Denitrification Control Method and System for Biomass Boilers Based on Cryogenic Catalytic Reduction

Abstract

This application provides a denitrification control method and system for biomass boilers based on cryogenic catalytic reduction, relating to the field of combustion product treatment technology. The method includes: arranging a denitrification reactor in the tail flue of the biomass boiler and monitoring the associated data of the outlet flue gas in real time; performing multi-level control analysis on the boiler denitrification control space and establishing a multi-level denitrification control channel; monitoring the reaction temperature and dynamically identifying zones in the denitrification reactor to determine the cryogenic catalyst reaction zone set; using the multi-level denitrification control channel to perform dynamic strategy analysis on the associated data of the outlet flue gas and the cryogenic catalyst reaction zone set; monitoring the denitrification execution of the denitrification reactor based on the target boiler denitrification control parameters, and performing dynamic compensation control through flue gas denitrification feedback data. This application can solve the technical problem of unstable denitrification control efficiency in existing biomass boiler technologies, achieving the technical effect of improving the stability of denitrification control efficiency.

Description

Technical Field

[0001] This application relates to the field of combustion product treatment technology, and in particular to a denitrification control method and system for biomass boilers based on ultra-low temperature catalytic reduction. Background Technology

[0002] Due to the complex composition of biomass fuel, uneven calorific value distribution, and changes in combustion temperature and air distribution during combustion, the concentration of nitrogen oxides in the flue gas at the boiler tail end exhibits time-varying and spatial non-uniformity, which poses a significant challenge to denitrification control.

[0003] Currently, existing biomass boiler denitrification technologies mainly rely on single control strategies, such as constant ammonia injection, fixed catalyst arrangement, and static temperature control. These methods have significant shortcomings in optimizing denitrification efficiency, failing to provide zoned or graded adjustments for different operating stages and flue gas characteristics. This results in low denitrification efficiency in some areas and excessive ammonia slip in others, failing to balance overall effectiveness and economy. Furthermore, existing control methods have limited monitoring and data analysis capabilities for real-time flue gas conditions, lacking dynamic response mechanisms to changes in catalyst activity distribution and flue gas flow field, causing control strategies to lag behind actual reaction conditions.

[0004] In summary, existing technologies suffer from several technical problems. These include the use of limited denitrification control methods, insufficient real-time monitoring capabilities, and a lack of dynamic response mechanisms. These issues lead to unstable denitrification efficiency, failure to meet expected nitrogen oxide emission targets, and low catalyst utilization in biomass boilers under different loads and operating conditions. Consequently, these problems further impact the boiler's environmental emission compliance, operational economy, and long-term equipment reliability. Summary of the Invention

[0005] The purpose of this application is to provide a denitrification control method and system for biomass boilers based on ultra-low temperature catalytic reduction, in order to solve the technical problems in the prior art, such as the instability of denitrification efficiency, failure to meet expected targets for nitrogen oxide emissions, and low catalyst utilization rate of biomass boilers under different loads and operating conditions due to factors such as the single denitrification control method, insufficient real-time monitoring capability, and lack of dynamic response mechanism. These problems further affect the environmental emission compliance, operating economy, and long-term reliability of the boiler.

[0006] In view of the above problems, this application provides a denitrification control method and system for biomass boilers based on cryogenic catalytic reduction.

[0007] Firstly, this application provides a denitrification control method for biomass boilers based on cryogenic catalytic reduction, implemented through a denitrification control system for biomass boilers based on cryogenic catalytic reduction. The method includes: arranging a denitrification reactor in the tail flue of the biomass boiler; monitoring the outlet flue gas correlation data in real time using a laser gas analyzer and pressure / flow sensors; performing historical data mining based on the denitrification reactor to construct a boiler denitrification control space; performing multi-level control analysis on the boiler denitrification control space to establish a multi-level denitrification control channel; monitoring the reaction temperature and dynamically identifying zones in the denitrification reactor to determine a set of cryogenic catalyst reaction regions; using the multi-level denitrification control channel to perform dynamic strategy analysis on the outlet flue gas correlation data and the set of cryogenic catalyst reaction regions to determine target boiler denitrification control parameters; monitoring the denitrification execution of the denitrification reactor based on the target boiler denitrification control parameters to obtain flue gas denitrification feedback data; and performing dynamic compensation control using the flue gas denitrification feedback data.

[0008] Preferably, the biomass boiler denitrification control method based on cryogenic catalytic reduction further includes: performing historical data mining based on the denitrification reactor to obtain a boiler denitrification control dataset, wherein the boiler denitrification control dataset includes historical boiler outlet flue gas correlation data, cryogenic catalyst reaction data, denitrification control data, and denitrification effect data; performing outlier cleaning and data normalization on the boiler denitrification control dataset to obtain a usable boiler denitrification control dataset; performing association rule analysis and confidence assessment on the usable boiler denitrification control dataset to obtain a denitrification control rule confidence set; and filtering the usable boiler denitrification control dataset based on the denitrification control rule confidence set to construct the boiler denitrification control space.

[0009] Preferably, the biomass boiler denitrification control method based on ultra-low temperature catalytic reduction further includes: obtaining the biomass boiler denitrification target; decomposing the biomass boiler denitrification target into effect indicators to obtain a set of boiler denitrification effect evaluation indicators; performing hierarchical cluster analysis and priority ranking on the set of boiler denitrification effect evaluation indicators to determine graded denitrification effect sequence evaluation indicators; constructing a denitrification control multi-objective function based on the graded denitrification effect sequence evaluation indicators; and using the denitrification control multi-objective function to perform multi-level control analysis on the boiler denitrification control space to build a multi-level denitrification control channel.

[0010] Preferably, the biomass boiler denitrification control method based on cryogenic catalytic reduction further includes: using the denitrification control multi-objective function to evaluate the effect and optimize the parameters of the boiler denitrification control space to obtain an initial boiler denitrification control parameter solution; performing random perturbation and cross-mutation expansion on the initial boiler denitrification control parameter solution to generate a boiler denitrification control parameter population; using the denitrification control multi-objective function to perform global optimization within the boiler denitrification control parameter population to obtain a feasible boiler denitrification control parameter solution; and performing multi-level control training based on the associated boiler denitrification dataset of the feasible boiler denitrification control parameter solution to build the multi-level denitrification control channel.

