A heat storage fluidized bed control method, device, equipment and medium

Abstract

Embodiments of the present application provide a heat storage fluidized bed control method, device, equipment and medium, the method comprises: obtaining the heating state and heating environment information of each fluidized bed partition of the heat storage fluidized bed;According to the heating state and heating environment information, determine the current heat distribution data and heat distribution prediction data of each fluidized bed partition;According to the current heat distribution data and heat distribution prediction data, determine the parameter value of the target control parameter;According to the parameter value of the target control parameter corresponding to each fluidized bed partition, adjust the heating environment of each fluidized bed partition. Make the heat storage fluidized bed can flexibly adjust the operation mode under different power supply conditions, always maintain stable heating environment, guarantee the safe, stable and efficient operation of the heat storage fluidized bed when facing unstable power.

Description

Technical Field

[0001] This invention relates to the field of thermal storage devices, and in particular to a method, apparatus, equipment and medium for controlling a thermal storage fluidized bed. Background Technology

[0002] With the widespread application of renewable energy, ultra-high temperature fluidized bed energy storage devices face numerous challenges when utilizing unstable electrical energy such as wind power for energy storage. Due to the characteristics of energy sources like wind power, the power supply may exhibit significant instability, which directly leads to instability in the fluidized bed heating environment. Therefore, ensuring that the thermal fluidized bed can maintain a stable and reasonable heating environment in the face of energy fluctuations is not only related to the safe operation of the system but also directly affects the overall performance and reliability of the energy storage device. Summary of the Invention

[0003] In view of the above problems, embodiments of the present invention are proposed to provide a method, apparatus, equipment and medium for controlling a thermal storage fluidized bed that overcomes or at least partially solves the problem of how to ensure that the thermal storage fluidized bed can maintain a stable and reasonable heating environment in the face of energy fluctuations.

[0004] To address the aforementioned problems, this invention discloses a method for controlling a thermally heated fluidized bed, the method comprising: Obtain heating status and heating environment information for each fluidized bed zone of the thermal storage fluidized bed; Based on the heating state and the heating environment information, determine the current heat distribution data and predicted heat distribution data for each fluidized bed zone; Based on the current heat distribution data and the predicted heat distribution data, determine the parameter values ​​of the target control parameters; The heating environment of each fluidized bed zone is adjusted according to the parameter values ​​of the target control parameters corresponding to each fluidized bed zone.

[0005] Optionally, each fluidized bed zone of the thermal storage fluidized bed is determined based on the size information of the thermal storage fluidized bed and a preset height-to-diameter ratio, where the height-to-diameter ratio refers to the ratio of the height to the diameter of the fluidized bed.

[0006] Optionally, the heating state includes at least one of heating power, fluidization state data of the fluidized bed partition, state data of the thermal storage particles, and heat transfer parameters, and the heating environment information includes temperature data and power supply condition data.

[0007] Optionally, determining the current heat distribution data of each fluidized bed zone based on the heating state and the heating environment information includes: Based on the heating power, the fluidization state data, the state data of the thermal storage particles, the heat transfer parameters, and the temperature data, the current heat distribution data of each fluidized bed zone is determined using the transient thermal field equation.

[0008] Optionally, determining the predicted heat distribution data for each fluidized bed zone based on the heating state and the heating environment information includes: Based on the power supply condition data, the heating power prediction data is determined; the power supply condition data includes power output data and power stability parameters. Based on the fluidization state data, the state data of the thermal storage particles, the heat transfer parameters, the temperature data, and the heating power prediction data, the heat distribution prediction data for each fluidized bed zone is determined using the transient thermal field equation.

[0009] Optionally, determining the parameter value of the target control parameter based on the current heat distribution data and the predicted heat distribution data includes: The first heat data is determined based on the current heat distribution data and the preset first weight; The second heat data is determined based on the predicted heat distribution data and the preset second weight; The sum of the first heat data and the second heat data is used as the current state data; Determine the first score of the current state data; If the first score is less than or equal to a preset score threshold, the parameter values ​​of the control parameters of the fluidized bed partition are maintained in the current state. If the first score is greater than a preset score threshold, the parameter value of the target control parameter is determined.

[0010] Optionally, the current state data includes maximum temperature, minimum temperature, temperature gradient, average temperature, and operating power, and determining the first score of the current state data includes: A heat distribution score is determined based on the maximum temperature, the minimum temperature, the temperature gradient, and the average temperature. Based on the operating power, determine the energy consumption score; A safety score is determined based on the maximum temperature. The heat distribution score, energy consumption score, and safety score are added together to determine the first score.

[0011] Optionally, determining the parameter value of the target control parameter when the first score is greater than a preset score threshold includes: In the preset parameter selection pool, at least one first data pair is determined, wherein the first data pair is a data pair of heating power and fluidization state data; Determine the second score of the state data corresponding to the first data pair; The first data pair corresponding to the second score that is less than the preset score threshold is used as the parameter value of the target control parameter.

