Multi-physical field regulated solid waste-based high-temperature heat storage system mode switching control method

Abstract

The application discloses a kind of solid waste-based high-temperature heat storage system mode switching control methods of multi-physical field regulation and control, it includes the following steps: S1: using substrate preparation solid waste-based gradient composite heat storage material;S2: solid waste-based gradient composite heat storage material is placed into heat storage unit target area, sealing treatment is carried out after filling is completed, so that it has heat storage capacity;S3: using intelligent controller realizes the dynamic switching between heat storage and heat release mode, when being in heat storage mode, phase change occurs in solid waste-based gradient composite heat storage material, absorbs and stores heat;When being in heat release mode, the stored heat is extracted, and output as a heat source externally.The application realizes the precise control and overshoot suppression of solid waste-based high-temperature heat storage system mode switching by gradient composite heat storage material design, multi-physical field coupling predictive control algorithm model construction and intelligent controller collaborative structure.

Description

Technical Field

[0001] This invention relates to the fields of industrial solid waste resource utilization, sustainable energy supply, and building energy conservation, specifically to a mode switching control method for a solid waste-based high-temperature thermal energy storage system based on multi-physics field synergistic regulation. Background Technology

[0002] In the field of solid waste-based high-temperature thermal energy storage systems, technologies such as CN120212555A and CN120300642A already provide solutions for molten salt thermal energy storage capacity regulation and power supply for hydrogen production in distribution cabinets.

[0003] CN120212555A proposes a molten salt thermal storage system based on deep learning and an adaptive PID controller, which can monitor the system status in real time and perform precise control. However, this method suffers from steam parameter overshoot and slow response speed during mode switching, such as rapidly switching from thermal storage mode to heat release mode, resulting in limited system efficiency and stability. In addition, the high-temperature corrosion problem of molten salt also affects the long-term operation and lifespan of the system.

[0004] Although CN120300642A achieves high efficiency and intelligent safety control in the power distribution cabinet for hydrogen production power, it does not involve mode switching control of solid waste-based high-temperature thermal storage system, and its heat dissipation system design is different from the multi-physics field collaborative regulation technology of this invention.

[0005] To address the aforementioned technical shortcomings, this invention proposes a mode switching control method for a solid waste-based high-temperature thermal energy storage system based on multi-physics field synergistic regulation. This method aims to solve the problems of steam parameter overshoot, slow response speed, and molten salt high-temperature corrosion during mode switching in existing technologies, thereby achieving efficient, rapid, and stable system operation and extending system lifespan. Summary of the Invention

[0006] This invention provides a mode switching control method for a solid waste-based high-temperature thermal storage system with multi-physics field regulation, in order to solve the technical problems existing in the prior art.

[0007] To achieve the above objectives, this invention provides a mode switching control method for a solid waste-based high-temperature thermal storage system with multi-physics field regulation, comprising the following steps: S1: Preparation of solid waste-based gradient composite thermal storage materials using substrates; S2: Place the solid waste-based gradient composite thermal storage material into the target area of ​​the thermal storage unit, and seal it after filling to enable it to have thermal storage capacity. S3: The intelligent controller enables dynamic switching between heat storage and heat release modes. When in heat storage mode, the solid waste-based gradient composite heat storage material undergoes a phase change to absorb and store heat. When in heat release mode, the stored heat is extracted and output as a heat source.

[0008] In one embodiment of the present invention, optionally, the construction of the intelligent controller is divided into three collaborative parts: the first part is a feedforward compensation module, the second part is a core control module, and the third part is a feedback compensation module.

