Self-adaptive optimization method and device of system, electronic equipment and storage medium

Abstract

The invention discloses a self-adaptive optimization method and device of a system, electronic equipment and a storage medium, and relates to the technical field of intelligent regulation and control. Compared with the prior art, the particle size, stacking density and humidity data of ash materials are collected in real time, and a three-dimensional dynamic ash material characteristic map is constructed in combination with coal quality parameters; on the basis of the dynamic map and the pressure gradient data, the ash conveying gas flow is optimized in real time by adopting a double-constraint optimization algorithm, the gas flow can be dynamically adjusted according to the ash material characteristics and the working condition change instead of adopting fixed gas flow configuration, and therefore the gas flow adjustment efficiency is improved. The technical problems of pipe blockage, high energy consumption, equipment abrasion and the like caused by excessive air supply under low load and insufficient air supply under high load due to fixed air quantity in the existing pneumatic ash conveying control method can be solved, and the technical effects of ensuring long-term reliable operation of the pneumatic ash conveying system and reducing energy consumption and equipment loss are achieved.

Description

Technical Field

[0001] This disclosure relates to the field of intelligent control technology, and in particular to a system adaptive optimization method and apparatus, electronic device and storage medium. Background Technology

[0002] Pneumatic ash conveying systems are a crucial component of ash and slag treatment in thermal power plants, widely used in key processes such as boiler ash discharge, dust collector ash hopper conveying, and ash silo feeding. In related technologies, a traditional pneumatic ash conveying control system is constructed through the coordinated operation of fixed air volume configuration, pressure monitoring, and manual adjustment.

[0003] In this pneumatic ash conveying control method, the worst-case scenario is used to set a fixed air volume, which may lead to excessive air supply at low loads and insufficient air supply at high loads, resulting in problems such as pipe blockage, high energy consumption and equipment wear, which seriously restricts the long-term reliable operation of the system. Summary of the Invention

[0004] This disclosure provides an adaptive optimization method, apparatus, electronic device, and storage medium for a system. Its main purpose is to address the problem that setting a fixed gas volume under worst-case conditions may lead to oversupply at low loads and insufficient gas supply at high loads, resulting in problems such as pipe blockage, high energy consumption, and equipment wear, severely restricting the long-term reliable operation of the system.

[0005] According to a first aspect of this disclosure, an adaptive optimization method for a system is provided, comprising: Real-time acquisition of particle size, bulk density, and moisture content data of ash materials; A three-dimensional dynamic ash characteristic map is constructed based on the particle size, bulk density, moisture content data and coal quality parameters of the ash material. Based on the dynamic ash material characteristic spectrum and pressure gradient data, a dual-constraint optimization algorithm is used to optimize the ash conveying gas volume in real time.

[0006] Optionally, after optimizing the ash conveying gas volume in real time using a dual-constraint optimization algorithm based on the dynamic ash material characteristic spectrum and pressure gradient data, the method further includes: The utilization rate of each gray pipe is calculated based on the operating status of multiple gray pipes. When the utilization rate is lower than the set threshold, the pipe borrowing mechanism is activated, and the gas volume after the pipe borrowing is adjusted through the gas volume redistribution formula to achieve load balancing and collaborative optimization among multiple gray pipes.

[0007] Optionally, the construction of a three-dimensional dynamic ash characteristic map based on the particle size, bulk density, moisture content data, and coal quality parameters of the ash further includes: The ash content parameters of the ash material characteristic spectrum are dynamically adjusted by using boiler load signal, dust collector secondary current signal and ash hopper weighing signal as real-time signal inputs.

[0008] Optionally, before using a dual-constraint optimization algorithm to optimize the ash conveying gas volume in real time based on the dynamic ash material characteristic spectrum and pressure gradient data, the method includes: The pressure gradient data is obtained by collecting pressure under different operating conditions based on preset sensors.

[0009] Optional, also includes: In response to the startup phase, the fluidizing gas flow rate is set to the rated value.

[0010] According to a second aspect of this disclosure, an adaptive optimization apparatus for a system is provided, comprising: The data acquisition unit is used to collect data on particle size, bulk density, and moisture content of the ash material in real time. The construction unit is used to construct a three-dimensional dynamic ash characteristic map based on the particle size, bulk density, moisture data and coal quality parameters of the ash. The optimization unit is used to optimize the ash conveying gas volume in real time based on the dynamic ash material characteristic spectrum and pressure gradient data using a dual-constraint optimization algorithm.

