Multivariable adaptive collaborative optimization control system and method for high-alkali coal boiler

Abstract

The invention discloses a multivariable self-adaptive collaborative optimization control system and method for a high-alkali coal boiler, and relates to the technical field of high-alkali coal boiler optimization control, the system is based on real-time operation data acquisition and multi-working-condition state recognition, and dynamic evaluation and collaborative adjustment of combustion heat transfer and physical coupling effects are achieved; by collecting a first data set, a second data set and a third data set, a high-alkali coal quality and load mapping relation is constructed, and a multi-working-condition state label is output; based on the label, the multivariable cooperative adjustment module calculates a combustion heat transfer coupling coefficient and performs efficiency judgment; the physical coupling effect evaluation module evaluates the boiler internal structure risk; and the dynamic control optimization module integrates multiple indexes to realize control response optimization. According to the system, through threshold value judgment and strategy feedback, the combustion efficiency of the high-alkali coal boiler is improved, structural safety guarantee and dynamic regulation and control optimization are achieved, and the operation stability and economical efficiency of the high-alkali coal boiler are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of optimization control of high-alkali coal boilers, and in particular to a multivariable adaptive collaborative optimization control system and method for high-alkali coal boilers. Background Art

[0002] High-alkali coal reserves are abundant in many western and northern regions of my country, such as Inner Mongolia, Xinjiang, Ningxia, and Shanxi. Its mining costs are low, it's easily accessible, and it offers good resource sustainability. Due to its high impurity content and large calorific value fluctuations, high-alkali coal generally commands a lower market price than high-quality bituminous or lean coal. Using high-alkali coal in boilers can significantly reduce fuel procurement costs for power generation companies. However, high-alkali coal, due to its high proportion of alkali metals such as potassium, sodium, and their oxides in its ash, easily forms low-melting-point viscous substances during combustion. These substances easily deposit, slag, and coke in the boiler furnace, water-cooled walls, and aft heating surfaces, severely impacting the boiler's combustion efficiency and heat transfer performance. Especially under high-load, fluctuating operating conditions, the temperature and airflow fields in the combustion zone experience severe disturbances, leading to unstable deposition and re-reaction processes of the high-alkali coal. This, in turn, can cause problems such as reduced boiler performance, high-temperature corrosion of tube walls, and amplified thermal deviations, posing significant risks to operational safety and energy efficiency control. During the combustion process, the alkali metal content in the ash of ordinary coal boilers is low, the risk of slagging and coking is relatively small, the risk of combustion consequence deposition is low, the cleanliness of the heated area is high, and the state changes are relatively mild relative to the operation disturbance response. The system has a large acceptable disturbance range; compared with the operation risk, the operation reliability is strong within the conventional heat load and structural design range; the control parameters are relatively stable, and the traditional PID control has strong adaptability.

[0003] Currently, industrial sites primarily rely on manual experience-based adjustments and traditional PID control strategies for parameter control and abnormality intervention in high-alkali coal-fired boilers. However, in operating environments with multiple parameters, multiple couplings, and multiple disturbances, these control methods struggle to achieve real-time responses to complex state changes and accurately identify operating conditions and the dynamic relationships between key variables. This results in significant lag, instability, and the risk of local optimality in control performance. In particular, under the unique physical coupling of high-alkali coal combustion, the complex nonlinear synergistic relationship between system heat transfer efficiency, structural stress response, and control execution performance makes it difficult for traditional control models to achieve unified optimization and dynamic correction in this nonlinear, multi-objective constraint environment.

[0004] At the same time, existing boiler operation control systems generally lack a fusion judgment mechanism among operating status, physical risks and control responses. They are unable to effectively distinguish whether the decline in combustion efficiency is caused by fuel supply deviation, heat transfer obstacles or equipment response imbalance. They lack the ability to generate linkage optimization strategies driven by actual data, resulting in difficulty in improving system operating efficiency and ensuring operational safety. Summary of the Invention

[0005] In view of the deficiencies in the prior art, the present invention provides a multivariable adaptive collaborative optimization control system and method for high-alkali coal boilers to solve the problems mentioned in the background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a multivariable adaptive collaborative optimization control system and method for high-alkali coal boilers, comprising: an operation data acquisition module, an operating condition identification and state mapping module, a multivariable collaborative adjustment module, a physical coupling effect evaluation module, and a dynamic control optimization module; The operation data acquisition module is used to collect the first data group in the operation process of the high-alkali coal boiler in real time, including the high-alkali coal boiler water flow Fw, the high-alkali coal fuel supply Ff, the high-alkali coal boiler air supply Fa, the high-alkali coal furnace outlet flue gas temperature and inlet air temperature ; The second data set includes: high alkali coal furnace water-cooled wall outer wall temperature , High alkali coal furnace flue gas main channel flow rate and the internal pressure of the high-alkali coal furnace ; Collect the third data group including: response stabilization time and high alkali coal boiler feed water valve opening ; The operating condition identification and state mapping module is used to analyze the key parameter change characteristics of the first data group, identify the current typical operating conditions of the high-alkali coal boiler, establish a mapping relationship between high-alkali coal quality and load, and output a multi-operating condition state label S; The multivariable coordinated adjustment module is used to calculate the combustion heat transfer coupling coefficient RCO based on the multi-operating state label S as the judgment basis, combined with the data of the first data group, and compare and analyze it with the first threshold Q1 to determine whether the combustion heat transfer efficiency of the high-alkali coal boiler is qualified. If it is unqualified, a strategy is given; The physical coupling effect evaluation module is used to call the collected data under the current operating condition in the second data group based on the multi-operating state label S, calculate and obtain the physical coupling risk coefficient Wphy, and compare and analyze it with the second threshold value Q2 to determine whether the current physical coupling state of the high-alkali coal boiler is qualified. If it is unqualified, a strategy is given; The dynamic control optimization module is used to extract the third data group data based on the multi-operating state label S, and calculate the dynamic control weighted deviation coefficient DJX in combination with the combustion heat transfer coupling coefficient RCO and the physical coupling risk coefficient Wphy. The coefficient is compared and analyzed with the third threshold Q3 to determine whether the current state is qualified for the control response. If it is unqualified, a strategy is given.

[0007] Preferably, the operation data acquisition module includes a first acquisition unit, a second acquisition unit and a third acquisition unit; The first acquisition unit is used to collect key parameter data of the high-alkali coal boiler in real time during operation. The key parameter data include: collecting the high-alkali coal boiler water flow rate Fw by installing a differential pressure flow meter on the main water supply pipe, collecting the high-alkali coal fuel supply Ff by installing a fuel mass flow meter at the coal feeding belt outlet, collecting the high-alkali coal boiler air supply Fa by installing a differential pressure anemometer, and collecting the high-alkali coal furnace outlet flue gas temperature by installing a thermocouple and an infrared thermometer. and inlet air temperature , constituting a first data group; The second acquisition unit is used to collect the temperature of the outer wall of the high-alkali coal furnace water-cooled wall through the high-temperature temperature sensor arranged on the surface of the water-cooled wall. , the wind speed sensor installed in the main flue is used to collect the flow rate of the main channel of the high-alkali coal furnace flue gas The internal pressure of the high-alkali coal furnace is collected in real time through the micro-pressure difference sensor arranged on the inner wall of the furnace. , constituting a second data group; The third collection unit is used to track the flue gas temperature at the high alkali coal furnace outlet in real time. Draw a time-temperature response curve, automatically record, and obtain the response stabilization time , through the valve position opening sensor on the main water supply electric regulating valve, collect the main high-alkali coal boiler water supply valve opening , constituting the third data group.