[0009] In one embodiment of the present invention, optionally, The first part, the feedforward compensation module, is constructed based on a four-field coupling model of thermo-fluid-mechanical-chemical processes. Before the mode switching command is issued, the feedforward compensation module uses the current system state as the initial condition and calls the four-field coupling model to perform rapid numerical prediction, calculating the predicted trajectory of key system parameters and potential predicted overshoot. Key system parameters include steam temperature and pressure. Based on the predicted trajectory of key system parameters and potential predicted overshoot, the feedforward compensation module generates a feedforward control signal that directly acts on the actuator, aiming to pre-compensate the main modelable dynamic disturbances, thereby significantly reducing the regulation burden of subsequent feedback control loops. The second core control module employs a derivative-first PID algorithm. The improvement of this algorithm lies in the fact that its derivative term only performs derivative operations on the actual measured values ​​of the system; that is, the derivative output is... This effectively avoids differential shocks caused by abrupt changes in the setpoint, eliminating system oscillations. Simultaneously, the core control module features gain self-tuning, automatically employing a larger proportional and differential gain in the initial transient phase of mode switching to achieve rapid response, and smoothly transitioning to a smaller gain as the system approaches steady state, thus facilitating steady-state precision control. The third part, the feedforward compensation module, employs a fuzzy adaptive algorithm based on model-driven early warning and performance supervision. This module continuously monitors the predicted overshoot calculated by the feedforward module, as well as the actual system error and its rate of change. When the predicted overshoot exceeds a preset safety threshold or poor control performance of the PID controller is detected, the feedforward compensation module is activated. Through a built-in fuzzy rule base, it adjusts the proportional, integral, and derivative parameters of the core PID controller online in real time, ensuring that the dynamic characteristics of the PID controller match the current nonlinear and time-varying operating conditions of the system. This guarantees the robustness and stability of the system under the worst-case scenario. The intelligent controller ultimately outputs the total control signal. For feedforward control signal With the fuzzy adaptively optimized derivative-ahead PID output signal The sum of the total control signals The actuator controls the valve opening, which is continuously adjusted from 0% to 100%.

[0010] In one embodiment of the present invention, optionally, the solid waste-based gradient composite thermal storage material uses a high-porosity porous ceramic as the matrix, with a high thermal conductivity silicon carbide fiber network embedded in its pores, and the network pores are further filled with NaNO3-KNO3 eutectic salt as a phase change thermal storage medium. The high-porosity porous ceramic matrix raw material is one or more of fly ash, tailings, slag, and mudstone. The mass fraction of the NaNO3-KNO3 eutectic salt is ≥90%.

[0011] In one embodiment of the present invention, the preparation of the solid waste-based gradient composite thermal storage material optionally includes the following steps: first, the matrix raw material is crushed to a particle size of less than 8 mm, cleaned and dried, and then placed into a mold; then, a mixed slurry is added, compacted, cured, and demolded; finally, sintering, grinding, and porosity treatment are performed.

[0012] In one embodiment of the present invention, optionally, the composition and proportion of the mixed slurry are 50-60 parts water, 30-45 parts kaolin, 5-7 parts aluminum powder, and 3-8 parts organic binder.

[0013] In one embodiment of the present invention, optionally, the sintering environment atmosphere is oxygen-enriched air, the sintering temperature is 1250-1400°C, and the holding time is 4-6 hours.

[0014] In one embodiment of the present invention, optionally, the material is ground to a fineness of less than 200 mesh.

[0015] In one embodiment of the present invention, the optional process parameters for porosity treatment are: gas flow rate of 5 L / min, gas temperature of 80-100°C, and treatment time of 60-120 min.

[0016] In one embodiment of the present invention, optionally, the thermo-fluid-mechanical-chemical four-field coupling model is the mathematical core of the feedforward compensation module, used to describe and predict the transient behavior of the system. The thermo-fluid-mechanical-chemical four-field coupling model consists of the following mutually coupled control equations: (1) The governing equations of the thermal field: The governing equations of the thermal field obey energy conservation.

[0017] In the formula, T represents the temperature field. , These are composite materials and fluid density, respectively. , These refer to the effective specific heat capacity of the composite material and the specific heat capacity of the fluid, respectively. For the effective thermal conductivity of composite materials, For the fluid velocity field, For the latent heat source term of phase change, The term represents the heat source of the chemical reaction, where t is time. (2) Control equations of the flow field: Flow field governing equations for Darcy flow in porous media and mass conservation.

[0018] In the formula, For fluid pressure field, For porous media permeability, For fluid viscosity, For dynamic porosity, For quality source items; (3) Force field governing equations: The force field governing equations obey linear elastic equilibrium and geometric coupling.

[0019] Geometric coupling is manifested as follows: Dynamic porosity , Dynamic penetration rate , In the formula, For stress tensor, For solid displacement field, For strain tensor, The coefficient of thermal expansion; (4) Description of the chemical field: The phase transition process is represented in the energy equation using the equivalent heat capacity method, and the potential salt decomposition reaction is described by the Arrhenius equation, with its heat of reaction incorporated into the chemical reaction heat source term. , Solving this model simultaneously can predict the trajectory of temperature changes in the short time domain. and the trajectory of the fluid pressure field in the short time domain in the future. And calculate the predicted overshoot. .

