state parameters. Using the smoothed state parameters, combined with the logic of result verification, the stability of the polymerization state is reconfirmed to determine the final reaction process adjustment plan. Based on the final reaction process adjustment plan, feedback data on the improvement results is recorded to obtain adjusted polymerization state information of the urea molecules and determine whether the preset target is achieved.

[0037] S107: If the fluctuation range exceeds the preset safety interval, the flow precision control strategy is dynamically updated based on the environmental parameters collected by real-time data to obtain a final flow regulation solution.

[0038] If the fluctuation range exceeds the preset safety interval, a preliminary analysis of the fluctuation range is conducted using real-time collected environmental parameter data to obtain an initial determination of abnormal fluctuations. Based on this initial determination, the data is processed using a pre-established analysis model to determine the specific deviation direction of the fluctuation range based on the changing trends of the environmental parameters. If the deviation direction indicates that the fluctuation range continues to deviate from the safety interval, the environmental parameters are calibrated using a data processing tool to obtain a calibrated parameter dataset. Based on the calibrated parameter dataset, the existing control strategy is dynamically adjusted to meet the needs of flow control and an adjusted policy framework is obtained. If the adjusted policy framework deviates from the control target, the policy framework is optimized using a parameter analysis tool to determine an optimized flow control solution. Based on the optimized flow control solution, the execution process of flow control is monitored in conjunction with the real-time collected data to obtain dynamic feedback information during the execution process. If the dynamic feedback information indicates that the flow control does not meet the preset interval judgment criteria, the control solution is adjusted again using the policy update mechanism to obtain the final flow control solution.

[0039] S108. Based on the final flow rate regulation scheme, combined with the reaction process control logic, a closed-loop feedback signal is generated to determine whether the mixing uniformity meets the expected standard.

[0040] To determine the degree of mixing uniformity, data collection results from the real-time reaction process are obtained and, in conjunction with a closed-loop feedback mechanism, preliminary signal data is generated. This preliminary signal data is processed using a pre-established analysis model to determine the mixing uniformity during the reaction process and obtain a state assessment result. If the state assessment result indicates that the mixing uniformity deviates from the preset standard, the flow control scheme is dynamically adjusted using control logic to obtain the adjusted control parameters. Based on the adjusted control parameters and information collected from real-time data, a new closed-loop feedback signal is generated to determine whether the preset standard is met. If the new closed-loop feedback signal still does not meet the preset standard, the reaction process is further calibrated using data processing tools to obtain calibrated process data. Based on the calibrated process data, a support vector machine model is used to perform a secondary analysis of the mixing uniformity to determine the final adjustment direction. Based on the final adjustment direction, the corresponding flow control instructions are generated, and the execution status is tracked through the process monitoring mechanism to obtain execution feedback information.

[0041] S109: Continuously monitor the operating status of the entire control system according to the closed-loop feedback signal, obtain stability data of the system operation, and determine the optimization effect of the dynamic environmental response.

[0042] Based on the closed-loop feedback signal, a pre-established monitoring tool is used to track the operating status of the control system in real time. Stability data reflecting system performance is obtained from this data to determine the system's initial operating status. Based on this stability data, the information processing module analyzes the system's response to the dynamic environment. If the response deviation exceeds a preset threshold, the control system parameters are calibrated to obtain calibrated operating parameters. Based on these calibrated operating parameters, real-time system monitoring information is obtained and combined with the closed-loop feedback signal to determine whether the system has achieved the desired level of optimization. If the judgment indicates that the optimization level is not met, the data processing tool conducts in-depth analysis of the operating status and, combined with dynamic environment adaptability data, determines the direction of adjustment required. Based on the determined adjustment direction, corresponding control instructions are generated. The system monitoring mechanism tracks the execution process and obtains post-execution status feedback. Based on the obtained status feedback, a support vector machine model is used to comprehensively analyze the stability data and environmental adaptability to determine whether the control system has adapted to the dynamic environment. Based on the comprehensive analysis results, if deviations still exist, the information processing module recalibrates the closed-loop feedback signal to obtain the final system operating status.