[0008] Preferably, the operating condition identification and state mapping module is used to perform real-time dynamic analysis and feature extraction on the key operating parameters included in the first data group, and identify the current high-alkali coal boiler in a typical operating condition by analyzing the change trend and coupling relationship of the parameters of the high-alkali coal boiler feed water flow Fw, the high-alkali coal fuel supply Ff, the high-alkali coal boiler air supply Fa, the high-alkali coal furnace outlet flue gas temperature and the inlet air temperature in the time series, and construct a nonlinear mapping relationship model between the high-alkali coal quality characteristics and the boiler load condition based on the identification result; extract the dynamic pattern of parameter changes, and then output a multi-condition state label that describes the boiler operation behavior. , including: steady-state combustion state, load increase critical state, load decrease critical state and low load deviation state.

[0009] Preferably, the multivariable collaborative adjustment module includes a first calculation unit and a first analysis unit; The first calculation unit is used to dynamically select the first data group data of the typical operating condition of the current system under different states based on the multi-operating condition state label S output by the operating condition identification and state mapping module as the judgment basis, and calculate and obtain the combustion heat transfer coupling coefficient RCO after dimensionless processing.

[0010] Preferably, the first analysis unit is used to preset a first threshold value Q1 in advance, and compare and analyze the combustion heat transfer coupling coefficient RCO with the first threshold value Q1, and obtaining the first evaluation result includes: When the combustion heat transfer coupling coefficient RCO ≥ the first threshold Q1, it means that the combustion heat transfer efficiency of the high-alkali coal boiler is qualified and continuous monitoring is required; When the combustion heat transfer coupling coefficient RCO is less than the first threshold value Q1, it means that the combustion heat transfer efficiency of the high-alkali coal boiler is unqualified, and there is a risk of combustion degradation, increased slagging and decreased tail heat transfer. The first warning instruction is triggered and the first strategy is generated: increase the high-alkali coal fuel supply by 3%-8%, improve the fuel heat release intensity, and enhance the center temperature of the furnace flame; reduce the high-alkali coal boiler air supply by 2%-6% to suppress excess air and increase the local heat load density in the combustion area; reduce the high-alkali coal boiler water flow by 3%-7% to reduce the boiler evaporation load and alleviate the heat load mismatch; and start the physical coupling risk monitoring mechanism.

[0011] Preferably, the physical coupling effect evaluation module includes a second calculation unit and a second analysis unit; The second calculation unit is used to call the collected data under the current working condition in the second data group based on the multi-working condition state label S as the judgment basis, and on the basis of identifying the coordinated change characteristics between the outer wall temperature of the high-alkali coal furnace water-cooled wall and the flow velocity of the main channel of the high-alkali coal furnace flue gas, calculate and obtain the physical coupling risk coefficient Wphy through normalization and dynamic gradient weighted processing, and dimensionless processing.

[0012] Preferably, the second analysis unit is used to preset a second threshold Q2 in advance, and compare and analyze the physical coupling risk coefficient Wphy with the second threshold Q2, and obtain the second evaluation result including: When the physical coupling risk coefficient Wphy is less than the second threshold Q2, it indicates that the current physical coupling state of the high-alkali coal boiler is qualified, the heat load and structural strength are in balance, and continuous monitoring is required; When the physical coupling risk coefficient Wphy ≥ the second threshold Q2, it indicates that the current physical coupling state of the high-alkali coal-fired boiler is unqualified, and there is a potential trend of coupling deterioration inside the high-alkali coal-fired boiler, with risks of slagging, deposition, furnace pressure fluctuations, and poor flue gas circulation. This triggers the second early warning instruction and generates the second strategy: stage-by-stage fluctuation control of the high-alkali coal fuel supply Ff: set it to slowly increase by 2% within 5 minutes and then maintain it in a steady state to avoid local coking caused by short-term thermal shock; enable the furnace zone air distribution adjustment function: redistribute the total high-alkali coal-fired boiler air supply volume Fa to the main combustion zone and the upper combustion zone under the premise of keeping it unchanged, to improve flame stability and burnout rate; maintain the current high-alkali coal boiler feed water flow unchanged, but enable the feed water preheating bypass adjustment: by adjusting the feed water temperature by 5℃–8℃, the stability of the superheated steam outlet temperature response is improved; and start the physical risk dynamic joint control mechanism.

[0013] Preferably, the dynamic control optimization module includes a third calculation unit and a third analysis unit; The third calculation unit is used to extract the time correlation between the opening change of the main high-alkali coal furnace water supply valve and the high-alkali coal furnace outlet flue gas temperature response of the third data group based on the multi-condition state label S, and the response stabilization time Adjust time with target The relative deviation of the dynamic control weighted deviation coefficient DJX is calculated after dimensionless processing, combined with the combustion heat transfer coupling coefficient RCO and the physical coupling risk coefficient Wphy.

[0014] Preferably, the third analysis unit is used to preset a third threshold Q3 in advance and compare and analyze the dynamic control weighted deviation coefficient DJX with the third threshold Q3 to obtain a third evaluation result including: dynamically correcting the high-alkali coal furnace feed water flow Fw: according to the response stabilization time Adjust time with target The deviation is reduced by 4%–6% to reduce the regulation pressure of the main steam system; the fuel supply fluctuation Ff is smoothed: a 2%–3% fine-tuning strategy is adopted to suppress the drastic changes in the fuel supply rate and stabilize the combustion center; the staged air damper opening curve adjustment Fa is introduced: a short cycle adjustment of 5 to 8 seconds and a periodic reverse adjustment of 3% are introduced to enhance the furnace disturbance absorption capacity.

[0015] When the dynamic control weighted deviation coefficient DJX is less than the third threshold Q3, it indicates that the current state is qualified for control response, there is no risk of multivariable imbalance, and continuous monitoring is required; When the dynamic control weighted deviation coefficient DJX ≥ the third threshold Q3, the current state indicates an unqualified control response and the risk of multivariable imbalance. This triggers the third warning instruction and generates the third strategy: In the high-alkali coal-fired boiler operation monitoring system, new high-sensitivity abnormal warning nodes are added, and their number should be no less than 15% of the total number of key monitoring points in the boiler. A multi-level redundant monitoring mechanism is activated for important parameters such as furnace temperature, pressure, and flue gas composition to ensure that abnormal fluctuations are captured and reported by the system within 5 seconds. Under control, the system automatically adjusts the operating frequency and load of the boiler's auxiliary equipment to maintain stable equipment operation. The adjustment range of the stable operating frequency of the auxiliary equipment shall not exceed ±10%, ensuring equipment safety while reducing the failure rate. For the boiler fan, feedwater pump, and fuel delivery system, a preventive maintenance cycle optimization strategy is implemented, shortening maintenance intervals by 5% in advance to improve equipment reliability and operational continuity. The dynamic load limit module is activated to control the load command change rate to no more than 3% to prevent system instability due to excessively rapid adjustment. The first and second strategies are continuously linked to form a multi-level and multi-dimensional dynamic collaborative optimization control.

