A semi-gasification multi-parameter coordinated matching industrial steam stable supply control system

Abstract

The present application relates to the technical field of industrial steam boiler control, and discloses a kind of semi-gasification multi-parameter collaborative matching industrial steam stable supply control system, including feedforward prediction module, for based on the state data of end steam pipe network constructs fluid network topology model, and solves steam pipe network's transient fluctuation characteristic quantity, as the feedforward prediction signal of system;State observation module is used to calculate the combined virtual elastic energy storage margin containing semi-gasification bed layer latent heat and steam pipe network volume.Through based on the state data of end steam pipe network constructs fluid network topology model, effectively overcome the steam supply lag problem caused by semi-gasification combustion large inertia physical process, and by the multi-parameter collaborative control increment obtained by orthogonal decoupling is analyzed as absolute instruction, and is issued in parallel to the bottom execution mechanism of primary air, secondary air and feeder, ensure the continuity and efficiency of heat energy to steam conversion, improve the overall combustion quality.

Description

Technical Field

[0001] This invention relates to the field of industrial steam boiler control technology, specifically a semi-gasification multi-parameter collaborative matching industrial steam stable supply control system. Background Technology

[0002] In modern industrial production, a stable supply of industrial steam is crucial for the normal and continuous operation of downstream processes such as printing and dyeing, chemical industry, papermaking, and pharmaceuticals. In recent years, semi-gasification combustion technology has been widely used in various industrial boilers due to its significant advantages in improving the utilization rate of low-rank fuels and reducing emissions of pollutants such as nitrogen oxides and dust. This technology can stage the entire combustion process, allowing solid fuels to first undergo pyrolysis and gasification in an oxygen-deficient or weak-oxygen bed region, releasing a large amount of combustible gas, and then complete high-temperature combustion in a secondary oxygen-enriched space. However, the semi-gasification combustion process is essentially an extremely complex thermodynamic system with multivariable, strong nonlinear, and large inertial pure time delay characteristics, which places extremely high demands on the underlying automated control.

[0003] Currently, the control of semi-gasification industrial boilers generally adopts a hardware architecture based on conventional DCS or PLC. The core control logic mostly follows the traditional single-loop PID control or simple error-based cascade feedback control. When faced with the complex and ever-changing industrial conditions, this conventional control system exposes deep-seated technical limitations. There is a strong cross-physical coupling between key execution parameters such as the feeding rate, primary gasification air volume, and secondary combustion air volume inside the semi-gasification furnace. Adjusting the primary air can control the gasification production rate, but it will affect the furnace bed temperature; adjusting the secondary air can promote the complete combustion of volatiles, but it will change the system's flue gas heat loss and flow field distribution. The existing single-loop control system forcibly separates these parameters for independent adjustment, lacking system-level overall coordination. When the load changes, it is very easy to cause mutual interference and system oscillation between different control loops.

[0004] Meanwhile, the semi-gasification reaction itself is accompanied by huge thermophysical inertia. From the solid fuel entering the furnace, absorbing latent heat for pyrolysis, releasing combustible gas, to finally burning and releasing heat and converting into steam, there is a pure lag time of several minutes. Existing control schemes usually only use the main steam pressure or temperature at the boiler outlet as a single passive feedback signal, which is a post-compensation control. When the industrial terminal pipeline network experiences sudden and drastic fluctuations in steam load due to machine start-up and shutdown, this system, which lacks front-end prediction and effective feedforward mechanism, cannot predict the load evolution trend in advance. This results in the boiler's gas production response always being slower than the demand cycle, which in turn causes a precipitous drop in steam header pressure or dangerous overpressure release, failing to meet the requirements of stable steam supply for industrial production. Therefore, this invention designs a semi-gasification multi-parameter collaborative matching industrial steam stable supply control system to address the above-mentioned problems. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a semi-gasification multi-parameter collaborative matching industrial steam stable supply control system, which solves the problems of parameter matching mismatch, low combustion efficiency, and unstable steam supply under terminal pipeline load fluctuations caused by strong coupling of multiple variables, large lag in physical processes, and heavy reliance on manual solidification of experience in existing semi-gasification control technologies.