[0012] On the other hand, the present invention also provides a thermal storage fluidized bed control device, the device comprising: The data acquisition module is used to acquire the heating status and heating environment information of each fluidized bed zone of the thermal storage fluidized bed; The heat data determination module is used to determine the current heat distribution data and heat distribution prediction data of each fluidized bed partition based on the heating state and the heating environment information. The parameter value determination module is used to determine the parameter value of the target control parameter based on the current heat distribution data and the predicted heat distribution data. The environment adjustment module is used to adjust the heating environment of each fluidized bed zone according to the parameter values ​​of the target control parameters corresponding to each fluidized bed zone.

[0013] Optionally, the heat data determination module includes: The first heat distribution data determination submodule is used to determine the current heat distribution data of each fluidized bed partition based on the heating power, the fluidization state data, the state data of the heat storage particles, the heat transfer parameters, and the temperature data, using the transient thermal field equation.

[0014] Optionally, the heat data determination module includes: The heating power prediction submodule is used to determine the heating power prediction data based on the power condition data; the power condition data includes power output data and power stability parameters. The second heat distribution data determination submodule is used to determine the heat distribution prediction data of each fluidized bed zone based on the fluidization state data, the state data of the thermal storage particles, the heat transfer parameters, the temperature data, and the heating power prediction data, using the transient thermal field equation.

[0015] Optionally, the parameter value determination module includes: The first heat data determination submodule is used to determine the first heat data based on the current heat distribution data and a preset first weight. The second heat data determination submodule is used to determine the second heat data based on the heat distribution prediction data and the preset second weight; The status data determination submodule is used to take the sum of the first heat data and the second heat data as the current status data; The first score determination submodule is used to determine the first score of the current state data; The first target parameter value determination submodule is used to maintain the parameter value of the control parameter of the fluidized bed partition in the current state when the first score is less than or equal to a preset score threshold. The second target parameter value determination submodule is used to determine the parameter value of the target control parameter when the first score is greater than a preset score threshold.

[0016] Optionally, the current status data includes maximum temperature, minimum temperature, temperature gradient, average temperature, and operating power, and the first score determination submodule includes: A heat distribution score determination unit is used to determine a heat distribution score based on the maximum temperature, the minimum temperature, the temperature gradient, and the average temperature. An energy consumption score determination unit is used to determine an energy consumption score based on the operating power. A safety score determination unit is used to determine a safety score based on the maximum temperature. The second scoring unit is used to add the heat distribution score, energy consumption score and safety score to determine the first score.

[0017] Optionally, the second target parameter value determining submodule includes: The data pair determination unit is used to determine at least one first data pair in a preset parameter selection pool, wherein the first data pair is a data pair of heating power and fluidization state data; The third score determination unit is used to determine the second score of the state data corresponding to the first data pair; The third target parameter value determination unit is used to take the first data pair corresponding to the second score that is less than the preset score threshold as the parameter value of the target control parameter.

[0018] Accordingly, this invention discloses an electronic device, including: a processor, a memory, and a computer program stored in the memory and capable of running on the processor. When the computer program is executed by the processor, it implements the various steps of the above-described embodiment of a thermal storage fluidized bed control method.

[0019] Accordingly, this invention discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the various steps of the above-described embodiment of a thermal storage fluidized bed control method.

[0020] The embodiments of the present invention have the following advantages: By acquiring the heating status and heating environment information of each zone of the thermal storage fluidized bed, and combining the current heat distribution data and heat distribution prediction data, the system can grasp the current and future operating status of the fluidized bed zone in real time. Based on the current heat distribution data and the heat distribution prediction data, the system determines the parameter values ​​of the target control parameters. Based on the parameter values ​​of the target control parameters corresponding to each fluidized bed zone, the system adjusts the heating environment of each fluidized bed zone. By introducing forward-looking heat distribution prediction data based on the current environment, the system possesses forward-looking control capabilities, enabling it to respond proactively to energy fluctuations. This significantly improves the stability and rationality of the system in the face of energy fluctuations, allowing the thermal storage fluidized bed to flexibly adjust its operating mode under different power supply conditions, always maintaining a stable heating environment, and ensuring the safe, stable, and efficient operation of the thermal storage fluidized bed in the face of unstable power. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating the steps of an embodiment of a thermal storage fluidized bed control method according to the present invention; Figure 2 This is a schematic diagram of the fluidized bed partitioning according to an embodiment of the thermal storage fluidized bed control method of the present invention; Figure 3 This is a structural block diagram of an embodiment of a thermal storage fluidized bed control device according to the present invention. Detailed Implementation

[0022] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0023] One of the core concepts of this invention is that by partitioning the fluidized bed, the control precision is improved and heating state data and heating environment data are fully acquired. Based on the current environment, forward-looking heat distribution prediction data is introduced, enabling the system to have forward-looking regulation capabilities and to make forward-looking responses to energy actions in advance.

[0024] Reference Figure 1 The diagram illustrates a step flowchart of an embodiment of a thermal storage fluidized bed control method according to the present invention, which may specifically include the following steps: Step 101: Obtain the heating status and heating environment information of each fluidized bed zone of the thermal storage fluidized bed.

