Automatic bale biomass boiler fuel gasification coupling control method

Abstract

The present application belongs to the field of boiler control, and particularly relates to an automatic baling biomass boiler fuel gasification coupling control method. The method comprises: extracting raw material activity index and dynamically adjusting baling machine parameters to produce customized fuel; collecting multi-modal data of a gasification furnace and a boiler in real time and constructing a data vector; using a gasification combustion digital twin to calculate the nonlinear coupling relationship between fuel properties and the data vector to generate an optimal operating parameter combination; and using a model predictive control algorithm to issue coordinated control instructions to the fuel forming, gasification, distribution and heat exchange whole chain. The present application realizes source compensation for raw material fluctuations and accurate mapping of dynamic characteristics through whole-process closed-loop control, improves the system comprehensive energy efficiency and load tracking rate, optimizes the safety and environmental performance, and improves the intelligent level of the biomass boiler system.

Description

Technical Field

[0001] This invention belongs to the field of boiler control, specifically relating to an automatic fuel gasification coupling control method for bundled biomass boilers. Background Technology

[0002] With the rapid development of biomass energy conversion technology, the coupled control of biomass fuel gasification and boilers has become an important direction for achieving cleaner and more efficient industrial energy supply. Biomass energy supply systems typically encompass multiple physicochemical reaction stages, including fuel pretreatment, pyrolysis gasification, and boiler heat exchange, aiming to meet the continuous heating needs of industrial production through the orderly extraction of energy from biomass resources. Against the backdrop of diversified utilization and high energy efficiency requirements, the system places higher demands on fuel utilization, process stability, and the accuracy of cross-process energy conversion.

[0003] The automated bale forming and gasification coupling control of biomass fuel is the core link to ensure the overall performance of the system. By automating the compression and bale processing of biomass raw materials and dynamically matching it with the downstream feeding, gasification and combustion processes, the system can achieve precise conversion of biomass energy from solid to gas and then to thermal energy. This involves complex chemical reaction kinetics, thermal detection technology and cross-domain nonlinear multivariate control logic.

[0004] Existing technologies typically treat fuel pretreatment, gasification reaction, and boiler combustion as independent processes, often employing single-loop feedback control, which struggles to address random disturbances caused by fluctuations in biomass feedstock moisture content, calorific value, and bale density. Traditional control strategies exhibit significant feedback delays and response lags when handling sudden boiler load changes, leading to a mismatch between gas generation rate and load demand, potentially causing safety hazards such as gasifier deflagration, backfire, or sudden boiler pressure drops. Furthermore, there is a strong physical coupling between parameters such as temperature distribution and pressure state in the gasification layer and air-fuel ratio in the combustion process. Conventional control logic cannot achieve deep decoupling and synergistic optimization of multi-dimensional variables, resulting in limited overall system thermal efficiency and excessive reliance on manual intervention. Summary of the Invention

[0005] The purpose of this invention is to provide an automatic fuel gasification coupling control method for biomass boilers, which can solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is: an automatic bundling biomass boiler fuel gasification coupling control method, comprising the following specific steps: Step 1: Real-time detection and feature extraction of raw materials. The physical property parameters of the biomass raw materials to be processed are acquired in real time by the sensor array deployed at the front end of the automatic bundling machine. The physical property parameters are input into the preset molding reaction activity prediction model to calculate the activity index reflecting the chemical activity of biomass fuel. Step 2: Active forming and dynamic bundling control. Based on the activity index, the control unit sends execution instructions to the automatic bundling machine. By dynamically adjusting the compression ratio and mechanical geometric parameters of the bundling machine, customized biomass bundling fuel that matches the activity index is produced. Step 3: Multimodal production operation status reconstruction. Using a multi-sensor network deployed on the gasifier body and boiler body, multimodal operation data including temperature distribution, pressure status, flue gas composition and flame morphology are collected in real time to construct a real-time data vector reflecting the current production load and reaction process. Step 4: Digital twin calculation and coupling optimization. The physical properties of the customized biomass bundled fuel and the real-time data vector are input into the preset gasification and combustion digital twin. In the virtual space, the nonlinear coupling relationship between gasification efficiency, gas calorific value, burner stability and boiler heat absorption efficiency is calculated in real time to generate the optimal combination of operating parameters. Step 5: Multi-dimensional collaborative command execution and closed-loop regulation. Based on the optimal combination of operating parameters, collaborative control commands are simultaneously issued to the fuel forming stage, gasification reaction stage, gas transmission and distribution stage, and combustion heat exchange stage through model predictive control algorithm to realize dynamic characteristic mapping and closed-loop control of the entire link.

[0007] Preferably, in step 1, the sensing array includes a near-infrared spectral sensor and a microwave moisture meter. The process of acquiring the physical properties of the biomass raw material in real time is as follows: The near-infrared spectral sensor emits a beam of light of a specific wavelength onto the surface of the crushed biomass raw material. By analyzing the intensity of the characteristic peaks in the diffuse reflectance spectrum, the ash content and volatile matter content of the biomass raw material are determined. Simultaneously, the microwave moisture meter emits a microwave signal that penetrates the material layer. Based on the degree of microwave energy attenuation and phase shift changes, the moisture content of the biomass raw material is calculated.

[0008] Preferably, in step 1, the molding reaction activity prediction model is constructed based on massive historical experimental data and is used to describe the logical correspondence between the moisture content, ash content, volatile matter content of biomass raw materials and the subsequent gasification reaction rate. The activity index is a scalar value; when the moisture content or ash content increases, the activity index shows a decreasing trend; when the volatile matter increases, the activity index shows an increasing trend.