[0006] To achieve the above objectives, the present invention provides a semi-gasification multi-parameter collaborative matching industrial steam stable supply control system, comprising:

[0007] The feedforward prediction module is used to construct a fluid network topology model based on the state data of the end steam pipeline network and solve for the transient fluctuation characteristics of the steam pipeline network as the feedforward prediction signal of the system.

[0008] The state observation module is used to calculate the joint virtual elastic energy storage margin, which includes the latent heat of the semi-gasified bed and the volume of the steam pipeline network, and treat it as one of the independent state variables of the system.

[0009] The adaptive reconstruction module is used to construct a reference mapping matrix between the system control input vector and the system state response vector, and dynamically adjust the weight matrix of the state features according to the feedforward prediction signal, thereby generating a reconstruction mapping matrix.

[0010] The orthogonal decoupling optimization module is used to perform singular value decomposition on the reconstructed mapping matrix, extract mutually independent control principal components in the dimensionality reduction space, and obtain multi-parameter collaborative control increments in combination with the target state deviation.

[0011] The collaborative execution module is used to parse the collaborative control increment into absolute control commands for the semi-gasification underlying actuators and perform actions, and to achieve closed-loop iteration by collecting feedback data.

[0012] Preferably, the feedforward prediction module is specifically used for:

[0013] Real-time acquisition of valve opening, local flow rate, and main pipe node pressure of steam-consuming terminal equipment is used as boundary conditions;

[0014] A set of one-dimensional unsteady partial differential equations containing the spatial and temporal coordinates of the pipeline is established based on the conservation of mass and momentum.

[0015] The total demand flow of the entire pipeline network is predicted by solving the partial differential equations in real time, and the transient derivative of the total demand flow with respect to time is extracted and used as the feedforward prediction signal.

[0016] Preferably, the calculation of the joint virtual elastic energy storage margin by the state observation module specifically includes:

[0017] The pipeline volume can be calculated using the elastic energy storage component, which is determined based on the total pipeline volume of the entire plant, the specific heat capacity of steam at constant volume, and the difference between the current average measured pressure of the pipeline and the set minimum pressure threshold.

[0018] The available energy storage component of the latent heat of the semi-gasification bed is calculated. This component is determined by spatial integration based on the fuel distribution mass density at the height of the gasification chamber bed, the fuel lower heating value, and the gasification heat release rate that dynamically changes with local temperature.

[0019] The available elastic energy storage component of the pipeline volume and the available latent heat energy storage component of the semi-gasified bed are linearly summed according to a preset weighting coefficient to obtain the joint virtual elastic energy storage margin.

[0020] Preferably, the system state response vector includes multi-dimensional observed physical quantities, specifically including: gasification chamber characteristic temperature, secondary combustion chamber characteristic temperature, tail exhaust oxygen concentration, main steam pressure, and the joint virtual elastic energy storage margin;

[0021] The system control input vector includes the operating frequency of the multi-dimensional execution unit, specifically including: the frequency of the primary fan, the frequency of the secondary fan, and the operating frequency of the feeder.

[0022] Preferably, the adaptive reconstruction module generates the reconstruction mapping matrix in the following specific way:

[0023] The system physical partial derivative relationship obtained from the previous control cycle is used as the reference mapping matrix;

[0024] The feedforward prediction signal is introduced into the diagonal weight matrix, so that the penalty weight factor corresponding to each state feature in the matrix is ​​dynamically mapped with the transient fluctuation feature.

[0025] The reconstructed mapping matrix is ​​obtained by multiplying the diagonal weight matrix with the reference mapping matrix.

[0026] Preferably, the specific logic of the dynamic mapping includes: when the absolute value of the feedforward prediction signal is determined to be in a surge state, the penalty weight factor corresponding to the flue gas oxygen concentration in the diagonal weight matrix decreases, while the penalty weight factor corresponding to the main steam pressure and the joint virtual elastic energy storage margin increases.

[0027] Preferably, the orthogonal decoupling optimization module specifically performs the following when performing singular value decomposition:

[0028] The reconstructed mapping matrix is ​​decomposed into a left singular orthogonal matrix in the state space, a right singular orthogonal matrix in the control input space, and a diagonal matrix containing singular values ​​representing the decoupling strength in each orthogonal direction.