[0016] Preferably, the multivariable adaptive collaborative optimization control method for high-alkali coal boilers includes the following steps: Step 1: collect the first data set in real time during the operation of the high-alkali coal boiler, including: high-alkali coal boiler water flow Fw, high-alkali coal fuel supply Ff, high-alkali coal boiler air flow Fa, high-alkali coal furnace outlet flue gas temperature and inlet air temperature ; The second data set includes: high alkali coal furnace water-cooled wall outer wall temperature , High alkali coal furnace flue gas main channel flow rate and the internal pressure of the high-alkali coal furnace ; Collect the third data group including: response stabilization time and high alkali coal boiler feed water valve opening ; Step 2: By analyzing the key parameter change characteristics of the first data group, the current typical operating conditions of the high-alkali coal boiler are identified, a mapping relationship between high-alkali coal quality and load is constructed, and a multi-condition state label S is output; Step 3: Based on the multi-condition state label S as the basis for judgment, combined with the data of the first data group, the combustion heat transfer coupling coefficient RCO is calculated and compared with the first threshold Q1 to determine whether the combustion heat transfer efficiency of the high-alkali coal boiler is qualified. If it is unqualified, a strategy is given; Step 4: Based on the multi-operating state label S, the collected data under the current operating condition in the second data group is called to calculate the physical coupling risk coefficient Wphy, and compared with the second threshold Q2 to determine whether the current physical coupling state of the high-alkali coal boiler is qualified. If it is unqualified, a strategy is given; Step 5: Based on the multi-condition state label S, extract the data of the third data group, combine the combustion heat transfer coupling coefficient RCO and the physical coupling risk coefficient Wphy, calculate the dynamic control weighted deviation coefficient DJX, and compare and analyze it with the third threshold Q3 to determine whether the current state is qualified for the control response. If it is unqualified, a strategy is given.

[0017] The present invention provides a multivariable adaptive collaborative optimization control system and method for high-alkali coal boilers. It has the following beneficial effects: (1) The multivariable adaptive collaborative optimization control system and method for high-alkali coal boilers collects the first data group of thermal parameters of high-alkali coal boilers, the second data group of structural operation risk related parameters and the third data group of dynamic response behavior parameters through the three-data group collection mechanism constructed by the operation data collection module, thereby achieving comprehensive coverage and functional stratification of high-alkali coal boiler operation data, providing high-precision data support for subsequent operating condition identification, coupling coefficient calculation and control strategy formulation, and significantly improving the dynamic response capability and prediction accuracy of the control system.

[0018] (2) The multivariable adaptive collaborative optimization control system and method for high-alkali coal boilers, with the help of the combustion heat transfer coupling coefficient RCO introduced by the multivariable collaborative adjustment module, can accurately evaluate the combustion heat transfer efficiency under different operating conditions, and achieve dynamic optimal control of the heat release intensity, local air coefficient and load heat matching of the high-alkali coal furnace by strategically adjusting the synergistic ratio between the high-alkali coal fuel supply, the high-alkali coal boiler air supply and the high-alkali coal boiler feed water flow, thereby effectively suppressing slagging, reducing the risk of tail heat transfer degradation, and improving the operating stability and energy conversion efficiency of the high-alkali coal boiler.

[0019] (3) The multivariable adaptive collaborative optimization control system and method for high-alkali coal boilers, based on the physical coupling risk coefficient Wphy proposed by the physical coupling effect evaluation module, can identify the coordinated abnormal trend of the flue gas flow and the water-cooled wall structure inside the boiler. Especially during the load change or high-alkali coal quality switching stage, it can perceive the hidden dangers such as furnace slagging, air pressure fluctuation and increased flow resistance in advance. Through the linkage strategy, it triggers the optimization of air flow distribution and the adjustment of feed water temperature, realizes the pre-emptive control of physical risks, and reduces the probability of thermal stress shock and structural damage of the system.

[0020] (4) The multivariable adaptive collaborative optimization control system and method for high-alkali coal boilers, the dynamic control optimization module introduces the third data group parameters such as response stabilization time, target adjustment time, valve opening, etc., to construct the dynamic control weighted deviation coefficient DJX, which can effectively reflect the changes in the boiler control response performance. Combined with the multi-condition identification results and historical operation modes, it realizes the intelligent evaluation and differentiated adjustment of the multivariable control strategy, and links the control measures of the first and second modules at the strategy triggering level to build a closed-loop control chain of "early warning-adjustment-monitoring-joint control", thereby improving the overall adaptive adjustment capability and operation stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a flowchart of the multivariable adaptive collaborative optimization control system for high-alkali coal boilers of the present invention; Figure 2 This is a schematic diagram of the steps of the multivariable adaptive collaborative optimization control method for high-alkali coal boilers of the present invention. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0023] Example 1 See also Figure 1 The present invention provides a multivariable adaptive collaborative optimization control system and method for high-alkali coal boilers, including: an operation data acquisition module, an operating condition identification and state mapping module, a multivariable collaborative adjustment module, a physical coupling effect evaluation module and a dynamic control optimization module; The operation data acquisition module is used to collect the first data group in the operation process of the high-alkali coal boiler in real time, including the high-alkali coal boiler water flow Fw, the high-alkali coal fuel supply Ff, the high-alkali coal boiler air supply Fa, the high-alkali coal furnace outlet flue gas temperature and inlet air temperature ; The second data set includes: high alkali coal furnace water-cooled wall outer wall temperature , High alkali coal furnace flue gas main channel flow rate and the internal pressure of the high-alkali coal furnace ; Collect the third data group including: response stabilization time and high alkali coal boiler feed water valve opening ; The operating condition identification and state mapping module is used to analyze the key parameter change characteristics of the first data group, identify the current typical operating conditions of the high-alkali coal boiler, establish a mapping relationship between high-alkali coal quality and load, and output a multi-operating condition state label S; The multivariable coordinated adjustment module is used to calculate the combustion heat transfer coupling coefficient RCO based on the multi-operating state label S as the judgment basis, combined with the data of the first data group, and compare and analyze it with the first threshold Q1 to determine whether the combustion heat transfer efficiency of the high-alkali coal boiler is qualified. If it is unqualified, a strategy is given; The physical coupling effect evaluation module is used to call the collected data under the current operating condition in the second data group based on the multi-operating state label S, calculate and obtain the physical coupling risk coefficient Wphy, and compare and analyze it with the second threshold value Q2 to determine whether the current physical coupling state of the high-alkali coal boiler is qualified. If it is unqualified, a strategy is given; The dynamic control optimization module is used to extract the third data group data based on the multi-operating state label S, and calculate the dynamic control weighted deviation coefficient DJX in combination with the combustion heat transfer coupling coefficient RCO and the physical coupling risk coefficient Wphy. The coefficient is compared and analyzed with the third threshold Q3 to determine whether the current state is qualified for the control response. If it is unqualified, a strategy is given.

[0024] In this embodiment, through the collaborative work of multiple modules, accurate identification and dynamic regulation of the operating status of the high-alkali coal boiler are achieved. It can collect multiple sets of key operating data in real time, comprehensively analyze the combustion heat transfer efficiency, physical coupling risk and control response performance, and form a multi-dimensional risk assessment and optimization strategy, which significantly improves the stability and safety of the high-alkali coal boiler operation, reduces the risk of combustion degradation and equipment failure, and realizes efficient and intelligent high-alkali coal boiler operation management.

[0025] High-alkali coal contains a high proportion of alkali metals such as sodium and potassium. During high-temperature combustion, it easily forms a low-melting-point viscous substance, leading to frequent problems such as furnace coking, slagging of heating surfaces, and high-temperature corrosion. Conventional boiler control systems struggle to cope with these nonlinear dynamic changes. This invention establishes a data input foundation strongly correlated with the physical effects of high-alkali coal through customized collection of key parameters such as furnace flue gas temperature, wind temperature, water-cooled wall temperature, and airflow disturbances. This enhances the control strategy's adaptability to coal quality.