[0011] Preferably, the biomass boiler denitrification control method based on ultra-low temperature catalytic reduction further includes: arranging K-type thermocouple temperature sensors at intervals along the flue gas flow direction inside the denitrification reactor; monitoring the reaction temperature through the K-type thermocouple temperature sensors and recording a reactor temperature distribution dataset; generating a reactor temperature distribution map based on the reactor temperature distribution dataset; dividing the reactor temperature distribution map into zones according to the ultra-low temperature catalyst activity temperature window to obtain catalyst zone clustering results; and performing dynamic region identification based on the catalyst zone clustering results to determine the ultra-low temperature catalyst reaction region set.

[0012] Preferably, the biomass boiler denitrification control method based on cryogenic catalytic reduction further includes: using the multi-stage denitrification control channel to perform dynamic strategy analysis on the outlet flue gas correlation data and the cryogenic catalyst reaction region set, and outputting zoned boiler denitrification control parameters; performing correlation influence analysis on the cryogenic catalyst reaction region set to obtain a catalyst reaction region influence coefficient set; and globally correcting the zoned boiler denitrification control parameters based on the catalyst reaction region influence coefficient set to determine the target boiler denitrification control parameters.

[0013] Preferably, the biomass boiler denitrification control method based on ultra-low temperature catalytic reduction further includes: performing twin simulation modeling based on the catalytic reduction denitrification process mechanism of the denitrification reactor to generate a denitrification reaction twin simulation model; and using the denitrification reaction twin simulation model to perform correlation influence analysis on the ultra-low temperature catalyst reaction region set to obtain a catalyst reaction region influence coefficient set.

[0014] Preferably, the biomass boiler denitrification control method based on ultra-low temperature catalytic reduction further includes: predicting the denitrification effect of the flue gas denitrification feedback data to obtain boiler denitrification prediction effect parameters; presetting boiler denitrification effect expectation parameters; performing compensation calculations on the boiler denitrification prediction effect parameters based on the boiler denitrification effect expectation parameters to determine denitrification control compensation parameters; and performing dynamic compensation control through the denitrification control compensation parameters.

[0015] Preferably, the biomass boiler denitrification control method based on ultra-low temperature catalytic reduction further includes: initializing a PID denitrification controller according to the multi-stage denitrification control channel; calculating the boiler denitrification effect deviation parameter between the predicted boiler denitrification effect parameter and the expected boiler denitrification effect parameter; and performing correction calculation on the boiler denitrification effect deviation parameter based on the PID denitrification controller to determine the denitrification control compensation parameter.

[0016] Secondly, this application also provides a biomass boiler denitrification control system based on cryogenic catalytic reduction, used to execute the biomass boiler denitrification control method based on cryogenic catalytic reduction as described in the first aspect, including: a data monitoring module, used to arrange a denitrification reactor in the tail flue of the biomass boiler, and monitor the outlet flue gas correlation data of the tail flue in real time through a laser gas analyzer and a pressure and flow sensor; a channel construction module, used to perform historical data mining based on the denitrification reactor, construct a boiler denitrification control space, perform multi-level control analysis on the boiler denitrification control space, and construct a multi-level denitrification control channel; a parameter determination module, used to monitor the reaction temperature and dynamically identify the zones of the denitrification reactor, determine the cryogenic catalyst reaction zone set, and use the multi-level denitrification control channel to perform dynamic strategy analysis on the outlet flue gas correlation data and the cryogenic catalyst reaction zone set to determine the target boiler denitrification control parameters; and a compensation control module, used to monitor the denitrification execution of the denitrification reactor based on the target boiler denitrification control parameters, obtain flue gas denitrification feedback data, and perform dynamic compensation control through the flue gas denitrification feedback data.

[0017] The technical solution provided in this application has at least the following technical effects or advantages: by achieving the technical objectives of dynamic, zoned, and graded denitrification process of boiler, it can automatically adjust the denitrification control parameters according to the real-time state of flue gas and the distribution of catalyst activity, optimize the distribution of ammonia injection, reaction temperature and flow rate, improve the overall denitrification efficiency and reduce ammonia escape, and achieve the technical effects of stably controlling nitrogen oxide emissions within the environmental protection standard range, improving catalyst utilization, reducing energy consumption and ensuring safe and reliable operation of boiler.

[0018] The above description is merely an overview of the technical solution of this application. To enable a clearer understanding of the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the denitrification control method for biomass boilers based on ultra-low temperature catalytic reduction, as described in this application.

[0021] Figure 2 This is a schematic diagram of the denitrification control system for a biomass boiler based on cryogenic catalytic reduction, as described in this application.

[0022] Figure labeling: Data monitoring module 1, channel construction module 2, parameter determination module 3, compensation control module 4. Detailed Implementation

[0023] This application provides a denitrification control method and system for biomass boilers based on cryogenic catalytic reduction. It addresses the technical problems in existing technologies, such as the instability of denitrification efficiency, failure to meet expected nitrogen oxide emissions targets, and low catalyst utilization due to factors like limited denitrification control methods, insufficient real-time monitoring capabilities, and a lack of dynamic response mechanisms. These issues further impact the boiler's environmental emission compliance, operational economy, and long-term equipment reliability. The system achieves dynamic, zoned, and graded control of the boiler denitrification process. It automatically adjusts denitrification control parameters based on real-time flue gas conditions and catalyst activity distribution, optimizing ammonia injection, reaction temperature, and flow rate distribution. This improves overall denitrification efficiency and reduces ammonia slip, achieving stable control of nitrogen oxide emissions within environmental standards, increased catalyst utilization, reduced energy consumption, and ensured safe and reliable boiler operation.

[0024] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0025] Example 1, please refer to the appendix. Figure 1 This application provides a denitrification control method for biomass boilers based on cryogenic catalytic reduction, which is applied to the denitrification control system of biomass boilers based on cryogenic catalytic reduction. The method specifically includes the following steps:

[0026] S1: A denitrification reactor is installed in the tail flue of the biomass boiler, and the outlet flue gas correlation data of the tail flue is monitored in real time by a laser gas analyzer and a pressure and flow sensor.