[0025] A thermal energy storage fluidized bed is a device that utilizes fluidization technology for efficient heat storage and transfer. It is filled with heat storage particles such as ceramic balls or molten salt particles, and heat storage and release are achieved through the flow of gases such as air or nitrogen. Its core principle is to enhance heat transfer efficiency through fluidization (the suspension state of particles under the influence of gas or liquid). It is widely used in wind power consumption, industrial waste heat recovery, and other fields, possessing highly efficient heat storage and release capabilities.

[0026] However, because thermal storage fluidized beds are generally large in scale, the heat distribution throughout the entire fluidized bed may exhibit regional differences, such as higher temperatures in the central area and lower temperatures in the peripheral areas. A globally unified control strategy may not effectively address these regional differences. Therefore, this invention proposes a method of dividing the thermal storage fluidized bed into multiple zones according to certain rules. After dividing the fluidized bed into zones, zone control can be implemented for each zone. Through zone control, the heating power and fluidization velocity can be adjusted according to the specific state of each zone, thereby achieving more precise heat distribution control. Furthermore, based on different operational needs, different zones may also undertake different functions or tasks, such as some zones for rapid heating and others for long-term energy storage. Zone control allows the system to be flexibly adjusted according to actual needs, adapting to changing operating scenarios.

[0027] For fluidized beds (fluidized bed zones), there are two representative types of parameters: heating state and heating environment information. Heating state refers to the operating parameters within the thermal storage fluidized bed zone that are directly related to heat input, transfer, and distribution. These parameters reflect the current thermodynamic characteristics, particle behavior, and heat transfer mechanisms of the fluidized bed zone. They describe the dynamic changes in heat within the fluidized bed zone and provide key input data required for optimized control. Heating state changes over time, especially under transient conditions, and includes thermodynamic parameters, hydrodynamic parameters, and material property parameters.

[0028] Heating environment information refers to the external environmental conditions of a fluidized bed storage section and the factors influencing its operation. This information describes the interaction between the fluidized bed and the external environment, reflecting the impact of the external environment on the fluidized bed's heat distribution and operational performance, and providing the basic data needed to predict future heat distribution trends. For example, external environmental conditions may indicate impending heat loss or suggest the need for additional heat input. Furthermore, environmental fluctuations such as changes in power supply conditions or ambient temperature fluctuations can significantly impact the stability of the fluidized bed. Heating environment information typically describes the boundary conditions between the fluidized bed and the external environment, including physical environmental parameters and operational support conditions. While heating status focuses on the operational characteristics within the fluidized bed section, heating environment information focuses on the impact of the external environment on the fluidized bed. The two complement each other, together forming the complete data foundation for the operation of a fluidized bed system.

[0029] In this invention, the main body executing the method can be an intelligent control system, which typically consists of two parts: hardware and software. The hardware part may include a sensor network responsible for real-time acquisition of heating status data and heating environment information of the thermal fluidized bed zones; a controller responsible for executing control commands and adjusting control parameters such as heating power and fluidization velocity; and computing devices for solving and optimizing the thermal field equations. The software part may include a data processing module for preprocessing the data acquired by the sensors to ensure the accuracy of the input data; an optimization module responsible for possible optimizations; a control command generation module for generating specific control commands based on optimization results or static rules; and a strategy feedback module for monitoring the system's operating status in real time and adjusting the control based on feedback. Furthermore, depending on the actual situation, the system can also be connected to edge computing devices and cloud computing platforms to improve response speed.

[0030] In one embodiment, the various fluidized bed zones of the thermal storage fluidized bed are determined based on the size information of the thermal storage fluidized bed and a preset height-to-diameter ratio, wherein the height-to-diameter ratio refers to the ratio of the height to the diameter of the fluidized bed.

[0031] The aspect ratio (HRR) refers to the ratio of the height to the diameter of a fluidized bed, i.e., HRR = diameter / height. The HRR directly affects the fluid dynamics and heat distribution within the fluidized bed. Different HRRs lead to different particle behaviors, such as fluidization state, particle concentration distribution, and heat transfer efficiency. Zoning can optimize the geometry of each region based on the HRR, thereby improving heat distribution uniformity and system operating efficiency. Prior to this, it is necessary to obtain the dimensions of the fluidized bed, as these dimensions are the basis for calculating the HRR and also serve as the physical boundary conditions for zoning. The HRR of each zone can be derived based on empirical rules or experimental data; for example, the HRR value can range from 3 to 5. Based on the overall dimensions of the thermal storage fluidized bed and the selected target HRR, the geometric parameters of each zone are calculated.

