Intelligent control system and method for ultra-low nitrogen combustion

Abstract

This invention provides an intelligent control system and method for ultra-low NOx combustion. By using fuzzy logic to self-tune PID parameters in real time, it can quickly respond to sudden changes in key parameters, avoiding a disconnect between the initial air / feed ratio and the actual combustion state. This improves the adaptability of the control algorithm to complex combustion processes. The total feed rate is precisely controlled by the fuel regulating valve, the oxygen content in the main combustion zone is controlled by the main combustion air regulating valve, and the airflow and residence time in the reduction zone are adjusted by the secondary / tertiary air regulating valve. This allows the reducing atmosphere of staged combustion to be constructed as needed, effectively suppressing thermal NOx and fuel NOx, ensuring that ultra-low NOx emissions meet standards. By directly outputting the specific adjustment parameters of each valve, fuzzy operations during execution are avoided, ensuring that the adjustment actions of each valve under different operating conditions precisely match the target requirements of the control module. This reduces combustion state fluctuations caused by human intervention or execution deviations, improving the stability and consistency of system operation.

Description

Technical Field

[0001] This invention relates to the field of industrial combustion equipment control and environmental protection retrofitting technology, and in particular to an intelligent control system and method for ultra-low nitrogen combustion. Background Technology

[0002] Traditional burners and control schemes are no longer sufficient to meet nitrogen oxide emission standards. Low-NOx combustion technology is the core pathway to achieve NOx emission reduction. Among these, multi-stage combustion technology, through segmented feeding and staged combustion to create a reducing atmosphere, can effectively suppress the formation of thermal NOx (generated under high-temperature, oxygen-rich conditions) and fuel NOx (generated by the oxidation of nitrogen in fuel), and is currently the mainstream low-NOx retrofit technology direction. However, existing low-NOx burner control retrofit schemes suffer from the following core technical defects, making it difficult to stably achieve ultra-low emissions and exhibiting poor adaptability:

[0003] 1. Insufficient control precision: Existing technologies mostly adopt fixed parameters and open-loop control modes, which only preset the air / fuel ratio based on the combustion load. They cannot respond in real time to the dynamic changes in furnace temperature distribution and flue gas composition, resulting in excessive fluctuations in oxygen content in the main combustion zone. This leads to either the generation of a large amount of NOx due to oxygen enrichment or incomplete combustion of fuel due to oxygen deficiency.

[0004] 2. Poor adaptability: The existing control algorithm does not take into account the influence of fuel composition and combustion load. The fixed stage ratio cannot adapt to different operating conditions, which makes it easy for NOx emissions to exceed the standard when the load changes.

[0005] 3. Insufficient reliability: The system lacks fault redundancy design, and when sensors or actuators fail, the system is prone to shutdown or loss of control, resulting in excessive emissions;

[0006] 4. Poor economic efficiency of the retrofit: Some solutions require large-scale modification of the main structure of the burner, which results in long construction period, high retrofit cost, and poor compatibility with old equipment. Summary of the Invention

[0007] This invention provides an intelligent control system and method for ultra-low nitrogen combustion, in order to solve the problems mentioned in the background art.

[0008] A smart control system for ultra-low NOx combustion includes:

[0009] The detection module is used to collect temperature parameters, flue gas composition parameters, and material flow parameters of the multi-stage combustion structure in real time based on a multi-point distributed high-precision detection instrument to obtain key parameters.

[0010] The control module is used to analyze key parameters based on the fuzzy PID control algorithm to obtain the initial air / feed ratio, and optimize the initial air / feed ratio based on fuel composition and combustion load to obtain the target air / feed ratio.

[0011] The execution module is used to determine the adjustment parameters of the main combustion air supply regulating valve, the secondary air supply regulating valve, and the tertiary air supply regulating valve based on the target air supply / feed ratio.

[0012] The fault redundancy handling module is used to immediately activate the backup parameter mode and issue an early warning when an abnormality in a critical parameter is detected.

[0013] Preferably, the detection module includes:

[0014] The temperature detection unit is used to install K-type thermocouple sensors in the main combustion chamber, secondary combustion chamber and tertiary combustion chamber to monitor the temperature distribution of each combustion zone in real time and obtain temperature parameters.

[0015] The flue gas component detection unit is used to install a zirconia oxygen content analyzer and an infrared absorption NOx analyzer in the flue gas outlet pipe to monitor the flue gas distribution in real time and obtain flue gas component parameters.

[0016] The material flow detection unit is used to install electromagnetic flow sensors in the main fuel supply pipeline and vortex flow sensors in the main combustion air pipeline, secondary air pipeline and tertiary air pipeline to monitor the flow in real time and obtain material flow parameters.

[0017] Preferably, the control module includes:

[0018] The preprocessing unit is used to perform data purification and normalization on key parameters to obtain the target key parameters.

[0019] The membership function establishment unit is used to establish a membership function by taking the parameter quantity as the input variable and the fuzzy level of the PID as the output variable, and combining the Gaussian membership function with dynamic parameter adjustment.

[0020] The clustering unit is used to cluster historical operating data based on clustering algorithms, using temperature, flue gas composition and material flow rate as clustering dimensions, to obtain exclusive operating data corresponding to three core operating conditions: low load, medium load and high load.

[0021] The rule base establishment unit is used to obtain fuzzy input variables of exclusive operating data under each core operating condition, perform statistical analysis on fuzzy input variables and input precision quantities, and generate a mapping relationship between input precision quantities and output fuzzy levels based on membership functions. The core combustion mechanism of the multi-stage combustion structure is transformed into rule constraints. The mapping relationship is differentiated based on the rule constraints to obtain the target mapping relationship. The operating condition-specific rule base is generated based on the target mapping relationship.

[0022] The fuzzy set determination unit is used to calculate the trigger strength of each rule in the working condition-specific rule base based on a preset inference algorithm, select the minimum value between the membership degree of the corresponding output fuzzy level and the trigger strength of the input precision of each rule as the target membership degree, and generate the output fuzzy set of each rule based on the target membership degree.

[0023] Fuzzy sets are used to synthesize the output fuzzy sets of multiple rules according to the rule of taking the maximum value when multiple rules are triggered at the same time, so as to obtain an intermediate output fuzzy set. The intermediate output fuzzy set is then eliminated and smoothed to obtain the target output fuzzy set.

[0024] The initial ratio determination unit is used to defuzzify the target output fuzzy set based on the centroid method to obtain the PID precise value, and to normalize the PID precise value to obtain the ratio correction coefficient. Based on the product of the ratio correction coefficient and the theoretical air / material feeding ratio, the initial air / material feeding ratio is obtained.

[0025] Preferably, the preprocessing unit includes:

[0026] The purification unit is used to perform primary purification of key parameters based on Kalman filtering, followed by secondary purification based on wavelet threshold denoising to obtain purified key parameters.

[0027] The normalization unit is used to normalize the key parameters of purification to obtain the target key parameters.

[0028] Preferably, the initial ratio determination unit includes:

[0029] The coefficient determination unit is used to obtain the proportional, integral, and derivative outputs from the PID precise value, and after normalization, the sum of the proportional, integral, and derivative outputs and the reference output value is used as the proportional correction coefficient.

