Intelligent control system for biomass fuel gasification combustion based on adaptive air supply

Abstract

The application provides a biomass fuel gasification combustion intelligent control system based on adaptive air supply, belongs to the technical field of biomass fuel gasification combustion control, and aims at the problems of fixed air supply mode, single parameter sensing, low regulation and control precision and the like of the existing system. The system collects multi-dimensional parameters of fuel characteristics and combustion states through a sensing unit, generates a regulation and control instruction by using an adaptive PID algorithm of a fusion neural network after data processing of a control unit, accurately adjusts air volume, air speed and air supply distribution of an adaptive air supply unit, and forms a closed loop control in cooperation with a gasification combustion unit. The system can dynamically adapt to different biomass fuel characteristics and combustion conditions, improves combustion efficiency and stability, expands the fuel adaptation range, guarantees operation safety, and is suitable for intelligent management and control of various biomass fuel gasification combustion equipment.

Description

Technical Field

[0001] This invention relates to the field of biomass fuel gasification and combustion control technology, specifically to an intelligent control system for biomass fuel gasification and combustion based on adaptive air supply. Background Technology

[0002] As a clean and renewable energy source, the combustion efficiency and stability of biomass fuels during gasification and combustion directly depend on the matching degree between air supply parameters, fuel characteristics, and combustion conditions. Existing biomass gasification and combustion systems mostly employ fixed air supply modes or rely solely on simple temperature feedback for coarse air supply regulation, which has several shortcomings. Firstly, biomass fuels are diverse, with significant differences in moisture content, particle size, and component content. Fixed air supply parameters cannot adapt to the combustion requirements of different fuels, often resulting in incomplete combustion, low thermal efficiency, and potentially generating large amounts of pollutants due to insufficient oxygen supply or causing heat loss due to excessive oxygen supply. Secondly, existing systems have a limited range of parameters for sensing the combustion process, often only monitoring furnace temperature while neglecting key parameters such as furnace pressure, flue gas composition, and fuel characteristics. This leads to an inability to fully grasp changes in combustion conditions, resulting in delayed and inaccurate air supply regulation, which can easily cause furnace slagging and flameout. Furthermore, existing control systems lack differentiated air supply control for different combustion zones, leading to uneven distribution of temperature and airflow fields within the furnace, further affecting combustion performance. To address the aforementioned issues, this application proposes an intelligent control system capable of dynamically adapting air distribution parameters based on fuel characteristics and combustion conditions, thereby improving the efficiency and stability of biomass fuel gasification and combustion. Summary of the Invention

[0003] The present invention aims to solve the problems mentioned in the background art by providing an intelligent control system for biomass fuel gasification and combustion based on adaptive air supply.

[0004] The specific technical solution is as follows: An intelligent control system for biomass fuel gasification and combustion based on adaptive air supply includes a sensing unit, a control unit, an adaptive air supply unit, and a gasification and combustion unit, wherein: The sensing unit is signal-connected to the control unit and is used to collect multi-dimensional operating parameters during the biomass fuel gasification and combustion process and transmit them to the control unit. The control unit is control-connected to the adaptive air supply unit and is used to generate adaptive air supply control commands based on the operating parameters transmitted by the sensing unit. The adaptive air supply unit is connected to the gasification and combustion unit and is used to adjust the air volume, air speed, and air supply mode supplied to the gasification and combustion unit according to the control commands. The gasification and combustion unit is used to carry out the gasification and combustion reaction of biomass fuel, and its operating status parameters are fed back to the control unit in real time through the sensing unit to form a closed-loop control.

[0005] As a preferred embodiment of the present invention, the sensing unit includes a fuel characteristic sensing module and a combustion state sensing module; the fuel characteristic sensing module is disposed at the feed end of the gasification combustion unit and is used to collect the moisture content, particle size and component content of biomass fuel; the combustion state sensing module is disposed inside the gasification combustion unit and the flue gas passage and is used to collect the furnace temperature, furnace pressure, flue gas oxygen content and alkali metal volatilization concentration.

[0006] As a preferred embodiment of the present invention, the control unit has a built-in parameter processing module and an intelligent decision-making module; the parameter processing module is used to filter, normalize and couple the multi-dimensional operating parameters collected by the sensing unit; the intelligent decision-making module adopts an adaptive PID algorithm that integrates neural networks to calculate the optimal air supply parameters based on the parameter processing results and generate corresponding control commands.

[0007] In a preferred embodiment of the present invention, the adaptive air supply unit includes an air supply power module, an air volume regulation module, an air speed regulation module, and an air supply distribution module; the air supply power module is a variable frequency fan used to provide adjustable pressure airflow; the air volume regulation module and the air speed regulation module are connected in series in the air supply duct and are used to precisely control the total air supply volume and airflow speed, respectively; the air supply distribution module is connected to multiple air supply ports of the vaporization combustion unit and is used to distribute the airflow to different combustion zones according to a preset ratio.

[0008] As a preferred embodiment of the present invention, the temperature sensors in the combustion state sensing module are arranged in a distributed manner, with at least three detection points uniformly set along the furnace height direction of the vaporization combustion unit, to collect temperature distribution data of different areas in real time and transmit them to the control unit.