[0029] This allows us to extract a mathematical characteristic space in which the gasification endothermic process, the combustion exothermic process, and the solid fuel replenishment process do not interfere with each other.

[0030] Preferably, the specific method for obtaining the multi-parameter collaborative control increment is as follows:

[0031] Obtain the target reference state set by the system and subtract it from the current measured actual state to obtain the target state deviation vector;

[0032] Using the left singular orthogonal matrix, the right singular orthogonal matrix, and the generalized inverse matrix of the diagonal matrix obtained by singular value decomposition, the target state deviation vector is solved by inverse mapping along the orthogonal space, and the multi-parameter collaborative control increment is output.

[0033] Preferably, the collaborative execution module accumulates the collaborative control increment to the control reference value of the previous cycle to form the absolute control command, and the parsed absolute control command is sent out in parallel to:

[0034] The primary air control unit of the semi-gasification boiler is used to adjust the bottom penetrating drag force and drag oxygen concentration to change the gasification pyrolysis rate.

[0035] The secondary air control unit of the semi-gasification boiler is used to match the combustible gas released in the gasification zone to maintain oxygen-rich combustion.

[0036] The feeding drive unit of the semi-gasification boiler is used to supplement the amount of solid base material required for subsequent gasification reactions.

[0037] Preferably, after the collaborative execution module sends the absolute control command to the underlying actuator, it triggers the sensor network to synchronously collect the updated pipeline boundary state and boiler thermodynamic state, and sends the updated data back to the feedforward prediction module and the state observation module, thereby entering the dynamic optimization iteration process of the next control cycle.

[0038] This invention provides a semi-gasification multi-parameter collaborative matching industrial steam stable supply control system. It has the following beneficial effects:

[0039] 1. This invention constructs a fluid network topology model based on the state data of the terminal steam pipeline network and solves the transient derivative of the total demand flow of the steam pipeline network as a feedforward prediction signal. This changes the situation of traditional boilers that rely solely on the passive and lagging adjustment of the outlet steam pressure. It effectively overcomes the problem of steam supply lag caused by the large inertia of the semi-gasification combustion physical process and realizes a stable response under sudden load changes in the industrial steam pipeline network.

[0040] 2. This invention analyzes the multi-parameter collaborative control increment obtained by orthogonal decoupling into absolute commands, and sends them in parallel to the underlying actuators of the primary air, secondary air and feeder. Combined with multi-dimensional sensor feedback to form a dynamic closed-loop iteration, it realizes the seamless connection of the entire physical process of semi-gasification staged combustion. This fundamentally suppresses black smoke emissions and incomplete gasification caused by air distribution imbalance, ensures the continuous and efficient conversion of heat energy into steam, and improves the overall combustion quality.

[0041] 3. This invention introduces the characteristic quantities of transient fluctuations in the pipeline network into the diagonal weight matrix as penalty weight factors to adaptively reconstruct the system's baseline mapping matrix, giving the control system the ability to dynamically switch optimization targets. When the load at the steam consumption end is stable, the system balances combustion economy and steam supply stability. When extreme conditions such as sudden load changes are detected, the reconstructed matrix automatically adjusts the solution priority of the high-dimensional space, temporarily relaxes the constraints on local indicators such as tail-end exhaust oxygen content, and fully ensures the stability of the main steam pressure, thus realizing the intelligent and flexible transition of the control logic between steady-state and transient conditions.

[0042] 4. This invention extracts non-interfering orthogonal control principal components in a multidimensional mathematical dimensionality reduction space by performing singular value decomposition (SVD) on the reconstructed system state space mapping matrix. This completely eliminates the dependence of semi-gasification boiler bottom parameter matching on manual experience curves and fixed air-fuel ratio. This decoupling algorithm uses pure matrix algebra features to accurately remove the strong nonlinear coupling interference between gasification heat absorption rate, combustion heat release rate and feed replenishment amount. It effectively avoids the system oscillation phenomenon commonly seen in traditional single-loop PID independent regulation and ensures optimal coordinated matching of multiple parameters under different coal quality fluctuations.