[0011] Optionally, the device further includes: The adjustment unit is used to optimize the ash conveying gas volume in real time by the optimization unit based on the dynamic ash material characteristic spectrum and pressure gradient data using a dual-constraint optimization algorithm. Then, it calculates the utilization rate of each ash pipe according to the operating status of multiple ash pipes. When the utilization rate is lower than a set threshold, it activates the pipe borrowing mechanism and adjusts the gas volume after pipe borrowing through the gas volume redistribution formula to achieve load balancing and collaborative optimization among multiple ash pipes.

[0012] Optionally, the building unit is further configured to: The ash content parameters of the ash material characteristic spectrum are dynamically adjusted by using boiler load signal, dust collector secondary current signal and ash hopper weighing signal as real-time signal inputs.

[0013] Optionally, the device includes: The acquisition unit is used to acquire pressure under different operating conditions based on preset sensors and obtain the pressure gradient data before the optimization unit optimizes the ash conveying gas volume in real time based on the dynamic ash material characteristic spectrum and pressure gradient data using a dual-constraint optimization algorithm.

[0014] Optional, also includes: A setting unit is used to set the fluidizing gas flow rate to a rated value in response to the startup phase.

[0015] According to a third aspect of this disclosure, an electronic device is provided, comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect above.

[0016] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are configured to cause the computer to perform the method described in the first aspect above.

[0017] According to a fifth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method described in the first aspect above.

[0018] The adaptive optimization method, apparatus, electronic equipment, and storage medium provided in this disclosure mainly include: real-time acquisition of particle size, bulk density, and moisture data of ash; construction of a three-dimensional dynamic ash characteristic map based on the particle size, bulk density, and moisture data of the ash and coal quality parameters; and real-time optimization of the ash conveying gas volume using a dual-constraint optimization algorithm based on the dynamic ash characteristic map and pressure gradient data. Compared with related technologies, this application, by real-time acquisition of particle size, bulk density, and moisture data of ash, combined with coal quality parameters to construct a three-dimensional dynamic ash characteristic map, and then using a dual-constraint optimization algorithm to optimize the ash conveying gas volume in real time based on the dynamic map and pressure gradient data, can dynamically adjust the gas volume according to the ash characteristics and changes in operating conditions, rather than using a fixed gas volume configuration. Therefore, it can solve the technical problems in existing pneumatic ash conveying control methods, such as excessive gas supply at low loads and insufficient gas supply at high loads caused by fixed gas volumes, as well as the resulting pipe blockage, high energy consumption, and equipment wear, achieving the technical effect of ensuring long-term reliable operation of the pneumatic ash conveying system and reducing energy consumption and equipment wear.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0020] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein: Figure 1 A flowchart illustrating an adaptive optimization method for a system provided in an embodiment of this disclosure; Figure 2 This is a schematic diagram of the structure of an adaptive optimization device for a system provided in an embodiment of the present disclosure; Figure 3 A schematic diagram of the structure of an adaptive optimization device for another system provided in this disclosure embodiment; Figure 4 A schematic block diagram of an example electronic device provided for embodiments of this disclosure. Detailed Implementation

[0021] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0022] The adaptive optimization method, apparatus, electronic device, and storage medium of the system according to embodiments of the present disclosure are described below with reference to the accompanying drawings.

[0023] Figure 1 This is a flowchart illustrating an adaptive optimization method for a system provided in an embodiment of this disclosure.

[0024] like Figure 1 As shown, the method includes the following steps: Step 101: Collect real-time data on particle size, bulk density, and moisture content of the ash material; The particle size of ash refers to the size and distribution of ash particles. Ash particles of different sizes exhibit different flow characteristics during pneumatic conveying. Fine-grained ash is more easily carried by airflow, while coarse-grained ash requires higher airflow velocities to avoid sedimentation. Bulk density refers to the mass per unit volume of ash, directly reflecting its compactness. Ash with high bulk density requires greater airflow thrust during conveying to overcome its gravity and inter-particle friction. Moisture content refers to the water content of the ash. Ash with excessive moisture content is prone to particle agglomeration, increasing conveying resistance and even raising the risk of pipe blockage. To achieve real-time data acquisition, the system deploys corresponding detection devices at key nodes in the ash conveying path, such as the silo pump outlet and below the ash hopper. For example, a particle size sensor using laser diffraction is used to monitor the particle size distribution of the ash in real time, a density sensor is used to obtain the bulk density data of the ash in real time, and a moisture sensor is used to continuously monitor the moisture content of the ash. These detection devices can quickly respond to changes in ash characteristics, transmitting the collected particle size, bulk density, and moisture data to the system's control unit in real-time. This provides accurate and timely raw data support for the subsequent dynamic generation of a three-dimensional characteristic map of ash quantity (t / h) × pressure loss (kPa) × ash-to-gas ratio, ensuring that the map accurately reflects the actual characteristics of the ash. This lays the data foundation for adaptive gas volume control, effectively addressing the problem of ash characteristic changes caused by coal quality fluctuations, and avoiding the drawbacks of high energy consumption and easy pipe blockage caused by the use of fixed gas volume configuration in traditional systems due to the lack of real-time ash characteristic data.