[0032] For example: If the overall height of the thermal storage fluidized bed is H=15m, the diameter is D=3m, and the target height-to-diameter ratio is between 3:1 and 5:1, then this thermal storage fluidized bed can be divided into three zones. The specific zoning scheme can be that the zones have the same height but different diameters, i.e., each zone has the same height: H1=H2=H3=5m. The diameter of each zone can be dynamically selected based on operational needs, with the height-to-diameter ratio set dynamically. For example, the height-to-diameter ratio of zone 1 can be 3:1, then the diameter is D1=1.67; the height-to-diameter ratio of zone 2 can be 4:1, then the diameter is D2=1.25m; similarly, the diameter of zone 3 can be determined using a height-to-diameter ratio of 5:1. In addition, a scheme with the same diameter but different heights, or a scheme with the same height and diameter, can also be adopted. The number of zones and geometric parameters can be dynamically adjusted according to actual needs, increasing the number of zones to improve control accuracy, or reducing the number of zones to reduce complexity.

[0033] A higher aspect ratio typically leads to stronger axial flow, while a lower aspect ratio favors radial flow. This difference affects the particle trajectory and heat transfer path. Zones with a larger aspect ratio may form a high-temperature zone in the central region and a lower temperature zone at the edge, while zones with a smaller aspect ratio are more likely to achieve a uniform heat distribution.

[0034] Reference Figure 2 The diagram shows a fluidized bed partitioning schematic of an embodiment of a thermal storage fluidized bed control method according to the present invention: The entire fluidized bed is divided into two fluidized bed zones, namely fluidized bed zone 1 and fluidized bed zone 2. Each fluidized bed zone has an independent heater, a shut-off valve for independently adjusting the fluidization air velocity, and an overflow port for discharging the heat storage particles. All fluidized bed zones share a unified air chamber.

[0035] In one embodiment, the heating state includes at least one of heating power, fluidization state data of the fluidized bed partition, state data of the thermal storage particles, and heat transfer parameters, and the heating environment information includes temperature data and power supply condition data.

[0036] The significance and uses of heating state and heating environment information have been introduced above. In this invention, heating data may include at least one of the following: heating power, fluidization state data of the fluidized bed partition, state data of the thermal storage particles, and heat transfer parameters. Heating environment information includes temperature data and power supply condition data. Heating power refers to the amount of heat energy input provided to the fluidized bed partition, which determines the rate of temperature change and heat accumulation within the fluidized bed partition. Fluidization state data, including fluidization velocity, particle concentration, and fluidization model, is used to describe the motion state and flow characteristics of particles within the fluidized bed. The state data of the thermal storage particles describes the physical and thermodynamic properties of the thermal storage particles themselves, specifically including the following: current particle temperature, specific heat capacity, thermal conductivity, particle size, and density. Heat transfer parameters describe the heat transfer characteristics within the fluidized bed, specifically including the following: thermal conductivity, convective heat transfer coefficient, and radiative heat transfer coefficient. Heat transfer parameters reflect the heat transfer mechanism and efficiency within the fluidized bed. Temperature data refers to the ambient temperature surrounding the fluidized bed zones; power supply data describes the characteristics and fluctuations of the power supply system, specifically including voltage, frequency, and wind power fluctuations. Heating status and heating environment information together form the data foundation for the thermal storage fluidized bed control method, ensuring that the system can achieve a balance between uniform heat distribution, minimized energy consumption, and safety.

[0037] Step 102: Based on the heating state and the heating environment information, determine the current heat distribution data and predicted heat distribution data for each fluidized bed zone.

[0038] Using operational data from a thermal storage fluidized bed, physical models or computational methods are employed to generate current heat distribution data and predict future heat distribution trends. This data forms the foundation for subsequent optimal control. Current heat distribution data refers to the specific heat distribution within a fluidized bed zone at a given moment, reflecting the actual operating state of the zone and used to assess the system's current performance. This data may include parameters such as temperature field, heat gradient, and heat flux density. Predicted heat distribution data, based on the current operating state and external environmental conditions, estimates the heat distribution over a future period. This provides a forward-looking basis for optimal control, ensuring the system can adapt to future changes. This data may include predicted parameters such as temperature field, heat gradient, and heat flux density. The transient thermal field equation is a mathematical model used to describe the heat transfer process within the fluidized bed zone over time. By solving the transient thermal field equation, both current and predicted heat distribution data can be calculated. The transient thermal field equation is a common existing technique for calculating heat distribution data and will not be elaborated upon here.

[0039] Two exemplary embodiments are presented below to explain step 102; In a first exemplary embodiment, the current heat distribution data of each fluidized bed partition can be determined based on the heating state and the heating environment information by using a transient thermal field equation to determine the current heat distribution data of each fluidized bed partition based on the heating power, the fluidization state data, the state data of the thermal storage particles, the heat transfer parameters, and the temperature data.

[0040] The transient thermal field equation is a partial differential equation describing the temporal and spatial distribution of heat, and its general form is: Where ρ represents density, c p Here, represents specific heat capacity, T represents temperature, t represents time, k represents thermal conductivity, and Q represents the heat source term, which typically includes heating power, convective heat transfer, etc. To solve this equation, input data such as heating power, fluidization state data, state data of the heat storage particles, heat transfer parameters, temperature data, and power supply conditions need to be correlated with the terms in the equation, and then calculated using numerical methods.