[0020] Compared with the prior art, the present invention has the following beneficial effects: Collaborative control with superior performance: Through a three-level collaborative control architecture of "feedforward predictive compensation + derivative-first PID + fuzzy adaptive supervision", fast, smooth and overshoot-free control is achieved during mode switching, with overshoot <5% and stabilization time shortened to 30-120 seconds.

[0021] The combination of material and system innovation: The design of gradient composite thermal storage materials fundamentally improves thermal storage and conduction performance, and together with anti-corrosion coatings and intelligent control methods, it solves the problems of molten salt corrosion and slow system response.

[0022] Model-driven, accurate prediction: The use of a four-field coupled model for feedforward prediction enables the control behavior to be forward-looking, significantly overcoming the inherent difficulties of controlling large time delay systems and improving the accuracy of control and system stability.

[0023] Improved lifespan and reliability: The combined application of the above methods significantly reduces the thermomechanical stress and parameter fluctuations of the system under frequent switching conditions, thereby extending the overall service life of the system and improving operational reliability. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of the solid waste-based gradient composite thermal storage material structure involved in this invention; Figure 2 This is a flowchart illustrating the mode switching control of the solid waste-based high-temperature thermal energy storage system based on multi-physics field synergistic regulation involved in this invention. Figure 3 This is a block diagram of the collaborative structure of the core intelligent controller involved in this invention (showing the signal flow and collaborative relationship of the feedforward compensation module, the derivative-first PID core module, and the fuzzy adaptive module). Figure 4 This is a schematic diagram of a high-porosity porous ceramic matrix with a high thermal conductivity silicon carbide fiber network embedded inside the present invention. Figure 5 This is a graph showing the temperature / pressure changes before and after the mode conversion involved in this invention (indicating overshoot <5% and settling time within the range). Figure 6 This is a graph showing the changes in the physical field responses of the system before and after the mode conversion involved in this invention. Detailed Implementation

[0026] The technical solutions of 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.

[0027] The innovative approach of this invention is to achieve precise control and overshoot suppression of mode switching in a solid waste-based high-temperature thermal storage system through gradient composite thermal storage material design, multi-physics field coupled predictive control algorithm model construction, and intelligent controller collaborative structure.

[0028] This invention provides a mode switching control method for a solid waste-based high-temperature thermal energy storage system with multi-physics field regulation, which includes the following steps: S1: Preparation of solid waste-based gradient composite thermal storage materials using substrates; S2: Place the solid waste-based gradient composite thermal storage material into the target area of ​​the thermal storage unit, and seal it after filling to enable it to have thermal storage capacity. S3: The intelligent controller enables dynamic switching between heat storage and heat release modes. When in heat storage mode, the solid waste-based gradient composite heat storage material undergoes a phase change to absorb and store heat. When in heat release mode, the stored heat is extracted and output as a heat source.

[0029] In one embodiment of the present invention, optionally, the construction of the intelligent controller is divided into three collaborative parts: the first part is a feedforward compensation module, the second part is a core control module, and the third part is a feedback compensation module.

[0030] Figure 4 This is a schematic diagram of the high-porosity porous ceramic matrix with a high thermal conductivity silicon carbide fiber network embedded inside, as involved in this invention. Figure 4 In the diagram, 1 represents the porous ceramic matrix, 2 represents the pore structure, 3 represents the silicon carbide fiber network, 4 represents the NaNO3-KNO3 eutectic salt, and 5 represents the porosity gradient direction. Figure 5 This is a graph showing the temperature / pressure changes before and after the mode conversion involved in this invention (indicating overshoot <5% and settling time within the range). Figure 5 In the diagram, 1 is the switching time indicator line (t=0s), 2 is the temperature response curve (T), 3 is the pressure response curve (P), and 4 is the stabilization time interval indicator (30-120s). Figure 6 This is a graph showing the changes in the physical field responses of the system before and after the mode conversion involved in this invention. Figure 6 In the diagram, 5 is the thermal response curve (q), 6 is the flow response curve (v), 7 is the force response curve (s), and 8 is the chemical response curve (c).