[0009] As a preferred embodiment of the present invention, the air distribution module includes multiple electrically adjustable dampers, each of which corresponds to an air supply port, and each electrically adjustable damper is individually controlled and connected to a control unit to realize independent adjustment of the air volume in each air supply area.

[0010] As a preferred embodiment of the present invention, the control unit also has a built-in fault warning module. The fault warning module determines whether there is a risk of slagging, flameout or excessive corrosion in the vaporization combustion process based on the analysis results output by the parameter processing module. When a risk is detected, a warning signal is output and the corresponding emergency air supply control strategy is triggered.

[0011] As a preferred embodiment of the present invention, it further includes a human-machine interaction unit, which is bidirectionally connected to the control unit and is used to display system operating parameters, set control thresholds, and manually intervene in air supply regulation commands.

[0012] As a preferred embodiment of the present invention, the adaptive air supply unit further includes an air preheating module, which is disposed between the air supply duct and the exhaust duct of the vaporization combustion unit. The air preheating module uses the waste heat of the exhaust to preheat the incoming cold air. The preheated air is then processed by the air volume regulation module and the air speed regulation module before being sent into the vaporization combustion unit.

[0013] As a preferred embodiment of the present invention, the intelligent decision-making module of the control unit can dynamically adjust the matching ratio between the air supply parameters and the fuel feed amount based on the biomass fuel component content collected by the fuel characteristic sensing module, so as to achieve adaptive adaptation of gasification and combustion of different types of biomass fuels.

[0014] The present invention has the following beneficial effects: 1. Improve combustion efficiency and energy utilization: Through comprehensive perception of fuel characteristics and combustion status, combined with an adaptive PID algorithm that integrates neural networks, the air supply parameters are precisely controlled to ensure dynamic matching between air supply and fuel combustion demand. This avoids incomplete combustion due to insufficient oxygen supply or heat loss due to excessive oxygen supply. At the same time, the air preheating module recovers waste heat from the exhaust to heat the air supply, further improving fuel vaporization and combustion efficiency and increasing energy utilization.

[0015] 2. Enhanced combustion stability and adaptability to operating conditions: The closed-loop control system can respond to changes in combustion conditions in real time and dynamically correct air supply control commands; the multi-zone independent air supply distribution design ensures uniform distribution of temperature and airflow fields within the furnace, effectively avoiding faults such as local slagging and flameout; at the same time, it can dynamically adjust the matching ratio of air supply and feed rate according to the characteristics of different biomass fuels, adapting to the gasification and combustion needs of various biomass fuels and expanding the system's applicability.

[0016] 3. Ensure safe system operation: The fault early warning module relies on multi-parameter coupled analysis to identify risks such as slagging, excessive corrosion, and flameout in advance and trigger emergency ventilation strategies to prevent the fault from escalating; comprehensive monitoring of operating parameters also provides support for operators to detect and handle anomalies in a timely manner, reducing potential safety hazards.

[0017] 4. Improved ease of operation and control accuracy: The human-machine interaction unit enables visualization of operating parameters, setting of control thresholds, and manual intervention, simplifying the operation process; the parameter processing module optimizes the collected data and, together with the precise calculation of the intelligent decision-making algorithm, makes the air supply control commands more in line with actual working conditions, greatly improving control accuracy and reducing the frequency of manual intervention. Attached Figure Description

[0018] Figure 1 This is a schematic diagram showing the connection relationship of each unit in the intelligent control system for biomass fuel gasification and combustion based on adaptive air supply provided in an embodiment of the present invention. Figure 2 A flowchart illustrating the use of the intelligent control system for biomass fuel gasification and combustion based on adaptive air supply, provided in an embodiment of the present invention. Figure 3 A graph showing the relationship between time and combustion efficiency; Figure 4 A graph showing the relationship between time and furnace temperature fluctuations; Figure 5 A graph showing the relationship between time and furnace pressure fluctuations; Figure 6 This is a graph showing the relationship between fuel water content and combustion efficiency. Detailed Implementation

[0019] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0020] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual images. They should not be construed as limiting the scope of this application. To better illustrate the embodiments of the present invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual dimensions of the product. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0021] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "inner," and "outer" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present application. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0022] In the description of this invention, unless otherwise explicitly specified and limited, the term "connection" or similar designation indicating a connection between components should be interpreted broadly. For example, it can refer to a fixed connection, a detachable connection, or an integral part; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can refer to the internal communication between two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0023] Example Reference 1- Figure 6 ,in Figure 3The experimental group maintained a high and stable combustion efficiency of 89%, while the control group had a combustion efficiency of only 72% with significant fluctuations. Figure 4 The experimental group's temperature fluctuation of ±25℃ was significantly better than that of the control group's ±80℃. Figure 5 The experimental group's ±0.8 kPa fluctuation range was only 1 / 3 that of the control group; Figure 6 The experimental group maintained high efficiency in the 15%-35% moisture content range, while the control group was only suitable for ≤20% moisture content.

[0024] The intelligent control system for biomass fuel gasification and combustion based on adaptive air supply provided in this embodiment includes a sensing unit, a control unit, an adaptive air supply unit, and a gasification and combustion unit. The sensing unit is signal-connected to the control unit and is used to collect multi-dimensional operating parameters during the biomass fuel gasification and combustion process and transmit them to the control unit. The control unit is control-connected to the adaptive air supply unit and is used to generate adaptive air supply control commands based on the operating parameters transmitted by the sensing unit. The adaptive air supply unit is connected to the gasification and combustion unit and is used to adjust the air volume, air velocity, and air supply mode supplied to the gasification and combustion unit according to the control commands. The gasification and combustion unit is used to carry out the gasification and combustion reaction of biomass fuel, and its operating status parameters are fed back to the control unit in real time through the sensing unit, forming a closed-loop control.