[0041] For heat source terms, they are generally divided into heating power and heat transfer caused by convection and radiation. If the obtained heating power is uniformly distributed, it can be directly substituted into the heating power in Q and added to the equation. If the heating power is non-uniformly distributed, it needs to be allocated to different grid cells according to the characteristics of the partition. For example, the heating power of a certain partition is P=50kW, and the volume is V=1m³. 3Therefore, the heating power component of the heat source term Q is = P / V = 50,000 W / m 3 The fluidization state data is substituted into the equations by influencing the convective heat transfer coefficient h. Specifically, the heat transfer caused by convective heat transfer can be expressed as: Q conv =h*A(T fluid T solid Where: h represents the convective heat transfer coefficient, A represents the heat transfer area, and T fluid and T solid These represent the temperatures of the fluid and the solid particles, respectively, and h can be expressed as h = C * v n Where C and n are preset coefficients determined experimentally, and v represents the fluidization velocity. Meanwhile, in high-temperature scenarios, radiative heat transfer cannot be ignored; therefore, the heat transfer caused by radiative heat transfer can be expressed as: Q rad =σ* *(T solid4 T env4 Where: σ represents the Stefan-Boltzmann constant, The preset emission rate. T solid and T env The temperatures of the particles and the environment can be confirmed based on the acquired temperature data. Information such as density and specific heat capacity from the state data of the thermal storage particles can be directly substituted into the equations. The thermal conductivity k can be determined using heat transfer parameters, while the boundary conditions of the equations are conventionally determined based on the temperature data.

[0042] Once the required parameters in the equations are determined, the transient thermal field equations can be solved using the finite difference method or the finite element method. Since the solution process is generally applicable, it will not be elaborated here. The time step and spatial grid size used in the solution process can be flexibly selected based on experience or operational rules; this application does not impose any restrictions on these. The combination of these data ensures that the transient thermal field equations accurately reflect the heat transfer process within the thermal storage fluidized bed partitions, providing a scientific basis for optimized control.

[0043] In a second exemplary embodiment, the method for determining the predicted heat distribution data for each fluidized bed zone based on the heating state and the heating environment information can be: Based on the power supply condition data, heating power prediction data is determined; the power supply condition data includes power output data and power stability parameters; based on the fluidization state data, the state data of the thermal storage particles, the heat transfer parameters, the temperature data, and the heating power prediction data, the heat distribution prediction data of each fluidized bed zone is determined using the transient thermal field equation.

[0044] When the power supply fluctuates, the heating power may fluctuate in the future due to energy fluctuations. Since the current heating power collected by sensors cannot effectively simulate and predict the fluctuation of power output in the future window, this paper uses the current power condition data to simulate and predict the power supply situation in the future time window. The power stability parameter can be the fluctuation pattern of the power supply, which can be determined based on the historical log data of the power supply. The prediction method can also be to use a long short-term memory neural network. After obtaining the predicted power output data, the heating power prediction data can be determined according to the corresponding physical formulas, such as P=V*I, P=V2 / R, etc.

[0045] Step 103: Determine the parameter values ​​of the target control parameters based on the current heat distribution data and the predicted heat distribution data.

[0046] By inputting current heat distribution data and predicted heat distribution data into the control output model of a pre-defined built-in optimization algorithm, the corresponding target control parameter values ​​can be output. Target control parameters refer to the key variables in a thermal fluidized bed system that directly affect the system's operating state. They are the core outputs of the control system, determining the actual operating mode of the system and directly affecting key performance indicators such as heat distribution, energy consumption, and safety. These parameters generally include heating power allocation, fluidizing air velocity adjustment, particle feed rate, cooling condition adjustment, and the start / stop status of the energy storage device. In this invention, the target control parameters typically used are heating power allocation and fluidizing air velocity adjustment. The optimization algorithm can be, for example, linear programming, nonlinear programming, genetic algorithms, or particle swarm optimization.

[0047] In one embodiment, step 103 may include the following sub-steps: Sub-step S11: Determine the first heat data based on the current heat distribution data and the preset first weight.

[0048] The sources of heat distribution data have been described above. Now, it needs to be multiplied by a corresponding preset first weight to dynamically change the degree of influence of the current heat distribution data in the final decision. Generally, if there are abnormalities in the current heat distribution data, the first weight will account for a larger proportion so that the system can give more consideration to the current situation and handle abnormalities in real time.

[0049] Sub-step S12: Determine the second heat data based on the predicted heat distribution data and the preset second weight.

[0050] Correspondingly, the predicted heat distribution data also needs to be multiplied by a preset second weight to dynamically change the influence of the predicted heat distribution data in the final decision. Generally, if the predicted heat distribution data shows abnormalities, the weight of the preset second weight will be increased accordingly to alert the system to potential power fluctuations and ensure the heating stability of the system.

[0051] Sub-step S13: The sum of the first heat data and the second heat data is used as the current state data.

[0052] By combining current and future heat distribution information, a comprehensive evaluation index can be formed, facilitating subsequent overall assessment.

[0053] Sub-step S14: Determine the first score of the current state data.