[0043] 5. This invention constructs a joint virtual elastic energy storage margin that includes the incomplete latent heat of the semi-gasified bed and the volumetric potential energy of the steam pipeline network, and introduces it as an independent system physical state variable into the control space. This breaks the conventional thinking of treating the physical inertia of a large system as a control burden. This mechanism successfully transforms the passive lag characteristics of the wide-area pipeline network and the bottom boiler into an active and dispatchable "virtual buffer pool". When the control system faces a sudden steam consumption shock, it can smoothly call the system's internal energy storage through algorithms to smooth the supply and demand imbalance, avoid combustion deterioration caused by forced transient adjustment, and greatly enhance the system's shock resistance to external disturbances. Attached Figure Description

[0044] Figure 1 This is a schematic diagram of the system architecture of the present invention;

[0045] Figure 2 This is one of the system flow diagrams of the present invention;

[0046] Figure 3This is the second system flow diagram of the present invention;

[0047] Figure 4 This is the third system flow diagram of the present invention. Detailed Implementation

[0048] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] Please see the appendix Figure 1 -Appendix Figure 4 This invention provides a semi-gasification multi-parameter collaborative matching industrial steam stable supply control system, comprising:

[0050] The feedforward prediction module is used to construct a fluid network topology model based on the state data of the end-point steam pipeline network and solve for the transient fluctuation characteristics of the steam pipeline network as the feedforward prediction signal of the system. Specifically, the feedforward prediction module is used to: acquire the valve opening, local flow rate, and main pipe node pressure of the end-point steam equipment in real time as boundary conditions; establish a one-dimensional unsteady partial differential equation system containing pipeline spatial coordinates and time coordinates based on mass conservation and momentum conservation; predict the total demand flow rate of the entire pipeline network by solving the partial differential equation system in real time, and extract the transient derivative of the total demand flow rate with respect to time, and use this transient derivative as the feedforward prediction signal.

[0051] Specifically, the core principle of this module is to break away from the traditional lagging control thinking that relies solely on the main steam pressure at the boiler outlet as a single feedback source, and to extend the perception boundary of the control system to the entire steam pipeline network.

[0052] First, the system acquires the dynamic boundary conditions of steam-consuming terminal equipment (such as machines in various workshops) in real time through an industrial IoT interface. Specifically, this includes the valve opening degree, local flow rate, and main pipe node pressure at each node. Based on the basic principles of fluid mechanics, the system establishes a system that includes the spatial coordinates of the pipeline. With time coordinates The system of one-dimensional unsteady fluid topological partial differential equations, derived from the theorems of mass and momentum conservation, includes: a balance equation between the fluid compressibility term and the spatial derivative of the mass flow rate, and an equation incorporating vapor density. Effective cross-sectional area Isothermal compressibility and overall friction coefficient The momentum transfer equation;

[0053] By using an online numerical solver to solve the above partial differential equations in real time, the module can pre-calculate the delay time of the pressure wave generated by the terminal steam load propagating to the boiler outlet and accurately predict the total demand flow of the entire pipeline network. At this point, the system calculates the time derivative of the total demand flow and extracts the transient fluctuation characteristic (i.e., the transient derivative). This transient derivative represents the intensity and direction of fluctuations in external steam load. The system uses it as an advanced feedforward prediction signal to send to the downstream calculation module, fundamentally overcoming the large hysteresis characteristic of the thermal system.

[0054] The state observation module is used to calculate the joint virtual elastic energy storage margin, which includes the latent heat of the semi-gasification bed and the volume of the steam pipeline network. This margin is treated as one of the system's independent state variables. Specifically, the calculation includes: calculating the available elastic energy storage component of the pipeline network volume, which is determined based on the total volume of the plant's pipeline network, the specific heat capacity of the steam at constant volume, and the difference between the current average measured pressure of the pipeline network and the set minimum pressure threshold; calculating the available energy storage component of the latent heat of the semi-gasification bed, which is determined by spatial integration based on the fuel distribution mass density at the height of the gasification chamber bed, the lower heating value of the fuel, and the gasification heat release rate that dynamically changes with local temperature; and linearly summing the available elastic energy storage component of the pipeline network volume and the available energy storage component of the latent heat of the semi-gasification bed according to preset weighting coefficients to obtain the joint virtual elastic energy storage margin.