[0027] Specifically, a biomass boiler is a boiler system that uses renewable biomass such as agricultural and forestry waste, straw, and sawdust as fuel. During combustion, it produces flue gas containing various components, including nitrogen oxides, carbon dioxide, and water vapor. The tail flue, located near the chimney outlet, has a relatively low temperature and stable flue gas composition, making it suitable for installing a denitrification device that performs a catalytic reaction. A denitrification reactor is installed in the tail flue of the biomass boiler; this means installing a reaction device in the duct section after combustion is complete and just before the flue gas is discharged, to reduce the concentration of nitrogen oxides in the flue gas. The denitrification reactor is a device filled with a catalyst that works in conjunction with a reducing agent to convert nitrogen oxides into nitrogen and water; it is the core unit for achieving selective catalytic reduction denitrification.

[0028] Real-time monitoring of flue gas output data at the tail-end flue gas duct is achieved using a laser gas analyzer and pressure / flow sensors. This involves continuous measurement and data acquisition of the flue gas composition, state, and flow characteristics before emission. The laser gas analyzer is an online monitoring device based on the principle of spectral absorption. It uses a laser of a specific wavelength to penetrate the flue gas, analyzing the absorption characteristics of gas molecules to quickly measure the concentrations of gases such as nitrogen oxides, oxygen, and sulfur dioxide. The pressure / flow sensor simultaneously acquires information on the static pressure, dynamic pressure, and flow velocity of the flue gas within the duct, thereby determining the flue gas volumetric flow rate, density, and transport status. The output flue gas output data refers to a collection of various monitoring parameters related to the denitrification reaction, including nitrogen oxide concentration, flue gas temperature, flow velocity, oxygen content, and pressure, providing real-time data for optimizing the control strategy of the subsequent denitrification reactor.

[0029] S2: Based on the historical data mining of the denitrification reactor, construct the boiler denitrification control space, perform multi-level control analysis on the boiler denitrification control space, and build a multi-level denitrification control channel.

[0030] Furthermore, this application also includes: performing historical data mining based on the denitrification reactor to obtain a boiler denitrification control dataset, wherein the boiler denitrification control dataset includes historical boiler outlet flue gas correlation data, cryogenic catalyst reaction data, denitrification control data, and denitrification effect data; performing outlier cleaning and data normalization on the boiler denitrification control dataset to obtain a usable boiler denitrification control dataset; performing association rule analysis and confidence assessment on the usable boiler denitrification control dataset to obtain a denitrification control rule confidence set; and filtering the usable boiler denitrification control dataset based on the denitrification control rule confidence set to construct the boiler denitrification control space.

[0031] Furthermore, this application also includes: obtaining the denitrification target of the biomass boiler; decomposing the denitrification target of the biomass boiler into effect indicators to obtain a set of boiler denitrification effect evaluation indicators; performing hierarchical cluster analysis and priority ranking on the set of boiler denitrification effect evaluation indicators to determine the graded denitrification effect sequence evaluation indicators; constructing a denitrification control multi-objective function based on the graded denitrification effect sequence evaluation indicators; and using the denitrification control multi-objective function to perform multi-level control analysis on the boiler denitrification control space to build a multi-level denitrification control channel.

[0032] Furthermore, this application also includes: using the denitrification control multi-objective function to evaluate the effect and optimize the parameters of the boiler denitrification control space to obtain an initial boiler denitrification control parameter solution; performing random perturbation and cross-mutation expansion on the initial boiler denitrification control parameter solution to generate a boiler denitrification control parameter population; using the denitrification control multi-objective function to perform global optimization within the boiler denitrification control parameter population to obtain a feasible boiler denitrification control parameter solution; and performing multi-level control training based on the associated boiler denitrification dataset of the feasible boiler denitrification control parameter solution to build the multi-level denitrification control channel.

[0033] Specifically, historical data mining based on the denitrification reactor involves collecting and analyzing a large amount of historical operational information generated under different operating conditions, loads, and time periods, centered on the reactor's operation. Valuable patterns and characteristics for denitrification control are extracted through data analysis, forming a boiler denitrification control dataset. This dataset includes historical boiler outlet flue gas correlation data, cryogenic catalyst reaction data, denitrification control data, and denitrification effect data. Historical boiler outlet flue gas correlation data reflects past changes in operating parameters such as flue gas concentration, temperature, pressure, and flow rate; cryogenic catalyst reaction data records the catalyst's activity and conversion efficiency under different temperatures, flow rates, and reaction conditions; denitrification control data records the control system's operating status, control commands, and adjustment strategies; and denitrification effect data characterizes the final results of the denitrification reaction, such as nitrogen oxide removal rate and emission compliance rate.

[0034] Outlier cleaning and data normalization of boiler denitrification control datasets involve quality control of the collected data, removing or correcting abnormal data caused by measurement errors, equipment malfunctions, or sudden abnormal operating conditions. Then, data with different dimensions and ranges are proportionally converted to a unified numerical interval for subsequent algorithm processing and comparison. This eliminates noise and bias in the data, improves the accuracy and stability of subsequent analysis, and makes the data comparable and computable, thus yielding a usable boiler denitrification control dataset.

[0035] Association rule analysis and confidence assessment were performed on the available boiler denitrification control dataset. This involved using data mining algorithms to find statistical relationships and potential logical connections between different data items, identifying factors that significantly impact denitrification performance. Association rule analysis is a data analysis method that can reveal co-occurrence relationships or causal trends between different variables, such as the relationship between increased flue gas temperature and improved catalyst conversion efficiency. Confidence assessment is a quantitative indicator of the reliability of association rules, reflecting their probability and stability in the dataset.

[0036] Filtering available boiler denitrification control datasets based on the confidence set of denitrification control rules refers to selecting the most representative and decision-valuable data samples from the available datasets using rules with high confidence and strong correlation as criteria, in order to construct the boiler denitrification control space. The boiler denitrification control space is a multi-dimensional data structure or model space that maps and combines various factors such as flue gas characteristics, reaction conditions, catalyst state, and control parameters in a unified coordinate system to describe and predict denitrification response behavior under different control strategies.

[0037] Denitrification targets for biomass boilers are determined by environmental regulations, equipment capacity, and operational economics, specifying the required denitrification performance and emission requirements. These targets describe the overall requirements and control direction of the entire denitrification process, including the acceptable range for nitrogen oxide emission concentrations, denitrification reaction efficiency, energy consumption levels, and system operational stability.