[0030] The determination unit is used to obtain the initial air / material ratio based on the product of the proportional correction coefficient and the theoretical air / material ratio.

[0031] Preferably, the control module further includes:

[0032] The detection unit is used to install near-infrared spectroscopy sensors and laser-induced breakdown spectroscopy at the feed port of the multi-stage combustion structure to perform multi-dimensional detection of fuel composition and obtain real-time fuel composition. Key parameters are extracted by pre-constructed load characteristic quantities that fuse multiple parameters of flow, pressure and temperature to obtain real-time combustion load.

[0033] The component correction determination unit is used to determine the core reaction mechanism of multi-stage combustion structure. It establishes fuel-type NOx formation rate equation, thermal NOx formation rate equation, and combustion completeness equation. Based on the formation rate and combustion completeness as constraints, it solves the optimal oxygen content in the main combustion zone under the fuel-type NOx formation rate equation, thermal NOx formation rate equation, and combustion completeness equation in combination with real-time fuel composition. It also calculates the fuel component correction coefficient by combining the fuel nitrogen content, volatile matter, and moisture content in the real-time fuel composition.

[0034] The load correction determination unit is used to establish a correction mechanism under different sub-conditions based on the control priority under different sub-conditions, and to determine the combustion load correction coefficient based on the real-time combustion load and the correction mechanism.

[0035] The fusion unit is used to design a dynamic weight allocation mechanism based on fuel composition and combustion load fluctuations. Based on the dynamic weight allocation mechanism, the fuel composition correction coefficient and the combustion load correction coefficient are weighted and fused to obtain the initial correction coefficient.

[0036] The prediction and verification unit is used to establish a digital twin model of the multi-stage combustion structure. Based on the digital twin model, it predicts the combustion parameters over a period of time under the target correction coefficient. When the combustion parameters meet the preset requirements, it optimizes the initial air supply / feed ratio based on the initial correction coefficient to obtain the target air supply / feed ratio. Otherwise, it initiates the correction iteration mechanism of the digital twin model to obtain the target correction coefficient and optimizes the initial air supply / feed ratio to obtain the target air supply / feed ratio.

[0037] Preferably, the load correction determination unit includes:

[0038] The priority determination unit is used to determine the current sub-condition based on the real-time combustion load and retrieve the target control priority corresponding to the current sub-condition.

[0039] The coefficient calculation unit is used to retrieve the correction coefficient calculation method corresponding to the target control priority from the correction mechanism, and calculate the combustion load correction coefficient based on the correction coefficient calculation method and the real-time combustion load.

[0040] Preferably, the execution module includes:

[0041] The fuel regulating unit is used to determine the opening of the fuel regulating valve based on the target feeding ratio, combined with real-time material flow data and a pre-set flow-opening curve.

[0042] The air supply regulating unit is used to determine the valve opening of the main combustion air supply regulating valve, the secondary air supply regulating valve, and the tertiary air supply regulating valve based on the target air supply ratio and in combination with the graded air supply ratio of the main combustion air supply regulating valve, the secondary air supply regulating valve, and the tertiary air supply regulating valve, through a pre-set air volume-opening curve.

[0043] Preferably, the fault redundancy processing module includes:

[0044] An anomaly identification unit is used to acquire real-time key parameters, and determine that an anomaly has occurred when the real-time key parameters are not within the preset normal threshold range;

[0045] The startup unit is used to retrieve the dedicated backup parameters corresponding to the current operating condition to start the backup parameter mode.

[0046] The early warning unit is used to issue early warnings based on the location and type of the anomaly.

[0047] A smart control method for ultra-low NOx combustion, comprising:

[0048] S1: Based on multi-point distributed high-precision detection instruments, the temperature parameters, flue gas composition parameters and material flow parameters of the multi-stage combustion structure during the combustion process are collected in real time to obtain key parameters;

[0049] S2: Based on the fuzzy PID control algorithm, the key parameters are analyzed to obtain the initial air supply / feed ratio, and the initial air supply / feed ratio is optimized based on the fuel composition and combustion load to obtain the target air supply / feed ratio;

[0050] S3: Based on the target air / material supply ratio, determine the adjustment parameters of the main combustion air supply regulating valve, the secondary air supply regulating valve, and the tertiary air supply regulating valve;

[0051] S4: When an abnormality is detected in a key parameter, immediately activate the backup parameter mode and issue an early warning.

[0052] Compared with the prior art, the present invention has achieved the following beneficial effects:

[0053] By deploying high-precision detection instruments at multiple distributed points, the system achieves full coverage and real-time acquisition of temperature, flue gas composition, and material flow parameters. This avoids the limitations of single-point detection and accurately captures temperature distribution and flue gas component concentration differences in different areas of the furnace, such as oxygen content and NOx concentration gradients between the main combustion zone and the reduction zone. This solves the control blindness caused by traditional single-parameter detection. Simultaneously, real-time data acquisition replaces the lag of traditional sampling or timed detection, ensuring that key parameters dynamically reflect instantaneous changes in the combustion process. This provides accurate and timely data support for subsequent control algorithms, improving control accuracy from the source. By simultaneously acquiring three core parameters—temperature, flue gas, and material flow—the system avoids the one-sidedness of relying solely on a single load parameter, providing a complete data dimension for subsequent optimization adjustments based on fuel composition and combustion load. Real-time self-tuning of PID parameters using fuzzy logic enables rapid response to sudden changes in key parameters, preventing the initial air / feed ratio from deviating from the actual combustion state. This improves the adaptability of the control algorithm to complex combustion processes, reduces oxygen content fluctuations in the main combustion zone, and improves control accuracy through fuel regulating valves. Precise control of the total feed rate, with the main combustion air regulating valve controlling the oxygen content in the main combustion zone and the secondary / tertiary air regulating valve adjusting the airflow and residence time in the reduction zone, ensures that the reducing atmosphere of staged combustion is constructed as needed, effectively suppressing thermal NOx and fuel NOx, and ensuring that ultra-low NOx emissions meet standards. By directly outputting the specific adjustment parameters of each valve, ambiguous operations during execution are avoided, ensuring that the adjustment actions of each valve under different operating conditions precisely match the target requirements of the control module, reducing combustion state fluctuations caused by human intervention or execution deviations, and improving the stability and consistency of system operation. When an abnormality of key parameters is detected, the backup parameter mode is immediately activated to prevent the system from falling into a shutdown or irregular operation state due to parameter abnormalities, ensuring continuous combustion and reducing production interruption losses caused by failures. Through the modular integrated design of detection, control, and execution, the system can achieve ultra-low NOx retrofit without changing the main structure of the burner's staged combustion, only by optimizing the control logic and execution adjustment. It is compatible with old combustion equipment, reducing retrofit costs and construction cycle, and solving the problems of high cost, long cycle, and poor adaptability of traditional retrofits.

[0054] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in this application.