[0054] The first score is a global evaluation metric obtained by overlaying the current state data of all fluidized bed partitions. It reflects whether the heat distribution of the entire system meets the optimization objective. The first score is used to evaluate whether the current and future operating states of the entire system meet expectations. The first score is a global metric and is only obtained after overlaying the current state data of all partitions.

[0055] Sub-step S15: If the first score is less than or equal to a preset score threshold, maintain the parameter values ​​of the control parameters of the fluidized bed partition in the current state.

[0056] If the first score is less than or equal to the preset score threshold, it means that the current state has already met the optimization objective, and there is no need to adjust the control parameters. In this case, the control parameter values ​​of the fluidized bed partition will remain unchanged under the current state.

[0057] Sub-step S16: If the first score is greater than a preset score threshold, determine the parameter value of the target control parameter.

[0058] If the initial score exceeds the preset score threshold, it indicates that the current state deviates from the optimization objective, and the target control parameters need to be recalculated. The system recalculates the specific values ​​of the target control parameters based on the current state data and the optimization algorithm to improve the system state. By combining the current heat distribution data and predicted heat distribution data with different weights, a more accurate comprehensive judgment can be made on whether the current situation meets expectations.

[0059] In one embodiment, the current state data includes maximum temperature, minimum temperature, temperature gradient, average temperature, and operating power. The relevant indicators such as maximum temperature, minimum temperature, temperature gradient, average temperature, and operating power can be determined based on heat distribution data. Sub-step S14 may include the following sub-steps: Sub-step S141: Determine the heat distribution score based on the maximum temperature, the minimum temperature, the temperature gradient, and the average temperature.

[0060] The heat distribution score is a comprehensive index used to evaluate the uniformity and rationality of heat distribution within a fluidized bed zone. It can be determined by substituting the maximum temperature, the minimum temperature, the temperature gradient, and the average temperature into the following formula: Among them, Score heat This represents the heat distribution score. Tmax represents the maximum temperature under the current conditions. min G represents the minimum temperature under the current conditions, G represents the temperature gradient under the current conditions, and T represents the minimum temperature under the current conditions. avg Represents the average temperature under the current conditions, T target w1, w2, and w3 represent the target average temperature, and w1, w2, and w3 represent preset weights, which respectively indicate the importance of the maximum temperature difference, temperature gradient, and average temperature deviation from the target value.

[0061] Sub-step S142: Determine the energy consumption score based on the operating power.

[0062] The energy consumption score is a comprehensive indicator used to evaluate the system's energy efficiency under current conditions. Similarly, the energy consumption score can be determined based on the operating power according to the following formula: Among them, Score energy Represents energy consumption score, P current w4 represents the operating power under the current state, and w4 represents the preset weight, indicating the degree of influence of operating power on energy consumption score.

[0063] Sub-step S143: Determine the safety score based on the maximum temperature.

[0064] The safety score can be determined based on the maximum temperature using the following formula: Among them, Score safety Represents security score, T max Represents the maximum temperature under the current condition, T limit w5 represents the maximum temperature limit allowed by the system, and w5 represents the preset weight, indicating the degree of influence of the maximum temperature on the safety score.

[0065] Sub-step S144: Add the heat distribution score, energy consumption score and safety score together to determine the first score.

[0066] The first score is a global evaluation metric used to comprehensively assess the system's overall performance in its current state. After obtaining these scores, the first score can be determined using the following formula:

[0067] The first score, as a single numerical value, can intuitively reflect whether the current state of the system meets the optimization objective.

[0068] In one embodiment, sub-step S15 may include the following sub-steps: Sub-step S151: In the preset parameter selection pool, at least one first data pair is determined, wherein the first data pair is a data pair of heating power and fluidization state data.

[0069] The preset parameter selection pool is a predefined dataset containing multiple possible combinations of heating power and fluidization state data. These combinations cover a range of different control parameters that may be used during system operation. Hereinafter, fluidization velocity will be used as representative data for fluidization state; each data pair consists of two elements: heating power and fluidization velocity; the first data pair is one or more combinations of heating power and fluidization velocity selected from the preset parameter selection pool.

[0070] Sub-step S152: Determine the second score of the state data corresponding to the first data pair.

[0071] Based on the currently selected first data, the transient thermal field equation is reapplied to calculate new heat distribution data, and the second score is recalculated based on this heat distribution data in the same way as the first score was calculated above.

[0072] Sub-step S153: The first data pair corresponding to the second score that is less than the preset score threshold is used as the parameter value of the target control parameter.

[0073] If the second score corresponding to a data pair is lower than the threshold, it means that the combination can meet the system's optimization objective. In this case, the parameter value of the target control parameter for that data pair can be adjusted. Because the process of continuously selecting data pairs and performing optimization iterations is itself an optimization iteration process, the above process can be implemented using the particle swarm optimization algorithm.

[0074] Step 104: Adjust the heating environment of each fluidized bed zone according to the parameter values ​​of the target control parameters corresponding to each fluidized bed zone.

[0075] Once the parameters that need to be adjusted and their corresponding values ​​are determined, adjustments can be made to each fluidized bed zone to ensure the stability of the heating environment.

