A biomass mixed combustion boiler minimum stable combustion load prediction and regulation method and system

Abstract

Embodiments of the present disclosure provide a biomass mixed combustion boiler minimum stable combustion load prediction and regulation method and system. The method comprises: collecting the operation parameters, biomass characteristic parameters and combustion state parameters of the boiler in real time; inputting the multiple parameters into a preset multi-parameter coupling influence model to calculate the predicted value of the minimum stable combustion load, the model being a linear weighting model; comparing the predicted value with the target value, if the predicted value is higher than the target value, adjusting the boiler operation parameters according to the hierarchical regulation strategy; collecting the combustion state parameters after regulation and feeding back to the model for iterative optimization. Through multi-parameter coupling and dynamic calibration, embodiments of the present disclosure realize accurate prediction and active regulation of the minimum stable combustion load, can significantly reduce the minimum stable combustion load of the boiler, improve the deep peak shaving capacity, and at the same time guarantee the combustion stability and environmental protection.

Description

Technical Field

[0001] The embodiments disclosed herein belong to the field of biomass co-firing boiler control technology, specifically relating to a method and system for predicting and controlling the minimum stable combustion load of a biomass co-firing boiler. Background Technology

[0002] The proportion of new energy power generation continues to increase, but the volatility and uncertainty of wind and solar power pose challenges to the safe and stable operation of the power grid, requiring thermal power units to have deep peak-shaving capabilities, that is, the ability to operate stably under lower loads. As a core piece of equipment in large thermal power units, the minimum stable combustion load of the swirl-counter boiler directly determines the peak-shaving depth. Biomass co-firing, due to its carbon neutralization properties and stable combustion efficiency enhancement characteristics, has become a key technological path for reducing the minimum stable combustion load of boilers.

[0003] Existing research indicates that co-firing biomass can shorten the ignition distance, increase the high-temperature combustion zone, and improve combustion stability through its high volatile matter content. For example, co-firing 10% cotton stalks can reduce the minimum stable combustion load of a 1000MW swirl-opposed boiler from 40% to 30%.

[0004] However, existing technologies have significant bottlenecks: First, there is a lack of quantitative impact models, and the stable combustion effect under specific operating conditions can only be obtained through experiments or numerical simulations, which cannot be extended to different biomass types, co-firing ratios and boiler load ranges; second, the parameter correlation analysis is insufficient, and the coupling relationship between multiple parameters such as furnace temperature, ignition distance, and oxygen concentration and the minimum stable combustion load has not been established; third, the control strategy is static, making it difficult to dynamically adjust the operating parameters according to the real-time operating status, resulting in unstable stable combustion effect. Summary of the Invention

[0005] The embodiments disclosed herein aim to at least solve one of the technical problems existing in the prior art, and provide a method and system for predicting and controlling the minimum stable combustion load of a biomass co-firing boiler.

[0006] One aspect of this disclosure provides a method for predicting and controlling the minimum stable combustion load of a biomass co-firing boiler, the method comprising: Real-time acquisition of multiple parameters of the boiler, including operating parameters, biomass characteristic parameters and combustion status parameters; The multivariate parameters are input into a preset multi-parameter coupled influence model to calculate and output the predicted value of the minimum stable combustion load; wherein, the multi-parameter coupled influence model is a linear weighted model; Compare the predicted value of the minimum stable combustion load with the target value. If the predicted value is higher than the target value, adjust the boiler's operating parameters according to the preset graded control strategy. The combustion state parameters after regulation are collected and fed back to the multi-parameter coupled influence model for iterative optimization.

[0007] Furthermore, the operating parameters include primary wind speed, inner secondary wind speed, and outer secondary wind speed; The biomass characteristic parameters include biomass blending ratio and biomass volatile matter content; The combustion state parameters include the proportion of the high-temperature zone in the furnace, the fuel ignition distance, the oxygen concentration at the furnace outlet, the pulverized coal burnout rate, and the biomass burnout rate.