[0055] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0056] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0057] Figure 1 This is a structural diagram of an intelligent control system for ultra-low nitrogen combustion in an embodiment of the present invention;

[0058] Figure 2 This is a flowchart of an intelligent control method for ultra-low nitrogen combustion in an embodiment of the present invention. Detailed Implementation

[0059] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0060] Example 1: This embodiment of the invention provides an intelligent control system for ultra-low nitrogen combustion, such as... Figure 1 As shown, it includes:

[0061] The detection module is used to collect temperature parameters, flue gas composition parameters, and material flow parameters of the multi-stage combustion structure in real time based on a multi-point distributed high-precision detection instrument to obtain key parameters.

[0062] The control module is used to analyze key parameters based on the fuzzy PID control algorithm to obtain the initial air / feed ratio, and optimize the initial air / feed ratio based on fuel composition and combustion load to obtain the target air / feed ratio.

[0063] The execution module is used to determine the adjustment parameters of the main combustion air supply regulating valve, the secondary air supply regulating valve, and the tertiary air supply regulating valve based on the target air supply / feed ratio.

[0064] The fault redundancy handling module is used to immediately activate the backup parameter mode and issue an early warning when an abnormality in a critical parameter is detected.

[0065] In this embodiment, the backup parameter mode is a set of dedicated parameters pre-set according to each operating condition.

[0066] In this embodiment, the multi-stage combustion structure is modified based on the existing burner main structure without changing the furnace shape. Only a secondary combustion chamber and a tertiary combustion chamber made of high-temperature resistant stainless steel and welded together are added to the original combustion chamber. The main combustion chamber is equipped with the main burner, and the secondary / tertiary combustion chambers are evenly equipped with atomizing combustion nozzles. The number of nozzles is matched according to the furnace volume, usually 4-6 secondary combustion chambers and 2-4 tertiary combustion chambers. The fuel supply main pipeline is connected to each burner / nozzle through branch pipelines, and the branch pipelines are equipped with small regulating valves. The air supply pipelines are connected to each combustion chamber to achieve staged air supply.

[0067] In this embodiment, multi-point distributed means setting up corresponding high-precision detection instruments at locations such as the main combustion chamber, secondary combustion chamber, tertiary combustion chamber, fuel supply main pipeline, main combustion, secondary and tertiary air supply pipelines, and flue gas outlet pipeline.

[0068] In this embodiment, the high-precision detection instruments include a K-type thermocouple sensor (measurement range 0-1500℃, accuracy ±1℃), a zirconia oxygen content analyzer (measurement accuracy ±0.1%), an infrared absorption NOx analyzer (measurement range 0-100mg / m³, accuracy ±0.5mg / m³), an electromagnetic flow sensor (accuracy ±0.5%), and a vortex flow sensor (accuracy ±1%).

[0069] In this embodiment, NOx is a general term for nitrogen oxides, which refers to compounds composed of nitrogen and oxygen. The core components include nitric oxide and nitrogen dioxide, and it is one of the main pollutants in the combustion process.

[0070] The beneficial effects of the above design scheme are as follows: By setting up high-precision detection instruments at multiple distributed points, it achieves full coverage and real-time acquisition of temperature parameters, flue gas composition parameters, and material flow parameters, avoiding the limitations of single-point detection. It accurately captures the temperature distribution and flue gas composition concentration differences in different areas of the furnace, such as the oxygen content and NOx concentration gradient between the main combustion zone and the reduction zone, solving the control blindness caused by traditional single-parameter detection. Simultaneously, real-time data acquisition replaces the lag of traditional sampling or timed detection, ensuring that key parameters dynamically reflect the instantaneous changes in the combustion process, providing accurate and timely data support for subsequent control algorithms, and improving control accuracy from the source. By simultaneously acquiring three core parameters—temperature, flue gas, and material flow—it avoids the one-sidedness of relying solely on a single load parameter, providing a complete data dimension for subsequent optimization and adjustment based on fuel composition and combustion load. Through real-time self-tuning of PID parameters using fuzzy logic, it can quickly respond to sudden changes in key parameters, avoiding the initial air / feed ratio from deviating from the actual combustion state, improving the adaptability of the control algorithm to complex combustion processes, and reducing oxygen content fluctuations in the main combustion zone. The system precisely controls the total feed rate through fuel regulating valves, controls the oxygen content in the main combustion zone through main combustion air regulating valves, and adjusts the airflow and residence time in the reduction zone through secondary / tertiary air regulating valves. This allows the reducing atmosphere of staged combustion to be constructed as needed, effectively suppressing thermal NOx and fuel NOx, ensuring that ultra-low NOx emissions meet standards. By directly outputting the specific adjustment parameters of each valve, the system avoids ambiguous operations during execution, ensuring that the adjustment actions of each valve under different operating conditions precisely match the target requirements of the control module. This reduces combustion state fluctuations caused by human intervention or execution deviations, improving the stability and consistency of system operation. When an abnormality in a key parameter is detected, the system immediately activates the backup parameter mode to prevent the system from shutting down or operating irregularly due to parameter abnormalities, ensuring continuous combustion and reducing production interruption losses caused by malfunctions. Through modular integrated design of detection, control, and execution, the system can achieve ultra-low NOx retrofit without changing the main structure of the burner's staged combustion. It only requires optimizing the control logic and execution adjustment to adapt to old combustion equipment, reducing retrofit costs and construction time, and solving the problems of high cost, long cycle, and poor adaptability of traditional retrofits.

[0071] Example 2: Based on Example 1, this embodiment of the invention provides an intelligent control system for ultra-low NOx combustion, wherein the detection module includes:

[0072] The temperature detection unit is used to install K-type thermocouple sensors in the main combustion chamber, secondary combustion chamber and tertiary combustion chamber to monitor the temperature distribution of each combustion zone in real time and obtain temperature parameters.

[0073] The flue gas component detection unit is used to install a zirconia oxygen content analyzer and an infrared absorption NOx analyzer in the flue gas outlet pipe to monitor the flue gas distribution in real time and obtain flue gas component parameters.

[0074] The material flow detection unit is used to install electromagnetic flow sensors in the main fuel supply pipeline and vortex flow sensors in the main combustion air pipeline, secondary air pipeline and tertiary air pipeline to monitor the flow in real time and obtain material flow parameters.

[0075] The beneficial effects of the above design scheme are as follows: By setting up high-precision detection instruments at multiple distributed points, it achieves full coverage and real-time acquisition of temperature parameters, flue gas composition parameters, and material flow parameters, avoiding the limitations of single-point detection. It accurately captures the temperature distribution and flue gas composition concentration differences in different areas of the furnace, such as the oxygen content and NOx concentration gradient between the main combustion zone and the reduction zone, solving the control blindness caused by traditional single-parameter detection. At the same time, real-time data acquisition replaces the lag of traditional sampling or timed detection, ensuring that key parameters can dynamically reflect the instantaneous changes in the combustion process, providing accurate and timely data support for subsequent control algorithms, and improving control accuracy from the source. By simultaneously acquiring three core parameters—temperature, flue gas, and material flow—it avoids the one-sidedness of relying solely on a single load parameter, providing a complete data dimension for subsequent optimization and adjustment based on fuel composition and combustion load.

[0076] Example 3: Based on Example 1, this embodiment of the invention provides an intelligent control system for ultra-low NOx combustion, wherein the control module includes:

[0077] The preprocessing unit is used to perform data purification and normalization on key parameters to obtain the target key parameters.