[0076] By acquiring the heating status and heating environment information of each zone of the thermal storage fluidized bed, and combining it with current heat distribution data and predicted heat distribution data, the system can monitor the current and future operating status of the fluidized bed zones in real time. Based on the current heat distribution data and the predicted heat distribution data, the system determines the parameter values ​​of the target control parameters. According to the parameter values ​​of the target control parameters corresponding to each fluidized bed zone, the system adjusts the heating environment of each fluidized bed zone. By introducing forward-looking heat distribution prediction data based on the current environment, the system possesses forward-looking control capabilities, enabling it to respond proactively to energy fluctuations. This significantly improves the stability and rationality of the system in the face of energy fluctuations, allowing the thermal storage fluidized bed to flexibly adjust its operating mode under different power supply conditions, maintaining a stable heating environment and ensuring the safe, stable, and efficient operation of the thermal storage fluidized bed in the face of unstable power.

[0077] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0078] Reference Figure 3 The diagram shows a structural block diagram of an embodiment of a thermal storage fluidized bed control device according to the present invention, which may specifically include the following modules: The data acquisition module 201 is used to acquire the heating status and heating environment information of each fluidized bed zone of the thermal storage fluidized bed; The heat data determination module 202 is used to determine the current heat distribution data and heat distribution prediction data of each fluidized bed partition based on the heating state and the heating environment information. The parameter value determination module 203 is used to determine the parameter value of the target control parameter based on the current heat distribution data and the heat distribution prediction data; The environment adjustment module 204 is used to adjust the heating environment of each fluidized bed partition according to the parameter values ​​of the target control parameters corresponding to each fluidized bed partition.

[0079] In one embodiment, the heat data determination module includes: The first heat distribution data determination submodule is used to determine the current heat distribution data of each fluidized bed partition based on the heating power, the fluidization state data, the state data of the heat storage particles, the heat transfer parameters, and the temperature data, using the transient thermal field equation.

[0080] In one embodiment, the heat data determination module includes: The heating power prediction submodule is used to determine the heating power prediction data based on the power condition data; the power condition data includes power output data and power stability parameters. The second heat distribution data determination submodule is used to determine the heat distribution prediction data of each fluidized bed zone based on the fluidization state data, the state data of the thermal storage particles, the heat transfer parameters, the temperature data, and the heating power prediction data, using the transient thermal field equation.

[0081] In one embodiment, the parameter value determination module includes: The first heat data determination submodule is used to determine the first heat data based on the current heat distribution data and a preset first weight. The second heat data determination submodule is used to determine the second heat data based on the heat distribution prediction data and the preset second weight; The status data determination submodule is used to take the sum of the first heat data and the second heat data as the current status data; The first score determination submodule is used to determine the first score of the current state data; The first target parameter value determination submodule is used to maintain the parameter value of the control parameter of the fluidized bed partition in the current state when the first score is less than or equal to a preset score threshold. The second target parameter value determination submodule is used to determine the parameter value of the target control parameter when the first score is greater than a preset score threshold.

[0082] In one embodiment, the current state data includes maximum temperature, minimum temperature, temperature gradient, average temperature, and operating power, and the first score determination submodule includes: A heat distribution score determination unit is used to determine a heat distribution score based on the maximum temperature, the minimum temperature, the temperature gradient, and the average temperature. An energy consumption score determination unit is used to determine an energy consumption score based on the operating power. A safety score determination unit is used to determine a safety score based on the maximum temperature. The second scoring unit is used to add the heat distribution score, energy consumption score and safety score to determine the first score.

[0083] In one embodiment, the second target parameter value determination submodule includes: The data pair determination unit is used to determine at least one first data pair in a preset parameter selection pool, wherein the first data pair is a data pair of heating power and fluidization state data; The third score determination unit is used to determine the second score of the state data corresponding to the first data pair; The third target parameter value determination unit is used to take the first data pair corresponding to the second score that is less than the preset score threshold as the parameter value of the target control parameter.