[0055] Specifically, this module can transform the combined physical inertia of both into a buffer pool in the control system. First, it calculates the available elastic energy storage component of the pipeline network volume. This component is based on the total volume of the plant's pipeline network. Specific heat capacity of steam at constant volume It is determined by the difference between the current average measured pressure of the pipeline network and the minimum pressure threshold required for the terminal process to maintain production. In physical terms, it represents the steam energy that the pipeline network can currently overdraw.

[0056] Simultaneously, the module calculates the latent heat available for energy storage in the semi-gasified bed. This component is not simply the calorific value of coal, but is determined by spatial integration based on the mass density of fuel distribution in the height direction of the gasification chamber bed, the lower heating value of fuel, and the gasification heat release rate function that dynamically changes with the local temperature of the bed. It represents the heat potential that has been pyrolyzed but not yet fully released on the grate.

[0057] Finally, the system uses preset dimensionless weighting coefficients. and By linearly summing the two, the joint virtual elastic energy storage margin is obtained. This margin is defined as a new and independent state variable in the system's state space, enabling the system to actively quantify and invoke the system's physical buffering capacity when responding to transient shocks.

[0058] The adaptive reconfiguration module constructs a baseline mapping matrix between the system control input vector and the system state response vector. It dynamically adjusts the weight matrix of the state features based on the feedforward prediction signal to generate the reconfiguration mapping matrix. The system state response vector contains multi-dimensional observed physical quantities, specifically including: gasification chamber characteristic temperature, secondary combustion chamber characteristic temperature, tail-end exhaust oxygen concentration, main steam pressure, and combined virtual elastic energy storage margin. The system control input vector contains multi-dimensional actuator operating frequencies, specifically including: primary fan frequency converter frequency, secondary fan frequency converter frequency, and feeder operating frequency. The adaptive reconfiguration module generates... The specific method for constructing the reconstructed mapping matrix is ​​as follows: the system physical partial derivative relationship identified in the previous control cycle is used as the reference mapping matrix; the feedforward prediction signal is introduced into the diagonal weight matrix, so that the penalty weight factors corresponding to each state feature in the matrix are dynamically mapped with the transient fluctuation feature; the diagonal weight matrix is ​​multiplied by the reference mapping matrix to obtain the reconstructed mapping matrix. The specific logic of the dynamic mapping includes: when the absolute value of the feedforward prediction signal is determined to be a surge state, the penalty weight factor corresponding to the flue gas oxygen concentration in the diagonal weight matrix is ​​reduced, and the penalty weight factor corresponding to the main steam pressure and the joint virtual elastic energy storage margin is increased.

[0059] Specifically, this module is responsible for constructing the mathematical mapping link between the actuator commands and the boiler thermodynamic state. First, it defines the system's multidimensional state response vector. This includes: the characteristic temperature of the vaporization chamber. Characteristic temperature of secondary combustion chamber Tail-end exhaust oxygen concentration (Co2), main steam pressure and the joint virtual elastic energy storage margin calculated in the above steps Simultaneously define the system's multidimensional control input vector. This includes the underlying execution: primary fan frequency conversion. Secondary air fan frequency conversion and feeder operating frequency The system obtains the reference mapping partial derivative matrix between the two based on mechanism identification. ;

[0060] To achieve adaptive optimization for different operating conditions, this module uses the transient derivatives obtained in S1. By introducing a control structure at the bottom layer, a diagonal weight matrix is ​​constructed, consisting of penalty weight factors corresponding to each state feature. At this point, the module will use the diagonal weight matrix. Mapping matrix with reference Multiply to generate a reconstructed mapping matrix. ;

[0061] The dynamic mapping logic is as follows: when the system detects a feedforward prediction signal... When the absolute value of oxygen concentration surges (i.e., the plant experiences a sudden surge in steam demand or a sudden shutdown), the penalty factor corresponding to the exhaust oxygen concentration in the diagonal weight matrix will automatically decrease, while the penalty factor corresponding to the main steam pressure and virtual elastic energy storage margin will be significantly amplified. This structural reconfiguration allows the system to automatically abandon the strict restrictions on micro-combustion economy (oxygen content) when subjected to load shocks, prioritizing the scheduling of energy storage and increasing gas production to ensure supply. When the load is stable, the weight factor automatically recovers, and oxygen content is strictly constrained again to improve combustion efficiency.