[0008] Furthermore, the multi-parameter coupling effect model is expressed as follows:

[0009] In the formula, This is the predicted value for the minimum stable fuel load. This represents the percentage of the high-temperature zone in the furnace. For fuel ignition distance, The oxygen concentration at the furnace outlet. The combustion rate of pulverized coal. For biomass burnout rate, For biomass co-firing ratio, The content of volatile matter in biomass. , , These are the primary wind speed, the inner secondary wind speed, and the outer secondary wind speed, respectively. to These are the model weight coefficients.

[0010] Furthermore, the method also includes: Boiler operating data is collected periodically, and the gradient descent method is used to adjust the weight coefficients of the model. to The model is iteratively updated to ensure that the prediction error of the multi-parameter coupling influence model meets the preset accuracy requirements.

[0011] Furthermore, the tiered control strategy includes: Level 1 control: Adjust the biomass co-firing ratio to the first preset range; Secondary control: If the predicted value is still higher than the target value after primary control, the ratio between the primary wind speed, the inner secondary wind speed and the outer secondary wind speed is adjusted to the second preset range. Level 3 control: If the predicted value is still higher than the target value after Level 2 control, then adjust the burner's operating combination mode.

[0012] Furthermore, the first preset range is 8% to 12%; The second preset range is : : =1 : (1.1~1.3) : (1.5~1.8).

[0013] Furthermore, the process of collecting and adjusting the combustion state parameters and feeding them back to the multi-parameter coupled influence model for iterative prediction includes: If the actual value of the minimum stable combustion load calculated based on the regulated combustion state parameters deviates from the predicted value by more than a preset deviation threshold, the multi-parameter coupled influence model is iteratively optimized.

[0014] Another aspect of this disclosure provides a minimum stable combustion load prediction and control system for biomass co-firing boilers, the system comprising: The multi-parameter acquisition module is used to acquire multi-parameters of the boiler in real time, including operating parameters, biomass characteristic parameters, and combustion status parameters. The coupled model prediction module is used to input the multivariate parameters into a preset multi-parameter coupled influence model and calculate and output the predicted value of the minimum stable combustion load; wherein, the multi-parameter coupled influence model is a linear weighted model; The operating parameter control module is used to compare the predicted value of the minimum stable combustion load with the target value. If the predicted value is higher than the target value, the operating parameters of the boiler are adjusted according to the preset graded control strategy. The closed-loop feedback optimization module is used to collect the combustion state parameters after regulation and feed them back to the multi-parameter coupled influence model for iterative optimization.

[0015] Another aspect of this disclosure provides an electronic device, comprising: At least one processor; and, A memory communicatively connected to the at least one processor is used to store one or more programs that, when executed by the at least one processor, enable the at least one processor to implement the method for predicting and controlling the minimum stable combustion load of a biomass co-firing boiler as described above.

[0016] Another aspect of this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for predicting and controlling the minimum stable combustion load of a biomass co-firing boiler described above.

[0017] This disclosure discloses a method and system for predicting and controlling the minimum stable combustion load of a biomass co-firing boiler. Through multi-parameter coupling and dynamic model calibration, the model prediction error is small, accurately reflecting the variation law of the minimum stable combustion load under different operating conditions, overcoming the limitations of "empirical" prediction. It can significantly reduce the minimum stable combustion load of the swirl-counter boiler, improving its deep peak-shaving capacity and meeting the peak-shaving needs of thermal power units for new energy consumption. The dynamic control strategy balances stable combustion effect and combustion efficiency, resulting in high pulverized coal burnout rate and biomass burnout rate under co-firing conditions, and low NOx emissions. xEmissions are reduced compared to pure coal operation, achieving environmental protection and high efficiency; the model can be adapted to different biomass types, co-firing ratios and swirl counter-current boiler capacities, without the need for remodeling for specific boilers, resulting in low promotion costs; the closed-loop control mechanism operates automatically without manual intervention, reducing the labor intensity of operators and improving the safety and reliability of boiler operation. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a method for predicting and controlling the minimum stable combustion load of a biomass co-firing boiler according to an embodiment of this disclosure. Figure 2 This is a schematic diagram of the structure of a minimum stable combustion load prediction and control system for a biomass co-firing boiler according to another embodiment of the present disclosure; Figure 3 This is a schematic diagram of the structure of an electronic device according to another embodiment of the present disclosure. Detailed Implementation

[0019] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. Based on the embodiments of this disclosure, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this disclosure.