[0043] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A deep denitrification spray system with improved uniformity of urea solution injection and flue gas mixing, characterized in that: include: Real-time data collection of flue gas temperature changes and flue gas flow rate fluctuations is performed through a sensor network, and the collected environmental parameter signals are digitally processed to obtain dynamic change characteristic values ​​of the flue gas working conditions; Based on the dynamic change characteristic value, a pre-established analysis model is used to predict the fluctuation trend of the flue gas operating conditions to determine the degree of influence of the current flue gas environment on the injection of urea solution; If the impact exceeds a preset threshold, the urea solution flow rate is preliminarily adjusted to obtain an adjusted flow parameter value for subsequent precise control; Based on the adjusted flow parameter value and the flue gas velocity fluctuation data, the deviation value of the injection distribution uniformity is calculated to obtain the optimization requirement index of the injection distribution; According to the optimization requirement index, the angle and speed of the injection device are adjusted, corresponding control instructions are generated, and the real-time improvement results of the injection distribution uniformity are determined; By analyzing the correlation between the improvement results and the molecular polymerization state, the structural stability data of the urea molecules during the reaction process is collected to obtain the fluctuation range of the polymerization state; If the fluctuation range exceeds the preset safety interval, the flow precision control strategy is dynamically updated based on the environmental parameters collected by real-time data to obtain the final flow regulation solution; Based on the final flow regulation scheme, combined with the reaction process control logic, a closed-loop feedback signal is generated to determine whether the mixing uniformity meets the expected standard; Based on the closed-loop feedback signal, the operating status of the entire control system is continuously monitored, the stability data of the system operation is obtained, and the optimization effect of the dynamic environmental response is determined.

2. The deep denitrification spray system with improved uniformity of urea solution injection and flue gas mixing according to claim 1 is characterized in that: The real-time data collection of flue gas temperature changes and flue gas flow rate fluctuations is performed through the sensor network, and the collected environmental parameter signals are digitally processed to obtain dynamic change characteristic values ​​of the flue gas working conditions, including: The original signal data of flue gas temperature and flow rate are obtained through the sensor network, and the collected analog signals are digitized using analog-to-digital conversion technology to obtain a digital environmental parameter sequence; Preprocess the digital environmental parameter sequence, use the fast Fourier transform algorithm to decompose the signal frequency components, extract the low-frequency and high-frequency features, and obtain the frequency domain eigenvalues ​​of the signal; According to the frequency domain eigenvalue, if the proportion of high-frequency components exceeds the preset threshold, it is judged that there is a significant fluctuation in the flue gas flow rate, and a flow rate fluctuation feature sequence is generated; For the velocity fluctuation characteristic sequence, the sliding window technology is used to calculate the mean and variance within the time window to obtain the dynamic change trend of the flue gas velocity; Based on the dynamic change trend, the K-means clustering algorithm is used to classify the flue gas working conditions and determine the category label of the current working condition; According to the working condition category label, if the category label matches the preset abnormal working condition, a feature description of the abnormal working condition is generated to obtain the dynamic monitoring result of the flue gas working condition; The dynamic monitoring results are analyzed in time series, and the autoregressive model is used to predict the trend of working condition changes in the future time period to obtain the predicted working condition characteristic values.

3. The deep denitrification spraying system with improved uniformity of urea solution injection and flue gas mixing according to claim 1 is characterized in that: The method of predicting the fluctuation trend of the flue gas operating conditions based on the dynamically changing characteristic values ​​using a pre-established analysis model to determine the degree of influence of the current flue gas environment on the injection of the urea solution includes: The real-time data of flue gas working conditions is obtained through the sensor network, and the dynamic change characteristic values ​​are extracted using signal processing technology to obtain the working condition characteristic sequence; Based on the operating condition feature sequence, a pre-trained regression model is used to analyze and predict the fluctuation trend of the flue gas operating condition, and a fluctuation trend sequence is obtained; According to the fluctuation trend sequence, the statistical characteristics within the time window are calculated, and the mean and variance analysis methods are used to determine the stability of the fluctuation trend and obtain the stability index; If the stability index is lower than the preset threshold, it is determined that the flue gas operating conditions have abnormal fluctuations and a description of the abnormal fluctuation characteristics is generated; According to the description of abnormal fluctuation characteristics, the adjustment coefficient of urea solution injection parameters is calculated using a mapping function to obtain the injection parameter adjustment value; According to the injection parameter adjustment value and combined with the working condition data, the linear interpolation method is used to optimize the urea solution injection amount to obtain the optimized injection parameters; Through the optimized injection parameters, the control signal of the flue gas working condition is generated to determine the final urea solution injection plan.