[0062] The orthogonal decoupling optimization module performs singular value decomposition on the reconstructed mapping matrix, extracts independent control principal components in the reduced-dimensional space, and calculates the multi-parameter collaborative control increment by combining the target state deviation. Specifically, the orthogonal decoupling optimization module performs singular value decomposition by decomposing the reconstructed mapping matrix into a left singular orthogonal matrix in the state space, a right singular orthogonal matrix in the control input space, and a diagonal matrix containing singular values ​​representing the decoupling strength in each orthogonal direction. This extracts a mathematical feature space where the gasification endothermic process, combustion exothermic process, and solid fuel replenishment process do not interfere with each other. The specific method for calculating the multi-parameter collaborative control increment is as follows: The target reference state set by the system is obtained, and the difference between this and the currently measured actual state is calculated to obtain the target state deviation vector. Using the generalized inverse matrices of the left, right, and diagonal matrices obtained from the singular value decomposition, the target state deviation vector is solved by inverse mapping along the orthogonal space, outputting the multi-parameter collaborative control increment.

[0063] Specifically, in semi-gasification combustion, there is an extremely complex physical coupling between primary air, secondary air, and feed (for example, adding primary air not only increases the gasification rate but also reduces the bed temperature). This module utilizes matrix space dimensionality reduction technology to decouple this coupling. Within each control calculation cycle, the system reconstructs the mapping matrix output by the adaptive reconstruction module. Perform singular value decomposition (SVD);

[0064] Through mathematical operations, it is decomposed into a left singular orthogonal matrix in the state space. The right singular orthogonal matrix of the control input space and diagonal matrices containing singular values. In the complex high-dimensional space of the system, mutually non-interfering orthogonal directions were found: namely, the dimension that purely controls the gasification endothermic process, the dimension that purely controls the combustion exothermic process, and the dimension of the solid fuel replenishment process. Subsequently, the module obtains the difference between the currently set target reference state and the actual measured physical state of the system, which constitutes the target state deviation vector. Construct the generalized inverse matrix using the SVD decomposition results. And solve by inverse mapping along orthogonal space: The result obtained is the multi-parameter collaborative control incremental vector after removing the influence of cross-interference. This method abandons the traditional experience-based ratio of manual table lookup and realizes intelligent parameter matching that relies purely on the algebraic features of real-time data.

[0065] The collaborative execution module parses the collaborative control increment into absolute control commands for the semi-gasification bottom-level actuators, enabling the actuators to take action. It also achieves closed-loop iteration by collecting feedback data. The collaborative execution module accumulates the collaborative control increment to the control baseline of the previous cycle, forming absolute control commands. These parsed absolute control commands are then sent in parallel to: the primary air control unit of the semi-gasification boiler, used to adjust the bottom penetration drag force and drag oxygen concentration to change the gasification pyrolysis rate; the secondary air control unit of the semi-gasification boiler, used to match the combustible gas released in the gasification zone to maintain oxygen-rich combustion; and the feeding drive unit of the semi-gasification boiler, used to replenish the amount of solid base material required for subsequent gasification reactions. After sending the absolute control commands to the bottom-level actuators, the collaborative execution module triggers the sensor network to synchronously collect updated pipeline boundary states and boiler thermodynamic states, and sends the updated data back to the feedforward prediction module and the state observation module, thus entering the dynamic optimization iteration process of the next control cycle.

[0066] Specifically, this module is responsible for transforming abstract mathematical calculations into actual physical device actions and forming a closed loop. The system will use the cooperative control increment obtained in S4. The value accumulated to the baseline of the previous control cycle is parsed into an absolute control command and strictly synchronized and sent to the three core underlying units:

[0067] 1. The primary air control unit receives the corresponding command and instantly changes the penetrating drag force and drag oxygen concentration at the bottom of the gasification chamber, thereby precisely controlling the pyrolysis gasification rate (gas production) of solid fuel.

[0068] 2. The secondary air control unit receives the corresponding instructions and uses strong swirling air to provide a precisely matched oxygen-rich environment for the combustible gas that has just been released in the gasification zone, maintaining the stability of the fire surface and complete combustion.

[0069] 3. The feeding drive unit receives the corresponding instruction and adjusts the feeding speed to supplement the amount of solid base material required for the subsequent continuous gasification reaction.