[0078] The membership function establishment unit is used to establish a membership function by taking the parameter quantity as the input variable and the fuzzy level of the PID as the output variable, and combining the Gaussian membership function with dynamic parameter adjustment.

[0079] The clustering unit is used to cluster historical operating data based on clustering algorithms, using temperature, flue gas composition and material flow rate as clustering dimensions, to obtain exclusive operating data corresponding to three core operating conditions: low load, medium load and high load.

[0080] The rule base establishment unit is used to obtain fuzzy input variables of exclusive operating data under each core operating condition, perform statistical analysis on fuzzy input variables and input precision quantities, and generate a mapping relationship between input precision quantities and output fuzzy levels based on membership functions. The core combustion mechanism of the multi-stage combustion structure is transformed into rule constraints. The mapping relationship is differentiated based on the rule constraints to obtain the target mapping relationship. The operating condition-specific rule base is generated based on the target mapping relationship.

[0081] The fuzzy set determination unit is used to calculate the trigger strength of each rule in the working condition-specific rule base based on a preset inference algorithm, select the minimum value between the membership degree of the corresponding output fuzzy level and the trigger strength of the input precision of each rule as the target membership degree, and generate the output fuzzy set of each rule based on the target membership degree.

[0082] Fuzzy sets are used to synthesize the output fuzzy sets of multiple rules according to the rule of taking the maximum value when multiple rules are triggered at the same time, so as to obtain an intermediate output fuzzy set. The intermediate output fuzzy set is then eliminated and smoothed to obtain the target output fuzzy set.

[0083] The initial ratio determination unit is used to defuzzify the target output fuzzy set based on the centroid method to obtain the PID precise value, and to normalize the PID precise value to obtain the ratio correction coefficient. Based on the product of the ratio correction coefficient and the theoretical air / material feeding ratio, the initial air / material feeding ratio is obtained.

[0084] In this embodiment, the synthesized total output fuzzy set may be discontinuous or fluctuate locally, requiring simple normalization: ① Remove redundant values ​​with membership degree ≤ 0 (regions without rule influence); ② Smooth the membership degree values ​​in continuous intervals (e.g., using moving average) to avoid sudden changes in results during subsequent centroid defuzzification. After normalization, the final output variable fuzzy set is obtained, which can be directly used for the next step of centroid defuzzification.

[0085] In this embodiment, the trigger strength is the minimum value of the membership degree of the input variables; for example, taking the rule under low load conditions as an example: IF temperature=NB, flue gas volume=NS, flow rate=NB, THEN ΔKp=PS, ΔKi=PM, ΔKd=NS, assuming the current purified precise input is temperature=-0.9, flue gas volume=-0.3, flow rate=-0.8: the membership degree of temperature under the fuzzy level of NB (calculated by Gaussian function) is 0.9; the membership degree of flue gas volume under the fuzzy level of NS is 0.7; the membership degree of flow rate under the fuzzy level of NB is 0.8; then the trigger strength of this rule is min(0.9,0.7,0.8)=0.7.

[0086] In this embodiment, the minimum value between the membership degree of the corresponding output fuzzy level and the trigger strength of the input precision of each rule is used as the target membership degree. For example, when x=0.3, the original membership degree is 1.0, and when x=0.2, the original membership degree is 0.89. The trigger strength of 0.7 is used for judgment to obtain the membership degree of each value in the output fuzzy set of a single rule (when x=0.3, min(1.0,0.7)=0.7, when x=0.2, min(0.89,0.7)=0.7). In this way, the trigger strength is integrated into the output fuzzy set to realize the logic that the stronger the rule is, the higher the upper limit of the membership degree of the output fuzzy set.

[0087] In this embodiment, the input precise quantities are, for example, precise values ​​corresponding to temperature, flow rate, and flue gas composition.

[0088] In this embodiment, the clustering algorithm is the K-means algorithm.

[0089] In this embodiment, the core characteristics of low-load conditions are low combustion intensity and low NOx generation, but easy oxygen deficiency and incomplete combustion. The core characteristics of medium-load conditions are stable combustion and low difficulty in balancing NOx generation and combustion efficiency. The core characteristics of high-load conditions are high combustion intensity and high risk of thermal NOx generation, requiring key control of oxygen content in the main combustion zone.

[0090] In this embodiment, the fuzzy levels of the PID include NB (negative large), NM (negative medium), NS (negative small), ZO (zero), PS (positive small), PM (positive medium), and PB (positive large), including proportional, derivative, and integral components, each corresponding to the above fuzzy levels.

[0091] In this embodiment, a membership function is established by combining a Gaussian membership function with dynamic parameter adjustments. For example: under low load conditions, the membership function of flue gas components decreases (from 0.3 to 0.2), thus increasing the sensitivity to changes in CO concentration; under high load conditions, the membership function of temperature shifts to the right (from 0 to 0.1), strengthening the early warning response to high-temperature areas. The expression for the Gaussian membership function is as follows:

[0092] Where x represents the current parameter correction value, This represents the mean. It represents the standard deviation.

[0093] In this embodiment, the core combustion mechanism of the multi-stage combustion structure is transformed into rule constraints. For example: Low load condition: The combustion mechanism requires prioritizing combustion sufficiency and avoiding oxygen deficiency to generate CO, which is transformed into constraints—the rule output Ki must be ≥0.5 (to ensure strong integral action and eliminate steady-state oxygen deficiency deviation), and the air supply ratio of the main combustion zone must be ≥65%; High load condition: The mechanism requires prioritizing the suppression of thermal NOx and controlling the temperature and oxygen content of the main combustion zone, which is transformed into constraints—the rule output Kp must be ≥10 (to enhance the rapidity of proportional adjustment), and the air supply ratio of the main combustion zone must be ≤55%; Medium load condition: The mechanism requires balancing combustion efficiency and NOx emissions, which is transformed into constraints—Kp∈[8,10], Ki∈[0.4,0.6], NOx≤25mg / m³ and CO≤40mg / m³.

[0094] In this embodiment, the mapping relationship is adjusted differently based on rule constraints. For example, the operating condition rule is optimized by adding a CO concentration-sensitive rule. When the CO-related component in the E flue gas characteristic increases, the rule output ΔKi is increased by 1 fuzzy level (e.g., adjusted from PS to PM), while ΔKd is decreased by 1 level (e.g., adjusted from ZO to NS) to ensure rapid elimination of hypoxia deviation.