[0031] In one embodiment of the present invention, optionally, The first part, the feedforward compensation module, is constructed based on a four-field coupling model of thermo-fluid-mechanical-chemical processes. Before the mode switching command is issued, the feedforward compensation module uses the current system state as the initial condition and calls the four-field coupling model to perform rapid numerical prediction, calculating the predicted trajectory of key system parameters and potential predicted overshoot. Key system parameters include steam temperature and pressure. Based on the predicted trajectory of key system parameters and potential predicted overshoot, the feedforward compensation module generates a feedforward control signal that directly acts on the actuator, aiming to pre-compensate the main modelable dynamic disturbances, thereby significantly reducing the regulation burden of subsequent feedback control loops. The second core control module employs a derivative-first PID algorithm. The improvement of this algorithm lies in the fact that its derivative term only performs derivative operations on the actual measured values ​​of the system; that is, the derivative output is... , For differential control output, For differential gain, The actual rate of change of the system output is given by t, where t is time. This effectively avoids differential shocks caused by abrupt changes in the setpoint, eliminating system oscillations. Simultaneously, the core control module possesses gain self-tuning capabilities, automatically employing a larger proportional and differential gain in the initial transient phase of mode switching to achieve rapid response. As the system approaches steady state, it smoothly transitions to a smaller gain, facilitating steady-state precision control. In the formula in the previous paragraph, The output is the derivative control component, where t is time. At time t, the output value of the derivative control component of the PID controller is... It is part of the controller's total output signal, specifically designed to reflect the trend of changes in the controlled variable and to suppress or compensate for that trend, thereby improving the system's dynamic performance (such as reducing overshoot and shortening settling time). The differential gain is an adjustable positive real parameter whose magnitude determines the strength of the differential control action. The larger the value, the more sensitive the controller is to changes in the system output rate, and the stronger its ability to suppress changing trends. In the intelligent controller of this invention, this parameter can be adjusted online by the feedback compensation module (fuzzy adaptive supervisor) based on the real-time performance of the system. This represents the rate of change of the system's actual output. The third part, the feedforward compensation module, employs a fuzzy adaptive algorithm based on model warning and performance supervision. This module continuously monitors the predicted overshoot calculated by the feedforward module, as well as the actual system error and its rate of change. When the predicted overshoot exceeds a preset safety threshold (e.g., 15%) or when poor control performance of the PID controller is detected, the feedforward compensation module is activated. Using a built-in fuzzy rule base (e.g., "If the error is large positive and the rate of change is small negative, then significantly increase the proportional gain"), it adjusts the proportional, integral, and derivative parameters of the core PID controller online in real time. This ensures that the dynamic characteristics of the PID controller match the current nonlinear and time-varying operating conditions of the system, guaranteeing the robustness and stability of the system under the worst-case scenario. The intelligent controller ultimately outputs the total control signal. For feedforward control signal With the fuzzy adaptively optimized derivative-ahead PID output signal The sum of the total control signals Control the action of the actuator (such as a piezoelectric valve), and continuously adjust the valve opening from 0% to 100%.

[0032] In one embodiment of the present invention, optionally, the solid waste-based gradient composite thermal storage material uses a high-porosity porous ceramic as the matrix, with a high thermal conductivity silicon carbide fiber network embedded in its pores, and the network pores are further filled with NaNO3-KNO3 eutectic salt as a phase change thermal storage medium. The high-porosity porous ceramic matrix raw material is one or more of fly ash, tailings, slag, and mudstone. The mass fraction of the NaNO3-KNO3 eutectic salt is ≥90%.

[0033] In one embodiment of the present invention, the preparation of the solid waste-based gradient composite thermal storage material optionally includes the following steps: first, the matrix raw material is crushed to a particle size of less than 8 mm, cleaned and dried, and then placed into a mold; then, a mixed slurry is added, compacted, cured, and demolded; finally, sintering, grinding, and porosity treatment are performed.

[0034] In one embodiment of the present invention, optionally, the composition and proportion of the mixed slurry are 50-60 parts water, 30-45 parts kaolin, 5-7 parts aluminum powder, and 3-8 parts organic binder.

[0035] In one embodiment of the present invention, optionally, the sintering environment atmosphere is oxygen-enriched air, the sintering temperature is 1250-1400°C, and the holding time is 4-6 hours.

[0036] In one embodiment of the present invention, optionally, the material is ground to a fineness of less than 200 mesh.