[0025] Step 102: Construct a three-dimensional dynamic ash characteristic map based on the particle size, bulk density, moisture content data and coal quality parameters of the ash material; The particle size, bulk density, and moisture content of ash, as key parameters directly reflecting the physical properties of ash, provide real-time dynamic data support for map construction: particle size distribution determines the suspension and flowability of ash in airflow, bulk density reflects the compactness of ash and the thrust required for conveying, and moisture content is related to the agglomeration tendency and conveying resistance characteristics of ash. Coal quality parameters, especially ash content, are core factors affecting the essential properties of ash. A coal quality-map mapping library is established to achieve precise adaptation of map types—when the ash content is >25%, the ash often exhibits higher compactness and conveying difficulty. In this case, the system automatically uses pre-stored high-density ash maps to ensure that the maps can specifically match the ash characteristics under high-ash coal conditions.

[0026] The coordinate axes of the three-dimensional dynamic ash material characteristic map are clearly defined as ash content (t / h) × pressure loss (kPa) × ash-to-gas ratio. Ash content represents the amount of ash transported per unit time, pressure loss reflects the pipeline pressure loss during transport, and the ash-to-gas ratio reflects the proportional relationship between ash mass and transport gas flow rate. These three elements together constitute the core dimensions describing the ash material transport status. During map construction, the system first uses pre-stored basic characteristic maps as an initial reference based on real-time collected particle size, bulk density, moisture data, and current coal quality parameters. Then, it enters a dynamic correction stage. When the deviation between the measured pressure loss and the map's estimated value is greater than 10%, the system updates the map parameters in real time according to the preset formula "new ash-to-gas ratio = original ash-to-gas ratio × (measured pressure loss / estimated pressure loss) 0.8," ensuring that the map can dynamically track subtle changes in ash material characteristics. Through this construction process, the three-dimensional dynamic ash material characteristic map can reflect the correlation between ash content, pressure loss and ash-to-gas ratio under different ash material characteristics and coal quality parameters in a real and real time. It provides an accurate characteristic benchmark for subsequent adaptive gas volume control, effectively solves the problem of unreasonable gas volume configuration caused by rigid ash material characteristic characterization in traditional systems, and lays a key characteristic understanding foundation for coping with the ash transportation challenges brought about by coal quality fluctuations.

[0027] Step 103: Based on the dynamic ash material characteristic spectrum and pressure gradient data, the ash conveying gas volume is optimized in real time using a dual-constraint optimization algorithm.

[0028] The dynamic ash characteristic map provides a precise benchmark for ash characteristics for gas volume optimization. This three-dimensional map uses ash content (t / h) × pressure loss (kPa) × ash-to-gas ratio as the coordinate axis, reflecting in real time the ash conveying characteristics under the correlation of current ash particle size, bulk density, moisture content, and coal quality parameters (such as coal ash content). For example, when the coal ash content is >25% and the high-density ash map is activated, the map will output a reference range of ash-to-gas ratio suitable for high-density ash, providing an initial characteristic basis for gas volume optimization. The pressure gradient data is acquired in real time by pressure sensors deployed every 3 meters along the ash conveying pipeline. It reflects the pressure change trend at different locations in the pipeline. The pressure difference ΔP between adjacent sensors can intuitively reflect abnormal changes in ash flow resistance. For example, an increase in ΔP may indicate the risk of local pipe blockage, providing real-time operating condition feedback for the algorithm.

[0029] The dual-constraint optimization algorithm uses "minimizing total gas volume" as its core objective function, i.e., min(total gas volume) = f(ash quantity, ash mass, pipe pressure). It aims to solve the problem of excessive gas supply under low load caused by the fixed gas volume configuration of traditional systems, reducing energy consumption through dynamic adjustment of gas volume. Simultaneously, the algorithm sets two key constraints to ensure ash conveying safety: first, the terminal velocity ≤ 15 m / s. The terminal velocity is the airflow velocity at the end of the ash conveying pipeline; excessively high velocity will significantly exacerbate wear on the inner wall of the pipeline. This constraint avoids excessive equipment wear by limiting the terminal airflow velocity. Second, the starting Froude number ≥ 1.25. The Froude number is a dimensionless parameter reflecting the ratio of airflow inertial force to ash weight. Insufficient Froude number at the ash conveying start (such as the outlet of a silo pump) will lead to insufficient ash fluidization and particle agglomeration. This constraint ensures that the ash can be effectively carried by the airflow, preventing pipe blockage from the source.