[0095] The beneficial effects of the above design scheme are as follows: By purifying and normalizing the key parameters, the target key parameters are obtained, improving the quality of control input data, eliminating potential deviations at the source, and strengthening the correlation between the target key parameters and the control target. By using parameters as input variables and PID fuzzy levels as output variables, combined with the smoothing characteristics of Gaussian membership functions and dynamic parameter adjustment design, precise adaptation to the highly nonlinear and time-varying characteristics of the combustion process is achieved, solving the problem that fixed membership functions cannot adapt to all operating conditions. By using temperature, flue gas composition, and material flow rate as core clustering dimensions, the clustering algorithm separates exclusive operating data for three core operating conditions—low load, medium load, and high load—from historical operating data, accurately capturing different operating conditions. The differences in combustion characteristics provide data support for the establishment of a condition-specific rule base. Clustered operational data focuses on the common characteristics of similar operating conditions, avoiding cross-interference between data from different operating conditions. This quickly uncovers the core mapping relationship between input parameters and PID fuzzy levels under that operating condition, reducing rule redundancy and contradictions, and improving the efficiency and accuracy of rule base construction. Statistical analysis of the operational data reveals the verified mapping relationship between input fuzzy variables and output fuzzy levels in actual operation, ensuring the rule base aligns with the actual operating characteristics of the equipment. Furthermore, by transforming multi-level combustion core mechanisms into rule constraints, control failures caused by the rule base deviating from the essence of combustion are avoided. Ultimately, a condition-specific rule base is obtained, enabling each rule to accurately match the combustion of its corresponding operating condition. This feature ensures a balance between NOx emissions and combustion efficiency under different loads. By calculating the trigger strength of each rule, essentially quantifying the matching degree between input parameters and rule conditions, and then determining the target membership degree by taking the minimum value between the membership degree of the input precision quantity and the trigger strength, the upper limit of the membership degree of the single rule output fuzzy set is linked to the actual effectiveness of the rule. This avoids the decision bias caused by the equal effect of all rules after triggering in traditional fuzzy inference. By adopting a synthesis rule that takes the maximum value, the control intent of multiple triggering rules can be effectively integrated. The higher the effectiveness of the rule, the greater its contribution to the synthesis result. This avoids logical conflicts when multiple rules are triggered and achieves the effect of multiple related rules working together to support control decisions, solving the problem of rule conflicts in traditional multi-rule inference. To address the issue of mutually canceling or overlapping effects, redundant values ​​are removed and the intermediate output fuzzy set is smoothed to optimize the quality of the synthesized result and provide a stable input for defuzzification. The centroid method is used to defuzzify the target output fuzzy set, improving defuzzification accuracy and ensuring the reliability of the PID precision value. The proportional correction coefficient obtained by normalizing the PID precision value is then multiplied by the theoretical air / material feeding ratio to obtain the initial ratio. This ensures the theoretical rationality of the initial ratio and incorporates real-time operating condition feedback through the PID precision value, enabling the initial ratio to quickly respond to the current combustion state while maintaining a balance between combustion efficiency and NOx emissions. This solves the problem of excessive emissions or incomplete combustion caused by the disconnect between fixed presets and actual operating conditions. Ultimately, each unit is interconnected.A complete closed loop is formed from data input to initial proportional output. The output of each stage serves as the precise input for the next stage, ensuring that control decisions can respond in real time to the dynamic changes in the combustion process. This solves the problems of response lag and deviation accumulation caused by traditional open-loop control and segmented control.

[0096] Example 4: Based on Example 3, this embodiment of the invention provides an intelligent control system for ultra-low NOx combustion, wherein the pretreatment unit includes:

[0097] The purification unit is used to perform primary purification of key parameters based on Kalman filtering, followed by secondary purification based on wavelet threshold denoising to obtain purified key parameters.

[0098] The normalization unit is used to normalize the key parameters of purification to obtain the target key parameters.

[0099] In this embodiment, the key purification parameters are normalized, and the parameter values ​​are mapped to the [-1,1] interval to eliminate dimensional differences.

[0100] The beneficial effects of the above design scheme are: by performing primary purification of key parameters based on Kalman filtering to suppress slowly varying noise, and by performing secondary purification based on wavelet threshold denoising to eliminate instantaneous impulse interference, the target key parameters are obtained by normalization after purification, providing a standard data basis for subsequent parameter analysis.

[0101] Example 5: Based on Example 3, this embodiment of the invention provides an intelligent control system for ultra-low NOx combustion, wherein the initial ratio determination unit includes:

[0102] The coefficient determination unit is used to obtain the proportional, integral, and derivative outputs from the PID precise value, and after normalization, the sum of the proportional, integral, and derivative outputs and the reference output value is used as the proportional correction coefficient.

[0103] The determination unit is used to obtain the initial air / material ratio based on the product of the proportional correction coefficient and the theoretical air / material ratio.

[0104] The beneficial effects of the above design scheme are as follows: by normalizing the PID precise value to obtain the proportional correction coefficient, and then multiplying it by the theoretical air supply / feed ratio to obtain the initial ratio, the theoretical rationality of the initial ratio is guaranteed. Furthermore, by incorporating the PID precise value into real-time operating condition feedback, the initial ratio can not only respond quickly to the current combustion state, but also maintain the balance between combustion efficiency and NOx emissions, thus solving the problem of excessive emissions or incomplete combustion caused by the disconnect between fixed preset and actual operating conditions.

[0105] Example 6: Based on Example 1, this embodiment of the invention provides an intelligent control system for ultra-low NOx combustion, wherein the control module further includes:

[0106] The detection unit is used to install near-infrared spectroscopy sensors and laser-induced breakdown spectroscopy at the feed port of the multi-stage combustion structure to perform multi-dimensional detection of fuel composition and obtain real-time fuel composition. Key parameters are extracted by pre-constructed load characteristic quantities that fuse multiple parameters of flow, pressure and temperature to obtain real-time combustion load.

[0107] The component correction determination unit is used to determine the core reaction mechanism of multi-stage combustion structure. It establishes fuel-type NOx formation rate equation, thermal NOx formation rate equation, and combustion completeness equation. Based on the formation rate and combustion completeness as constraints, it solves the optimal oxygen content in the main combustion zone under the fuel-type NOx formation rate equation, thermal NOx formation rate equation, and combustion completeness equation in combination with real-time fuel composition. It also calculates the fuel component correction coefficient by combining the fuel nitrogen content, volatile matter, and moisture content in the real-time fuel composition.

[0108] The load correction determination unit is used to establish a correction mechanism under different sub-conditions based on the control priority under different sub-conditions, and to determine the combustion load correction coefficient based on the real-time combustion load and the correction mechanism.

[0109] The fusion unit is used to design a dynamic weight allocation mechanism based on fuel composition and combustion load fluctuations. Based on the dynamic weight allocation mechanism, the fuel composition correction coefficient and the combustion load correction coefficient are weighted and fused to obtain the initial correction coefficient.

[0110] The prediction and verification unit is used to establish a digital twin model of the multi-stage combustion structure. Based on the digital twin model, it predicts the combustion parameters over a period of time under the target correction coefficient. When the combustion parameters meet the preset requirements, it optimizes the initial air supply / feed ratio based on the initial correction coefficient to obtain the target air supply / feed ratio. Otherwise, it initiates the correction iteration mechanism of the digital twin model to obtain the target correction coefficient and optimizes the initial air supply / feed ratio to obtain the target air supply / feed ratio.

[0111] In this embodiment, the constraints are based on the generation rate and combustion completeness, for example: NOx generation rate ≤ 0.5 mg / (m³·s) and combustion completeness ≥ 99%.

[0112] In this embodiment, the real-time fuel composition includes fuel nitrogen content, volatile matter, moisture, fixed carbon, etc.