[0037] In one embodiment of the present invention, the optional process parameters for porosity treatment are: gas flow rate of 5 L / min, gas temperature of 80-100°C, and treatment time of 60-120 min.

[0038] In one embodiment of the present invention, optionally, the thermo-fluid-mechanical-chemical four-field coupling model is the mathematical core of the feedforward compensation module, used to describe and predict the transient behavior of the system. The thermo-fluid-mechanical-chemical four-field coupling model consists of the following mutually coupled control equations: (1) The governing equations of the thermal field: The governing equations of the thermal field obey energy conservation.

[0039] In the formula, T represents the temperature field. , These are composite materials and fluid density, respectively. , These refer to the effective specific heat capacity of the composite material and the specific heat capacity of the fluid, respectively. For the effective thermal conductivity of composite materials, For the fluid velocity field, For the latent heat source term of phase change, The term represents the heat source of the chemical reaction, where t is time. (2) Control equations of the flow field: Flow field governing equations for Darcy flow in porous media and mass conservation.

[0040] In the formula, For fluid pressure field, For porous media permeability, For fluid viscosity, For dynamic porosity, For quality source items; (3) Force field governing equations: The force field governing equations obey linear elastic equilibrium and geometric coupling.

[0041] Geometric coupling is manifested as follows: Dynamic porosity , Dynamic penetration rate , In the formula, For stress tensor, For solid displacement field, For strain tensor, The coefficient of thermal expansion; (4) Description of the chemical field: The phase transition process is represented in the energy equation using the equivalent heat capacity method, and the potential salt decomposition reaction is described by the Arrhenius equation, with its heat of reaction incorporated into the chemical reaction heat source term. , Solving this model simultaneously can predict the trajectory of temperature changes in the short time domain. and the trajectory of the fluid pressure field in the short time domain in the future. And calculate the predicted overshoot. .

[0042] The following is another specific embodiment of the present invention: Figure 1 This is a schematic diagram of the solid waste-based gradient composite thermal storage material structure involved in this invention. Figure 2 This is a flowchart illustrating the mode switching control of the solid waste-based high-temperature thermal energy storage system based on multi-physics field synergistic regulation, as described in this invention. Figure 1 In the diagram, 1 represents the porous ceramic matrix (fly ash / slag), 2 represents the silicon carbide fiber network, 3 represents the NaNO3-KNO3 eutectic salt, 4 represents the pore structure, and 5 represents the porosity gradient direction. For example... Figure 1 , Figure 2 As shown, the mode switching control method for a solid waste-based high-temperature thermal energy storage system based on multi-physics field synergistic regulation provided by the present invention includes the following steps: Step S1: Preparation of gradient composite thermal storage material. A high-porosity porous ceramic matrix is ​​prepared using industrial waste (such as fly ash) as the main substrate. A silicon carbide fiber network is constructed within the matrix pores and filled with NaNO3-KNO3 eutectic salt. The mixture is then sintered to form a gradient composite thermal storage material, which is placed within the thermal storage unit.

[0043] Step S2: System Initialization and Model Prediction. After system startup, the intelligent controller loads the THMC four-field coupled model. Upon receiving a mode switching command, the feedforward compensation module immediately uses the current sensor data as initial values ​​to run the model for rapid simulation, predicting the steam temperature, pressure trajectory, and overshoot risk within the next tens of seconds, and generating corresponding feedforward control signals.

[0044] Step S3: Coordinated control execution. For example... Figure 3 As shown, the feedforward control signal is superimposed with the derivative-first PID output signal optimized by the fuzzy adaptive module to form the overall control command, driving the actuators such as the high-performance piezoelectric valve. The derivative-first PID ensures smooth adjustment that is insensitive to sudden changes in the setpoint, while the fuzzy adaptive module dynamically adjusts the PID parameters based on the predicted overshoot and real-time error as a safety redundancy.

[0045] Step S4: Process Monitoring and Dynamic Adjustment. Throughout the switching process, the controller continuously compares the model's predicted values ​​with the actual sensor feedback. If unmodeled disturbances occur or the prediction deviation increases, the feedback compensation module will further strengthen the parameter adjustment rules to ensure that the actual overshoot is always suppressed within 5% until the system reaches stability near the target value (stabilization time controlled within 30-120 seconds).