[0009] Preferably, in step 2, the specific method for dynamically adjusting the compression ratio and mechanical geometric parameters of the bale machine is as follows: When the activity index is lower than a preset first activity threshold, the control unit determines that the chemical activity of the raw material is insufficient, and then increases the pre-pressure of the hydraulic system of the bale machine to increase the compression ratio of the forming mold, increase the density of the biomass bales, and prolong their pyrolysis residence time in the gasifier. When the activity index is higher than a preset second activity threshold, the control unit determines that the chemical activity of the raw material is too high, and then reduces the pre-pressure of the hydraulic system to produce low-density bales with higher porosity, so as to promote the penetration of the gasifying agent into the fuel bed and accelerate the reaction rate.

[0010] Preferably, in step 2, the mechanical geometric parameters include the length, diameter, and weight of the bale. The control unit, based on a preset load prediction, adjusts the operating cycle of the cutting mechanism to change the geometric dimensions of the customized biomass bale fuel. Under conditions of anticipated load increase, the volume of the bale is reduced to increase the total specific surface area of ​​the fuel layer; under conditions of anticipated load stability or decrease, the volume of the bale is increased to maintain a stable fuel layer height.

[0011] Preferably, in step 3, the multi-sensor network includes a thermocouple array, a pressure transmitter, an acoustic thermometer, and a flue gas analyzer. The real-time temperature distribution acquisition process is as follows: by using a thermocouple array arranged at different height levels on the inner wall of the gasifier, the temperature gradients of the drying layer, pyrolysis layer, oxidation layer, and reduction layer are obtained. Simultaneously, the acoustic thermometer emits acoustic signals inside the boiler furnace, and by calculating the propagation speed of sound waves in the high-temperature medium, a three-dimensional temperature field distribution map inside the furnace is reconstructed.

[0012] Preferably, in step 3, the flue gas component monitoring includes real-time online analysis of the concentrations of oxygen, carbon monoxide, carbon dioxide, and nitrogen oxides. The real-time data vector also includes the pressure difference between the gasifier outlet and the boiler inlet, the flow rate of the main gas pipe, and the pressure and temperature parameters of the boiler feedwater system.

[0013] Preferably, in step 4, the gasification combustion digital twin is a digital simulation system integrating fluid dynamics, thermochemical reaction kinetics, and heat conduction models. After receiving the physical properties and real-time data vectors, the digital twin calculates the proportion of gas components and lower heating value generated in the gasifier under the current operating conditions by numerically iteratively solving the mass conservation equation, momentum conservation equation, and energy conservation equation.

[0014] Preferably, in step 4, the process of generating the optimal combination of operating parameters involves multi-objective optimization calculations. These multi-objectives include maximizing gasification efficiency, minimizing tar generation, maintaining boiler pressure fluctuations within a preset range, and ensuring that nitrogen oxide emission concentrations are below a preset environmental threshold. The digital twin uses a genetic algorithm or particle swarm optimization algorithm to find an equilibrium point within the parameter search space that satisfies these multi-objectives.

[0015] Preferably, in step 5, the coordinated control commands include commands for the feeding speed of the gasifier, primary air volume, secondary air volume, and combustion air ratio of the burner, as well as commands for the opening degree of the pressure regulating valve on the gas transmission and distribution pipeline.

[0016] Preferably, in step 5, the model predictive control algorithm has a predictive function. When it is predicted that the boiler load will increase significantly in a predetermined period of time in the future, the controller will execute the following logic in advance: First, instruct the automatic bundling machine to produce a batch of low-density, high-porosity bundled fuel and increase the speed of the feed screw; second, increase the primary air volume in advance to preheat the material layer; and finally, increase the energy reserve in the gas pipeline by adjusting the back pressure valve of the buffer tank.

[0017] Preferably, in step 5, the dynamic matching of gas production and consumption rhythm is achieved by adjusting the pressure of the quick-shut-off valve and buffer tank on the gas main pipe between the gasifier outlet and the burner. When the boiler load drops sharply, the controller reduces the frequency of the primary air fan in the gasifier to suppress the gasification reaction intensity and opens the bypass circulation valve to temporarily store or safely dispose of excess gas, preventing backfire.

[0018] Preferably, the automatic bundling biomass boiler fuel gasification coupling control method further includes a safety self-diagnosis step. The system monitors the gasification furnace pressure in real time, and when the pressure exceeds a preset safety upper limit threshold, it immediately triggers an emergency shutdown procedure, stops all fuel supply, and activates the nitrogen purging system to ensure the inherent safety of the equipment.

[0019] Preferably, the automatic biomass boiler fuel gasification coupling control method continuously compares the actual collected operating results with the calculation results of the digital twin during operation. If the deviation exceeds the preset allowable range, the system will automatically activate the online learning mechanism to correct the empirical correlations within the digital twin using current production data, thereby achieving self-evolution of control accuracy.

[0020] Preferably, the moisture content of the customized biomass bale fuel is controlled within a preset moisture range. Through the linkage between the front-end drying pretreatment equipment and the automatic bale machine, it is ensured that the fuel entering the gasifier has good calorific value consistency.

[0021] Preferably, the combustion air ratio command is finely adjusted based on the residual oxygen content fed back by the flue gas analyzer. The controller adjusts the inverter output of the combustion air fan to maintain the excess air coefficient in the furnace at a preset value, thereby suppressing the formation of thermal nitrogen oxides while ensuring complete combustion.

[0022] Preferably, the gasification efficiency is calculated based on the ratio of the total energy of the fuel entering the gasifier to the chemical energy of the produced gas. This invention optimizes the proportion of effective components such as hydrogen and carbon monoxide in the gas by precisely controlling the forming density, thereby reducing the condensation loss of tar in the cold gas.

[0023] Preferably, the optimization of the boiler's heat absorption efficiency is achieved by dynamically adjusting the center position of the combustion flame. The digital twin, based on the temperature field reconstructed from acoustic wave temperature measurement, instructs the actuator to adjust the burner's injection angle, making the heat radiation distribution on the boiler's heating surface more uniform and preventing the risk of water-cooled wall tube rupture due to localized overheating.