[0113] In this embodiment, load characteristics include static load, dynamic load change rate, load stability index, etc.

[0114] In this embodiment, the sub-conditions include combinations of low load, medium load, and high load in steady state, rising, and falling states, respectively.

[0115] In this embodiment, the rate equation for the formation of fuel-type NOx is A= ×H^0.6×V^0.4× The rate equation for the formation of thermodynamic NOx is B = 0.8 × T^1.2. × 1.0×T^1.5×exp(-E / (R×T)), the equation for complete combustion is C=1- ×exp(- × ^0.5×T^0.3 / V^0.2), where ~ The reaction rate constant (calibrated through industrial testing) =0.02、 =0.005、 =0.8、 =0.1), The values ​​represent oxygen content, H represents fuel nitrogen content, V represents volatile matter, E represents activation energy, R represents gas constant, and T represents the average temperature of the main combustion zone.

[0116] In this embodiment, the calculation process of the fuel composition correction coefficient is as follows: based on the real-time parameters, the optimal oxygen content is obtained by substituting them into the three kinetic equations. Then, based on the real-time parameters, the theoretical air volume correction coefficient G = 1 + 0.05 × (H - 1.5) - 0.03 × (V - 25) + 0.02 × (M - 8) is calculated, where 1.5 is the baseline nitrogen content percentage, 25 is the baseline volatile matter percentage, 8 is the baseline moisture percentage, and M represents the moisture content. Finally, the fuel composition correction coefficient K = G * (optimal oxygen content / 10.5) is calculated, where 10.5 is the baseline oxygen content percentage in the main combustion zone.

[0117] In this embodiment, the control priorities under different sub-conditions are as follows: In low-load sub-conditions: the priority is combustion completeness > NOx suppression > smooth transition, and the core of the correction is to increase the air supply ratio to avoid oxygen deficiency; In high-load sub-conditions: the priority is NOx suppression > combustion completeness > smooth transition, and the core of the correction is to limit the air supply ratio to avoid oxygen enrichment; In medium-load sub-conditions: the priority is combustion efficiency and NOx balance > smooth transition, and the core of the correction is to maintain a stable air supply ratio; In variable-load sub-conditions: the priority of smooth transition is temporarily increased to the top to avoid excessive correction that could lead to combustion fluctuations.

[0118] In this embodiment, the correction mechanism is based on the following: under steady-state conditions, the air supply ratio is increased at low loads to ensure complete combustion, and the air supply ratio is limited at high loads to suppress NOx; under load-increasing conditions, the correction coefficient is increased to improve the following speed of the air supply ratio, avoiding oxygen deficiency caused by increased fuel supply and delayed air supply when the load increases; under load-decreasing conditions, the correction coefficient is reduced to slow down the rate of decrease in the air supply ratio, avoiding oxygen-rich NOx generation caused by reduced fuel and excessive air supply when the load decreases; under unstable conditions, a stability weight is introduced to weaken the correction intensity, avoiding combustion oscillations caused by the superposition of corrections when the load fluctuates.

[0119] In this embodiment, the dynamic weight allocation mechanism is as follows: when the fuel composition fluctuates greatly, the fuel composition correction coefficient is 0.6 and the combustion load correction coefficient is 0.4; when the load change rate is large, the fuel composition correction coefficient is 0.3 and the combustion load correction coefficient is 0.7; under steady-state conditions and when the fuel composition fluctuates little, the fuel composition correction coefficient is 0.5 and the combustion load correction coefficient is 0.5.

[0120] In this embodiment, the correction iteration mechanism of the digital twin model is activated to obtain the target correction coefficient, which is specifically required to be ≤5 iterations. If the standard is still not met, a safety mode is activated (using a preset conservative target ratio to ensure that emissions do not exceed the standard). If NOx exceeds the standard, the initial correction coefficient is reduced; if CO exceeds the standard, the initial correction coefficient is increased.

[0121] The beneficial effects of the above design scheme are as follows: By using multi-dimensional fuel component detection and multi-parameter fusion load characterization, it solves the problems of data distortion and one-sided characterization; by constructing fuel-type NOx, thermal NOx formation rate equations, and combustion completeness equations based on multi-stage combustion core reaction mechanisms, it ensures that the correction logic aligns with the essence of combustion, avoiding the subjectivity and one-sidedness of empirical corrections; by constraining targets, it guarantees the core requirement of ultra-low nitrogen; the correction coefficient calculation is directly related to real-time fuel components, enabling dynamic response to fuel type switching, solving the problem that fuel component correction coefficients cannot adapt to fuel changes, easily leading to excessive emissions or low combustion efficiency; and by prioritizing sub-operating conditions... This improves the targeting of control and avoids the problems of low-load hypoxia and high-load NOx exceedance caused by using the same correction logic for all operating conditions. It solves the problem of fuel and load correction conflict by dynamically balancing the correction needs of two dimensions. By introducing a predictive feedback mechanism, it solves the problem of no prediction and easy loss of control after correction. Iterative correction improves the accuracy of target ratio. It forms a complete closed loop from real-time detection, mechanism correction, dynamic fusion, prediction verification and target optimization. The output of each unit serves as the input of the next level, ensuring that the control decision can continuously adapt to the dual needs of fuel composition fluctuation and load dynamic changes, and solves the problems of response lag and deviation accumulation in traditional open-loop control.

[0122] Example 7: Based on Example 6, this embodiment of the invention provides an intelligent control system for ultra-low NOx combustion, including a load correction determination unit, comprising:

[0123] The priority determination unit is used to determine the current sub-condition based on the real-time combustion load and retrieve the target control priority corresponding to the current sub-condition.

[0124] The coefficient calculation unit is used to retrieve the correction coefficient calculation method corresponding to the target control priority from the correction mechanism, and calculate the combustion load correction coefficient based on the correction coefficient calculation method and the real-time combustion load.

[0125] The beneficial effects of the above design scheme are: by directly linking the real-time fuel composition through the calculation of the correction coefficient, it can dynamically respond to the change of fuel type and solve the problem that the fuel composition correction coefficient cannot adapt to fuel changes, which can easily lead to excessive emissions or low combustion efficiency.

[0126] Example 8: Based on Example 1, this embodiment of the invention provides an intelligent control system for ultra-low NOx combustion, wherein the execution module includes:

[0127] The fuel regulating unit is used to determine the opening of the fuel regulating valve based on the target feeding ratio, combined with real-time material flow data and a pre-set flow-opening curve.

[0128] The air supply regulating unit is used to determine the valve opening of the main combustion air supply regulating valve, the secondary air supply regulating valve, and the tertiary air supply regulating valve based on the target air supply ratio and in combination with the graded air supply ratio of the main combustion air supply regulating valve, the secondary air supply regulating valve, and the tertiary air supply regulating valve, through a pre-set air volume-opening curve.

[0129] The beneficial effects of the above design scheme are: by directly outputting the specific adjustment parameters of each valve, fuzzy operations during the execution process are avoided, ensuring that the adjustment actions of each valve under different working conditions accurately match the target requirements of the control module, reducing combustion state fluctuations caused by human intervention or execution deviations, and improving the stability and consistency of system operation.