4. The deep denitrification spraying system with improved uniformity of urea solution injection and flue gas mixing according to claim 1, characterized in that: If the impact exceeds the preset threshold range, the urea solution flow rate is preliminarily adjusted to obtain the adjusted flow parameter value for subsequent precise control, including: Collect multi-dimensional sensor data from flue gas working conditions in real time and process it using data fusion technology to obtain a standardized working condition data set; If the fluctuation range of the standardized working condition data set exceeds the preset threshold, the data set is analyzed through the pre-trained random forest model to predict the dynamic adjustment requirements of the flow parameters and obtain the adjustment requirement sequence; According to the adjustment demand sequence, the statistical characteristic values ​​within the time window are calculated, and the mean analysis method is used to determine the preliminary adjustment coefficients of the flow parameters; By preliminarily adjusting the coefficients and combining the operating condition data set, the linear interpolation method is used to optimize the urea solution flow rate and obtain the optimized flow parameters; If the deviation between the optimized flow parameters and the real-time operating data exceeds the preset range, the parameters are iteratively adjusted using the gradient descent algorithm to obtain the fine-tuned flow parameters; Generate a control signal for urea solution injection based on the fine-tuning flow rate parameters and determine the final injection parameter value; The injection equipment is adjusted by a closed-loop control method based on the final injection parameter value to obtain a stable operating state.

5. The deep denitrification spraying system with improved uniformity of urea solution injection and flue gas mixing according to claim 1, characterized in that: The method of calculating the deviation value of the injection distribution uniformity based on the adjusted flow parameter value and the flue gas flow velocity fluctuation data to obtain the optimization requirement index of the injection distribution includes: By combining the adjusted flow parameters with the flue gas velocity fluctuation data and using data integration technology to process multi-source information, the preliminary uniformity value of the injection distribution is obtained; Based on the preliminary uniformity value, if there is a deviation from the preset threshold range, the specific deviation data of the injection distribution is calculated by the deviation analysis method to determine the deviation distribution characteristics; Based on the deviation distribution characteristics, the distribution adjustment requirements under the influence of flue gas flow velocity are obtained, and the correlation between flow velocity and distribution is analyzed using a linear regression model to obtain the reference coefficient for distribution adjustment; By using the reference coefficient of distribution adjustment and the flow parameter correlation data, the mean smoothing method is used to optimize the control parameters of the injection distribution and determine the optimized distribution parameter values; According to the optimized distribution parameter value, if its matching degree with the real-time flue gas flow rate fluctuation data is lower than the preset threshold, the distribution parameter is adjusted through iterative calculation to obtain the final distribution adjustment plan; Based on the final distribution adjustment plan, combined with the flow rate impact data, the control instructions of the injection equipment are generated to determine whether the injection distribution has reached a stable state; The real-time injection distribution index is obtained through the execution results of the control instructions. The data comparison method is used to analyze the consistency between the distribution index and the optimization requirements to determine the final distribution optimization result.

6. The deep denitrification spraying system with improved uniformity of urea solution injection and flue gas mixing according to claim 1, characterized in that: The method of adjusting the angle and speed of the injection device according to the optimization requirement index, generating corresponding control instructions, and determining the real-time improvement result of the injection distribution uniformity includes: Based on the optimization requirements and index data, the current parameter configuration of the injection device is obtained, and preliminary control instructions are generated based on the requirements of angle adjustment and rate control; Through preliminary control instructions, the operating status of the injection device is adjusted, real-time distribution uniformity data is obtained, and whether the distribution status meets the preset threshold range is determined; If the distribution state does not reach the preset threshold range, the linear regression model is used to analyze the matching degree between angle adjustment and rate control in combination with the real-time improvement data and parameter correlation information to obtain the improved device parameters; Generate new control instructions based on the improved device parameters, make secondary adjustments to the injection device, and obtain updated distribution uniformity data; By combining the updated uniform distribution data with the indicator data and optimization requirements, we analyze the stability of the distribution state and determine whether further parameter adjustments are needed. If there is still a deviation in the distribution state, the fluctuation information in the improved data is obtained, and the parameter correlation data is processed using the mean smoothing method to obtain the final control instruction; According to the final control instructions, the operating parameters of the injection device are adjusted, the real-time distribution status data is obtained, and it is determined whether the uniform distribution meets the optimization requirements.

7. The deep denitrification spraying system with improved uniformity of urea solution injection and flue gas mixing according to claim 1, characterized in that: The analysis of the correlation between the improvement results and the molecular polymerization state collects the structural stability data of the urea molecule during the reaction process and obtains the fluctuation range of the polymerization state, including: By collecting data during the reaction process, the structural stability information of the urea molecule is recorded to obtain a preliminary stability data set; Based on the preliminary stability data set, the correlation analysis method was used to extract the state change characteristics of urea molecules in the polymerized state and determine the key change nodes; If the key change node exceeds the preset threshold range, the state change data is calibrated using the fluctuation range calculation tool to obtain the calibrated fluctuation data; Based on the calibrated fluctuation data, the changing trends of molecular properties are continuously monitored to obtain dynamic adjustment information during the reaction process and determine whether the stability standards are met; If the dynamic adjustment information shows that there is a deviation in the structural stability, the process monitoring data is processed using the mean smoothing algorithm to obtain the smoothed state parameters; Through the smoothed state parameters and the logic of result verification, the stability of the polymerization state is reconfirmed to determine the final reaction process adjustment plan; According to the final reaction process adjustment plan, the feedback data of the improvement results is recorded, the adjusted urea molecule polymerization state information is obtained, and it is determined whether the preset target is achieved.