[0024] Preferably, the issuance cycle of the coordinated control command is on the order of milliseconds. The model predictive control algorithm re-solves the optimization problem within each control step and only executes the control output at the current moment. This rolling optimization method can overcome the large inertia and large hysteresis characteristics in the biomass combustion process.

[0025] Preferably, the automatic bundling biomass boiler fuel gasification coupling control method further includes monitoring the mechanical wear condition of the automatic bundling machine. By analyzing the fluctuation pattern of hydraulic pump current during the forming process, the mold replacement cycle is predicted, and this maintenance parameter is integrated into the overall production scheduling plan of the system.

[0026] Preferably, the calculation process of the activity index also takes into account the type of biomass raw material. The system has a built-in classification database of various biomass raw materials, including sawdust, rice husks, corn stalks, and cotton stalks. After the user selects the type of raw material in the operation interface, the model will automatically call the corresponding weighting coefficients to correct the activity index.

[0027] Preferably, the pressure regulation strategy in the gas transmission and distribution process employs a composite logic of feedforward and feedback. The feedforward signal originates from the command changes in boiler load, while the feedback signal originates from real-time pressure sampling of the gas main. Through the superposition of these two signals, the burner inlet pressure remains stable even during drastic load fluctuations.

[0028] Preferably, the automatic bundling biomass boiler fuel gasification coupling control method integrates an Internet of Things (IoT) communication module to achieve remote monitoring and diagnostic functions. All production data, alarm records, and control commands are transmitted to a cloud server via an encrypted protocol, and big data analytics are used to perform horizontal comparisons and energy efficiency benchmarking of the operating efficiency of multiple biomass boilers.

[0029] Preferably, the feeding speed command of the gasifier is inversely proportional to the weight of each individual customized biomass bale fuel. The controller accurately calculates the total mass of biomass fed into the furnace per unit time based on feedback from the weighing sensors on the conveyor belt, achieving precise quantitative control of the heat load entering the furnace.

[0030] Preferably, the control of the combustion heat exchange stage also includes adjusting the boiler blowdown rate. Based on the conductivity monitoring results of the circulating water, the system automatically adjusts the opening of the blowdown valve to ensure the boiler water quality meets standards while reducing heat loss due to blowdown.

[0031] Preferably, the model predictive control algorithm employs a combination of hard and soft constraints when handling multivariable constraints. Hard constraints ensure that the system operation does not exceed physical safety limits, while soft constraints provide the optimization algorithm with a larger optimization space, enabling it to achieve better control performance even under complex disturbances.

[0032] Compared with the prior art, the present invention has the following beneficial effects: 1. By introducing an activity index and dynamic molding control, this invention compensates for fluctuations in the physical properties of biomass raw materials. Customized molded fuel allows for more complete pyrolysis reactions within the gasifier, significantly suppressing tar production and remarkably improving gasification efficiency. Combined with precise calculations and collaborative optimization using a digital twin, the generation and consumption of fuel gas achieve a dynamic balance, and both boiler combustion efficiency and overall thermal efficiency overcome the technical bottlenecks of traditional biomass energy supply systems.

[0033] 2. This invention overcomes the lag inherent in traditional single-loop feedback control by employing a model predictive control algorithm to proactively anticipate boiler load commands. Load tracking speed is significantly improved, enabling minute-level precise responses to changes in user heating demand. In the face of sudden load changes, the system proactively adjusts fuel molding properties and pipeline energy storage status, avoiding the risk of sudden pressure drops or overpressure, thus enhancing the system's robustness under complex and variable operating conditions.

[0034] 3. By deeply decoupling the entire gasification and combustion process through digital twin technology, this invention eliminates the risks of backfire and deflagration caused by gas production-dissipation imbalance, thus improving the inherent safety level of the system. Simultaneously, based on real-time flue gas analysis and precise air distribution control, the initial emission concentration of nitrogen oxides is reduced to below the preset environmental threshold, achieving clean combustion and yielding both social and environmental benefits.

[0035] 4. This invention constructs a closed-loop control system covering the entire process from front-end raw material pretreatment to back-end energy output, transforming the traditional operation mode, which heavily relies on manual experience, into a data-driven and model-guided autonomous control mode. The system can adapt to various fuel fluctuations and load disturbances, realizing intelligent operation of the biomass boiler system and reducing the labor intensity of operators. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the overall technical solution architecture of the present invention; Figure 2 This is a schematic diagram of the core principle framework of digital twin calculation and nonlinear coupling optimization of gasification combustion in this invention; Figure 3 This is a flowchart outlining the main stages of the biomass fuel molding process in this invention, based on the extraction of activity index from raw material physical property parameters and the customized control of biomass fuel molding accordingly. Figure 4 This is a flowchart of the main stages of the reconstruction of the multimodal production and operation status of the gasifier and boiler body and the construction of real-time data vectors in this invention. Figure 5 This is a schematic diagram of the multi-level interaction and data flow between the fuel forming stage, gasification reaction stage, gas transmission and distribution stage and combustion heat exchange stage in this invention; Figure 6 This is a flowchart illustrating the multi-dimensional collaborative instruction issuance and end-to-end closed-loop adjustment logic based on the model predictive control algorithm in this invention. Detailed Implementation

[0037] Example 1: Please refer to Figures 1 to 5 To make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to specific embodiments.

[0038] The core of the automated biomass boiler fuel gasification coupling control method lies in constructing a full-link, multi-dimensional dynamic feature mapping and closed-loop regulation system. This method achieves precise control of the production process by intervening in the entire process of biomass feedstock, from physical shaping to thermochemical conversion and finally to energy output.