[0020] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0021] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0022] It should be understood that although the terms first, second, third, etc., may be used in this disclosure to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another. Therefore, the first component discussed below may be referred to as the second component without departing from the teachings of this disclosure. As used in this disclosure, the term "and / or" includes all combinations of any and more of the associated listed items.

[0023] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of exemplary embodiments, and the modules or processes in the drawings are not necessarily necessary for implementing this disclosure, and therefore cannot be used to limit the scope of protection of this disclosure.

[0024] like Figure 1 As shown, one embodiment of this disclosure provides a method for predicting and controlling the minimum stable combustion load of a biomass co-firing boiler, the method comprising: Step S1: Real-time acquisition of multiple parameters of the boiler, including operating parameters, biomass characteristic parameters, and combustion status parameters.

[0025] Specifically, multi-dimensional parameters of the cyclone counter-current boiler are collected in real time. These operating parameters include primary air velocity. Internal secondary wind speed and external secondary wind speed The wind speed is collected by a wind speed sensor installed inside the burner duct, with a measurement range of 0m / s to 50m / s and an accuracy of ±0.1m / s.

[0026] Biomass characteristic parameters include biomass blending ratio volatile matter content of biomass And other industrial analysis data (such as moisture, ash content, lower heating value, etc.). Among them, the biomass co-firing ratio The volatile matter content of biomass is monitored in real time using a mass flow sensor. The calculation is based on the fusion of data from third-party testing reports and online elemental analyzers.

[0027] Combustion state parameters include the proportion of the high-temperature zone in the furnace. fuel ignition distance Oxygen concentration at furnace outlet pulverized coal combustion rate Biomass burnout rate Among them, the high-temperature zone of the furnace accounts for a certain percentage. The percentage of the area above 1400K at the center cross-section of each burner layer in the furnace was determined by collecting temperature distribution cloud maps using an infrared thermometer array (arranged in three layers each on the front and rear walls of the furnace, with a total of 24 measuring points), and the percentage was calculated using an image segmentation algorithm; fuel ignition distance; Based on Semenov's ignition theory, the inflection point of the temperature curve along the pulverized coal jet direction is identified. The inflection point satisfies dT / dx ≥ 0 and d 2 T / dx 2 =0; Oxygen concentration at furnace outlet Data is collected using a zirconia sensor installed in the furnace outlet flue, with a response time ≤0.5s; pulverized coal combustion rate. With biomass burnout rate All calculations are based on the principle of gray balance, and the formula is:

[0028] In the formula, Ash content of fuel entering the furnace (dry basis). Ash content of fly ash at the furnace outlet.

[0029] All parameters can be obtained via industrial Ethernet, with a uniform sampling frequency of 10Hz and a data storage period of 3 months, used for model calibration and optimization.

[0030] Step S2: Input the multivariate parameters into a preset multi-parameter coupled influence model and calculate and output the predicted value of the minimum stable combustion load; wherein, the multi-parameter coupled influence model is a linear weighted model.

[0031] Specifically, based on the linear weighted coupling algorithm, the influence of various parameters on the minimum stable combustion load is integrated to construct a prediction model, namely the multi-parameter coupled influence model, as shown in the following equation:

[0032] In the formula, This is the predicted value for the minimum stable fuel load. This represents the percentage of the high-temperature zone in the furnace. For fuel ignition distance, The oxygen concentration at the furnace outlet. The combustion rate of pulverized coal. For biomass burnout rate, For biomass co-firing ratio, The content of volatile matter in biomass. , , These are the primary wind speed, the inner secondary wind speed, and the outer secondary wind speed, respectively. to These are the model weight coefficients.