[0038] The denitrification target of biomass boilers is broken down into performance indicators, refining the overall macro-level objective into multiple measurable and monitorable specific evaluation indicators for optimization in subsequent analysis and control. The boiler denitrification performance evaluation indicator set is the decomposed set, including indicators from multiple dimensions such as nitrogen oxide removal rate, denitrification reaction temperature control accuracy, catalyst activity retention rate, reducing agent utilization rate, and unit denitrification energy consumption, to comprehensively reflect the operating status and effectiveness of the denitrification system.

[0039] Hierarchical cluster analysis and prioritization were performed on the set of boiler denitrification effect evaluation indicators. Similarity clustering was used to group indicators with similar properties or significant mutual influence into different levels. Priority was then assigned based on the importance, sensitivity, and controllability of each indicator to the denitrification target. Hierarchical cluster analysis, a data mining technique, can reveal the inherent relationships between indicators; for example, denitrification temperature and catalyst activity may belong to the same performance category, while energy consumption and reducing agent utilization rate may belong to the economic category. Prioritization determines the optimization order based on the contribution of each group. For instance, if the nitrogen oxide emission concentration compliance rate accounts for 40% of the evaluation, that indicator will be given a higher priority. This results in a graded denitrification effect sequence evaluation indicator, i.e., an indicator sequence arranged at different levels and priorities, providing a structured basis for subsequent multi-objective optimization.

[0040] Based on the evaluation indicators of the graded denitrification effect sequence, a multi-objective function for denitrification control is constructed. This function, based on the quantitative expressions of each indicator, is a mathematical function that comprehensively measures and optimizes multiple objectives to guide control decisions. The multi-objective function for denitrification control is a comprehensive evaluation model that integrates multiple performance dimensions. Through weighting and constraints, it simultaneously considers multiple objectives such as emission compliance rate, reaction efficiency, operating cost, and system stability.

[0041] A multi-objective function for denitrification control is employed to evaluate the effectiveness and optimize parameters within the boiler denitrification control space. This involves comprehensively evaluating the performance of various control parameter combinations within the established boiler denitrification control space and selecting the optimal parameter solution that balances multiple performance indicators. Effectiveness evaluation refers to the quantitative analysis of the performance of different control strategies from multiple perspectives, including nitrogen oxide removal rate, catalyst activity maintenance, reaction temperature control accuracy, and energy consumption level. Parameter optimization involves selecting the solution that best meets the overall objective from a large number of candidate parameter combinations; this is the initial boiler denitrification control parameter solution. Thus, the initial boiler denitrification control parameter solution is obtained.

[0042] The initial boiler denitrification control parameter solution is expanded through random perturbation and crossover mutation. By introducing randomness and genetic evolution mechanisms, more parameter combinations with potential advantages are generated, thereby expanding the search space and improving the comprehensiveness of optimization. Random perturbation involves making small random adjustments to the initial boiler denitrification control parameter solution, resulting in diversity within the parameter space. Crossover mutation combines parts of different parameter solutions or mutates some parameters, simulating the process of gene recombination in natural evolution. This generates a population of boiler denitrification control parameters that may have higher denitrification efficiency or lower energy consumption under different operating conditions.

[0043] Global optimization within the boiler denitrification control parameter population using a multi-objective function refers to comprehensively searching and evaluating the generated parameter population over a larger scope using a multi-objective optimization model to find the control solution with optimal overall performance. Global optimization finds a feasible solution that achieves a balance among multiple indicators across the entire parameter space, rather than just a local optimum. This is achieved through iterative calculations, fitness evaluation, and convergence determination. The final feasible boiler denitrification control parameter solution ensures emission compliance while achieving high economic efficiency and stability.

[0044] Multi-level control training based on the associated boiler denitrification dataset of feasible boiler denitrification control parameter solutions refers to using the optimal solution obtained through global optimization as a basis, constructing a training set with its corresponding historical operating data, and then intelligently modeling and optimizing the control system using deep learning methods. The associated boiler denitrification dataset includes information such as boiler outlet flue gas parameters, cryogenic catalyst reaction data, and denitrification reaction conditions related to the feasible solution. Deep neural network training is performed using the associated boiler denitrification dataset as input features and the feasible boiler denitrification control parameter solutions as output labels. Multi-level control training implicitly encodes the control laws into the model, enabling the automatic derivation of the optimal control strategy based on different operating conditions, ultimately building a multi-level denitrification control channel that can be adaptively adjusted online.

[0045] S3: Monitor the reaction temperature and dynamically identify the zones of the denitrification reactor to determine the set of ultra-low temperature catalyst reaction zones. Use the multi-stage denitrification control channel to perform dynamic strategy analysis on the outlet flue gas correlation data and the set of ultra-low temperature catalyst reaction zones to determine the target boiler denitrification control parameters.

[0046] Furthermore, this application also includes: arranging K-type thermocouple temperature sensors at intervals along the flue gas flow direction inside the denitrification reactor; monitoring the reaction temperature through the K-type thermocouple temperature sensors and recording a reactor temperature distribution dataset; generating a reactor temperature distribution map based on the reactor temperature distribution dataset; dividing the reactor temperature distribution map into zones according to the ultra-low temperature catalyst activity temperature window to obtain catalyst zone clustering results; and performing dynamic region identification based on the catalyst zone clustering results to determine the ultra-low temperature catalyst reaction region set.

[0047] Furthermore, this application also includes: using the multi-level denitrification control channel to perform dynamic strategy analysis on the outlet flue gas correlation data and the ultra-low temperature catalyst reaction region set, and outputting denitrification control parameters for the zoned boiler; performing correlation influence analysis on the ultra-low temperature catalyst reaction region set to obtain a catalyst reaction region influence coefficient set; and globally correcting the denitrification control parameters for the zoned boiler based on the catalyst reaction region influence coefficient set to determine the denitrification control parameters for the target boiler.

[0048] Furthermore, this application also includes: performing twin simulation modeling based on the catalytic reduction denitrification process mechanism of the denitrification reactor to generate a denitrification reaction twin simulation model; and using the denitrification reaction twin simulation model to perform correlation influence analysis on the set of reaction regions of the ultra-low temperature catalyst to obtain a set of catalyst reaction region influence coefficients.