[0039] In the above method, step 1, namely real-time detection and feature extraction of raw materials, is implemented as follows: At the feeding hopper inlet of the automatic bale machine, an industrial-grade sensor array scans the material flow in real time. This sensor array is integrated above a closed detection conveyor belt, with the near-infrared spectral sensor employing a non-contact diffuse reflectance measurement principle. During detection, the near-infrared spectral sensor emits a specific wavelength beam covering 900 nm to 2500 nm. This beam illuminates the surface of the crushed biomass raw material, where the organic molecules' chemical bonds, such as carbon-hydrogen, oxygen-hydrogen, and nitrogen-hydrogen bonds, resonate and absorb energy at specific frequencies. The spectrometer inside the sensor captures the reflected spectral data. By analyzing the absorbance intensity of each characteristic peak in the diffuse reflectance spectrum, pre-set physical property analysis software calculates the percentage content of cellulose, hemicellulose, lignin, and ash in the biomass raw material.

[0040] Meanwhile, a microwave moisture analyzer deployed at the same workstation emits penetrating microwave signals, with its operating frequency set at 2.45 GHz or higher. The microwave signal penetrates a material layer of a certain thickness. Due to the high-frequency oscillations of the polar molecular structure of water molecules in the biomass within the alternating electric field, the microwave energy attenuates and undergoes a phase shift. The control unit receives the signal from the microwave moisture analyzer and, based on the linear mapping relationship between energy attenuation and phase difference, calculates the absolute moisture content of the biomass raw material to be processed in real time.

[0041] The extracted physical properties, including moisture content, ash content, volatile matter content, and calorific value data matched according to a species database, are transmitted in real time to the molding reactivity prediction model. This model is based on a deep learning network with a multilayer perceptron architecture, integrating pyrolysis kinetic experimental data spanning over five years from various biomass sources, including sawdust, rice husks, corn stalks, and cotton stalks, under different temperature and pressure conditions. After normalizing the input physical properties, the model performs a weighted summation operation to ultimately output a scalar value reflecting the fuel's chemical activity, namely the activity index.

[0042] The calculation logic for the activity index is set as follows: when the moisture content or ash content parameter increases, the penalty coefficient within the model causes the activity index to decrease, indicating that the fuel requires more preheating energy after entering the gasifier and the reaction rate is relatively slow. When the volatile matter parameter increases, the gain coefficient within the model causes the activity index to increase, indicating that the fuel has extremely high pyrolysis activity. Furthermore, the system's built-in raw material database automatically applies specific correction coefficients to perform secondary compensation on the activity index based on the material category selected by the user in the operating interface, such as selecting corn stalks or sawdust, to ensure that the prediction results conform to the physical characteristics of different fiber structures.

[0043] In the above method, step 2, namely active forming and dynamic bundling control, involves the control unit generating and issuing execution commands to the automatic bundling machine in real time based on the activity index output in step 1. These commands directly affect the hydraulic control system and mechanical cutting mechanism of the bundling machine. When the activity index falls below a preset first activity threshold, the system determines that the current raw material is a difficult-to-react fuel.

[0044] The control unit sends a pressurization command to the hydraulic pump station, increasing the pre-pressure of the bale forming chamber from the conventional 15 MPa to 20 MPa or even higher. By increasing the compression ratio and reducing the gaps between the raw material fibers, highly dense biomass bale fuel is produced. In subsequent gasification furnace operation, this high-density bale, due to its longer heat conduction path from the surface to the core and the increased mass of reduction reactants per unit volume, effectively prolongs the residence time of the fuel in the pyrolysis and reduction layers, compensating for insufficient chemical reactivity with increased physical density.

[0045] When the activity index exceeds the preset second activity threshold, the system determines that the raw material activity is extremely high. If high-density molding is maintained, it is easy for localized violent reactions to occur in the gasifier, leading to coking. At this time, the control unit instructs the hydraulic system to reduce the pre-pressure, thereby reducing the compression ratio of the molding die and producing low-density bales with higher internal porosity. This structure facilitates the penetration of gasifying agents, such as air or oxygen-enriched gas, into the fuel bed, accelerating the overall reaction rate by increasing the solid-liquid contact area.

[0046] Regarding the adjustment of mechanical geometric parameters, the control unit makes proactive adjustments based on the predicted trend of the downstream boiler load. Mechanical geometric parameters include the length and diameter of the bundles, and the weight of each individual unit. The operating cycle of the cutting mechanism is controlled by a servo motor-driven crank-connecting rod or a hydraulic shearing blade. Under conditions where the expected load increase is anticipated, the control unit instructs the cutting mechanism to shorten its operating cycle and reduce the length of the individual bundles, resulting in a greater number of individual units and a larger total specific surface area for the same mass of fuel, which is beneficial for improving gasification production intensity. Under conditions where the expected load is stable or decreasing, the volume of the individual bundles is increased to maintain a stable bed height and resistance characteristics within the gasifier.

[0047] In the above method, step 3, namely the multimodal production operation state reconstruction, involves the system utilizing a multi-sensor network deployed on the gasifier and boiler bodies for comprehensive data acquisition. Inside the gasifier, a thermocouple array of four or more layers is arranged at equal intervals along the height direction to monitor the temperature gradients of the drying layer, pyrolysis layer, oxidation layer, and reduction layer in real time. Each layer of thermocouples adopts an armored K-type or S-type structure, capable of withstanding high temperatures and airflow erosion.

[0048] An acoustic thermography system is deployed in the boiler furnace area. This system consists of multiple acoustic wave emitters and receiving sensors. The propagation speed of sound waves in the high-temperature flue gas medium is proportional to the square root of the absolute temperature of the flue gas. By calculating the propagation time difference of sound waves along different paths within the furnace and applying a basic tomographic imaging algorithm, the system can reconstruct a three-dimensional temperature field distribution map inside the furnace within milliseconds. This distribution map clearly shows the flame center location, the high-temperature zone range, and temperature fluctuations near the heated surfaces, providing direct basis for burner adjustment.