[0033] The logic of the multi-parameter coupling influence model is: the proportion of high-temperature regions above 1400K. The larger the distance to the fire The shorter the length, the stronger the combustion stability and the lower the minimum stable combustion load. The lower the temperature, the greater the proportion of high-temperature areas. With minimum stable combustion load Negative correlation; oxygen concentration Excessively high temperatures indicate incomplete combustion, which is related to the minimum stable combustion load. Positive correlation; burnout rate ( , The higher the ), the more complete the combustion, and the lower the minimum stable combustion load. The lower the value, the negative correlation; the coupling term between biomass blending ratio and volatile matter ( The synergistic combustion stabilization effect reflecting the characteristics of biomass, and the minimum combustion stabilization load. Negative correlation; primary wind With secondary wind speed , By influencing the airflow mixing effect to regulate the combustion state, the weighting coefficient to Determined based on air distribution optimization tests.

[0034] The initial coefficients of the model were obtained by fitting literature data and numerical simulation results (1000MW swirl counter-current boiler with cotton stalks). In field application, the model was calibrated iteratively using the gradient descent method. The objective function was to minimize the mean square error between the predicted and actual values. The iteration termination condition was that the mean square error was ≤0.04.

[0035] Model parameter calibration adopts a "batch acquisition-iterative update" calibration mechanism: each batch collects 100 sets of valid operational data (covering load 25%~100%, biomass co-firing ratio). =5%~15%, different biomass types), remove abnormal data (such as sensor failure, boiler start-up and shutdown data); update the weight coefficients using the mean square error as the objective function and the gradient descent method. to The learning rate was set to 0.01, and the number of iterations was 1000 to ensure the model's prediction accuracy across the entire operating range. A comprehensive calibration was performed every quarter, and a temporary calibration process was initiated when the prediction error of a single batch of data exceeded 3% to ensure the model's adaptability.

[0036] Step S3: Compare the predicted value of the minimum stable combustion load with the target value. If the predicted value is higher than the target value, adjust the boiler's operating parameters according to the preset graded control strategy.

[0037] Based on the prediction results of the multi-parameter coupled influence model, a graded control strategy is formulated to adjust the minimum stable combustion load. The rate will be reduced to below 30% to accommodate deeper peak-shaving demands. The tiered control strategy includes: Level 1 regulation (priority implementation): Adjust the biomass blending ratio When the minimum stable fuel load forecast value When >35%, The ignition distance is shortened by increasing the base value from 5% to 8%~12% and utilizing the high volatile matter content of biomass. (Expected to shorten by 0.24%~3.45%), increase the proportion of high-temperature areas. ; if =10% If it remains above 32%, then continue to increase. Up to 12%~15%, but needs to be controlled. ≤15%, to avoid excessive temperature drop inside the furnace; Secondary regulation (optimized powder-to-wind ratio): If after primary regulation Still not up to standard, adjust the wind speed ratio and optimize it to : : =1 : (1.1~1.3) : (1.5~1.8), which increases the entrainment effect of the external secondary air on the high-temperature flue gas, improves the combustion stability, and at the same time maintains the burnout wind speed at 30m / s to ensure that the fuel is fully burned; Three-level control (burner condition optimization): If the first two levels of control are ineffective, the coal mill combination mode is adjusted. When the load is reduced, the upper coal mill is shut down first, while the lower burner is kept running (the lower burner is equipped with a plasma stabilization system) to further improve the temperature concentration in the main combustion zone.

[0038] Step S4: Collect the adjusted combustion state parameters and feed them back to the multi-parameter coupled influence model for iterative optimization.

[0039] Specifically, a closed-loop mechanism of "prediction-control-feedback-re-prediction" is constructed: within 10 minutes after control, 200 sets of combustion state parameters are collected, and the actual value of the minimum stable combustion load is calculated. (Judgment criteria: average temperature of the main combustion zone ≥ 900K, burnout rate ≥ 95%, and flame shape intact and uninterrupted); compare with the predicted values ​​of the model. Compared with actual value If the deviation is ≤3%, the current parameters are maintained; if the deviation is >3%, the actual parameters are fed back to the model, the control instructions are recalculated, and iterative optimization is performed until the deviation meets the requirements.

[0040] Abnormal warning: When the actual value of the minimum stable combustion load... When the concentration is below 25%, activate the low-load stable combustion warning and appropriately increase the primary wind speed. Adjust the coal powder concentration to avoid the risk of flameout.