[0026] Example 2 This embodiment is explained in Example 3. Specifically, the operation data acquisition module includes a first acquisition unit, a second acquisition unit and a third acquisition unit; The first acquisition unit is used to collect key parameter data of the high-alkali coal boiler in real time during operation. The key parameter data include: collecting the high-alkali coal boiler water flow rate Fw by installing a differential pressure flow meter on the main water supply pipe, collecting the high-alkali coal fuel supply Ff by installing a fuel mass flow meter at the coal feeding belt outlet, collecting the high-alkali coal boiler air supply Fa by installing a differential pressure anemometer, and collecting the high-alkali coal furnace outlet flue gas temperature by installing a thermocouple and an infrared thermometer. and inlet air temperature , constituting a first data group; The second acquisition unit is used to collect the temperature of the outer wall of the high-alkali coal furnace water-cooled wall through the high-temperature temperature sensor arranged on the surface of the water-cooled wall. , the wind speed sensor installed in the main flue is used to collect the flow rate of the main channel of the high-alkali coal furnace flue gas The internal pressure of the high-alkali coal furnace is collected in real time through the micro-pressure difference sensor arranged on the inner wall of the furnace. , constituting a second data group; The third collection unit is used to track the flue gas temperature at the high alkali coal furnace outlet in real time. Draw a time-temperature response curve, automatically record, and obtain the response stabilization time , through the valve position opening sensor on the main water supply electric regulating valve, collect the main high-alkali coal boiler water supply valve opening , constituting the third data group.

[0027] In this embodiment, the parameters such as the high-alkali coal boiler feed water flow Fw, fuel supply Ff, air supply Fa and flue gas temperature collected by the first collection unit fully take into account the characteristics of high-alkali coal in the combustion process, such as large fluctuations in calorific value, high sodium and potassium components, and strong slag adhesion. Compared with ordinary coal boilers, these parameters have stronger nonlinear volatility and response sensitivity under the same load conditions. Therefore, through the configuration of special flow meters, differential pressure anemometers and infrared temperature measurement, the input and heat exchange status of the high-alkali coal combustion system can be more accurately reflected; secondly, the water-cooled wall outer wall temperature, flue gas main channel flow rate and furnace internal pressure collected by the second collection unit, in view of the risks of increased coking of the heat transfer surface, changes in flue resistance and furnace pressure difference fluctuations caused by the combustion of high-alkali coal, high-temperature sensitive sensors are arranged to achieve high-precision monitoring of the physical field coupling state inside the boiler, and these dynamic changes are in ordinary The performance in common coal boilers is not significant, and conventional acquisition methods are difficult to effectively perceive. Secondly, the third acquisition unit obtains the response stabilization time by dynamically tracking the flue gas temperature change curve, and combined with the valve opening signal, it can reflect the control characteristics such as adjustment hysteresis and thermal inertia enhancement under high-alkali coal conditions, effectively capturing the dynamic offset of the control system caused by the special characteristics of the coal type, thereby providing a real-time basis for the control strategy. In the design of the operation data acquisition structure, the present invention takes the physicochemical properties and combustion nonlinear response of high-alkali coal as the basic conditions to construct three types of data groups covering heat input, heat transfer state and control behavior. Compared with the general acquisition method of ordinary coal boilers, it has stronger adaptability, pertinence and dynamic accuracy, which helps to support the subsequent physical coupling risk assessment and the implementation of dynamic control optimization strategy, and fundamentally improves the stability, safety and intelligent response capability of high-alkali coal boiler operation. By setting a multi-dimensional parameter acquisition path specifically for high-alkali coal boilers, three sets of key data acquisition systems covering fuel, air, water-cooled wall and flue gas flow are constructed, which effectively adapts to the operation characteristics of high-alkali coal boilers with significant high-temperature deposition, coking and airflow disturbance. Compared to common coal boilers, high-alkali coal boilers experience more intense ash deposition and flow field disturbances during combustion. This system precisely captures thermal deviations, pressure fluctuations, and surface heat transfer anomalies during high-alkali coal combustion by combining infrared temperature measurement with thermocouples at the furnace outlet, deploying high-temperature temperature sensors on the water-cooled wall surfaces, and arranging micro-pressure differential sensors within the furnace. In particular, the system's ability to discern the control response speed and execution effectiveness of high-alkali coal boilers, coupled with response time tracking and valve opening acquisition, significantly improves the system's perception accuracy and dynamic discrimination of the complex combustion physics of high-alkali coal, surpassing the single-parameter, coarse-grained monitoring model used by conventional boilers for common coal.

[0028] Example 3 This example is an explanation of Example 2. Specifically, the operating condition identification and state mapping module is used to perform real-time dynamic analysis and feature extraction on the key operating parameters included in the first data group, and identify the typical operating condition of the current high-alkali coal boiler by analyzing the change trend and coupling relationship of the parameters of the high-alkali coal boiler feed water flow Fw, the high-alkali coal fuel supply Ff, the high-alkali coal boiler air supply Fa, the high-alkali coal furnace outlet flue gas temperature and the inlet air temperature in the time series, and construct a nonlinear mapping relationship model between the high-alkali coal quality characteristics and the boiler load condition based on the identification result; extract the dynamic pattern of parameter changes, and then output a multi-condition state label that describes the boiler operation behavior. , including: steady-state combustion state, load increase critical state, load decrease critical state and low load deviation state.

[0029] In this embodiment, through real-time dynamic analysis and feature extraction of key parameters of the first data group, the typical operating conditions of high-alkali coal boilers can be accurately identified, a nonlinear mapping relationship between high-alkali coal quality characteristics and load conditions can be constructed, and the dynamic change pattern of high-alkali coal boiler operation can be reflected in real time; through the output of multiple operating condition status labels, accurate distinction between different operating states can be achieved, which helps to improve the accuracy of high-alkali coal boiler operating status monitoring and the targeted response, thereby effectively supporting subsequent adjustment and optimization control, and improving the stability and combustion efficiency of high-alkali coal boiler operation.

[0030] Example 4 This embodiment is explained in Example 3. Specifically, the multivariable coordinated adjustment module includes a first calculation unit and a first analysis unit; The first calculation unit is used to dynamically select the first data set data of the typical working condition of the current system under different states based on the multi-working condition state label S output by the working condition identification and state mapping module as the judgment basis, and calculate the combustion heat transfer coupling coefficient RCO after dimensionless processing. The formula is as follows: ; Where, Indicates the flue gas temperature at the furnace outlet of high-alkali coal. Indicates the inlet air temperature, Indicates the high-alkali coal fuel supply, Indicates the feed water flow rate of high-alkali coal boiler, Indicates the air supply volume of high-alkali coal boiler, and represents the empirical adjustment coefficient, and e represents the base of the natural constant.

[0031] and Acquisition Method: Based on the multi-operating condition collection and analysis of key parameters of the boiler combustion system, statistical methods and multivariate regression models are used, combined with normalization processing and a dynamic gradient weighting algorithm. The coupling effects of parameters such as high-alkali coal furnace outlet flue gas temperature, inlet air temperature, high-alkali coal fuel supply, high-alkali coal boiler feedwater flow, and high-alkali coal boiler air supply are quantitatively fitted, and empirical adjustment coefficients reflecting the weights of each parameter are extracted. Through repeated verification and field operation feedback, combined with industry combustion optimization standards and equipment characteristics, the coefficient value is adjusted to ensure the calculation accuracy and dynamic adaptability of the combustion heat transfer coupling coefficient RCO, providing solid data support for real-time monitoring of combustion efficiency and the formulation of adjustment strategies.