[0046] Step S5: System stabilizes. After the switch is complete, the system enters a new steady-state mode (heat storage or heat release), and the controller switches to steady-state accuracy maintenance mode. The resulting gradient composite thermal storage system can be applied to energy storage, steel smelting, building energy conservation, and other fields.

[0047] Compared with the prior art, the present invention has the following beneficial effects: Collaborative control with superior performance: Through a three-level collaborative control architecture of "feedforward predictive compensation + derivative-first PID + fuzzy adaptive supervision", fast, smooth and overshoot-free control is achieved during mode switching, with overshoot <5% and stabilization time shortened to 30-120 seconds.

[0048] The combination of material and system innovation: The design of gradient composite thermal storage materials fundamentally improves thermal storage and conduction performance, and together with anti-corrosion coatings and intelligent control methods, it solves the problems of molten salt corrosion and slow system response.

[0049] Model-driven, accurate prediction: The use of a four-field coupled model for feedforward prediction enables the control behavior to be forward-looking, significantly overcoming the inherent difficulties of controlling large time delay systems and improving the accuracy of control and system stability.

[0050] Improved lifespan and reliability: The combined application of the above methods significantly reduces the thermomechanical stress and parameter fluctuations of the system under frequent switching conditions, thereby extending the overall service life of the system and improving operational reliability.

[0051] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of one embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing the present invention.

[0052] Those skilled in the art will understand that the modules in the apparatus of the embodiments can be distributed in the apparatus of the embodiments as described in the embodiments, or they can be located in one or more devices different from this embodiment with corresponding changes. The modules of the above embodiments can be combined into one module, or they can be further divided into multiple sub-modules.

[0053] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A mode switching control method for a solid waste-based high-temperature thermal storage system regulated by multiple physics fields, characterized in that, Includes the following steps: S1: Preparation of solid waste-based gradient composite thermal storage materials using substrates; S2: Place the solid waste-based gradient composite thermal storage material into the target area of ​​the thermal storage unit, and seal it after filling to enable it to have thermal storage capacity. S3: The intelligent controller enables dynamic switching between heat storage and heat release modes. When in heat storage mode, the solid waste-based gradient composite heat storage material undergoes a phase change to absorb and store heat. When in heat release mode, the stored heat is extracted and output as a heat source.

2. The mode switching control method for a solid waste-based high-temperature thermal storage system with multi-physics field regulation according to claim 1, characterized in that, The construction of the intelligent controller is divided into three collaborative parts: the first part is the feedforward compensation module, the second part is the core control module, and the third part is the feedback compensation module.

3. The mode switching control method for a solid waste-based high-temperature thermal storage system with multi-physics field regulation according to claim 2, characterized in that, The first part, the feedforward compensation module, is constructed based on a four-field coupling model of thermo-fluid-mechanical-chemical processes. Before the mode switching command is issued, the feedforward compensation module uses the current system state as the initial condition and calls the four-field coupling model to perform rapid numerical prediction, calculating the predicted trajectory of key system parameters and potential predicted overshoot. Key system parameters include steam temperature and pressure. Based on the predicted trajectory of key system parameters and potential predicted overshoot, the feedforward compensation module generates a feedforward control signal that directly acts on the actuator, aiming to pre-compensate the main modelable dynamic disturbances, thereby significantly reducing the regulation burden of subsequent feedback control loops. The second core control module employs a derivative-first PID algorithm. The improvement of this algorithm lies in the fact that its derivative term only performs derivative operations on the actual measured values ​​of the system; that is, the derivative output is... , For differential control output, For differential gain, The actual rate of change of the system output is given by t, where t is time. This effectively avoids differential shocks caused by abrupt changes in the setpoint, eliminating system oscillations. Simultaneously, the core control module possesses gain self-tuning capabilities, automatically employing a larger proportional and differential gain in the initial transient phase of mode switching to achieve rapid response. As the system approaches steady state, it smoothly transitions to a smaller gain, facilitating steady-state precision control. The third part, the feedforward compensation module, employs a fuzzy adaptive algorithm based on model-driven early warning and performance supervision. This module continuously monitors the predicted overshoot calculated by the feedforward module, as well as the actual system error and its rate of change. When the predicted overshoot exceeds a preset safety threshold or poor control performance of the PID controller is detected, the feedforward compensation module is activated. Through a built-in fuzzy rule base, it adjusts the proportional, integral, and derivative parameters of the core PID controller online in real time, ensuring that the dynamic characteristics of the PID controller match the current nonlinear and time-varying operating conditions of the system. This guarantees the robustness and stability of the system under the worst-case scenario. The intelligent controller ultimately outputs the total control signal. For feedforward control signal With the fuzzy adaptively optimized derivative-ahead PID output signal The sum of the total control signals The actuator controls the valve opening, which is continuously adjusted from 0% to 100%.