[0130] Example 9: Based on Example 1, this embodiment of the invention provides an intelligent control system for ultra-low NOx combustion, wherein the fault redundancy processing module includes:

[0131] An anomaly identification unit is used to acquire real-time key parameters, and determine that an anomaly has occurred when the real-time key parameters are not within the preset normal threshold range;

[0132] The startup unit is used to retrieve the dedicated backup parameters corresponding to the current operating condition to start the backup parameter mode.

[0133] The early warning unit is used to issue early warnings based on the location and type of the anomaly.

[0134] The beneficial effects of the above design scheme are: when an abnormality of a key parameter is detected, the backup parameter mode is immediately activated to avoid the system from falling into a shutdown or irregular operation state due to parameter abnormalities, ensuring the continuous operation of the combustion process and reducing production interruption losses caused by faults.

[0135] Example 10: This embodiment of the invention provides an intelligent control method for ultra-low nitrogen combustion, such as... Figure 2 As shown, it includes:

[0136] S1: Based on multi-point distributed high-precision detection instruments, the temperature parameters, flue gas composition parameters and material flow parameters of the multi-stage combustion structure during the combustion process are collected in real time to obtain key parameters;

[0137] S2: Based on the fuzzy PID control algorithm, the key parameters are analyzed to obtain the initial air supply / feed ratio, and the initial air supply / feed ratio is optimized based on the fuel composition and combustion load to obtain the target air supply / feed ratio;

[0138] S3: Based on the target air / material supply ratio, determine the adjustment parameters of the main combustion air supply regulating valve, the secondary air supply regulating valve, and the tertiary air supply regulating valve;

[0139] S4: When an abnormality is detected in a key parameter, immediately activate the backup parameter mode and issue an early warning.

[0140] In this embodiment, the backup parameter mode is a set of dedicated parameters pre-set according to each operating condition.

[0141] In this embodiment, the multi-stage combustion structure is modified based on the existing burner main structure without changing the furnace shape. Only a secondary combustion chamber and a tertiary combustion chamber made of high-temperature resistant stainless steel and welded together are added to the original combustion chamber. The main combustion chamber is equipped with the main burner, and the secondary / tertiary combustion chambers are evenly equipped with atomizing combustion nozzles. The number of nozzles is matched according to the furnace volume, usually 4-6 secondary combustion chambers and 2-4 tertiary combustion chambers. The fuel supply main pipeline is connected to each burner / nozzle through branch pipelines, and the branch pipelines are equipped with small regulating valves. The air supply pipelines are connected to each combustion chamber to achieve staged air supply.

[0142] In this embodiment, multi-point distributed means setting up corresponding high-precision detection instruments at locations such as the main combustion chamber, secondary combustion chamber, tertiary combustion chamber, fuel supply main pipeline, main combustion, secondary and tertiary air supply pipelines, and flue gas outlet pipeline.

[0143] In this embodiment, the high-precision detection instruments include a K-type thermocouple sensor (measurement range 0-1500℃, accuracy ±1℃), a zirconia oxygen content analyzer (measurement accuracy ±0.1%), an infrared absorption NOx analyzer (measurement range 0-100mg / m³, accuracy ±0.5mg / m³), an electromagnetic flow sensor (accuracy ±0.5%), and a vortex flow sensor (accuracy ±1%).

[0144] The beneficial effects of the above design scheme are as follows: By setting up high-precision detection instruments at multiple distributed points, it achieves full coverage and real-time acquisition of temperature parameters, flue gas composition parameters, and material flow parameters, avoiding the limitations of single-point detection. It accurately captures the temperature distribution and flue gas composition concentration differences in different areas of the furnace, such as the oxygen content and NOx concentration gradient between the main combustion zone and the reduction zone, solving the control blindness caused by traditional single-parameter detection. Simultaneously, real-time data acquisition replaces the lag of traditional sampling or timed detection, ensuring that key parameters dynamically reflect the instantaneous changes in the combustion process, providing accurate and timely data support for subsequent control algorithms, and improving control accuracy from the source. By simultaneously acquiring three core parameters—temperature, flue gas, and material flow—it avoids the one-sidedness of relying solely on a single load parameter, providing a complete data dimension for subsequent optimization and adjustment based on fuel composition and combustion load. Through real-time self-tuning of PID parameters using fuzzy logic, it can quickly respond to sudden changes in key parameters, avoiding the initial air / feed ratio from deviating from the actual combustion state, improving the adaptability of the control algorithm to complex combustion processes, and reducing oxygen content fluctuations in the main combustion zone. The system precisely controls the total feed rate through fuel regulating valves, controls the oxygen content in the main combustion zone through main combustion air regulating valves, and adjusts the airflow and residence time in the reduction zone through secondary / tertiary air regulating valves. This allows the reducing atmosphere of staged combustion to be constructed as needed, effectively suppressing thermal NOx and fuel NOx, ensuring that ultra-low NOx emissions meet standards. By directly outputting the specific adjustment parameters of each valve, the system avoids ambiguous operations during execution, ensuring that the adjustment actions of each valve under different operating conditions precisely match the target requirements of the control module. This reduces combustion state fluctuations caused by human intervention or execution deviations, improving the stability and consistency of system operation. When an abnormality in a key parameter is detected, the system immediately activates the backup parameter mode to prevent the system from shutting down or operating irregularly due to parameter abnormalities, ensuring continuous combustion and reducing production interruption losses caused by malfunctions. Through modular integrated design of detection, control, and execution, the system can achieve ultra-low NOx retrofit without changing the main structure of the burner's staged combustion. It only requires optimizing the control logic and execution adjustment to adapt to old combustion equipment, reducing retrofit costs and construction time, and solving the problems of high cost, long cycle, and poor adaptability of traditional retrofits.

[0145] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of this application and its equivalents, this invention also intends to include these modifications and variations.