[0049] Specifically, arranging K-type thermocouple temperature sensors at intervals along the flue gas flow direction inside the denitrification reactor refers to installing multiple sets of K-type thermocouple sensors at fixed intervals along the flue gas flow path for temperature measurement. K-type thermocouples are temperature measuring elements composed of two metals: nickel-chromium alloy and nickel-silicon alloy. When the temperature at the contact point of the two metals changes, a potential difference proportional to the temperature is generated, thus achieving accurate temperature measurement. K-type thermocouple temperature sensors can be used to monitor the temperature inside the denitrification reactor in real time, acquiring temperature information from different locations from the inlet to the outlet, and recording it in temporal and spatial order to form a temperature distribution dataset reflecting the temperature change characteristics throughout the entire reactor.

[0050] Next, a reactor temperature distribution map is generated based on the reactor temperature distribution dataset. This involves using collected multi-point temperature data and methods such as interpolation, isotherm plotting, or 3D visualization to transform the originally discrete data points into spatially continuous graphical information. The temperature distribution map not only clearly shows the temperature distribution at different locations inside the denitrification reactor but also reveals the existence of temperature gradients, localized overheated regions, or low-temperature regions. This allows for a direct analysis of the heat transfer characteristics and temperature field structure of the flue gas inside the reactor, providing a reference for subsequent catalyst layout and reaction optimization.

[0051] Subsequently, the reactor temperature distribution map was partitioned and clustered according to the ultra-low temperature catalyst activity window. This involves clustering and partitioning the temperature distribution regions within the reactor based on the temperature range within which the catalyst can maintain high activity. The ultra-low temperature catalyst activity window refers to the temperature range within which the catalyst still exhibits high catalytic reaction efficiency at relatively low temperatures, defined by an upper and lower limit temperature range. By partitioning and clustering the temperature distribution map, regions with temperatures within the active range, as well as those that are too high or too low, can be identified, aiding in the analysis of spatial differences in reaction efficiency.

[0052] Finally, based on the catalyst partitioning and clustering results, dynamic regional identification is performed to determine the cryogenic catalyst reaction region set. This involves further dynamic characteristic analysis of the divided temperature ranges to identify the set of catalyst regions that truly participate in the effective denitrification reaction. Dynamic regional identification includes not only static spatial division but also analysis of the dynamic response of reaction activity to changes in operating conditions, considering factors such as flue gas flow rate and load fluctuations. This allows for the identification of the core reaction region set where the catalyst truly plays a dominant role under actual operating conditions, providing crucial information for subsequent catalyst replacement, layout adjustments, and control strategy optimization.

[0053] Furthermore, dynamic strategy analysis of the outlet flue gas correlation data and the cryogenic catalyst reaction zone set using a multi-level denitrification control channel refers to the use of a multi-level denitrification control channel constructed through deep learning or multi-objective optimization to dynamically decide and match strategies for key parameters in actual operation. A multi-level denitrification control channel is an intelligent control path containing different control levels and adjustment mechanisms, which can automatically switch control strategies according to different operating stages, load states, and reaction requirements. Outlet flue gas correlation data refers to multi-dimensional data related to the denitrification process in the flue gas discharged from the boiler tail, including flue gas temperature, nitrogen oxide concentration, oxygen content, flow rate, and pressure, reflecting the operating status of the denitrification reaction. The cryogenic catalyst reaction zone set refers to the set of regions identified that still have high catalytic activity under low temperature conditions, representing the optimal operating area for denitrification efficiency. Dynamic strategy analysis means performing time-series and state analysis, dynamically adjusting control parameters such as ammonia injection rate, reaction temperature, and flow field distribution according to changes in flue gas state and catalytic zone, thereby outputting zoned boiler denitrification control parameters for different temperature zones and flow field zones, achieving more precise and regionalized denitrification regulation.

[0054] Furthermore, twin simulation modeling based on the catalytic reduction denitrification process mechanism of the denitrification reactor refers to establishing a computationally achievable and interactive virtual simulation system based on the selective catalytic reduction process occurring inside the actual denitrification reactor, used to simulate the reaction behavior under real operating conditions. The catalytic reduction denitrification process mechanism refers to the chemical process in which a reducing agent (usually ammonia or ammonia produced from the decomposition of urea) reacts with nitrogen oxides in flue gas on the catalyst surface, converting them into nitrogen and water. This process is influenced by multiple factors such as temperature, flow rate, concentration, and catalyst activity. Twin simulation modeling is an application of digital twin technology, that is, constructing a virtual model in a digital environment that is highly consistent with the structure, operating mechanism, and dynamic behavior of the real physical system. Through mathematical descriptions of the reactor structure, gas flow path, chemical reaction kinetics, and thermodynamic processes, a simulation system capable of responding to changes in operating conditions in real time is generated. The denitrification reaction twin simulation model thus becomes a virtual mirror of the real reactor, enabling prediction and optimization analysis without affecting actual operation, and also providing a verification platform for control strategy adjustments.

[0055] Subsequently, a correlation analysis was conducted on the reaction regions of the cryogenic catalyst using a twin simulation model of the denitrification reaction. This involves mapping the identified highly active catalytic reaction regions into the denitrification reaction twin simulation model to analyze the role and impact of each region on the overall denitrification performance under different operating conditions. The cryogenic catalyst reaction region set refers to the collection of catalyst regions that maintain high reactivity at relatively low temperatures. The correlation analysis involves quantifying the contribution and coupling relationship of different regions in the overall reaction by simulating and calculating indicators such as reaction rate, nitrogen oxide conversion rate, and ammonia utilization rate. The catalyst reaction region influence coefficient set is a set of numerical parameters describing the degree of influence of each region on the total denitrification efficiency, used to guide subsequent control optimization and catalyst arrangement adjustment.

[0056] Finally, the global correction of the denitrification control parameters of the zoned boilers based on the catalyst reaction region influence coefficient set refers to weight adjustment and global optimization to obtain a more accurate and coordinated control strategy. The global correction process includes parameter weighting, constraint adjustment, and global consistency analysis, aiming to ensure that the control parameters not only apply to the optimal state in the local region but also achieve maximum efficiency and balance in the overall reaction system. The final determined target boiler denitrification control parameters are the control solutions after multi-level optimization, including ammonia injection rate, ammonia injection distribution, reaction temperature regulation, and flow field guidance, which can achieve optimal denitrification effect under the coupling effects of different regions.