4. The mode switching control method for a solid waste-based high-temperature thermal storage system with multi-physics field regulation according to claim 1, characterized in that, The solid waste-based gradient composite thermal storage material uses a high-porosity porous ceramic as the matrix, with a high thermal conductivity silicon carbide fiber network embedded in the pores. The pores of the network are then filled with NaNO3-KNO3 eutectic salt as the phase change thermal storage medium. The high-porosity porous ceramic matrix raw material is one or more of fly ash, tailings, slag, and mudstone. The mass fraction of the NaNO3-KNO3 eutectic salt is ≥90%.

5. The mode switching control method for a solid waste-based high-temperature thermal storage system with multi-physics field regulation according to claim 1, characterized in that, The preparation of solid waste-based gradient composite thermal storage materials includes the following steps: First, the base material is crushed to a particle size of less than 8mm, cleaned and dried, and then placed into a mold; then the mixed slurry is added, compacted, cured, and demolded; finally, sintering, grinding, and porosity treatment are carried out.

6. The mode switching control method for a solid waste-based high-temperature thermal storage system with multi-physics field regulation according to claim 5, characterized in that, The composition and proportion of the mixed slurry are 50-60 parts water, 30-45 parts kaolin, 5-7 parts aluminum powder, and 3-8 parts organic binder.

7. The mode switching control method for a solid waste-based high-temperature thermal storage system with multi-physics field regulation according to claim 5, characterized in that, The sintering environment is oxygen-rich air, the sintering temperature is 1250-1400°C, and the holding time is 4-6 hours.

8. The mode switching control method for a solid waste-based high-temperature thermal storage system with multi-physics field regulation according to claim 5, characterized in that, Grind to a fineness of less than 200 mesh.

9. The mode switching control method for a solid waste-based high-temperature thermal storage system with multi-physics field regulation according to claim 5, characterized in that, The process parameters for porosity treatment are: gas flow rate 5L / min, gas temperature 80-100°C, and treatment time 60-120min.

10. The mode switching control method for a solid waste-based high-temperature thermal storage system with multi-physics field regulation according to claim 3, characterized in that, The thermo-fluid-mechanical-chemical four-field coupling model is the mathematical core of the feedforward compensation module, used to describe and predict the transient behavior of the system. The thermo-fluid-mechanical-chemical four-field coupling model consists of the following mutually coupled control equations: (1) The governing equations of the thermal field: The governing equations of the thermal field obey energy conservation. In the formula, T is the real-time temperature. , These are composite materials and fluid density, respectively. , These refer to the effective specific heat capacity of the composite material and the specific heat capacity of the fluid, respectively. For the effective thermal conductivity of composite materials, For the fluid velocity field, For the latent heat source term of phase change, The term represents the heat source of the chemical reaction, where t is time. (2) Control equations of the flow field: Flow field governing equations for Darcy flow in porous media and mass conservation. In the formula, For fluid pressure field, For porous media permeability, For fluid viscosity, For dynamic porosity, For quality source items; (3) Force field governing equations: The force field governing equations obey linear elastic equilibrium and geometric coupling. Geometric coupling is manifested as follows: Dynamic porosity , Dynamic penetration rate , In the formula, For stress tensor, For solid displacement field, For strain tensor, The coefficient of thermal expansion; This is a volume force, with units of N / m³. Let be the elastic stiffness tensor. The initial temperature. For real-time current, For real-time pressure, This is the initial pressure. The initial porosity, , These are the sensitivity coefficients of porosity to pressure and temperature, respectively. It is the Kozeny-Carman constant; (4) Description of the chemical field: The phase transition process is represented in the energy equation using the equivalent heat capacity method, and the potential salt decomposition reaction is described by the Arrhenius equation, with its heat of reaction incorporated into the chemical reaction heat source term. , Solving this model simultaneously can predict the trajectory of temperature changes in the short time domain. and the trajectory of the fluid pressure field in the short time domain in the future. And calculate the predicted overshoot. .

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