[0025] This solution achieves real-time feedback of operating parameters and adaptive air supply regulation through a closed-loop connection between sensing, control, adaptive air supply and the vaporization combustion unit, ensuring the stability of the vaporization combustion process, avoiding operating condition fluctuations, and ensuring the continuous and efficient combustion reaction.

[0026] Specifically, in this embodiment, the sensing unit includes a fuel characteristic sensing module and a combustion state sensing module. The fuel characteristic sensing module is located at the feed end of the gasification combustion unit and is used to collect the moisture content, particle size, and component content of the biomass fuel. The combustion state sensing module is located inside the gasification combustion unit and in the flue gas passage and is used to collect the furnace temperature, furnace pressure, flue gas oxygen content, and alkali metal volatilization concentration. By subdividing the sensing unit into fuel characteristic and combustion state acquisition modules, the characteristics of the fuel itself and the real-time combustion state are comprehensively captured, providing comprehensive and accurate data support for subsequent air supply control, reducing control deviations caused by missing parameters, and improving the targeting of control.

[0027] Specifically, in this embodiment, the control unit incorporates a parameter processing module and an intelligent decision-making module. The parameter processing module filters, normalizes, and performs coupling analysis on the multi-dimensional operating parameters collected by the sensing unit. The intelligent decision-making module employs an adaptive PID algorithm with a fused neural network to calculate the optimal air supply parameters based on the parameter processing results and generate corresponding control commands. The parameter processing module of the control unit can eliminate interfering data and integrate multi-dimensional parameter correlation information to improve data reliability. The adaptive PID algorithm with a fused neural network can accurately match actual combustion needs to calculate the optimal air supply parameters, making the control commands more aligned with operating conditions and improving control accuracy and response speed.

[0028] The air supply parameter decision equation used in the intelligent decision module is as follows: ; in: Optimal air supply volume (m³) 3 / h); K: System calibration coefficient, determined experimentally (m 3 / h); α, β, γ: Dimensionless adaptive adjustment coefficients, optimized collaboratively by neural networks and PID algorithms; : Average furnace temperature (K); Temperature reference value, taken as the design furnace temperature (K); Fuel moisture content (normalized value, 0-1); apparent density of fuel (kg / m³) 3 ); Fuel reference density (kg / m³) 3 ); Median particle size of fuel particles (mm); Particle size reference value (mm); : These are the set and actual oxygen content (%) in the flue gas, respectively. Furnace pressure (kPa); Pressure reference value (kPa); : The temperature value collected by the i-th temperature sensor (K); Target furnace temperature (K); The weighting coefficient for the i-th temperature region is dynamically adjusted by the neural network.

[0029] The derivation process is as follows: 1. Basic air supply requirements: Based on the theory of fuel reaction rate and oxygen diffusion, the air supply volume is positively correlated with temperature, negatively correlated with moisture content, and negatively correlated with fuel density and particle size. Reference values ​​are introduced for normalization. 2. Oxygen content correction: The air supply is dynamically corrected based on the deviation of the flue gas oxygen content, taking into account the influence of furnace pressure on gas flow resistance. 3. Temperature field uniformity correction term: This term reflects the degree of temperature field non-uniformity by weighted summation of the deviations between the temperature in multiple regions and the target temperature. 4. Coefficient fusion: The three terms are multiplied by adaptive coefficients α, β, and γ, and the weights are adjusted by the neural network according to the real-time operating conditions. Then, they are multiplied by the system calibration coefficient K to convert them into the actual air volume.

[0030] Example: 1. Reference value setting: =1200K, =300 kg / m 3 , =3.0mm, =2.5kPa K=800m 3 / h (obtained from equipment calibration) 2. Input parameters: =1250K, =0.25, =350kg / m 3 , =3.5mm; =12%, =11.5%, =3.0 kPa; T=[1123,1253,1193] =1200K, weight w=[0.3,0.4,0.3]; α=0.85, β=1.2, γ=0.6; 3. Calculation process: First item Second item Third item Compared with the actual controlled air volume (650m³) 3 The coefficients ( / h) are close, and matching can be achieved through coefficient fine-tuning.

[0031] Technical effect 1. Correct dimensions and rigorous logic: The corrected equations are dimensionless and can be converted into actual air volume after calibration coefficients, with no physical contradictions.

[0032] 2. High adaptability: It can be dynamically adjusted through reference values ​​and coefficients to adapt to different equipment and fuels.

[0033] 3. High control precision: It integrates multi-dimensional information such as fuel characteristics, oxygen content, and temperature field, resulting in more precise air supply matching.

[0034] 4. Good engineering applicability: Reference values ​​can be obtained from design parameters, and calibration coefficients are easy to adjust on site.

[0035] The working principle and process are as follows: 1. Parameter normalization: After collecting actual parameters, divide them by the corresponding reference value to convert them into dimensionless ratios.

[0036] 2. Coefficient Decision: The neural network updates α, β, γ and... based on real-time operating conditions. .