[0057] S4: Based on the target boiler denitrification control parameters, monitor the denitrification process of the denitrification reactor to obtain flue gas denitrification feedback data, and perform dynamic compensation control using the flue gas denitrification feedback data.

[0058] Furthermore, this application also includes: predicting the denitrification effect of the flue gas denitrification feedback data to obtain boiler denitrification prediction effect parameters; presetting boiler denitrification effect expectation parameters; performing compensation calculations on the boiler denitrification prediction effect parameters based on the boiler denitrification effect expectation parameters to determine denitrification control compensation parameters; and performing dynamic compensation control through the denitrification control compensation parameters.

[0059] Furthermore, this application also includes: initializing a PID denitrification controller according to the multi-level denitrification control channel; calculating the boiler denitrification effect deviation parameter between the boiler denitrification prediction effect parameter and the boiler denitrification effect expectation parameter; and performing correction calculation on the boiler denitrification effect deviation parameter based on the PID denitrification controller to determine the denitrification control compensation parameter.

[0060] Specifically, monitoring the denitrification process of the denitrification reactor based on the target boiler denitrification control parameters refers to applying the optimal denitrification control parameters as control commands to the reactor's operation and dynamically monitoring its execution status throughout the process. The target boiler denitrification control parameters are a set of control variables obtained through comprehensive analysis of outlet flue gas data, catalyst reaction characteristics, and multi-level control strategies. These parameters include ammonia injection flow rate, reaction temperature, flue gas velocity, catalyst bed pressure drop, and other parameters that determine the efficiency and stability of the denitrification reaction. Denitrification execution monitoring refers to the real-time tracking and recording of the reactor's internal and outlet flue gas operating status using sensors, online analyzers, and the control system during the actual reactor operation phase, ensuring that the equipment's operation matches the set control targets. Subsequently, flue gas denitrification feedback data is obtained. This feedback data includes key variables such as denitrification efficiency, residual nitrogen oxide concentration, ammonia slip concentration, temperature distribution, and flow rate changes, reflecting the actual effect of the denitrification process and the system's dynamic response.

[0061] Furthermore, predicting the denitrification effect based on flue gas denitrification feedback data refers to using feedback data obtained from denitrification monitoring to estimate and analyze the actual denitrification effect of the boiler in advance. Flue gas denitrification feedback data includes indicators such as denitrification efficiency, nitrogen oxide concentration, ammonia slip, reaction temperature, and flow rate, reflecting the actual response of the denitrification process. Denitrification effect prediction involves mapping current feedback data to future denitrification performance using mathematical models, machine learning algorithms, or simulation methods, thereby obtaining boiler denitrification effect prediction parameters. These parameters represent predicted values ​​for boiler denitrification efficiency, residual nitrogen oxide concentration, or ammonia utilization rate within a short future period, reflecting the dynamic trend of the denitrification reaction.

[0062] Next, the preset boiler denitrification effect expectation parameters refer to the ideal denitrification effect indicators set in advance according to environmental protection standards, system design requirements or operation goals, including target denitrification efficiency, maximum allowable nitrogen oxide emission concentration, minimum ammonia escape, etc., which serve as the benchmark values ​​for system judgment and optimization.

[0063] Furthermore, based on the multi-level denitrification control channels, the PID denitrification controller is initialized, providing the basic configuration for closed-loop control of the denitrification system. The multi-level denitrification control channels are an intelligent control path combining different levels of control strategies, parameter optimization, and deep learning training, used for zoned, graded, and dynamic adjustment of the boiler denitrification process. The PID denitrification controller is an automatic control device based on proportional, integral, and derivative algorithms. The proportional element is used to quickly adjust the output according to the current deviation, the integral element is used to eliminate long-term steady-state errors, and the derivative element is used to predict future trends, thereby achieving stable control of the denitrification process. Initialization refers to setting and zeroing the controller's parameters before it is put into operation, enabling it to accurately respond to input signals and perform closed-loop adjustment.

[0064] Subsequently, the boiler denitrification effect deviation parameter, which is the predicted denitrification effect parameter and the expected denitrification effect parameter, is calculated. The predicted denitrification performance is compared with the set expected target to obtain the difference between the two. The predicted denitrification effect parameter is the future denitrification index value predicted by flue gas denitrification feedback data, such as a predicted nitrogen oxide concentration of 28 mg / m³. The expected denitrification effect parameter is a pre-set target value, such as an expected nitrogen oxide emission concentration of 20 mg / m³, used to reflect the degree of deviation between the current control strategy and the target.

[0065] Finally, based on the PID denitrification controller, the deviation parameters of the boiler denitrification effect are corrected and the denitrification control compensation parameters are determined. This involves inputting the deviation parameters into the PID controller, and using proportional, integral, and derivative algorithms to calculate the control quantities that need to be adjusted, thereby generating the operating parameters used to correct the denitrification process. The denitrification control compensation parameters include increasing or decreasing the ammonia injection rate, adjusting the local reaction temperature, and fine-tuning the flow rate, etc., with the aim of bringing the actual denitrification effect closer to the preset expectation. Dynamic compensation control is performed using the denitrification control compensation parameters, and the adjusted quantities are fed back to the denitrification control system in real time, continuously bringing the operating state closer to the desired target, thus achieving closed-loop optimal control.

[0066] In summary, the denitrification control method for biomass boilers based on ultra-low temperature catalytic reduction provided in this application has the following technical effects: by achieving the technical objectives of dynamic, zoned, and graded control of the boiler denitrification process, it can automatically adjust the denitrification control parameters according to the real-time state of flue gas and the distribution of catalyst activity, optimize the distribution of ammonia injection, reaction temperature, and flow rate, improve the overall denitrification efficiency, and reduce ammonia escape, thereby achieving the technical effects of stably controlling nitrogen oxide emissions within the environmental protection standard range, improving catalyst utilization, reducing energy consumption, and ensuring the safe and reliable operation of the boiler.