[0032] In this embodiment, key operating parameters under typical operating conditions are dynamically selected through a multivariable collaborative adjustment module, and the combustion heat transfer coupling coefficient RCO is calculated in combination with a dimensionless processing method to achieve an accurate quantitative evaluation of the combustion and heat transfer state of the high-alkali coal boiler; this coefficient can reflect the coupling relationship between multiple parameters and their dynamic changes, effectively improve the accuracy and real-time nature of combustion efficiency judgment, and provide a scientific basis for the formulation of subsequent adjustment strategies, thereby optimizing boiler operating performance and reducing energy consumption and emissions.

[0033] Example 5 This embodiment is an explanation of the fourth embodiment. Specifically, the first analysis unit is used to preset a first threshold value Q1 in advance, and compare and analyze the combustion heat transfer coupling coefficient RCO with the first threshold value Q1. Obtaining a first evaluation result includes: When the combustion heat transfer coupling coefficient RCO ≥ the first threshold Q1, it means that the combustion heat transfer efficiency of the high-alkali coal boiler is qualified and continuous monitoring is required; When the combustion heat transfer coupling coefficient RCO is less than the first threshold value Q1, it means that the combustion heat transfer efficiency of the high-alkali coal boiler is unqualified, and there is a risk of combustion degradation, increased slagging and decreased tail heat transfer. The first warning instruction is triggered and the first strategy is generated: increase the high-alkali coal fuel supply by 3%-8%, improve the fuel heat release intensity, and enhance the center temperature of the furnace flame; reduce the high-alkali coal boiler air supply by 2%-6% to suppress excess air and increase the local heat load density in the combustion area; reduce the high-alkali coal boiler water flow by 3%-7% to reduce the boiler evaporation load and alleviate the heat load mismatch; and start the physical coupling risk monitoring mechanism.

[0034] The first threshold Q1 is determined by statistically analyzing historical operating data and combustion efficiency test results from a large number of high-alkali coal-fired boilers under different loads and combustion conditions. The distribution characteristics of the combustion heat transfer coupling coefficient (RCO) under normal and abnormal combustion conditions are comprehensively evaluated. This is then combined with high-alkali coal-fired boiler operating safety regulations, combustion efficiency standards, and industry experience to determine a reasonable first threshold Q1. This threshold is set based on the dynamic response model of high-alkali coal-fired boiler combustion heat transfer, the performance indicators of equipment manufacturers, and the advice of combustion optimization experts to scientifically distinguish between acceptable and unacceptable combustion heat transfer efficiency states, providing timely warnings of combustion degradation, slagging, and reduced heat transfer efficiency, thereby ensuring the efficient and stable operation of high-alkali coal-fired boilers.

[0035] In this embodiment, the combustion heat transfer coupling coefficient RCO is compared with the preset threshold value Q1 in real time through the first analysis unit, which can timely and accurately identify the abnormal state of the combustion heat transfer efficiency of the high-alkali coal boiler, automatically trigger an early warning and generate a targeted adjustment strategy, thereby realizing the coordinated optimization adjustment of fuel supply, high-alkali coal boiler air supply volume and high-alkali coal boiler feed water flow, effectively preventing combustion degradation, slagging and reduced tail heat transfer efficiency, improving the stability and economy of boiler operation, and ensuring the safe and efficient operation of the boiler.

[0036] Example 6 This embodiment is explained in Example 5. Specifically, the physical coupling effect evaluation module includes a second calculation unit and a second analysis unit; The second calculation unit is used to call the collected data under the current working condition in the second data group based on the multi-working condition state label S as the judgment basis, and based on the identification of the coordinated change characteristics between the outer wall temperature of the high-alkali coal furnace water-cooled wall and the flow velocity of the high-alkali coal furnace flue gas main channel, calculate and obtain the physical coupling risk coefficient Wphy through normalization and dynamic gradient weighting processing after dimensionless processing. The formula is as follows: ; Where, Indicates the outer wall temperature of the water-cooled wall of the high-alkali coal furnace, Indicates the flow rate of the main channel of the high-alkali coal furnace flue gas, The internal gas pressure of the high-alkali coal furnace, a1, a2 and a3 represent the adjustment coefficients.

[0037] The method for obtaining a1, a2, and a3 is to collect and analyze operating data from high-alkali coal-fired boilers under various typical operating conditions. Based on the historical variations in the temperature of the water-cooled outer wall of the furnace, the flow velocity of the flue gas main channel, and the internal pressure of the furnace, a normalization and dynamic gradient weighting method are used to fit and optimize the coupling relationships between these parameters, thereby determining the appropriate range of values ​​for each adjustment coefficient. By referring to the thermodynamic characteristics of the boiler structure, the combustion and heat transfer process model, and industry experience data, adjustments are made based on actual operational feedback to ensure that the adjustment coefficients accurately reflect the weight of each parameter's impact on the physical coupling risk, thereby achieving accurate calculation and dynamic response of the risk coefficient Wphy.

[0038] In this embodiment, through the second calculation unit in the physical coupling effect evaluation module, based on the multi-condition state label S and the key parameters collected by the second data group, the coordinated change characteristics between the outer wall temperature of the high-alkali coal furnace water-cooled wall and the flow velocity of the main channel of the high-alkali coal furnace flue gas and the internal gas pressure of the high-alkali coal furnace are dynamically captured. The normalization and dynamic gradient weighted processing methods are used to accurately calculate the physical coupling risk coefficient Wphy, realize the accurate evaluation of the physical coupling state inside the boiler, effectively prevent equipment damage and operation risks caused by coupling abnormalities, and improve the safety and stability of the high-alkali coal boiler system.

[0039] Example 7 This embodiment is an explanation of Embodiment 6. Specifically, the second analysis unit is configured to preset a second threshold value Q2 in advance, and compare and analyze the physical coupling risk coefficient Wphy with the second threshold value Q2. Obtaining a second evaluation result includes: When the physical coupling risk coefficient Wphy is less than the second threshold Q2, it indicates that the current physical coupling state of the high-alkali coal boiler is qualified, the heat load and structural strength are in balance, and continuous monitoring is required; When the physical coupling risk coefficient Wphy ≥ the second threshold Q2, it indicates that the current physical coupling state of the high-alkali coal-fired boiler is unqualified, and there is a potential trend of coupling deterioration inside the high-alkali coal-fired boiler, with risks of slagging, deposition, furnace pressure fluctuations, and poor flue gas circulation. This triggers the second early warning instruction and generates the second strategy: stage-by-stage fluctuation control of the high-alkali coal fuel supply Ff: set it to slowly increase by 2% within 5 minutes and then maintain it in a steady state to avoid local coking caused by short-term thermal shock; enable the furnace zone air distribution adjustment function: redistribute the total high-alkali coal-fired boiler air supply volume Fa to the main combustion zone and the upper combustion zone under the premise of keeping it unchanged, to improve flame stability and burnout rate; maintain the current high-alkali coal boiler feed water flow unchanged, but enable the feed water preheating bypass adjustment: by adjusting the feed water temperature by 5℃–8℃, the stability of the superheated steam outlet temperature response is improved; and start the physical risk dynamic joint control mechanism.

[0040] The second threshold, Q2, was obtained by statistically analyzing historical operating data and abnormal events related to the physical coupling state of a large number of high-alkali coal-fired boilers under different operating conditions. This analysis identified the distribution range of typical indicators for heat load and structural strength balance and physical coupling anomalies in high-alkali coal-fired boilers. This was combined with safe operating specifications for high-alkali coal-fired boilers, performance limits provided by equipment manufacturers, industry risk assessment standards for high-alkali coal-fired boilers, and expert experience to develop a reasonable physical coupling risk threshold, Q2. This threshold is used to accurately determine the physical coupling state of the boiler, promptly identify potential risks of slagging, deposition, and pressure fluctuations, and ensure the stable and safe operation of the high-alkali coal-fired boiler system.