[0049] The flue gas analyzer monitors and provides real-time data on flue gas components, including residual oxygen, carbon monoxide concentration, carbon dioxide concentration, and real-time emission concentration of nitrogen oxides. The real-time data vector further integrates parameters such as the real-time pressure difference between the gasifier outlet and the boiler inlet, the instantaneous flow rate of the main gas pipe, the flow rate and pressure of the boiler feedwater system, and the saturated steam temperature. This multi-source heterogeneous data is aggregated to the central processor via a fieldbus, and after time-domain alignment and filtering, forms a real-time data vector reflecting the overall operation of the system.

[0050] In the above method, step 4, namely digital twin computation and coupling optimization, involves the gasification combustion digital twin running on a high-performance edge computing gateway or cloud server. This twin is not only a data display platform but also a digital simulation engine integrating computational fluid dynamics, thermochemical reaction dynamics, and multiphysics coupling characteristics. After receiving the customized fuel physical properties generated in step 2 and the real-time data vector from step 3, the digital twin performs numerical iterative computation in virtual space.

[0051] The calculation process strictly adheres to the mass conservation equation, momentum conservation equation, and energy conservation equation. The digital twin divides the spatial domain of the gasifier and boiler into tens of thousands of computational units. Within each unit, the chemical reaction rate equations are solved to calculate complex processes including volatile matter release, heterogeneous reactions, and homogeneous gas-phase reactions. Through calculation, the system can predict the proportions of hydrogen, carbon monoxide, and methane in the gas produced by the gasifier, as well as the lower heating value of the gas, under the current fuel ratio and process parameters.

[0052] The optimal combination of operating parameters is generated using a multi-objective optimization algorithm, such as an improved genetic algorithm or particle swarm optimization algorithm. The objective functions are set as follows: First, maximize gasification efficiency, i.e., maximize the ratio of the chemical energy of the produced gas to the total energy of the input fuel; second, minimize tar production by optimizing the reaction temperature and residence time distribution to suppress the generation of large molecular hydrocarbons; third, maintain the boiler steam pressure fluctuation within the set value; and fourth, ensure that the nitrogen oxide emission concentration is always below the hard threshold of the national environmental protection standards. The optimization algorithm searches for an equilibrium point that satisfies the above constraints within a multi-dimensional parameter space, including feed rate, air-to-coal ratio, and primary air to secondary air ratio, and uses this equilibrium point as the control benchmark for the next time step.

[0053] In the above method, step 5, namely multi-dimensional collaborative instruction execution and closed-loop regulation, employs a model predictive control algorithm as the core control strategy. This algorithm possesses the capabilities of rolling optimization, feedback correction, and handling multivariable constraints. Within each control step, typically 100 to 500 milliseconds, the model predictive control algorithm solves the optimization problem in the finite time domain based on the current system state and future load demand predictions.

[0054] The coordinated control commands cover all actuators in the entire process chain. Commands to the gasifier include feed screw speed commands, primary air fan frequency commands, and secondary damper opening commands. Commands to the burner include combustion fan frequency conversion commands and gas nozzle adjustment commands. Commands to the gas transmission and distribution system include the opening of the pressure regulating valve on the gas main and the back pressure adjustment commands for the buffer tank.

[0055] Model predictive control algorithms possess significant predictive execution characteristics. When the system predicts a significant increase in boiler load within the next 15 minutes based on upper computer allocation commands or historical heat consumption patterns, the controller will not wait for the pressure to drop before adjusting. Instead, it will proactively execute the following logic: instruct the automatic bundling machine to change its operating mode and begin producing a batch of customized low-density, high-porosity bales to ensure that these fuels can rapidly release heat when entering the furnace; simultaneously, by increasing the feed screw speed, the material inventory in the gasifier is pre-increased. Then, the primary air volume is increased slowly in advance, utilizing the heat storage in the fuel layer to preheat the new material that will participate in the reaction. By adjusting the back pressure valve of the buffer tank on the gas pipeline, the pipeline pressure is appropriately increased while ensuring safety, increasing the gas energy reserve and providing sufficient gas supply at the moment of load surge, achieving proactive coordination of load prediction, fuel pre-control, and reaction pre-adjustment.

[0056] To dynamically match the gas production and consumption rhythm, the system provides rapid feedback by monitoring the gas main pressure and load change rate. When a sudden drop in boiler load is detected, such as a steam demand shutdown due to downstream process shutdown, the model predictive control algorithm immediately executes pressure relief and suppression procedures. By rapidly reducing the primary air fan frequency and simultaneously opening the bypass circulation valve, excess gas is guided to the temporary storage unit or safety disposal unit to prevent backfire caused by excessive pressure in the gasifier. This dynamic matching mechanism ensures that the system maintains high safety even under drastically fluctuating operating conditions.

[0057] The system also integrates safety self-diagnostic procedures. A sensor network monitors the gasifier furnace pressure and the temperature of key components in real time. If the pressure sensor readings exceed the preset safety threshold, or if an abnormal increase in oxide layer temperature occurs due to feed blockage, the logic controller will immediately skip all optimization algorithms and trigger an emergency shutdown procedure. This procedure instantly cuts off all power, stops the screw feeder, closes the air inlet valve, and activates the nitrogen purging system to fill the gasifier and gas pipelines with inert gas, quickly extinguishing any remaining flames in the reaction zone and ensuring the intrinsic safety of equipment and personnel.

[0058] During operation, the system continuously compares the actual sensor readings, such as the actual calorific value of the gas and the measured temperature distribution, with the calculation and prediction results from the digital twin in real time. If the residual between the two exceeds a preset tolerance range of 5%, it indicates that the current digital model has drifted from the actual physical entity. At this point, the system automatically activates an online learning mechanism. This mechanism utilizes the production big data accumulated in the past 24 hours and employs stochastic gradient descent to correct the empirical correlations, heat transfer coefficients, and reaction kinetic parameters within the digital twin online. In this way, the control system can achieve self-evolution of accuracy as the equipment is used and the environment changes.