[0067] Example 2: Based on the same inventive concept as the biomass boiler denitrification control method based on ultra-low temperature catalytic reduction in the foregoing examples, this application also provides a biomass boiler denitrification control system based on ultra-low temperature catalytic reduction. Please refer to the appendix. Figure 2 The system includes: a data monitoring module 1, used to install a denitrification reactor in the tail flue of a biomass boiler, and monitor the outlet flue gas correlation data of the tail flue in real time using a laser gas analyzer and a pressure and flow sensor; a channel construction module 2, used to perform historical data mining based on the denitrification reactor, construct a boiler denitrification control space, perform multi-level control analysis on the boiler denitrification control space, and construct a multi-level denitrification control channel; a parameter determination module 3, used to monitor the reaction temperature and dynamically identify the zones of the denitrification reactor, determine the ultra-low temperature catalyst reaction zone set, and use the multi-level denitrification control channel to perform dynamic strategy analysis on the outlet flue gas correlation data and the ultra-low temperature catalyst reaction zone set to determine the target boiler denitrification control parameters; and a compensation control module 4, used to monitor the denitrification execution of the denitrification reactor based on the target boiler denitrification control parameters, obtain flue gas denitrification feedback data, and perform dynamic compensation control based on the flue gas denitrification feedback data.

[0068] Furthermore, the biomass boiler denitrification control system based on cryogenic catalytic reduction is also used for: performing historical data mining based on the denitrification reactor to obtain a boiler denitrification control dataset, which includes historical boiler outlet flue gas correlation data, cryogenic catalyst reaction data, denitrification control data, and denitrification effect data; performing outlier cleaning and data normalization on the boiler denitrification control dataset to obtain a usable boiler denitrification control dataset; performing association rule analysis and confidence assessment on the usable boiler denitrification control dataset to obtain a denitrification control rule confidence set; and filtering the usable boiler denitrification control dataset based on the denitrification control rule confidence set to construct the boiler denitrification control space.

[0069] Furthermore, the biomass boiler denitrification control system based on ultra-low temperature catalytic reduction is also used for: acquiring the denitrification target of the biomass boiler; decomposing the denitrification target of the biomass boiler into effect indicators to obtain a set of boiler denitrification effect evaluation indicators; performing hierarchical cluster analysis and priority ranking on the set of boiler denitrification effect evaluation indicators to determine the graded denitrification effect sequence evaluation indicators; constructing a denitrification control multi-objective function based on the graded denitrification effect sequence evaluation indicators; and using the denitrification control multi-objective function to perform multi-level control analysis on the boiler denitrification control space to build a multi-level denitrification control channel.

[0070] Furthermore, the biomass boiler denitrification control system based on cryogenic catalytic reduction is also used for: evaluating the effect and optimizing the parameters of the boiler denitrification control space using the denitrification control multi-objective function to obtain an initial boiler denitrification control parameter solution; expanding the initial boiler denitrification control parameter solution through random perturbation and cross-mutation to generate a boiler denitrification control parameter population; using the denitrification control multi-objective function to perform global optimization within the boiler denitrification control parameter population to obtain a feasible boiler denitrification control parameter solution; and performing multi-level control training based on the associated boiler denitrification dataset of the feasible boiler denitrification control parameter solution to build the multi-level denitrification control channel.

[0071] Furthermore, the biomass boiler denitrification control system based on cryogenic catalytic reduction is also used for: arranging K-type thermocouple temperature sensors at intervals along the flue gas flow direction inside the denitrification reactor; monitoring the reaction temperature through the K-type thermocouple temperature sensors and recording the reactor temperature distribution dataset; generating a reactor temperature distribution map based on the reactor temperature distribution dataset; dividing the reactor temperature distribution map into zones according to the cryogenic catalyst activity temperature window to obtain catalyst zone clustering results; and performing dynamic region identification based on the catalyst zone clustering results to determine the cryogenic catalyst reaction region set.

[0072] Furthermore, the biomass boiler denitrification control system based on cryogenic catalytic reduction is also used to: perform dynamic strategy analysis on the outlet flue gas correlation data and the cryogenic catalyst reaction region set using the multi-level denitrification control channel, and output the denitrification control parameters for the zoned boiler; perform correlation influence analysis on the cryogenic catalyst reaction region set to obtain the catalyst reaction region influence coefficient set; and perform global correction on the zoned boiler denitrification control parameters based on the catalyst reaction region influence coefficient set to determine the target boiler denitrification control parameters.

[0073] Furthermore, the biomass boiler denitrification control system based on ultra-low temperature catalytic reduction is also used for: performing twin simulation modeling based on the catalytic reduction denitrification process mechanism of the denitrification reactor to generate a denitrification reaction twin simulation model; and using the denitrification reaction twin simulation model to perform correlation influence analysis on the ultra-low temperature catalyst reaction region set to obtain the catalyst reaction region influence coefficient set.

[0074] Furthermore, the biomass boiler denitrification control system based on ultra-low temperature catalytic reduction is also used for: predicting the denitrification effect of the flue gas denitrification feedback data to obtain boiler denitrification prediction effect parameters; preset the boiler denitrification effect expectation parameters; perform compensation calculation on the boiler denitrification prediction effect parameters based on the boiler denitrification effect expectation parameters to determine the denitrification control compensation parameters; and perform dynamic compensation control through the denitrification control compensation parameters.

[0075] Furthermore, the biomass boiler denitrification control system based on ultra-low temperature catalytic reduction is also used for: initializing the PID denitrification controller according to the multi-level denitrification control channel; calculating the boiler denitrification effect deviation parameter between the predicted boiler denitrification effect parameter and the expected boiler denitrification effect parameter; and performing correction calculation on the boiler denitrification effect deviation parameter based on the PID denitrification controller to determine the denitrification control compensation parameter.