[0041] In this embodiment, the second analysis unit performs a real-time comparison of the physical coupling risk coefficient Wphy with a preset threshold value Q2, accurately determining the safety of the physical coupling state of the high-alkali coal boiler. When the risk coefficient exceeds the threshold, a timely warning is triggered and multiple targeted adjustment strategies are automatically generated. These strategies include gradually increasing the high-alkali coal fuel supply in stages, dynamically optimizing the high-alkali coal furnace air distribution by zone, and enabling feedwater preheating bypass regulation. These strategies effectively suppress slagging and deposition risks, stabilize the high-alkali coal furnace pressure and flue gas flow, significantly improve the safety and stability of high-alkali coal boiler operation, and achieve dynamic, coordinated control and preventive maintenance of physical coupling risks.

[0042] Example 8 This embodiment is explained in Example 7. Specifically, the dynamic control optimization module includes a third calculation unit and a third analysis unit; The third calculation unit is used to extract the time correlation between the opening change of the main high-alkali coal furnace water supply valve and the high-alkali coal furnace outlet flue gas temperature response of the third data group based on the multi-condition state label S, and the response stabilization time Adjust time with target The relative deviation of the dynamic control weighted deviation coefficient DJX is calculated after dimensionless processing, combined with the combustion heat transfer coupling coefficient RCO and the physical coupling risk coefficient Wphy. The formula is as follows:

[0043] Where, Indicates the response stabilization time, Indicates the target adjustment time, Indicates the opening of the main high-alkali coal boiler water supply valve, w1, w2, w3 and w4 are weight coefficients, , , , and .

[0044] The acquisition method of w1, w2, w3 and w4 is based on the statistical analysis of a large number of high-alkali coal high-alkali coal boiler operation data and expert experience guidance; combustion heat transfer coupling coefficient RCO, physical coupling risk coefficient Wphy, actual response stabilization time Adjust time with target A multi-dimensional regression model was developed based on the sensitivity and impact of the opening of the feedwater valve of the corresponding main high-alkali coal boiler. This method, combined with the safety regulations and combustion optimization requirements for high-alkali coal boiler operation, rationally allocated the numerical range of each weight coefficient. This method fully reflects the contribution of different parameters to the dynamic control weighted deviation coefficient, ensuring the scientific and practical nature of the weight coefficient, thereby effectively guiding the optimization and adjustment of high-alkali coal boiler operation control.

[0045] In this embodiment, a third calculation unit integrates the multi-condition status tag S and the temporal correlation between the main high-alkali coal boiler's feedwater valve opening and the high-alkali coal furnace outlet flue gas temperature response in the third data set. Combined with the combustion heat transfer coupling coefficient RCO and the physical coupling risk coefficient Wphy, it accurately calculates the dynamic control weighted deviation coefficient DJX, enabling a quantitative assessment of the control response of the high-alkali coal boiler. This mechanism effectively reflects the dynamic regulation performance and response stability of the control system, providing a scientific basis for subsequent optimization of the control strategy, improving the regulation accuracy and response efficiency of the high-alkali coal boiler, and ensuring safe and stable system operation.

[0046] Example 9 This embodiment is explained in Example 8. Specifically, the third analysis unit is used to preset a third threshold Q3 in advance and compare and analyze the dynamic control weighted deviation coefficient DJX with the third threshold Q3 to obtain a third evaluation result including: dynamically correcting the high-alkali coal furnace water flow rate Fw: according to the response stabilization time Adjust time with target The deviation is reduced by 4%–6% to reduce the regulation pressure of the main steam system; the fuel supply fluctuation Ff is smoothed: a 2%–3% fine-tuning strategy is adopted to suppress the drastic changes in the fuel supply rate and stabilize the combustion center; the staged air damper opening curve adjustment Fa is introduced: a short cycle adjustment of 5 to 8 seconds and a periodic reverse adjustment of 3% are introduced to enhance the furnace disturbance absorption capacity.

[0047] When the dynamic control weighted deviation coefficient DJX is less than the third threshold Q3, it indicates that the current state is qualified for control response, there is no risk of multivariable imbalance, and continuous monitoring is required; When the dynamic control weighted deviation coefficient DJX ≥ the third threshold Q3, the current state indicates an unqualified control response and the risk of multivariable imbalance. This triggers the third warning instruction and generates the third strategy: In the high-alkali coal-fired boiler operation monitoring system, new high-sensitivity abnormal warning nodes are added, and their number should be no less than 15% of the total number of key monitoring points in the boiler. A multi-level redundant monitoring mechanism is activated for important parameters such as furnace temperature, pressure, and flue gas composition to ensure that abnormal fluctuations are captured and reported by the system within 5 seconds. Under control, the system automatically adjusts the operating frequency and load of the boiler's auxiliary equipment to maintain stable equipment operation. The adjustment range of the stable operating frequency of the auxiliary equipment shall not exceed ±10%, ensuring equipment safety while reducing the failure rate. For the boiler fan, feedwater pump, and fuel delivery system, a preventive maintenance cycle optimization strategy is implemented, shortening maintenance intervals by 5% in advance to improve equipment reliability and operational continuity. The dynamic load limit module is activated to control the load command change rate to no more than 3% to prevent system instability due to excessively rapid adjustment. The first and second strategies are continuously linked to form a multi-level and multi-dimensional dynamic collaborative optimization control.

[0048] The third threshold Q3 is obtained as follows: This is based on a statistical analysis of a large amount of high-alkali coal-fired boiler operating stability data, combined with the comprehensive performance of the combustion heat transfer coupling coefficient RCO, the physical coupling risk coefficient Wphy, and the control response time. By extracting the fluctuation range of the dynamic control weighted deviation coefficient DJX under normal operating conditions and the critical change characteristics of this coefficient under abnormal conditions, and integrating the experience of industry experts and the power industry's safe operation standards, a reasonable threshold is determined. Referring to domestic and international coal-fired boiler operation control specifications and the performance limits of equipment manufacturers, Q3 is established as the key critical value for judging whether the control response is qualified or not, effectively realizing early warning and strategy triggering, and ensuring the safe and stable operation of the high-alkali coal-fired boiler system.

[0049] In this embodiment, a third analysis unit performs a comparative analysis based on the dynamic control weighted deviation coefficient DJX and a preset third threshold value Q3, enabling precise assessment and dynamic early warning of the control response status of high-alkali coal boilers. Upon detecting a substandard control response, the system immediately triggers multi-level redundant monitoring and highly sensitive anomaly warnings, effectively improving the speed and accuracy of response to abnormal events. Simultaneously, it automatically adjusts the operating frequency and load of auxiliary equipment, optimizing preventive maintenance cycles and ensuring safe and stable equipment operation. This mechanism enables timely identification of multivariable imbalance risks and multi-dimensional coordinated control, significantly enhancing the operational safety, reliability, and stability of the boiler system.