[0037] 3. Equation Calculation: Substitute into the equation to calculate. .

[0038] 4. Command Output: The control unit outputs commands according to... Outputs commands for variable frequency fan speed and damper opening.

[0039] 5. Closed-loop iteration: The sensing unit feeds back new parameters, and steps 1 to 4 are repeated to achieve continuous optimization.

[0040] Specifically, in this embodiment, the adaptive air supply unit includes an air supply power module, an air volume regulation module, an air speed regulation module, and an air supply distribution module. The air supply power module is a variable frequency fan, used to provide adjustable pressure airflow. The air volume regulation module and the air speed regulation module are connected in series in the air supply duct, respectively used to precisely control the total air supply volume and airflow speed. The air supply distribution module is connected to multiple air supply ports of the vaporization combustion unit, used to distribute the airflow to different combustion zones according to a preset ratio. The variable frequency fan provides adjustable pressure airflow, which, together with the series-connected air volume and air speed regulation modules, achieves precise control of the total air supply volume and speed. The multi-port air supply distribution allows the airflow to be distributed to different combustion zones as needed, ensuring uniform combustion conditions in each zone and improving fuel combustion completeness.

[0041] Specifically, in this embodiment, the temperature sensors in the combustion state sensing module are arranged in a distributed manner, with at least three detection points evenly set along the furnace height of the vaporization combustion unit. These sensors collect temperature distribution data from different areas in real time and transmit it to the control unit. This distributed arrangement of temperature sensors comprehensively captures the temperature distribution differences at different heights of the furnace, avoiding the biased judgment caused by single-point temperature measurement. This allows the control unit to accurately grasp the furnace temperature field, providing a more precise temperature basis for air supply control and further optimizing combustion uniformity.

[0042] Specifically, in this embodiment, the air distribution module includes multiple electrically adjustable dampers, each corresponding to an air supply port, and each damper is individually connected to the control unit to achieve independent adjustment of the airflow in each air supply zone. The independent control of multiple electrically adjustable dampers allows for precise adjustment of the corresponding airflow based on the different operating conditions of different combustion zones, avoiding airflow interference between zones, achieving personalized matching of airflow in each zone, and further improving the stability and uniformity of the combustion process.

[0043] Specifically, in this embodiment, the control unit also has a built-in fault early warning module. Based on the analysis results output by the parameter processing module, the fault early warning module determines whether there is a risk of slagging, flameout, or excessive corrosion in the vaporization and combustion process. When a risk is detected, it outputs an early warning signal and triggers the corresponding emergency air supply control strategy. By relying on parameter analysis to identify abnormal risks such as slagging and flameout in advance, and by issuing timely warnings and triggering emergency air supply strategies, the fault early warning module can prevent the fault from escalating, ensure the safe operation of the system, and reduce losses caused by downtime.

[0044] Specifically, this embodiment also includes a human-machine interface unit (HMI), which is bidirectionally connected to the control unit. The HMI is used to display system operating parameters, set control thresholds, and issue manual intervention commands for air supply regulation. The HMI enables visualization of operating parameters, setting of control thresholds, and manual intervention, allowing operators to monitor the system status in real time, flexibly adapt to different operating scenarios, and improve the system's ease of operation and applicability.

[0045] Specifically, in this embodiment, the adaptive air supply unit further includes an air preheating module. This module is located between the air supply duct and the exhaust duct of the vaporization combustion unit. It utilizes the waste heat from the exhaust to preheat the incoming cold air. The preheated air is then processed by the airflow regulation module and the wind speed regulation module before being sent to the vaporization combustion unit. The air preheating module recovers waste heat from the exhaust to heat the supply air, increasing the intake air temperature, reducing the heat consumption of fuel vaporization combustion, and improving energy utilization efficiency. Simultaneously, preheating the air promotes rapid and complete combustion of fuel, further improving combustion efficiency.

[0046] Specifically, in this embodiment, the intelligent decision-making module of the control unit can dynamically adjust the matching ratio between the air supply parameters and the fuel feed rate based on the biomass fuel composition content collected by the fuel characteristic sensing module, thereby achieving adaptive adaptation for the gasification and combustion of different types of biomass fuels. By dynamically adjusting the air supply and feed rate matching ratio according to the fuel composition, the intelligent decision-making module can adapt to the characteristic differences of different types of biomass fuels, avoiding incomplete combustion or operating condition fluctuations caused by changes in fuel type, improving the system's adaptability to different fuels, and expanding its applicable scope.

[0047] Working principle: This system constructs a closed-loop adaptive control system through the coordinated operation of sensing units, control units, adaptive air supply units, and vaporization combustion units, achieving precise control of biomass fuel vaporization combustion. The specific working principle is as follows: 1. Parameter sensing stage: The fuel characteristic sensing module in the sensing unit collects the moisture content, particle size and component content of biomass fuel in real time at the feed end, while the combustion state sensing module collects parameters such as furnace temperature, furnace pressure, flue gas oxygen content and alkali metal volatilization concentration in the furnace and flue gas passage. It comprehensively captures fuel characteristics and combustion condition information and transmits all collected multi-dimensional parameters to the control unit in real time.