[0059] Furthermore, this embodiment also includes real-time monitoring of the mechanical wear status of the automatic bundling machine. The current fluctuation curve of the hydraulic pump drive motor during the bundling process is collected using a current sensor. As the extrusion die wears, the forming resistance shifts, causing changes in the harmonic characteristics of the current signal. By analyzing these characteristics, the system can predict the die replacement cycle and send maintenance reminders to the cloud management platform via an IoT communication module, integrating them into the overall production and maintenance scheduling plan.

[0060] Controlling the combustion heat exchange process also involves fine-tuning the boiler blowdown rate. The system monitors the circulating water quality in real time using a conductivity meter installed below the steam-water separator. When the water hardness or salinity approaches a critical value, the opening of the blowdown valve is automatically adjusted. This dynamic blowdown based on water quality feedback reduces heat loss with wastewater while ensuring that the boiler heating surfaces do not scale.

[0061] The combustion air ratio command is corrected in a closed loop based on the residual oxygen content fed back by the flue gas analyzer. The controller adjusts the frequency converter output of the combustion air fan to precisely maintain the excess air coefficient in the furnace within a preset range of 1.05 to 1.20. While ensuring complete combustion of biomass gas and eliminating black smoke, it also reduces the formation of thermal nitrogen oxides by suppressing the formation of local high-temperature zones.

[0062] Example 2: Based on Example 1, this example further refines the implementation method in response to extreme fuel fluctuation conditions, especially the processing logic for agricultural waste raw materials with high moisture content and high impurity content.

[0063] In this implementation scenario, a dynamic weight adjustment mechanism is introduced into the feature extraction process in step 1. When the sensor array detects that the moisture content of the raw material exceeds the extreme upper limit of 35%, the molding reaction activity prediction model automatically increases the penalty weight of the moisture parameter. At this time, the activity index will drop to a low level.

[0064] Step 2, targeting this low activity index, executes an ultra-high-strength forming logic. The automatic bundling machine not only increases the hydraulic system pressure but also further enhances the forming density of the biomass by adjusting the resistance ring at the mold outlet, achieving an ultra-high density of over 1300 kg per cubic meter. This ultra-high-density bundling creates a relatively enclosed drying zone within the gasifier, utilizing the radiant heat within the furnace to slowly pre-dry the core moisture, preventing sudden evaporation of moisture that carries away a large amount of heat and causes furnace temperature to drop.

[0065] Simultaneously, the coordinated control command in step 5 will link with the front-end drying pretreatment equipment. Based on fluctuations in the activity index, the system adjusts the air temperature and velocity at the dryer's hot air inlet to ensure that the moisture content of the raw materials entering the bundling machine is pre-controlled within a relatively stable range. This cross-stage linkage control ensures that even during the rainy season or when raw materials are stored outdoors, leading to quality deterioration, the gasification coupling control system can maintain consistent calorific value output.

[0066] In the digital twin calculation, step 4 automatically recalculates the burner's air distribution scheme for low-calorific-value gas produced by high-moisture fuel. The digital twin simulation reveals that maintaining the original excess air coefficient leads to increased flue gas losses due to the lower flame temperature. Therefore, the optimization algorithm generates a combination of instructions to reduce combustion air volume and increase air preheating temperature, sacrificing some complete combustion to achieve a higher flame center temperature, ensuring that the boiler's steam pressure does not decline.

[0067] Example 3: This example focuses on describing the implementation details of the coupled control method under complex heat load variation conditions, especially for the frequent peak and valley switching of steam consumption caused by multiple process lines in parallel in industrial parks.

[0068] Under the above operating conditions, the real-time data vector in step 3 increases the sampling frequency of the external steam flow meter and pressure transmitter. The system assesses the intensity of user demand in real time by constructing a load change slope index.

[0069] When a peak steam consumption period is predicted to occur within the next 30 minutes, the model predictive control algorithm in step 5 activates the energy pre-storage mode. In addition to adjusting the bundling density and gas pipeline pressure, the system also fine-tunes the operating frequency of the boiler feedwater pump. By slightly raising the boiler operating water level, heat is stored in the water space inside the boiler.

[0070] In this process, the digital twin in step 4 calculates the impact of water level increase on steam dryness in real time. If the calculation result shows that the dryness will be lower than the quality standard of 0.98, the optimization algorithm will immediately limit the water level increase and instead enhance the instantaneous gas production capacity by increasing the primary air ratio of the gasifier.

[0071] To dynamically match the production and consumption rhythm of biomass fuel, the system introduces rapid online prediction of the fuel's calorific value in environments with frequent load fluctuations. Since there is approximately a 15-minute physical time delay between fuel bale formation and gasification, the controller in step 5 utilizes the fuel physicochemical profile generated in steps 1 and 2 to pre-determine the energy flow that will be generated when this batch of fuel enters the reaction zone 15 minutes later. This time-aligned predictive compensation allows the controller to adjust its throttle in advance, much like driving a car, based on road conditions, thus solving the regulation failure problem of biomass systems under conditions of high inertia and large hysteresis.

[0072] Example 4: This example describes the closed-loop implementation of the present invention for equipment status monitoring and performance optimization during long-term operation.

[0073] During the operation of the above method, the system encrypts and uploads all production and operation data to the cloud database via the IoT communication module. The cloud-based big data platform then uses the operating data from multiple boilers of the same type to perform energy efficiency benchmarking analysis.

[0074] During long-term operation, the digital twin in step 4 will automatically identify physical changes such as wear on the gasifier lining and ash accumulation on the heat exchange tube bundle. For example, when the system detects that the flue gas temperature continues to rise and the boiler efficiency decreases under the same fuel input and air volume, the heat transfer correction coefficient in the digital twin will be automatically lowered, indicating that there is ash accumulation on the heat exchange surface.

[0075] Subsequently, step 5 will issue a coordinated instruction to the automatic ash removal system based on this diagnostic result, increasing the frequency or duration of the ash blowing pulses. Simultaneously, to compensate for heat loss caused by ash accumulation, the controller will automatically fine-tune the parameters of the front-end strapping machine, slightly increasing the fuel activity index requirement. This is achieved by selecting higher-quality raw materials or improving molding activity to maintain the output energy efficiency at the back end.