[0050] Example 10 Multivariable adaptive collaborative optimization control method for high-alkali coal boilers, please refer to Figure 2 , including the following steps: Step 1: collect the first data set in real time during the operation of the high-alkali coal boiler, including: high-alkali coal boiler water flow Fw, high-alkali coal fuel supply Ff, high-alkali coal boiler air flow Fa, high-alkali coal furnace outlet flue gas temperature and inlet air temperature ; The second data set includes: high alkali coal furnace water-cooled wall outer wall temperature , High alkali coal furnace flue gas main channel flow rate and the internal pressure of the high-alkali coal furnace ; Collect the third data group including: response stabilization time and high alkali coal boiler feed water valve opening ; Step 2: By analyzing the key parameter change characteristics of the first data group, the current typical operating conditions of the high-alkali coal boiler are identified, a mapping relationship between high-alkali coal quality and load is constructed, and a multi-condition state label S is output; Step 3: Based on the multi-condition state label S as the basis for judgment, combined with the data of the first data group, the combustion heat transfer coupling coefficient RCO is calculated and compared with the first threshold Q1 to determine whether the combustion heat transfer efficiency of the high-alkali coal boiler is qualified. If it is unqualified, a strategy is given; Step 4: Based on the multi-operating state label S, the collected data under the current operating condition in the second data group is called to calculate the physical coupling risk coefficient Wphy, and compared with the second threshold Q2 to determine whether the current physical coupling state of the high-alkali coal boiler is qualified. If it is unqualified, a strategy is given; Step 5: Based on the multi-condition state label S, extract the data of the third data group, combine the combustion heat transfer coupling coefficient RCO and the physical coupling risk coefficient Wphy, calculate the dynamic control weighted deviation coefficient DJX, and compare and analyze it with the third threshold Q3 to determine whether the current state is qualified for the control response. If it is unqualified, a strategy is given.

[0051] In this embodiment, a comprehensive assessment of the operating status of high-alkali coal-fired boilers is conducted through systematic multi-step data collection and dynamic analysis, combined with multiple operating status tags. This enables multi-dimensional intelligent monitoring and precise identification of combustion heat transfer efficiency, physical coupling status, and control response. Based on real-time collaborative processing of three key data sets and multi-threshold comparative analysis, this system effectively identifies potential abnormal boiler operating conditions and responds promptly. Through strategic adjustments, it ensures the efficiency and safety of high-alkali coal-fired boiler operations, significantly improving operational stability and energy conservation and emissions reduction.

[0052] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technicians in this field for each set of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0053] The above formulas are obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The coefficients in the formula are set by those skilled in the art according to actual conditions. The above is only a preferred specific implementation method of the present invention, but the protection scope of the present invention is not limited to this. Any technician familiar with this technical field, within the technical scope disclosed by the present invention, can make equivalent replacements or changes based on the technical solution and inventive concept of the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A multivariable adaptive collaborative optimization control system for high-alkali coal boilers, characterized by: include: Operation data acquisition module, working condition identification and state mapping module, multivariable collaborative adjustment module, physical coupling effect evaluation module and dynamic control optimization module; The operation data acquisition module is used to collect the first data group in the operation process of the high-alkali coal boiler in real time, including the high-alkali coal boiler water flow Fw, the high-alkali coal fuel supply Ff, the high-alkali coal boiler air supply Fa, the high-alkali coal furnace outlet flue gas temperature and inlet air temperature ; The second data set includes: high alkali coal furnace water-cooled wall outer wall temperature , High alkali coal furnace flue gas main channel flow rate and the internal pressure of the high-alkali coal furnace ; Collect the third data group including: response stabilization time and high alkali coal boiler feed water valve opening ; The operating condition identification and state mapping module is used to analyze the key parameter change characteristics of the first data group, identify the current typical operating conditions of the high-alkali coal boiler, establish a mapping relationship between high-alkali coal quality and load, and output a multi-operating condition state label S; The multivariable coordinated adjustment module is used to calculate the combustion heat transfer coupling coefficient RCO based on the multi-operating state label S as the judgment basis, combined with the data of the first data group, and compare and analyze it with the first threshold Q1 to determine whether the combustion heat transfer efficiency of the high-alkali coal boiler is qualified. If it is unqualified, a strategy is given; The physical coupling effect evaluation module is used to call the collected data under the current operating condition in the second data group based on the multi-operating state label S, calculate and obtain the physical coupling risk coefficient Wphy, and compare and analyze it with the second threshold value Q2 to determine whether the current physical coupling state of the high-alkali coal boiler is qualified. If it is unqualified, a strategy is given; The dynamic control optimization module is used to extract the third data group data based on the multi-operating state label S, and calculate the dynamic control weighted deviation coefficient DJX in combination with the combustion heat transfer coupling coefficient RCO and the physical coupling risk coefficient Wphy. The coefficient is compared and analyzed with the third threshold Q3 to determine whether the current state is qualified for the control response. If it is unqualified, a strategy is given.

2. The multivariable adaptive collaborative optimization control system for high-alkali coal boilers according to claim 1 is characterized in that: The operation data acquisition module includes a first acquisition unit, a second acquisition unit and a third acquisition unit; The first acquisition unit is used to collect key parameter data of the high-alkali coal boiler in real time during operation. The key parameter data include: collecting the high-alkali coal boiler water flow rate Fw by installing a differential pressure flow meter on the main water supply pipe, collecting the high-alkali coal fuel supply Ff by installing a fuel mass flow meter at the coal feeding belt outlet, collecting the high-alkali coal boiler air supply Fa by installing a differential pressure anemometer, and collecting the high-alkali coal furnace outlet flue gas temperature by installing a thermocouple and an infrared thermometer. and inlet air temperature , constituting a first data group; The second acquisition unit is used to collect the temperature of the outer wall of the high-alkali coal furnace water-cooled wall through the high-temperature temperature sensor arranged on the surface of the water-cooled wall. , the wind speed sensor installed in the main flue is used to collect the flow rate of the main channel of the high-alkali coal furnace flue gas The internal pressure of the high-alkali coal furnace is collected in real time through the micro-pressure difference sensor arranged on the inner wall of the furnace. , constituting a second data group; The third collection unit is used to track the flue gas temperature at the high alkali coal furnace outlet in real time. Draw a time-temperature response curve, automatically record, and obtain the response stabilization time , through the valve position opening sensor on the main water supply electric regulating valve, collect the main high-alkali coal boiler water supply valve opening , constituting the third data group.

3. The multivariable adaptive collaborative optimization control system for high-alkali coal boilers according to claim 2 is characterized in that: The operating condition identification and state mapping module is used to perform real-time dynamic analysis and feature extraction on the key operating parameters included in the first data group. By analyzing the time series change trends and coupling relationships of the parameters of the high-alkali coal boiler feed water flow Fw, the high-alkali coal fuel supply Ff, the high-alkali coal boiler air supply Fa, the high-alkali coal furnace outlet flue gas temperature and the inlet air temperature, the module identifies the typical operating condition of the current high-alkali coal boiler, and constructs a nonlinear mapping relationship model between the high-alkali coal quality characteristics and the boiler load condition based on the identification results; extracts the dynamic pattern of parameter changes, and then outputs a multi-condition state label that describes the boiler operation behavior. , including: steady-state combustion state, load increase critical state, load decrease critical state and low load deviation state.

4. The multivariable adaptive collaborative optimization control system for high-alkali coal boilers according to claim 3 is characterized in that: The multivariable collaborative adjustment module includes a first calculation unit and a first analysis unit; The first calculation unit is used to dynamically select the first data group data of the typical operating condition of the current system under different states based on the multi-operating condition state label S output by the operating condition identification and state mapping module as the judgment basis, and calculate and obtain the combustion heat transfer coupling coefficient RCO after dimensionless processing.