[0048] 2. Data Processing and Decision-Making Stage: After receiving the parameters, the control unit first filters and normalizes the data through the parameter processing module, and then performs multi-parameter coupling analysis to remove interference information and clarify the correlation between parameters. Subsequently, the intelligent decision-making module, based on the adaptive PID algorithm of the fused neural network, combines the processed parameter data with the preset combustion optimization target to calculate the optimal air supply parameters suitable for the current operating conditions, and generates corresponding air volume, air speed and air supply distribution control commands.

[0049] 3. Adaptive air supply control stage: After receiving the control command from the control unit, the adaptive air supply unit adjusts the air supply pressure through the variable frequency fan, and then the air volume adjustment module and the air speed adjustment module precisely control the total air supply and airflow speed; at the same time, multiple electric regulating dampers of the air supply distribution module act independently according to the command, and distribute the airflow to different combustion zones of the vaporization combustion unit in the optimal ratio, so as to provide suitable oxygen supply conditions for fuel vaporization combustion.

[0050] 4. Closed-loop feedback stage: Under suitable air distribution conditions, the vaporization combustion unit completes the vaporization combustion reaction of biomass fuel. The real-time operating parameters during the combustion process are collected again by the sensing unit and fed back to the control unit. The control unit continuously corrects the control commands based on the feedback data, forming a closed-loop control of "sensing-decision-control-feedback" to ensure that the combustion process is always in the optimal state.

[0051] How to use: 1. Preparations before startup: Check the connection status of each unit of the system to ensure that the sensors of the sensing unit are installed in place, the pipelines and dampers of the adaptive air supply unit are unobstructed, and the signal connection between the control unit and each unit is normal; input the basic control threshold, combustion optimization target parameters and safety warning thresholds corresponding to different biomass fuels through the human-machine interaction unit.

[0052] 2. System Start-up and Feeding: Start the control unit, adaptive air supply unit and gasification combustion unit to feed biomass fuel into the feed inlet of the gasification combustion unit; at this time, the fuel characteristic sensing module immediately collects the fuel characteristic parameters and transmits them to the control unit.

[0053] 3. Adaptive Combustion Control: The control unit generates initial air supply control commands based on initial fuel characteristic parameters. The adaptive air supply unit adjusts the air supply parameters according to the commands and supplies air to the vaporization combustion unit. After the vaporization combustion unit starts combustion, the combustion status sensing module continuously collects parameters such as furnace temperature, pressure, and flue gas composition and feeds them back to the control unit. The control unit processes the feedback data in real time, dynamically corrects the air supply control commands, and continuously optimizes the air volume, air velocity, and air supply distribution through the adaptive air supply unit to ensure stable and efficient combustion.

[0054] 4. Operation monitoring and intervention: Operators can view system operating parameters, combustion status and air supply parameters in real time through the human-machine interface unit; when the system has a fault warning signal, the cause of the warning can be viewed through the human-machine interface unit, and if necessary, manual intervention can be performed to adjust the air supply parameters or suspend feeding. Automatic operation can be resumed after the fault is eliminated.

[0055] 5. Shutdown and Maintenance: After the combustion task is completed, a shutdown command is issued through the human-machine interface unit. First, the feeding is stopped, and the control unit controls the adaptive air supply unit to maintain a small amount of air supply. After the residual fuel in the furnace is completely burned, the adaptive air supply unit and the vaporization combustion unit are shut down. After shutdown, the sensors of the sensing unit are cleaned and calibrated, and the dampers and pipelines of the adaptive air supply unit are checked for unobstructed flow to ensure stable operation in the future.

[0056] In summary, the intelligent control system for biomass fuel gasification and combustion based on adaptive air supply provided in this embodiment has the following advantages: 1. Improve combustion efficiency and energy utilization: Through comprehensive perception of fuel characteristics and combustion status, combined with an adaptive PID algorithm that integrates neural networks, the air supply parameters are precisely controlled to ensure dynamic matching between air supply and fuel combustion demand. This avoids incomplete combustion due to insufficient oxygen supply or heat loss due to excessive oxygen supply. At the same time, the air preheating module recovers waste heat from the exhaust to heat the air supply, further improving fuel vaporization and combustion efficiency and increasing energy utilization.

[0057] 2. Enhanced combustion stability and adaptability to operating conditions: The closed-loop control system can respond to changes in combustion conditions in real time and dynamically correct air supply control commands; the multi-zone independent air supply distribution design ensures uniform distribution of temperature and airflow fields within the furnace, effectively avoiding faults such as local slagging and flameout; at the same time, it can dynamically adjust the matching ratio of air supply and feed rate according to the characteristics of different biomass fuels, adapting to the gasification and combustion needs of various biomass fuels and expanding the system's applicability.

[0058] 3. Ensure safe system operation: The fault early warning module relies on multi-parameter coupled analysis to identify risks such as slagging, excessive corrosion, and flameout in advance and trigger emergency ventilation strategies to prevent the fault from escalating; comprehensive monitoring of operating parameters also provides support for operators to detect and handle anomalies in a timely manner, reducing potential safety hazards.

[0059] 4. Improved ease of operation and control accuracy: The human-machine interaction unit enables visualization of operating parameters, setting of control thresholds, and manual intervention, simplifying the operation process; the parameter processing module optimizes the collected data and, together with the precise calculation of the intelligent decision-making algorithm, makes the air supply control commands more in line with actual working conditions, greatly improving control accuracy and reducing the frequency of manual intervention.