Claims

1. An intelligent control system for ultra-low nitrogen combustion, characterized in that, include: The detection module is used to collect temperature parameters, flue gas composition parameters, and material flow parameters of the multi-stage combustion structure in real time based on a multi-point distributed high-precision detection instrument to obtain key parameters. The control module is used to analyze key parameters based on the fuzzy PID control algorithm to obtain the initial air / feed ratio, and optimize the initial air / feed ratio based on fuel composition and combustion load to obtain the target air / feed ratio. The execution module is used to determine the adjustment parameters of the main combustion air supply regulating valve, the secondary air supply regulating valve, and the tertiary air supply regulating valve based on the target air supply / feed ratio. The fault redundancy handling module is used to immediately activate the backup parameter mode and issue an early warning when an abnormality in a key parameter is detected. The control module includes: The preprocessing unit is used to perform data purification and normalization on key parameters to obtain the target key parameters. The membership function establishment unit is used to establish a membership function by taking the target key parameters as input variables and the fuzzy level of the PID as the output variable, and by combining the Gaussian membership function with dynamic parameter adjustment. The clustering unit is used to cluster historical operating data based on clustering algorithms, using temperature, flue gas composition and material flow rate as clustering dimensions, to obtain exclusive operating data corresponding to three core operating conditions: low load, medium load and high load. The rule base establishment unit is used to obtain fuzzy input variables of exclusive operating data under each core operating condition, perform statistical analysis on fuzzy input variables and input precision quantities, and generate a mapping relationship between input precision quantities and output fuzzy levels based on membership functions. The core combustion mechanism of the multi-stage combustion structure is transformed into rule constraints. The mapping relationship is differentiated based on the rule constraints to obtain the target mapping relationship. The operating condition-specific rule base is generated based on the target mapping relationship. The fuzzy set determination unit is used to calculate the trigger strength of each rule in the working condition-specific rule base based on a preset inference algorithm, select the minimum value between the membership degree of the corresponding output fuzzy level and the trigger strength of the input precision of each rule as the target membership degree, and generate the output fuzzy set of each rule based on the target membership degree. Fuzzy sets are used to synthesize the output fuzzy sets of multiple rules according to the rule of taking the maximum value when multiple rules are triggered at the same time, so as to obtain an intermediate output fuzzy set. The intermediate output fuzzy set is then eliminated and smoothed to obtain the target output fuzzy set. The initial ratio determination unit is used to defuzzify the target output fuzzy set based on the centroid method to obtain the PID precise value, and to normalize the PID precise value to obtain the ratio correction coefficient. Based on the product of the ratio correction coefficient and the theoretical air / material feeding ratio, the initial air / material feeding ratio is obtained. Also includes: The detection unit is used to install near-infrared spectroscopy sensors and laser-induced breakdown spectroscopy at the feed port of the multi-stage combustion structure to perform multi-dimensional detection of fuel composition and obtain real-time fuel composition. Key parameters are extracted by pre-constructed load characteristic quantities that fuse multiple parameters of flow, pressure and temperature to obtain real-time combustion load. The component correction determination unit is used to determine the core reaction mechanism of multi-stage combustion structure. It establishes fuel-type NOx formation rate equation, thermal NOx formation rate equation, and combustion completeness equation. Based on the formation rate and combustion completeness as constraints, it solves the optimal oxygen content in the main combustion zone under the fuel-type NOx formation rate equation, thermal NOx formation rate equation, and combustion completeness equation in combination with real-time fuel composition. It also calculates the fuel component correction coefficient by combining the fuel nitrogen content, volatile matter, and moisture content in the real-time fuel composition. The load correction determination unit is used to establish a correction mechanism under different sub-conditions based on the control priority under different sub-conditions, and to determine the combustion load correction coefficient based on the real-time combustion load and the correction mechanism. The fusion unit is used to design a dynamic weight allocation mechanism based on fuel composition and combustion load fluctuations. Based on the dynamic weight allocation mechanism, the fuel composition correction coefficient and the combustion load correction coefficient are weighted and fused to obtain the initial correction coefficient. The prediction and verification unit is used to establish a digital twin model of the multi-stage combustion structure. Based on the digital twin model, it predicts the combustion parameters over a period of time under the target correction coefficient. When the combustion parameters meet the preset requirements, it optimizes the initial air supply / feed ratio based on the initial correction coefficient to obtain the target air supply / feed ratio. Otherwise, it initiates the correction iteration mechanism of the digital twin model to obtain the target correction coefficient and optimizes the initial air supply / feed ratio to obtain the target air supply / feed ratio.

2. The intelligent control system for ultra-low nitrogen combustion according to claim 1, characterized in that, The detection module includes: The temperature detection unit is used to install K-type thermocouple sensors in the main combustion chamber, secondary combustion chamber and tertiary combustion chamber to monitor the temperature distribution of each combustion zone in real time and obtain temperature parameters. The flue gas component detection unit is used to install a zirconia oxygen content analyzer and an infrared absorption NOx analyzer in the flue gas outlet pipe to monitor the flue gas distribution in real time and obtain flue gas component parameters. The material flow detection unit is used to install electromagnetic flow sensors in the main fuel supply pipeline and vortex flow sensors in the main combustion air pipeline, secondary air pipeline and tertiary air pipeline to monitor the flow in real time and obtain material flow parameters.

3. The intelligent control system for ultra-low nitrogen combustion according to claim 1, characterized in that, The preprocessing unit includes: The purification unit is used to perform primary purification of key parameters based on Kalman filtering, followed by secondary purification based on wavelet threshold denoising to obtain purified key parameters. The normalization unit is used to normalize the key parameters of purification to obtain the target key parameters.

4. The intelligent control system for ultra-low nitrogen combustion according to claim 1, characterized in that, The initial ratio determination unit includes: The coefficient determination unit is used to obtain the proportional, integral, and derivative outputs from the PID precise value, and after normalization, the sum of the proportional, integral, and derivative outputs and the reference output value is used as the proportional correction coefficient. The determination unit is used to obtain the initial air / material ratio based on the product of the proportional correction coefficient and the theoretical air / material ratio.

5. The intelligent control system for ultra-low nitrogen combustion according to claim 1, characterized in that, The load correction determination unit includes: The priority determination unit is used to determine the current sub-condition based on the real-time combustion load and retrieve the target control priority corresponding to the current sub-condition. The coefficient calculation unit is used to retrieve the correction coefficient calculation method corresponding to the target control priority from the correction mechanism, and calculate the combustion load correction coefficient based on the correction coefficient calculation method and the real-time combustion load.

6. The intelligent control system for ultra-low nitrogen combustion according to claim 1, characterized in that, The execution module includes: The fuel regulating unit is used to determine the opening of the fuel regulating valve based on the target feeding ratio, combined with real-time material flow data and a pre-set flow-opening curve. The air supply regulating unit is used to determine the valve opening of the main combustion air supply regulating valve, the secondary air supply regulating valve, and the tertiary air supply regulating valve based on the target air supply ratio and in combination with the graded air supply ratio of the main combustion air supply regulating valve, the secondary air supply regulating valve, and the tertiary air supply regulating valve, through a pre-set air volume-opening curve.

7. The intelligent control system for ultra-low nitrogen combustion according to claim 1, characterized in that, The fault redundancy processing module includes: An anomaly identification unit is used to acquire real-time key parameters, and determine that an anomaly has occurred when the real-time key parameters are not within the preset normal threshold range; The startup unit is used to retrieve the dedicated backup parameters corresponding to the current operating condition to start the backup parameter mode. The early warning unit is used to issue early warnings based on the location and type of the anomaly.

8. A smart control method for ultra-low NOx combustion, specifically used in the smart control system for ultra-low NOx combustion as described in claim 1, characterized in that, include: S1: Based on multi-point distributed high-precision detection instruments, the temperature parameters, flue gas composition parameters and material flow parameters of the multi-stage combustion structure during the combustion process are collected in real time to obtain key parameters; S2: Based on the fuzzy PID control algorithm, the key parameters are analyzed to obtain the initial air supply / feed ratio, and the initial air supply / feed ratio is optimized based on the fuel composition and combustion load to obtain the target air supply / feed ratio; S3: Based on the target air / material supply ratio, determine the adjustment parameters of the main combustion air supply regulating valve, the secondary air supply regulating valve, and the tertiary air supply regulating valve; S4: When an abnormality is detected in a key parameter, immediately activate the backup parameter mode and issue an early warning.

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