[0041] This disclosure discloses a method for predicting and controlling the minimum stable combustion load of a biomass co-firing boiler. Through multi-parameter coupling and dynamic model calibration, the model prediction error is small, accurately reflecting the variation law of the minimum stable combustion load under different operating conditions, thus overcoming the limitations of "empirical" prediction. It can significantly reduce the minimum stable combustion load of the swirl-counter boiler, improving its deep peak-shaving capacity and meeting the peak-shaving requirements of thermal power units for new energy consumption. The dynamic control strategy balances stable combustion effect and combustion efficiency, resulting in high pulverized coal burnout rate and biomass burnout rate under co-firing conditions, and low NOx emissions. xEmissions are reduced compared to pure coal operation, achieving environmental protection and high efficiency; the model can be adapted to different biomass types, co-firing ratios and swirl counter-current boiler capacities, without the need for remodeling for specific boilers, resulting in low promotion costs; the closed-loop control mechanism operates automatically without manual intervention, reducing the labor intensity of operators and improving the safety and reliability of boiler operation.

[0042] The following is an example of a 1000MW supercritical cyclone counter-current boiler in a power plant (furnace width 33973.4mm, depth 15558.4mm, height 66737.9mm, equipped with 48 low NO₂ units). x Using the swirl pulverized coal burner as an application example, the implementation process is explained in detail: 1. Multivariate parameter acquisition ① Install an infrared thermometer array: Arrange thermometers at the furnace positions corresponding to the burners on the front and rear walls, with a total of 24 measuring points, to collect the temperature distribution of the longitudinal section of the furnace and generate a temperature cloud map; ② Installation of wind speed sensors: Install wind speed sensors in the primary air, internal secondary air, and external secondary air ducts of each burner to monitor the primary air speed in real time. Internal secondary wind speed Secondary wind speed ; ③ Monitoring of biomass characteristic parameters: The amount of cotton stalks transported is monitored using a mass flow sensor, and the cotton stalk blending ratio is calculated in combination with the mass flow rate of coal. The volatile matter content of cotton stalks was monitored using an online elemental analyzer. =75.10%) ④ Calculation of combustion state parameters: Based on the temperature cloud map, the proportion of high-temperature areas above 1400K is calculated using an image segmentation algorithm. Temperature profiles along the direction of the pulverized coal jet indicate the fuel ignition distance. Zirconia sensors collect oxygen concentration at the furnace outlet. The fly ash content was analyzed using a fly ash sampler to calculate the pulverized coal combustion rate. Cotton stalk burnout rate .

[0043] 2. Model Building and Calibration Initial coefficient input: =52.3、 =0.35、 =18.6、 =1.2、 0.18 =0.12、 =0.42、 =0.85、 =0.56、 =0.32 Input model; Data Acquisition and Calibration: Collect 100 sets of operating data for pure coal and cotton stalk blending (load 300MW-1000MW). =5%-15%), the coefficients are iteratively calibrated using the gradient descent method, and the final calibrated coefficients are: =51.8、 =0.37、 =19.2、 =1.15、 =0.19、 =0.13、 =0.45、 =0.82、 =0.58、 =0.30, the model prediction error is 1.8%.

[0044] 3. Dynamic hierarchical regulation Target minimum stable combustion load: 30% (corresponding to 300MW load); Initial operating condition: pure coal condition, predicted value =40.2%, triggering Level 1 regulation to reduce the cotton stalk blending ratio. Increase to 10%, at which point the parameters are: =28%, =4.51m =8.92%, =98.51%, =95.03%, =16.49m / s =21.75m / s =29.27m / s, substitute into the model and recalculate the predicted value. =30.5%; Secondary regulation: due to forecast values Still slightly above the target value, the powder-to-air ratio is optimized as follows: : : =1:1.2:1.7, after adjustment =16.5m / s =19.8m / s =28.05m / s, recalculate =29.8%, meeting the target requirement; Closed-loop monitoring: After 2 hours of operation following adjustment, the combustion status is monitored in real time, and the actual value of the minimum stable combustion load is recorded. =29.5%, compared to the predicted value The deviation is 0.3%, which meets the accuracy requirements. Continue operating with the current parameters.