[0060] In addition, this embodiment also provides the following example.

[0061] This example provides an intelligent control system for biomass fuel gasification combustion based on adaptive air supply, specifically applied to a gasification combustion power generation device using a mixture of rice husks and corn stalks as biomass fuel. The specific configuration and connection relationships of each unit in the system are as follows: 1. Sensing Unit: This includes a fuel characteristic sensing module and a combustion state sensing module. The fuel characteristic sensing module consists of a moisture content sensor (model: CS200), a laser particle size analyzer (model: HL-2000), and a near-infrared component analyzer (model: NIRS6500), all installed at the discharge port of the screw feeder in the gasification combustion unit. The signal outputs of these three devices are connected to the control unit via an RS485 bus, respectively used to collect the moisture content (measurement range 0-60%), particle size (measurement range 0.1-10mm), and cellulose, hemicellulose, and lignin content of the rice husk-corn straw mixture. The combustion state sensing module includes 4 P... T100 platinum resistance temperature sensor (uniformly arranged along the height of the furnace, with detection points located at the bottom, middle, top, and outlet of the furnace), diffused silicon pressure sensor (model: MPX5700, installed in the middle of the furnace sidewall), zirconia oxygen analyzer (model: ZO-802, installed at the inlet of the flue gas duct), and alkali metal ion sensor (model: KMS-300, installed in the middle of the flue gas duct). The signal output terminals of all sensors are connected to the control unit through an analog acquisition module (model: ADAM-4017) to realize the acquisition of multi-dimensional combustion state parameters.

[0062] 2. Control Unit: An STM32H743ZIT6 microcontroller is used as the controller, integrating a parameter processing module, an intelligent decision-making module, and a fault early warning module. The parameter processing module uses the microcontroller's integrated filtering algorithm (moving average filtering) to remove impurities from the collected analog parameters, then uses a normalization program to convert parameters of different dimensions to the 0-1 range, and finally establishes a correlation model between fuel characteristics and combustion state parameters through a coupling analysis program. The intelligent decision-making module integrates an adaptive PID algorithm program with a BP neural network (the BP neural network includes an input layer, hidden layers, and an output layer; the input layer has 6 parameter nodes, the hidden layer has 12 nodes, and the output layer has 3 air supply parameter nodes), calculating the optimal air supply parameters based on the parameter processing results. The fault early warning module presets threshold ranges for slagging (furnace temperature > 1200℃ and pressure > 8kPa), flameout (furnace temperature < 600℃ and oxygen content > 18%), and excessive corrosion (alkali metal volatilization concentration > 50ppm), comparing parameter data in real time and triggering early warnings. The signal input terminal of the control unit is connected to the analog signal acquisition module of the sensing unit, and the signal output terminal is connected to the adaptive air supply unit through the relay module.

[0063] 3. Adaptive air supply unit: including variable frequency fan (model: 4-72-5A, rated power 7.5kW, speed range 500-2900r / min) and electric air volume regulating valve (model: ZDLP-16C, adjustment range 0-1000m). 3The system includes an electric wind speed regulating valve (model: ZDRL-16C, regulating accuracy ±0.5m / s) and an air distribution module (containing 4 electric regulating dampers, model: D941W-16P, corresponding to the air supply ports at the bottom, middle, top, and vaporization chambers of the furnace, respectively). The control terminal of the variable frequency fan is connected to the relay module of the control unit. The air outlet is connected in series with the electric air volume regulating valve and the electric wind speed regulating valve through the air duct, and then connected to the main air inlet of the air distribution module. The control terminals of the 4 electric regulating dampers are each connected to the control unit individually to achieve independent regulation.

[0064] 4. Gasification Combustion Unit: A fixed-bed gasification combustion furnace (furnace volume 5m³) is adopted. 3 (Rated evaporation capacity 2t / h) The feed inlet is connected to the screw feeder, and the feeder outlet corresponds to the detection position of the fuel characteristic sensing module; sensor mounting holes are reserved on the furnace sidewall and flue gas passage for fixing various sensors of the combustion state sensing module; air supply ports are set at the bottom, middle, upper and vaporization chambers of the furnace, which are connected to the four electric regulating dampers of the adaptive air supply unit; alkali metal ion sensor and zirconia oxygen analyzer are installed in sequence in the flue gas passage, and a flue gas baffle is set at the tail to adjust the flue gas resistance.

[0065] 5. Human-Machine Interaction Unit: It adopts a 7-inch touch screen (model: TPC7062Ki), which is bidirectionally connected to the control unit via RS232 bus. It is used to display fuel characteristic parameters, combustion status parameters, air supply parameters and fault warning information. It supports manual input of control thresholds for different fuels and start and stop commands.

[0066] Example of how it works: 1. Feeding and Parameter Acquisition: After the system is started, the screw feeder delivers the rice husk-corn straw mixture into the gasification combustion furnace. The moisture content sensor, laser particle size analyzer, and near-infrared component analyzer of the fuel characteristic sensing module simultaneously collect the fuel moisture content (e.g., 25%), particle size (e.g., 2-5mm), and component content (cellulose 45%, hemicellulose 25%, lignin 20%), and transmit the data to the control unit via RS485 bus. At the same time, the four PT100 temperature sensors of the combustion status sensing module collect the temperature of each area of ​​the furnace (bottom 850℃, middle 980℃, top 920℃, outlet 880℃), the pressure sensor collects the furnace pressure (3kPa), the oxygen analyzer collects the oxygen content of the flue gas (12%), and the alkali metal ion sensor collects the concentration (30ppm). All data are converted by the analog acquisition module and then transmitted to the control unit.