[0076] Furthermore, for the wear monitoring of the hydraulic pump in the automatic bundling machine, the system collects high-frequency current signals and extracts feature vectors using wavelet transform technology. When the feature vector deviates from the normal operating baseline value, the system simulates the impact of component failure on the entire production chain in the virtual space of the digital twin. If the simulation results show that the failure will lead to an uncontrolled imbalance between gas production and consumption, the system will issue a load reduction operation command in advance and automatically contact maintenance personnel.

[0077] Regarding fuel type switching, for example, switching from single-source sawdust to mixed crop straw, the system detects the change in raw material type through real-time extraction in step 1. At this time, the activity index calculation process automatically calls the corresponding weighting coefficient. For straw-based fuels, due to their high alkali metal content, they are prone to coking at high temperatures. When the digital twin calculates and optimizes the parameter combination, it lowers the hard constraint upper limit of the oxidation layer temperature by 50 degrees Celsius. The collaborative control command then reduces the concentration of combustion-supporting gas in the primary air, and by introducing some recirculated flue gas as a diluent, it reduces the local high temperature in the reaction zone, effectively preventing the risk of shutdown due to slagging while ensuring gasification efficiency.

[0078] In the combustion heat exchange stage, the system also achieves deep coupling between the frequency conversion control of the feedwater pump and the economizer outlet water temperature. During load fluctuations, the controller not only adjusts the gas flow rate but also changes the economizer inlet water flow rate to maximize the recovery of flue gas waste heat. The digital twin calculates the minimum feedwater temperature at which acidic dew point corrosion does not occur on the economizer inner wall under different operating conditions, using this as a soft constraint to guide the coordinated operation of blowdown and makeup water.

[0079] The end-to-end control logic also includes compensation for ambient temperature and humidity. Environmental sensors deployed outside the boiler room collect atmospheric pressure, temperature, and humidity data in real time. These parameters are incorporated into the real-time data vector in step 3. During humid, low-pressure rainy seasons, the controller in step 5 automatically increases the frequency of the combustion fan to compensate for the decrease in air density, ensuring that the oxygen mass flow rate entering the furnace is maintained near the calculated optimal stoichiometric ratio.

[0080] The above embodiments describe in detail the specific implementation logic of the present invention under different operating conditions and dimensions. By transforming the front-end physical forming process into a controllable execution variable and combining digital twin and model predictive control technologies, the present invention completely breaks through the technical barriers of the independent operation of the three stages of biomass boiler forming, gasification, and combustion. The system not only achieves a significant improvement in energy efficiency but also reaches a new level in safety, environmental protection, and automation. The issuance cycle of all control commands is maintained at the millisecond level, ensuring precise control of complex nonlinear processes.

[0081] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. An automatic biomass boiler fuel gasification coupling control method, characterized in that, Includes the following steps: Raw material real-time detection and feature extraction steps: The physical property parameters of the biomass raw materials to be processed are acquired in real time by the sensor array deployed at the front end of the automatic bundling machine, and the physical property parameters are input into the preset molding reaction activity prediction model to calculate the activity index reflecting the chemical activity of biomass fuel. Active forming and dynamic bundling control steps: Based on the activity index, the control unit sends execution instructions to the automatic bundling machine, and produces customized biomass bale fuel that matches the activity index by dynamically adjusting the compression ratio and mechanical geometric parameters of the automatic bundling machine; Multimodal production operation status reconstruction steps: Utilize a multi-sensor network deployed on the gasifier body and boiler body to collect multimodal operation data in real time, including temperature distribution, pressure status, flue gas composition and flame morphology, and construct a real-time data vector reflecting the current production load and reaction process; Digital twin calculation and coupling optimization steps: The physical properties of the customized biomass bundled fuel and the real-time data vector are input into the preset gasification combustion digital twin. In the virtual space, the nonlinear coupling relationship between gasification efficiency, gas calorific value, burner stability and boiler heat absorption efficiency is calculated in real time to generate the optimal combination of operating parameters. Multi-dimensional collaborative command execution and closed-loop regulation steps: Based on the optimal combination of operating parameters, collaborative control commands are simultaneously issued to the fuel forming stage, gasification reaction stage, gas transmission and distribution stage, and combustion heat exchange stage through model predictive control algorithm to realize dynamic characteristic mapping and closed-loop control of the entire link.

2. The automatic bundling biomass boiler fuel gasification coupling control method according to claim 1, characterized in that, In the real-time raw material detection and feature extraction step, the sensing array includes a near-infrared spectral sensor and a microwave moisture meter. The near-infrared spectral sensor emits a beam of light within a preset wavelength range onto the surface of the crushed biomass raw material. By analyzing the intensity of the characteristic peak absorbance formed by the resonant absorption of frequency energy by the chemical bonds of organic molecules in the diffuse reflectance spectrum, the ash content and volatile matter content of the biomass raw material are determined. The microwave moisture meter emits microwave signals that penetrate the material layer to be tested. Based on the energy attenuation and phase shift changes of the microwave signal during the penetration process, and combined with a preset mapping relationship, the moisture content of the biomass raw material is calculated. The extracted physical property parameters also include calorific value data obtained by matching the built-in raw material type database.

3. The automatic bundling biomass boiler fuel gasification coupling control method according to claim 2, characterized in that, The molding reaction activity prediction model is a deep learning network built based on historical experimental data, used to characterize the relationship between the moisture content, ash content, volatile matter of biomass raw materials and the subsequent gasification reaction rate. The activity index is a scalar value, and the activity index is negatively correlated with the moisture content and the ash content parameter, and positively correlated with the volatile matter parameter. During the calculation process, the molding reaction activity prediction model calls the corresponding weight correction coefficient from the built-in raw material type database based on the raw material type information input by the user, and performs secondary compensation on the activity index to eliminate the influence of different biomass fiber structures on the reaction activity prediction.