8. The deep denitrification spraying system with improved uniformity of urea solution injection and flue gas mixing according to claim 1, characterized in that: If the fluctuation range exceeds the preset safety interval, the flow precision control strategy is dynamically updated based on the environmental parameters collected by real-time data to obtain the final flow regulation solution, including: If the fluctuation range exceeds the preset safety interval, a preliminary analysis of the fluctuation range is performed through the environmental parameter data collected in real time to obtain an initial judgment result of abnormal fluctuation; Based on the initial judgment results, the pre-established analysis model is used to process the data according to the changing trends of environmental parameters to determine the specific deviation direction of the fluctuation range; If the deviation direction shows that the fluctuation range continues to deviate from the safe range, the environmental parameters are calibrated through data processing tools to obtain a calibrated parameter data set; Based on the calibrated parameter data set, the existing control strategy is dynamically adjusted to meet the needs of traffic control and obtain the adjusted policy framework; If there is a deviation between the adjusted policy framework and the regulation target, the policy framework is optimized through parameter analysis tools to determine the optimized traffic control plan; Based on the optimized flow control scheme and combined with real-time collected data, the flow control execution process is monitored to obtain dynamic feedback information during the execution process; If the dynamic feedback information shows that the flow regulation does not meet the preset interval judgment standard, the control plan will be adjusted twice through the strategy update mechanism to obtain the final flow regulation plan.

9. The deep denitrification spraying system with improved uniformity of urea solution injection and flue gas mixing according to claim 1, characterized in that: The final flow rate regulation scheme is combined with the reaction process control logic to generate a closed-loop feedback signal to determine whether the mixing uniformity meets the expected standard, including: To judge the degree of mixing uniformity, obtain data collection results during the real-time reaction process and generate preliminary signal data in combination with a closed-loop feedback mechanism; By processing the preliminary signal data and using the pre-established analysis model, the mixing uniformity state during the reaction process is determined and the state assessment result is obtained; If the state evaluation result shows that the mixing uniformity deviates from the preset standard, the flow regulation scheme is dynamically adjusted through the control logic to obtain the adjusted control parameters; Based on the adjusted control parameters and information collected from real-time data, a new closed-loop feedback signal is generated to determine whether the preset standards are met; If the new closed-loop feedback signal still does not meet the preset standard, the reaction process is further calibrated through data processing tools to obtain calibrated process data; Based on the calibrated process data, the support vector machine model is used to conduct a secondary analysis of the mixing uniformity to determine the final adjustment direction; According to the final adjustment direction, the corresponding flow adjustment instructions are generated, and the execution status is tracked through the process monitoring mechanism to obtain execution feedback information.

10. The deep denitrification spraying system with improved uniformity of urea solution injection and flue gas mixing according to claim 1, characterized in that: The method of continuously monitoring the operating status of the entire control system based on the closed-loop feedback signal, obtaining stability data of the system operation, and determining the optimization effect of the dynamic environmental response includes: Based on the closed-loop feedback signal, pre-established monitoring tools are used to track the operating status of the control system in real time, from which stability data reflecting the system performance is obtained to determine the preliminary operating status of the system; Based on the acquired stability data, the information processing module analyzes the response to the dynamic environment. If the response deviation exceeds the preset threshold, the control system is calibrated to obtain the calibrated operating parameters. Based on the calibrated operating parameters, real-time system monitoring information is obtained and combined with closed-loop feedback signals to determine whether the system has achieved the expected optimization level; If the judgment result shows that the optimization level does not meet the standard, the operating status will be deeply analyzed through data processing tools, and the adaptability data of the dynamic environment will be combined to determine the direction of adjustment; Generate corresponding control instructions based on the determined adjustment direction, track the execution process through the system monitoring mechanism, and obtain status feedback after execution; Based on the acquired state feedback, the support vector machine model is used to conduct a comprehensive analysis of the stability data and environmental adaptability to determine whether the control system has adapted to the dynamic environment; According to the results of the comprehensive analysis, if there is still a deviation, the closed-loop feedback signal is corrected again through the information processing module to obtain the final system operation status.

Images