[0070] Once the command is issued and executed, the sensor network distributed throughout the pipeline network and boiler body will immediately respond and synchronously collect updated pipeline boundary states and thermodynamic states. This real-time data is then transmitted back to the S1 feedforward prediction module and the S2 state observation module, thereby driving the system into the next round of dynamic optimization iteration at the millisecond or second level. Through this closed-loop execution mechanism, the system can maintain optimal coordination and matching of multiple parameters and absolute stability of steam supply when facing fluctuations in fuel calorific value or drastic fluctuations in industrial steam load.

[0071] In summary, this invention provides a semi-gasification multi-parameter collaborative matching industrial steam stable supply control system. By constructing a fluid network topology model based on the terminal steam pipeline network status data and solving for the transient derivative of the total demand flow rate of the steam pipeline network as a feedforward prediction signal, it changes the traditional boiler's passive and lagging adjustment based solely on outlet steam pressure. This method pushes the control sensing boundary forward to the steam-consuming end, enabling early prediction of the direction and amplitude of industrial load fluctuations. Therefore, it outputs adjustment commands in advance before a substantial drop or overpressure occurs in the steam header pressure, effectively overcoming the steam supply lag problem caused by the large inertia of the semi-gasification combustion physical process, and realizing stable industrial steam pipeline network control. The system achieves a smooth response under sudden load changes. By parsing the multi-parameter collaborative control increment obtained through orthogonal decoupling into absolute commands, it sends them in parallel to the underlying actuators of the primary air, secondary air, and feeder. Combined with multi-dimensional sensor feedback to form a dynamic closed-loop iteration, it achieves seamless connection of the entire physical process of semi-gasification staged combustion. This underlying synchronous control mechanism ensures millisecond-level precise matching between the rhythm of solid fuel pyrolysis and gasification to produce combustible gases and the air volume required for secondary oxygen-enriched combustion. This not only fundamentally suppresses black smoke emissions and incomplete gasification caused by air distribution imbalance, but also ensures the continuous and efficient conversion of heat energy into steam, thereby improving the overall combustion quality.

[0072] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A semi-gasification multi-parameter collaborative matching industrial steam stable supply control system, characterized in that, include: The feedforward prediction module is used to construct a fluid network topology model based on the state data of the end steam pipeline network and solve for the transient fluctuation characteristics of the steam pipeline network as the feedforward prediction signal of the system. The state observation module is used to calculate the joint virtual elastic energy storage margin, which includes the latent heat of the semi-gasified bed and the volume of the steam pipeline network, and treat it as one of the independent state variables of the system. The adaptive reconstruction module is used to construct a reference mapping matrix between the system control input vector and the system state response vector, and dynamically adjust the weight matrix of the state features according to the feedforward prediction signal, thereby generating a reconstruction mapping matrix. The orthogonal decoupling optimization module is used to perform singular value decomposition on the reconstructed mapping matrix, extract mutually independent control principal components in the dimensionality reduction space, and obtain multi-parameter collaborative control increments in combination with the target state deviation. The collaborative execution module is used to parse the collaborative control increment into absolute control commands for the semi-gasification underlying actuators and perform actions, and to achieve closed-loop iteration by collecting feedback data.

2. The semi-gasification multi-parameter collaborative matching industrial steam stable supply control system according to claim 1, characterized in that, The feedforward prediction module is specifically used for: Real-time acquisition of valve opening, local flow rate, and main pipe node pressure of steam-consuming terminal equipment is used as boundary conditions; A system of one-dimensional unsteady partial differential equations containing the spatial and temporal coordinates of the pipeline is established based on the conservation of mass and momentum. The total demand flow of the entire pipeline network is predicted by solving the partial differential equations in real time, and the transient derivative of the total demand flow with respect to time is extracted and used as the feedforward prediction signal.