5. The multivariable adaptive collaborative optimization control system for high-alkali coal boilers according to claim 4 is characterized in that: The first analysis unit is used to preset a first threshold value Q1 in advance, and compare and analyze the combustion heat transfer coupling coefficient RCO with the first threshold value Q1, and obtain a first evaluation result including: When the combustion heat transfer coupling coefficient RCO ≥ the first threshold Q1, it means that the combustion heat transfer efficiency of the high-alkali coal boiler is qualified and continuous monitoring is required; When the combustion heat transfer coupling coefficient RCO is less than the first threshold value Q1, it means that the combustion heat transfer efficiency of the high-alkali coal boiler is unqualified, and there is a risk of combustion degradation, increased slagging and decreased tail heat transfer. The first warning instruction is triggered and the first strategy is generated: increase the high-alkali coal fuel supply by 3%-8%, improve the fuel heat release intensity, and enhance the center temperature of the furnace flame; reduce the high-alkali coal boiler air supply by 2%-6% to suppress excess air and increase the local heat load density in the combustion area; reduce the high-alkali coal boiler water flow by 3%-7% to reduce the boiler evaporation load and alleviate the heat load mismatch; and start the physical coupling risk monitoring mechanism.

6. The multivariable adaptive collaborative optimization control system for high-alkali coal boilers according to claim 5, characterized in that: The physical coupling effect evaluation module includes a second calculation unit and a second analysis unit; The second calculation unit is used to call the collected data under the current working condition in the second data group based on the multi-working condition state label S as the judgment basis, and on the basis of identifying the coordinated change characteristics between the outer wall temperature of the high-alkali coal furnace water-cooled wall and the flow velocity of the main channel of the high-alkali coal furnace flue gas, calculate and obtain the physical coupling risk coefficient Wphy through normalization and dynamic gradient weighted processing, and dimensionless processing.

7. The multivariable adaptive collaborative optimization control system for high-alkali coal boilers according to claim 6, characterized in that: The second analysis unit is configured to preset a second threshold value Q2 in advance, and compare and analyze the physical coupling risk coefficient Wphy with the second threshold value Q2 to obtain a second evaluation result, including: When the physical coupling risk coefficient Wphy is less than the second threshold Q2, it indicates that the current physical coupling state of the high-alkali coal boiler is qualified, the heat load and structural strength are in balance, and continuous monitoring is required; When the physical coupling risk coefficient Wphy ≥ the second threshold Q2, it indicates that the current physical coupling state of the high-alkali coal-fired boiler is unqualified, and there is a potential trend of coupling deterioration inside the high-alkali coal-fired boiler, with risks of slagging, deposition, furnace pressure fluctuations, and poor flue gas circulation. This triggers the second early warning instruction and generates the second strategy: stage-by-stage fluctuation control of the high-alkali coal fuel supply Ff: set it to slowly increase by 2% within 5 minutes and then maintain it in a steady state to avoid local coking caused by short-term thermal shock; enable the furnace zone air distribution adjustment function: redistribute the total high-alkali coal-fired boiler air supply volume Fa to the main combustion zone and the upper combustion zone under the premise of keeping it unchanged, to improve flame stability and burnout rate; maintain the current high-alkali coal boiler feed water flow unchanged, but enable the feed water preheating bypass adjustment: by adjusting the feed water temperature by 5℃–8℃, the stability of the superheated steam outlet temperature response is improved; and start the physical risk dynamic joint control mechanism.

8. The multivariable adaptive collaborative optimization control system for high-alkali coal boilers according to claim 7, characterized in that: The dynamic control optimization module includes a third calculation unit and a third analysis unit; The third calculation unit is used to extract the time correlation between the opening change of the main high-alkali coal furnace water supply valve and the high-alkali coal furnace outlet flue gas temperature response of the third data group based on the multi-condition state label S, and the response stabilization time Adjust time with target The relative deviation of the dynamic control weighted deviation coefficient DJX is calculated after dimensionless processing, combined with the combustion heat transfer coupling coefficient RCO and the physical coupling risk coefficient Wphy.

9. The multivariable adaptive collaborative optimization control system for high-alkali coal boilers according to claim 8, characterized in that: The third analysis unit is used to obtain the third evaluation result by presetting the third threshold Q3 in advance and comparing the dynamic control weighted deviation coefficient DJX with the third threshold Q3, including: dynamically correcting the high-alkali coal furnace water flow Fw: according to the response stabilization time Adjust time with target The deviation is reduced by 4%–6% to reduce the regulation pressure of the main steam system; the fuel supply fluctuation Ff is smoothed: a 2%–3% fine-tuning strategy is adopted to suppress the drastic changes in the fuel supply rate and stabilize the combustion center; the staged air damper opening curve adjustment Fa is introduced: a short cycle adjustment of 5 to 8 seconds and a periodic reverse adjustment of 3% are introduced to enhance the furnace disturbance absorption capacity. When the dynamic control weighted deviation coefficient DJX is less than the third threshold Q3, it indicates that the current state is qualified for control response, there is no risk of multivariable imbalance, and continuous monitoring is required; When the dynamic control weighted deviation coefficient DJX ≥ the third threshold Q3, the current state indicates an unqualified control response and the risk of multivariable imbalance. This triggers the third warning instruction and generates the third strategy: In the high-alkali coal-fired boiler operation monitoring system, new high-sensitivity abnormal warning nodes are added, and their number should be no less than 15% of the total number of key monitoring points in the boiler. A multi-level redundant monitoring mechanism is activated for important parameters such as furnace temperature, pressure, and flue gas composition to ensure that abnormal fluctuations are captured and reported by the system within 5 seconds. Under control, the system automatically adjusts the operating frequency and load of the boiler's auxiliary equipment to maintain stable equipment operation. The adjustment range of the stable operating frequency of the auxiliary equipment shall not exceed ±10%, ensuring equipment safety while reducing the failure rate. For the boiler fan, feedwater pump, and fuel delivery system, a preventive maintenance cycle optimization strategy is implemented, shortening maintenance intervals by 5% in advance to improve equipment reliability and operational continuity. The dynamic load limit module is activated to control the load command change rate to no more than 3% to prevent system instability due to excessively rapid adjustment. The first and second strategies are continuously linked to form a multi-level and multi-dimensional dynamic collaborative optimization control.

10. A multivariable adaptive collaborative optimization control method for a high-alkali coal boiler, according to the multivariable adaptive collaborative optimization control system for a high-alkali coal boiler according to claims 1 to 9, characterized in that: The following steps are involved: Step 1: collect the first data set in real time during the operation of the high-alkali coal boiler, including: high-alkali coal boiler water flow Fw, high-alkali coal fuel supply Ff, high-alkali coal boiler air flow Fa, high-alkali coal furnace outlet flue gas temperature and inlet air temperature ; The second data set includes: high alkali coal furnace water-cooled wall outer wall temperature , High alkali coal furnace flue gas main channel flow rate and the internal pressure of the high-alkali coal furnace ; Collect the third data group including: response stabilization time and high alkali coal boiler feed water valve opening ; Step 2: By analyzing the key parameter change characteristics of the first data group, the current typical operating conditions of the high-alkali coal boiler are identified, a mapping relationship between high-alkali coal quality and load is constructed, and a multi-condition state label S is output; Step 3: Based on the multi-condition state label S as the basis for judgment, combined with the data of the first data group, the combustion heat transfer coupling coefficient RCO is calculated and compared with the first threshold Q1 to determine whether the combustion heat transfer efficiency of the high-alkali coal boiler is qualified. If it is unqualified, a strategy is given; Step 4: Based on the multi-operating state label S, the collected data under the current operating condition in the second data group is called to calculate the physical coupling risk coefficient Wphy, and compared with the second threshold Q2 to determine whether the current physical coupling state of the high-alkali coal boiler is qualified. If it is unqualified, a strategy is given; Step 5: Based on the multi-condition state label S, extract the data of the third data group, combine the combustion heat transfer coupling coefficient RCO and the physical coupling risk coefficient Wphy, calculate the dynamic control weighted deviation coefficient DJX, and compare and analyze it with the third threshold Q3 to determine whether the current state is qualified for the control response. If it is unqualified, a strategy is given.

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