[0084] By acquiring the heating status and heating environment information of each zone of the thermal storage fluidized bed, and combining it with current heat distribution data and predicted heat distribution data, the system can monitor the current and future operating status of the fluidized bed zones in real time. Based on the current and predicted heat distribution data, the system determines the parameter values ​​of the target control parameters. According to the parameter values ​​of the target control parameters for each fluidized bed zone, the system adjusts the heating environment of each fluidized bed zone. By introducing forward-looking heat distribution prediction data based on the current environment, the system possesses forward-looking control capabilities, enabling it to respond proactively to energy fluctuations. This significantly improves the stability and rationality of the system in the face of energy fluctuations, allowing the thermal storage fluidized bed to flexibly adjust its operating mode under different power supply conditions, maintaining a stable heating environment and ensuring the safe, stable, and efficient operation of the thermal storage fluidized bed in the face of unstable power. As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0085] This invention also provides an electronic device, including: a processor, a memory, and a computer program stored in the memory and capable of running on the processor. When the computer program is executed by the processor, it implements the various processes of the above-described embodiment of the thermal storage fluidized bed control method and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0086] This invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the various processes of the above-described embodiment of the thermal storage fluidized bed control method and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0087] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0088] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0089] Embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0090] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0091] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0092] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0093] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0094] The present invention provides a detailed description of a thermal storage fluidized bed control method, apparatus, equipment, and medium. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for controlling a thermally heated fluidized bed, characterized in that, The method includes: The heating status and heating environment information of each fluidized bed zone of the thermal storage fluidized bed are obtained; the heating status includes at least one of heating power, fluidization status data of the fluidized bed zone, status data of thermal storage particles, and heat transfer parameters; the heating environment information includes temperature data and power supply condition data. Based on the heating power, the fluidization state data, the state data of the thermal storage particles, the heat transfer parameters, and the temperature data, the current heat distribution data of each fluidized bed zone is determined using the transient thermal field equation. Based on the power supply condition data, the heating power prediction data is determined; the power supply condition data includes power output data and power stability parameters. Based on the fluidization state data, the state data of the thermal storage particles, the heat transfer parameters, the temperature data, and the heating power prediction data, the heat distribution prediction data for each fluidized bed zone is determined using the transient thermal field equation. The first heat data is determined based on the current heat distribution data and the preset first weight; The second heat data is determined based on the predicted heat distribution data and the preset second weight; The sum of the first heat data and the second heat data is used as the current state data; Determine the first score of the current state data; If the first score is less than or equal to a preset score threshold, the parameter values ​​of the control parameters of the fluidized bed partition are maintained in the current state. If the first score is greater than the preset score threshold, at least one first data pair is determined in the preset parameter selection pool. The first data pair is a data pair of heating power and fluidization state data. Determine the second score of the state data corresponding to the first data pair; The first data pair corresponding to the second score that is less than the preset score threshold is used as the parameter value of the target control parameter; The heating environment of each fluidized bed zone is adjusted according to the parameter values ​​of the target control parameters corresponding to each fluidized bed zone.

2. The method for controlling a thermal storage fluidized bed according to claim 1, characterized in that, The fluidized bed zones of the thermal storage fluidized bed are determined based on the size information of the thermal storage fluidized bed and the preset height-to-diameter ratio, where the height-to-diameter ratio is the ratio of the height of the fluidized bed to its diameter.

3. The method for controlling a thermal storage fluidized bed according to claim 1, characterized in that, The current status data includes maximum temperature, minimum temperature, temperature gradient, average temperature, and operating power. Determining the first score of the current status data includes: The heat distribution score is determined based on the maximum temperature, the minimum temperature, the temperature gradient, and the average temperature. Based on the operating power, determine the energy consumption score; A safety score is determined based on the maximum temperature. The heat distribution score, energy consumption score, and safety score are added together to determine the first score.

4. A thermal storage fluidized bed control device, characterized in that, The device includes: The data acquisition module is used to acquire the heating status and heating environment information of each fluidized bed zone of the thermal storage fluidized bed; the heating status includes at least one of heating power, fluidization status data of the fluidized bed zone, status data of thermal storage particles, and heat transfer parameters; the heating environment information includes temperature data and power supply condition data. The first heat distribution data determination submodule is used to determine the current heat distribution data of each fluidized bed partition based on the heating power, the fluidization state data, the state data of the heat storage particles, the heat transfer parameters and the temperature data, using the transient thermal field equation. The heating power prediction submodule is used to determine the heating power prediction data based on the power condition data; the power condition data includes power output data and power stability parameters. The second heat distribution data determination submodule is used to determine the heat distribution prediction data of each fluidized bed zone based on the fluidization state data, the state data of the thermal storage particles, the heat transfer parameters, the temperature data and the heating power prediction data, using the transient thermal field equation. The first heat data determination submodule is used to determine the first heat data based on the current heat distribution data and a preset first weight. The second heat data determination submodule is used to determine the second heat data based on the heat distribution prediction data and the preset second weight; The status data determination submodule is used to take the sum of the first heat data and the second heat data as the current status data; The first score determination submodule is used to determine the first score of the current state data; The first target parameter value determination submodule is used to maintain the parameter value of the control parameter of the fluidized bed partition in the current state when the first score is less than or equal to a preset score threshold. The data pair determination unit is used to determine at least one first data pair from a preset parameter selection pool when the first score is greater than a preset score threshold. The first data pair is a data pair of heating power and fluidization state data. The third score determination unit is used to determine the second score of the state data corresponding to the first data pair; The third target parameter value determination unit is used to take the first data pair corresponding to the second score that is less than the preset score threshold as the parameter value of the target control parameter; The environment adjustment module is used to adjust the heating environment of each fluidized bed zone according to the parameter values ​​of the target control parameters corresponding to each fluidized bed zone.

5. An electronic device, characterized in that, include: A processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the steps of a thermal storage fluidized bed control method as described in any one of claims 1-3.

6. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the steps of the thermal storage fluidized bed control method as described in any one of claims 1-3.

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