[0067] 2. Data Processing and Decision Making: The parameter processing module of the control unit first removes fluctuations in temperature and pressure data through moving average filtering, then normalizes all parameters, and uses a coupled analysis program to derive the correlation between "increased moisture content → decreased combustion temperature → need to increase air supply volume". The BP neural network of the intelligent decision-making module trains and learns from the processed data, and calculates the optimal air supply parameters using an adaptive PID algorithm: variable frequency fan speed 1800 r / min, air volume 650 m³ / min. 3 The system has a wind speed of 3.2 m / s and the air volume distribution ratio of the four air supply ports is 40% at the bottom, 30% in the middle, 20% at the top, and 10% in the vaporization chamber. Then, corresponding control commands are generated.

[0068] 3. Adaptive Air Supply and Combustion Control: The control unit transmits control commands to the adaptive air supply unit via a relay module. The variable frequency fan starts at 1800 r / min, and the electric air volume regulating valve is adjusted to 650 m³ / min. 3 The electric wind speed regulating valve is set to 3.2 m / s; simultaneously, the four electric regulating dampers are opened at 40%, 30%, 20%, and 10% openings respectively, precisely distributing the airflow to each area of ​​the furnace. After the airflow enters the gasification combustion furnace, it provides suitable oxygen for the gasification and combustion of the rice husk-corn straw mixture, and the fuel gradually completes the drying, pyrolysis, gasification, and combustion processes.

[0069] 4. Closed-loop feedback and fault early warning: During combustion, the sensing unit continuously collects various parameters and feeds them back to the control unit. If the temperature in the middle of the furnace is detected to rise to 1050℃ (close to the slagging threshold), the control unit immediately corrects the control command, increasing the air volume ratio of the middle air supply port to 35% to reduce the local temperature. If the concentration of alkali metal volatilization is detected to rise to 55ppm (exceeding the corrosion threshold), the fault early warning module immediately triggers an audible and visual warning and instructs the air volume ratio of the vaporization chamber air supply port to increase by 5% to dilute the alkali metal concentration and ensure a stable and safe combustion process.

[0070] Example experimental data: To verify the technical effectiveness of this example system, an existing fixed-air-supply biomass combustion system was selected as the control group, and this example system was selected as the experimental group. A comparative experiment was conducted under the conditions of the same gasification combustion furnace, the same rice husk-corn straw mixed fuel (ratio 1:1), and the same feed rate (500 kg / h). The experiment lasted for 10 hours, and the key performance data were recorded as follows: 1. Combustion efficiency: The average combustion efficiency of the control group was 72%, while the average combustion efficiency of the experimental group was 89%, an increase of 17 percentage points; 2. Stability of operating conditions: The furnace temperature fluctuation range of the control group was ±80℃, and the furnace pressure fluctuation range was ±2.5kPa; the furnace temperature fluctuation range of the experimental group was ±25℃, and the furnace pressure fluctuation range was ±0.8kPa. 3. Failure rate: The control group experienced two slagging warnings and one flameout, with a failure rate of 30%; the experimental group only experienced one alkali metal concentration warning (which was eliminated through adjustment), with no slagging or flameout, and a failure rate of 10%. 4. Fuel compatibility: The control group can only be used with fuels with a moisture content of ≤20%, and combustion is unstable if the moisture content exceeds this. The experimental group can be stably used with rice husk-corn straw mixed fuels with a moisture content of 15%-35% and a particle size of 0.5-8mm, which has a wider range of compatibility.

[0071] Example technical effect: 1. Improved combustion efficiency: This example achieves precise matching between air supply parameters and combustion requirements by collecting multi-dimensional parameters of fuel characteristics and combustion state, combined with an adaptive PID algorithm that integrates a BP neural network. This avoids the problem of insufficient or excessive oxygen supply in a fixed air supply mode. Experimental data shows that the combustion efficiency is 17 percentage points higher than the existing system, and the fuel energy is fully utilized.

[0072] 2. Enhanced operational stability: This example adopts a distributed temperature sensor layout and a multi-zone independent air supply distribution design, combined with a closed-loop feedback control mechanism, which can correct the air supply parameters of each zone in real time, greatly reducing the range of furnace temperature and pressure fluctuations, avoiding excessively high local temperatures or sudden pressure changes. In the experiment, the temperature fluctuation was only 31.25% of the control group, and the pressure fluctuation was only 32% of the control group, significantly improving the stability of combustion conditions.

[0073] 3. Reduced failure rate: This example is equipped with a fault early warning module, which can identify risks such as slagging, corrosion, and flameout in advance and trigger targeted control strategies. Abnormal operating conditions can be quickly alleviated without manual intervention. In the experiment, the failure rate was reduced by 66.7% compared with the control group, ensuring the long-term stable operation of the system and reducing downtime losses.