4. The automatic bundling biomass boiler fuel gasification coupling control method according to claim 1, characterized in that, In the active forming and dynamic bundling control steps, the specific method for adjusting the compression ratio of the automatic bundling machine is as follows: when the activity index is lower than the preset first activity threshold, the control unit determines that the chemical activity of the raw material is lower than the standard value, instructs the hydraulic system of the automatic bundling machine to increase the pre-pressure, increase the compression ratio of the forming mold, increase the density between the raw material fibers, and extend the pyrolysis residence time of the customized biomass bundling fuel in the gasifier. When the activity index is higher than the preset second activity threshold, the control unit determines that the chemical activity of the raw material is higher than the standard value, and instructs the hydraulic system to reduce the pre-pressure, reduce the compression ratio of the molding die, and increase the porosity inside the customized biomass bundled fuel.

5. The automatic bundling biomass boiler fuel gasification coupling control method according to claim 1, characterized in that, The mechanical geometric parameters include the length, diameter, and weight of the customized biomass bale fuel; In the active forming and dynamic bundling control steps, the control unit changes the geometric dimensions of the customized biomass bundling fuel by adjusting the action cycle of the cutting mechanism according to the preset load prediction trend. Under conditions of anticipated increased load, the operating cycle of the cutting mechanism is shortened to reduce the volume of the individual bundles and increase the total specific surface area of ​​fuel per unit mass. Under conditions where the expected load is stable or decreasing, the operating cycle of the cutting mechanism is extended to increase the volume of the individual bundles and maintain a stable material layer height in the gasifier.

6. The automatic bundling biomass boiler fuel gasification coupling control method according to claim 1, characterized in that, In the multimodal production operation status reconstruction step, the collection of operation data using the multi-sensor network includes: obtaining the temperature gradients of the drying layer, pyrolysis layer, oxidation layer and reduction layer by arranging thermocouple arrays at different height levels on the inner wall of the gasifier; Using an acoustic thermometer to emit acoustic signals inside the boiler furnace, and based on the physical property that the propagation speed of the acoustic signal in the high-temperature flue gas medium is proportional to the square root of the absolute temperature of the flue gas, the time difference of the acoustic signal on different propagation paths is calculated, and a three-dimensional temperature field distribution map inside the furnace is reconstructed by applying a basic tomographic imaging algorithm. The concentrations of oxygen, carbon monoxide, carbon dioxide, and nitrogen oxides in flue gas are analyzed in real time online using a flue gas analyzer.

7. The automatic bundling biomass boiler fuel gasification coupling control method according to claim 6, characterized in that, The gasification combustion digital twin is a digital simulation system that integrates fluid dynamics models, thermochemical reaction dynamics models, and heat conduction models. In the digital twin calculation and coupling optimization step, after receiving the input parameters, the gasification combustion digital twin solves the mass conservation equation, momentum conservation equation and energy conservation equation through numerical iteration to calculate the proportion of gas components and lower heating value generated in the gasifier under the current operating conditions. The calculation basis for the gasification efficiency is the ratio of the total energy of the fuel entering the gasifier to the chemical energy of the produced gas. The optimization of the boiler's heat absorption efficiency is achieved by adjusting the burner's injection angle based on the three-dimensional temperature field distribution map using the gasification combustion digital twin.

8. The automatic bundling biomass boiler fuel gasification coupling control method according to claim 1, characterized in that, The optimal combination of operating parameters is generated by finding an equilibrium point in the parameter search space using a multi-objective optimization algorithm. The objective functions of the multi-objective optimization algorithm include: maximizing the gasification efficiency, minimizing tar generation, maintaining boiler steam pressure fluctuations within a preset fluctuation range, and ensuring that nitrogen oxide emission concentrations are below a preset environmental protection threshold. In the multi-dimensional collaborative command execution and closed-loop regulation steps, the collaborative control commands include feeding speed commands, primary air volume commands, and secondary air volume commands issued to the gasifier, combustion air ratio commands issued to the burner, and pressure regulating valve opening commands issued to the gas transmission and distribution pipeline. The combustion air ratio command is finely adjusted based on feedback on the residual oxygen content in the flue gas.

9. The automatic bundling biomass boiler fuel gasification coupling control method according to claim 8, characterized in that, The model predictive control algorithm has a load prediction function; When it is predicted that the boiler load will increase in a predetermined period of time in the future, the model predictive control algorithm executes the following logic in advance: instructs the automatic bundling machine to produce low-density and high-porosity bundled fuel, and increases the operating frequency of the feeding mechanism; Increase the primary air volume command in advance to enhance the heat storage capacity of the material layer; Increase gas energy reserves by adjusting the back pressure valve of the buffer tank in the gas pipeline; For the dynamic matching of gas production and consumption rhythm, when the boiler load decreases, the model predictive control algorithm suppresses the gasification reaction intensity by reducing the primary fan frequency and opens the bypass circulation valve to guide excess gas to the storage unit.

10. The automatic bundling biomass boiler fuel gasification coupling control method according to claim 1, characterized in that, It also includes safety self-diagnosis and model evolution steps: real-time monitoring of the pressure status inside the gasifier; when the pressure status exceeds the preset safety upper limit threshold, an emergency shutdown procedure is triggered to stop the fuel supply and start the nitrogen purging system. The system continuously compares the actual operating results collected by the sensors with the calculation results of the gasification combustion digital twin. When the deviation between the actual operating results and the calculation results exceeds the preset allowable range, the online learning mechanism is activated to correct the empirical correlation and physical parameters inside the gasification combustion digital twin using the current production data. Meanwhile, by analyzing the fluctuation pattern of hydraulic pump current during the forming process of the automatic bundling machine, the wear condition and replacement cycle of the forming mold can be predicted.

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