[0045] 4. Verification of running results After one month of project operation, the following indicators were verified: Stable combustion load: The minimum stable combustion load is maintained at 29.5%~30%, which is 26.25% lower than that of pure coal (40%). Combustion stability: The proportion of high-temperature zones above 1400K increases by 15%-20%, the ignition distance is shortened by 0.24%~3.45%, and no flameout occurs; Environmental protection and efficiency indicators: Pulverized coal combustion rate ≥98.5%, cotton stalk combustion rate ≥95%, NO x Emissions were reduced by 7.2%, meeting the GB13223-2011 emission standards; Peak shaving capacity: The unit can operate stably in the load range of 300MW~1000MW, and its deep peak shaving capacity meets the grid requirements.

[0046] The above embodiments demonstrate that the method for predicting and controlling the minimum stable combustion load of biomass co-firing boilers disclosed herein can accurately predict and effectively reduce the minimum stable combustion load of swirl counter-current boilers, improve the deep peak-shaving capability of the unit, and has significant engineering application value.

[0047] like Figure 2 As shown, another embodiment of this disclosure provides a minimum stable combustion load prediction and control system for biomass co-firing boilers, the system comprising: The multi-parameter acquisition module 210 is used to acquire multi-parameters of the boiler in real time, including operating parameters, biomass characteristic parameters and combustion state parameters. The coupled model prediction module 220 is used to input the multivariate parameters into a preset multi-parameter coupled influence model and calculate and output the predicted value of the minimum stable combustion load; wherein, the multi-parameter coupled influence model is a linear weighted model; The operating parameter control module 230 is used to compare the predicted value of the minimum stable combustion load with the target value. If the predicted value is higher than the target value, the operating parameters of the boiler are adjusted according to the preset graded control strategy. The closed-loop feedback optimization module 240 is used to collect the combustion state parameters after regulation and feed them back to the multi-parameter coupled influence model for iterative optimization.

[0048] Specifically, the minimum stable combustion load prediction and control system for a biomass co-firing boiler according to an embodiment of this disclosure is used to implement the minimum stable combustion load prediction and control method for a biomass co-firing boiler described in the above embodiments. The specific implementation process has been described in detail in the above embodiments and will not be repeated here.

[0049] like Figure 3 As shown, another embodiment of this disclosure provides an electronic device, including: At least one processor 301; and a memory 302 communicatively connected to the at least one processor 301 for storing one or more programs that, when executed by the at least one processor 301, enable the at least one processor 301 to implement the method for predicting and controlling the minimum stable combustion load of a biomass co-firing boiler as described above.

[0050] The memory 302 and processor 301 are connected via a bus, which can include any number of interconnecting buses and bridges. The bus connects various circuits of one or more processors 301 and memory 302 together. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. A bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by processor 301 is transmitted over a wireless medium via an antenna, which further receives data and transmits it to processor 301.

[0051] Processor 301 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory 302 can be used to store data used by processor 301 during operation.

[0052] Another embodiment of this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for predicting and controlling the minimum stable combustion load of a biomass co-firing boiler described above.

[0053] The computer-readable storage medium may be included in the systems or electronic devices disclosed herein, or it may exist independently.

[0054] Computer-readable storage media can be any tangible medium that contains or stores a program, and can be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device. More specific examples include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, optical fibers, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0055] Computer-readable storage media may also include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code, specific examples of which include, but are not limited to, electromagnetic signals, optical signals, or any suitable combination thereof.

[0056] It is understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of this disclosure, and this disclosure is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and substance of this disclosure, and these modifications and improvements are also considered to be within the scope of protection of this disclosure.