[0074] 4. Expanding the range of fuel compatibility: This example can dynamically adapt to biomass fuels with different moisture contents and particle sizes by accurately collecting fuel characteristic parameters and adaptive air supply control. This breaks through the strict limitations of existing systems on fuel characteristics. In the experiment, it can stably adapt to fuels with a moisture content of 15%-35%, which greatly improves the applicability of the system and reduces fuel pretreatment costs.

[0075] 5. Enhanced ease of operation: This example is equipped with a human-computer interaction unit, enabling parameter visualization and manual intervention. Operators can monitor the system's operating status in real time and complete operations such as starting and stopping and setting thresholds without professional skills, reducing the difficulty of operation.

[0076] The above are merely preferred embodiments of the present invention and are not intended to limit the implementation methods and protection scope of the present invention. Those skilled in the art should recognize that any equivalent substitutions and obvious changes made based on the description and illustrations of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart control system for biomass fuel gasification and combustion based on adaptive air supply, characterized in that, It includes a sensing unit, a control unit, an adaptive air supply unit, and a vaporization combustion unit, wherein: The sensing unit is signal-connected to the control unit and is used to collect multi-dimensional operating parameters during the biomass fuel gasification and combustion process and transmit them to the control unit. The control unit is control-connected to the adaptive air supply unit and is used to generate adaptive air supply control commands based on the operating parameters transmitted by the sensing unit. The adaptive air supply unit is connected to the gasification and combustion unit and is used to adjust the air volume, air speed, and air supply mode supplied to the gasification and combustion unit according to the control commands. The gasification and combustion unit is used to carry out the gasification and combustion reaction of biomass fuel, and its operating status parameters are fed back to the control unit in real time through the sensing unit to form a closed-loop control.

2. The intelligent control system for biomass fuel gasification and combustion based on adaptive air supply according to claim 1, characterized in that, The sensing unit includes a fuel characteristic sensing module and a combustion state sensing module; the fuel characteristic sensing module is located at the feed end of the gasification combustion unit and is used to collect the moisture content, particle size and component content of biomass fuel; the combustion state sensing module is located inside the gasification combustion unit and in the flue gas passage and is used to collect the furnace temperature, furnace pressure, flue gas oxygen content and alkali metal volatilization concentration.

3. The intelligent control system for biomass fuel gasification and combustion based on adaptive air supply according to claim 1, characterized in that, The control unit has a built-in parameter processing module and an intelligent decision-making module. The parameter processing module is used to filter, normalize, and perform coupling analysis on the multi-dimensional operating parameters collected by the sensing unit. The intelligent decision-making module adopts an adaptive PID algorithm that integrates neural networks to calculate the optimal air supply parameters based on the parameter processing results and generate corresponding control commands.

4. The intelligent control system for biomass fuel gasification and combustion based on adaptive air supply according to claim 1, characterized in that, The adaptive air supply unit includes an air supply power module, an air volume regulation module, an air speed regulation module, and an air supply distribution module. The air supply power module is a variable frequency fan used to provide adjustable pressure airflow. The air volume regulation module and the air speed regulation module are connected in series in the air supply duct and are used to precisely control the total air supply volume and airflow speed, respectively. The air supply distribution module is connected to multiple air supply ports of the vaporization combustion unit and is used to distribute the airflow to different combustion zones according to a preset ratio.

5. The intelligent control system for biomass fuel gasification and combustion based on adaptive air supply according to claim 2, characterized in that, The temperature sensors in the combustion state sensing module are arranged in a distributed manner, with at least three detection points evenly set along the furnace height direction of the vaporization combustion unit to collect temperature distribution data of different areas in real time and transmit them to the control unit.

6. The intelligent control system for biomass fuel gasification and combustion based on adaptive air supply according to claim 4, characterized in that, The air distribution module includes multiple electrically adjustable dampers, each corresponding to an air supply port, and each electrically adjustable damper is individually connected to a control unit to achieve independent adjustment of the air volume in each air supply area.

7. The intelligent control system for biomass fuel gasification and combustion based on adaptive air supply according to claim 3, characterized in that, The control unit also has a built-in fault warning module. Based on the analysis results output by the parameter processing module, the fault warning module determines whether there is a risk of slagging, flameout or excessive corrosion in the vaporization combustion process. When a risk is detected, it outputs a warning signal and triggers the corresponding emergency air supply control strategy.

8. The intelligent control system for biomass fuel gasification and combustion based on adaptive air supply according to claim 1, characterized in that, It also includes a human-machine interaction unit, which is bidirectionally connected to the control unit and is used to display system operating parameters, set control thresholds, and manually intervene in air supply regulation commands.

9. The intelligent control system for biomass fuel gasification and combustion based on adaptive air supply according to claim 4, characterized in that, The adaptive air supply unit also includes an air preheating module, which is located between the air supply duct and the exhaust duct of the vaporization combustion unit. The air preheating module uses the waste heat of the exhaust to preheat the incoming cold air. The preheated air is then processed by the air volume regulation module and the air speed regulation module before being sent to the vaporization combustion unit.

10. The intelligent control system for biomass fuel gasification and combustion based on adaptive air supply according to claim 2, characterized in that, The intelligent decision-making module of the control unit can dynamically adjust the matching ratio between the air supply parameters and the fuel feed amount based on the biomass fuel composition content collected by the fuel characteristic sensing module, so as to achieve adaptive adaptation of gasification and combustion of different types of biomass fuels.

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