3. The semi-gasification multi-parameter collaborative matching industrial steam stable supply control system according to claim 1, characterized in that, The state observation module calculates the joint virtual elastic energy storage margin specifically including: The pipeline volume can be calculated using the elastic energy storage component, which is determined based on the total pipeline volume of the entire plant, the specific heat capacity of steam at constant volume, and the difference between the current average measured pressure of the pipeline and the set minimum pressure threshold. The available energy storage component of the latent heat of the semi-gasification bed is calculated. This component is determined by spatial integration based on the fuel distribution mass density at the height of the gasification chamber bed, the lower heating value of the fuel, and the gasification heat release rate that dynamically changes with local temperature. The available elastic energy storage component of the pipeline volume and the available latent heat energy storage component of the semi-gasified bed are linearly summed according to a preset weighting coefficient to obtain the joint virtual elastic energy storage margin.

4. The semi-gasification multi-parameter collaborative matching industrial steam stable supply control system according to claim 1, characterized in that, The system state response vector includes multi-dimensional observed physical quantities, specifically including: gasification chamber characteristic temperature, secondary combustion chamber characteristic temperature, tail exhaust oxygen concentration, main steam pressure, and the joint virtual elastic energy storage margin. The system control input vector includes the operating frequency of the multi-dimensional execution unit, specifically including: the frequency of the primary fan, the frequency of the secondary fan, and the operating frequency of the feeder.

5. The semi-gasification multi-parameter collaborative matching industrial steam stable supply control system according to claim 1, characterized in that, The specific method by which the adaptive reconstruction module generates the reconstruction mapping matrix is ​​as follows: The system physical partial derivative relationship obtained from the previous control cycle is used as the reference mapping matrix; The feedforward prediction signal is introduced into the diagonal weight matrix, so that the penalty weight factor corresponding to each state feature in the matrix is ​​dynamically mapped with the transient fluctuation feature. The reconstructed mapping matrix is ​​obtained by multiplying the diagonal weight matrix with the reference mapping matrix.

6. The semi-gasification multi-parameter collaborative matching industrial steam stable supply control system according to claim 5, characterized in that, The specific logic of the dynamic mapping includes: when the absolute value of the feedforward prediction signal is determined to be in a surge state, the penalty weight factor corresponding to the flue gas oxygen concentration in the diagonal weight matrix decreases, while the penalty weight factor corresponding to the main steam pressure and the joint virtual elastic energy storage margin increases.

7. The semi-gasification multi-parameter collaborative matching industrial steam stable supply control system according to claim 1, characterized in that, The orthogonal decoupling optimization module specifically performs the following during singular value decomposition: The reconstructed mapping matrix is ​​decomposed into a left singular orthogonal matrix in the state space, a right singular orthogonal matrix in the control input space, and a diagonal matrix containing singular values ​​representing the decoupling strength in each orthogonal direction. This allows us to extract a mathematical characteristic space in which the gasification endothermic process, the combustion exothermic process, and the solid fuel replenishment process do not interfere with each other.

8. A semi-gasification multi-parameter collaborative matching industrial steam stable supply control system according to claim 7, characterized in that, The specific method for obtaining the multi-parameter collaborative control increment is as follows: Obtain the target reference state set by the system and subtract it from the current measured actual state to obtain the target state deviation vector; Using the left singular orthogonal matrix, the right singular orthogonal matrix, and the generalized inverse matrix of the diagonal matrix obtained by singular value decomposition, the target state deviation vector is solved by inverse mapping along the orthogonal space, and the multi-parameter collaborative control increment is output.

9. A semi-gasification multi-parameter collaborative matching industrial steam stable supply control system according to claim 1, characterized in that, The collaborative execution module adds the collaborative control increment to the control reference value of the previous cycle to form the absolute control command, and the parsed absolute control command is sent out in parallel to: The primary air control unit of the semi-gasification boiler is used to adjust the bottom penetrating drag force and drag oxygen concentration to change the gasification pyrolysis rate. The secondary air control unit of the semi-gasification boiler is used to match the combustible gas released in the gasification zone to maintain oxygen-rich combustion. The feeding drive unit of the semi-gasification boiler is used to supplement the amount of solid base material required for subsequent gasification reactions.

10. A semi-gasification multi-parameter collaborative matching industrial steam stable supply control system according to claim 9, characterized in that, After the collaborative execution module sends the absolute control command to the underlying actuator, it triggers the sensor network to synchronously collect the updated pipeline boundary state and boiler thermodynamic state, and sends the updated data back to the feedforward prediction module and the state observation module, thereby entering the dynamic optimization iteration process of the next control cycle.

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