Claims

1. A method for predicting and controlling the minimum stable combustion load of a biomass co-firing boiler, characterized in that, The method includes: Real-time acquisition of multiple parameters of the boiler, including operating parameters, biomass characteristic parameters and combustion status parameters; The multivariate parameters are input into a preset multi-parameter coupled influence model to calculate and output the predicted value of the minimum stable combustion load; wherein, the multi-parameter coupled influence model is a linear weighted model; Compare the predicted value of the minimum stable combustion load with the target value. If the predicted value is higher than the target value, adjust the boiler's operating parameters according to the preset graded control strategy. The combustion state parameters after regulation are collected and fed back to the multi-parameter coupled influence model for iterative optimization.

2. The method for predicting and controlling the minimum stable combustion load of a biomass co-firing boiler according to claim 1, characterized in that, The operating parameters include primary wind speed, inner secondary wind speed, and outer secondary wind speed; The biomass characteristic parameters include biomass blending ratio and biomass volatile matter content; The combustion state parameters include the proportion of the high-temperature zone in the furnace, the fuel ignition distance, the oxygen concentration at the furnace outlet, the pulverized coal burnout rate, and the biomass burnout rate.

3. The method for predicting and controlling the minimum stable combustion load of a biomass co-firing boiler according to claim 2, characterized in that, The multi-parameter coupling effect model is expressed by the following equation: In the formula, This is the predicted value for the minimum stable fuel load. This represents the percentage of the high-temperature zone in the furnace. For fuel ignition distance, The oxygen concentration at the furnace outlet. For pulverized coal combustion rate, For biomass burnout rate, For biomass co-firing ratio, The content of volatile matter in biomass. , , These are the primary wind speed, the inner secondary wind speed, and the outer secondary wind speed, respectively. to These are the model weight coefficients.

4. The method for predicting and controlling the minimum stable combustion load of a biomass co-firing boiler according to claim 3, characterized in that, The method further includes: Boiler operating data is collected periodically, and the gradient descent method is used to adjust the weight coefficients of the model. to The model is iteratively updated to ensure that the prediction error of the multi-parameter coupling influence model meets the preset accuracy requirements.

5. A method for predicting and controlling the minimum stable combustion load of a biomass co-firing boiler according to claim 2 or 3, characterized in that, The tiered regulation strategy includes: Level 1 control: Adjust the biomass co-firing ratio to the first preset range; Secondary control: If the predicted value is still higher than the target value after primary control, the ratio between the primary wind speed, the inner secondary wind speed and the outer secondary wind speed is adjusted to the second preset range. Level 3 control: If the predicted value is still higher than the target value after Level 2 control, then adjust the burner's operating combination mode.

6. The method for predicting and controlling the minimum stable combustion load of a biomass co-firing boiler according to claim 5, characterized in that, The first preset range is 8% to 12%; The second preset range is : : =1 : (1.1~1.3) : (1.5~1.8).

7. The method for predicting and controlling the minimum stable combustion load of a biomass co-firing boiler according to claim 1, characterized in that, The process of collecting and adjusting combustion state parameters and feeding them back to the multi-parameter coupled influence model for iterative prediction includes: If the actual value of the minimum stable combustion load calculated based on the regulated combustion state parameters deviates from the predicted value by more than a preset deviation threshold, the multi-parameter coupled influence model is iteratively optimized.

8. A minimum stable combustion load prediction and control system for biomass co-firing boilers, characterized in that, The system includes: The multi-parameter acquisition module is used to acquire multi-parameters of the boiler in real time, including operating parameters, biomass characteristic parameters, and combustion status parameters. The coupled model prediction module is used to input the multivariate parameters into a preset multi-parameter coupled influence model and calculate and output the predicted value of the minimum stable combustion load; wherein, the multi-parameter coupled influence model is a linear weighted model; The operating parameter control module is used to compare the predicted value of the minimum stable combustion load with the target value. If the predicted value is higher than the target value, the operating parameters of the boiler are adjusted according to the preset graded control strategy. The closed-loop feedback optimization module is used to collect the combustion state parameters after regulation and feed them back to the multi-parameter coupled influence model for iterative optimization.

9. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor is used to store one or more programs, which, when executed by the at least one processor, enable the at least one processor to implement the minimum stable combustion load prediction and control method for a biomass co-firing boiler as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for predicting and controlling the minimum stable combustion load of a biomass co-firing boiler as described in any one of claims 1 to 7.

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