[0076] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The denitrification control method and specific examples of biomass boilers based on ultra-low temperature catalytic reduction in the foregoing embodiment 1 are also applicable to the denitrification control system of biomass boilers based on ultra-low temperature catalytic reduction in this embodiment. Through the foregoing detailed description of the denitrification control method of biomass boilers based on ultra-low temperature catalytic reduction, those skilled in the art can clearly understand the denitrification control system of biomass boilers based on ultra-low temperature catalytic reduction in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0077] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0078] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A biomass boiler denitration control method based on ultra-low temperature catalytic reduction, characterized in that, The method comprises: arranging a denitration reactor in the tail flue of a biomass boiler, monitoring the outlet flue gas correlation data of the tail flue in real time through a laser gas analyzer and a pressure flow sensor; based on the denitration reactor, performing historical data mining, constructing a boiler denitration control space, performing multi-level control analysis on the boiler denitration control space, and building a multi-level denitration control channel; monitoring the reaction temperature of the denitration reactor and performing dynamic identification on the partition, determining the ultra-low temperature catalyst reaction region set, using the multi-level denitration control channel to perform dynamic strategy analysis on the outlet flue gas correlation data and the ultra-low temperature catalyst reaction region set, and determining the target boiler denitration control parameter; based on the target boiler denitration control parameter, performing denitration execution monitoring on the denitration reactor, obtaining flue gas denitration feedback data, and performing dynamic compensation control through the flue gas denitration feedback data; constructing a boiler denitration control space, comprising: based on the denitration reactor, performing historical data mining to obtain a boiler denitration control data set, the boiler denitration control data set including historical boiler outlet flue gas correlation data, ultra-low temperature catalyst reaction data, denitration control data, and denitration effect data; performing outlier cleaning and data normalization on the boiler denitration control data set to obtain a usable boiler denitration control data set; performing association rule analysis and confidence evaluation on the usable boiler denitration control data set to obtain a denitration control rule confidence set; based on the denitration control rule confidence set, screening the usable boiler denitration control data set to construct the boiler denitration control space; building a multi-level denitration control channel, comprising: obtaining a biomass boiler denitration target, splitting the biomass boiler denitration target into effect index sets to obtain a boiler denitration effect evaluation index set; performing hierarchical clustering analysis and priority sorting on the boiler denitration effect evaluation index set to determine a hierarchical denitration effect sequence evaluation index; according to the hierarchical denitration effect sequence evaluation index, constructing a denitration control multi-objective function; using the denitration control multi-objective function to perform multi-level control analysis on the boiler denitration control space to build a multi-level denitration control channel; determining a target boiler denitration control parameter, comprising: using the multi-level denitration control channel to perform dynamic strategy analysis on the outlet flue gas correlation data and the ultra-low temperature catalyst reaction region set to output a partitioned boiler denitration control parameter; performing correlation impact analysis on the ultra-low temperature catalyst reaction region set to obtain a catalyst reaction region influence coefficient set; based on the catalyst reaction region influence coefficient set, globally correcting the partitioned boiler denitration control parameter to determine the target boiler denitration control parameter.

2. The ultra-low-temperature catalytic reduction-based biomass boiler denitration control method according to claim 1, characterized by, using the denitration control multi-objective function to perform multi-level control analysis on the boiler denitration control space to build a multi-level denitration control channel, comprising: using the denitration control multi-objective function to perform effect evaluation and parameter optimization on the boiler denitration control space to obtain an initial boiler denitration control parameter solution; performing random disturbance and cross variation expansion on the initial boiler denitration control parameter solution to generate a boiler denitration control parameter population; The multi-objective function is used for global optimization in the population of the boiler denitration control parameters to obtain a feasible boiler denitration control parameter solution; A multi-level control training is performed based on a relevant boiler denitration data set of the feasible boiler denitration control parameter solution to build the multi-level denitration control channel.

3. The ultra-low-temperature catalytic reduction-based biomass boiler denitration control method according to claim 1, characterized by, The super-low-temperature catalyst reaction region set is determined, including: K-type thermocouple temperature sensors are arranged in the denitration reactor in the direction of the flue gas flow, reaction temperature monitoring is performed by the K-type thermocouple temperature sensors, and a reactor temperature distribution data set is recorded; A reactor temperature distribution map is generated according to the reactor temperature distribution data set; The reactor temperature distribution map is divided by partition clustering according to the super-low-temperature catalyst activity temperature window to obtain a catalyst partition clustering result; The super-low-temperature catalyst reaction region set is determined based on the catalyst partition clustering result.

4. The ultra-low-temperature catalytic reduction-based biomass boiler denitration control method according to claim 1, characterized by, The catalyst reaction region influence coefficient set is obtained, including: A twin simulation model is generated based on the denitration catalyst reduction denitration process mechanism of the denitration reactor; The super-low-temperature catalyst reaction region set is analyzed for relevant influence by the denitration reaction twin simulation model to obtain the catalyst reaction region influence coefficient set.

5. The ultra-low-temperature catalytic reduction-based biomass boiler denitration control method according to claim 1, characterized by, Dynamic compensation control is performed by the flue gas denitration feedback data, including: Denitration effect prediction is performed on the flue gas denitration feedback data to obtain a boiler denitration prediction effect parameter; A boiler denitration effect expected parameter is preset, compensation calculation is performed on the boiler denitration prediction effect parameter based on the boiler denitration effect expected parameter to determine a denitration control compensation parameter, and dynamic compensation control is performed by the denitration control compensation parameter.

6. The ultra-low-temperature catalytic reduction-based biomass boiler denitration control method according to claim 5, characterized by, The denitration control compensation parameter is determined, including: A PID denitration controller is initialized according to the multi-level denitration control channel; A boiler denitration effect deviation parameter of the boiler denitration prediction effect parameter and the boiler denitration effect expected parameter is calculated; The PID denitration controller is used to correct and calculate the boiler denitration effect deviation parameter to determine the denitration control compensation parameter.

7. A biomass boiler denitration control system based on ultra-low temperature catalytic reduction, characterized in that, The steps of the biomass boiler denitration control method based on super-low-temperature catalyst reduction according to any one of claims 1 to 6 are implemented, including: A data monitoring module is configured to arrange a denitration reactor in the tail flue of a biomass boiler, and to monitor outlet flue gas correlation data of the tail flue in real time by a laser gas analyzer and a pressure flow sensor; A channel building module is configured to perform historical data mining based on the denitration reactor, to construct a boiler denitration control space, to perform multi-level control analysis on the boiler denitration control space, and to build a multi-level denitration control channel; A parameter determination module is configured to perform reaction temperature monitoring and partition dynamic identification on the denitration reactor, to determine a super-low-temperature catalyst reaction region set, to perform dynamic strategy analysis on the outlet flue gas correlation data and the super-low-temperature catalyst reaction region set by the multi-level denitration control channel, and to determine a target boiler denitration control parameter. The compensation control module is configured to perform denitration monitoring on the denitration reactor based on the target boiler denitration control parameter, obtain flue gas denitration feedback data, and perform dynamic compensation control through the flue gas denitration feedback data.

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