[0030] In the specific optimization process, the algorithm first calls the parameters such as the current ash volume, ash-to-gas ratio, and estimated pressure loss output from the dynamic ash characteristic map. Combined with the actual pipe pressure changes monitored by the pressure gradient data (e.g., when the deviation between the measured pressure loss and the map's estimated value is >10%, the ash-to-gas ratio after dynamic correction of the map is referenced), the algorithm calculates the initial gas volume value under the current operating conditions in real time. Then, it performs verification and adjustment based on dual constraints: if the final velocity corresponding to the calculated gas volume exceeds 15 m / s, the gas volume is reduced until the anti-wear constraint is met; if the initial Froude number is less than 1.25, the gas volume is appropriately increased to enhance fluidization carrying capacity until the anti-clogging constraint is met. Through this collaborative optimization logic of "characteristic map as the benchmark, pressure gradient to adjust operating conditions, and dual constraints to ensure safety," the algorithm can output the optimal gas volume value in real time that meets the current ash conveying requirements and minimizes energy consumption when coal quality fluctuations cause changes in ash characteristics. This effectively overcomes the industry pain points of rigid gas volume configuration, high energy consumption, and pipe clogging and wear in traditional systems.

[0031] In some embodiments, after optimizing the ash conveying gas volume in real time using a dual-constraint optimization algorithm based on the dynamic ash material characteristic spectrum and pressure gradient data, the method further includes: The utilization rate of each gray pipe is calculated based on the operating status of multiple gray pipes. When the utilization rate is lower than the set threshold, the pipe borrowing mechanism is activated, and the gas volume after the pipe borrowing is adjusted through the gas volume redistribution formula to achieve load balancing and collaborative optimization among multiple gray pipes.

[0032] By monitoring the actual operating data of each ash pipe in real time, the utilization rate of each ash pipe is calculated. The formula for calculating the utilization rate η is defined as η = actual ash volume / design throughput. This formula directly reflects the ratio of the actual ash conveying volume of the ash pipe to the maximum designed ash conveying capacity. A higher η value indicates that the ash pipe is fully loaded, while a low η value means that the ash pipe has idle capacity. The system sets a utilization rate threshold (e.g., η < 60%). When the utilization rate of a certain ash pipe is detected to be lower than this threshold, it indicates that the ash pipe is not fully utilizing its ash conveying capacity. At this time, the pipe borrowing mechanism is activated, that is, the ash material from other high-load ash pipes (such as the ash material generated by the second electric field) is transferred to the low-utilization ash pipe (such as the first electric field pipe) for transportation through system scheduling. This achieves a reasonable distribution of ash material transportation tasks among multiple ash pipes and avoids the situation where some ash pipes are overloaded while others are idle.

[0033] To ensure stable and efficient ash conveying after pipe borrowing, the system precisely adjusts the air volume after pipe borrowing using an air volume redistribution formula: Air volume after pipe borrowing = Original air volume * (1 = Borrowed ash volume / Original ash volume) 0.7. Here, "Original air volume" refers to the air volume value of the ash pipe before pipe borrowing, optimized by a dual-constraint optimization algorithm; "Borrowed ash volume" is the ash volume transferred from other ash pipes to this ash pipe; and "Original ash volume" is the actual ash conveying volume of the ash pipe before pipe borrowing. The exponent of 0.7 in the formula is based on experiments on ash conveying characteristics, aiming to balance the relationship between air supply and ash volume increase—when the borrowed ash volume increases, the air volume increases non-linearly, ensuring sufficient airflow thrust to carry the new ash while avoiding excessive air volume increase leading to increased energy consumption or excessive terminal velocity. Through this air volume redistribution mechanism, the air volume of the ash pipe after pipe borrowing can dynamically adapt to the conveying needs of the new ash, ensuring that key parameters such as ash-to-air ratio, terminal velocity, and starting Froude number still meet system constraints.

[0034] In summary, by calculating the utilization rate of the ash pipes and initiating the pipe borrowing mechanism when the utilization rate is low, and adjusting the gas volume in conjunction with the gas volume redistribution formula, this step achieves load balancing among multiple ash pipes, reduces the phenomenon of peak gas volume superposition, improves the resource utilization efficiency and operational stability of the overall ash conveying system, and further optimizes the collaborative operation performance of the pneumatic ash conveying system.

[0035] In some embodiments, the construction of a three-dimensional dynamic ash characteristic map based on the particle size, bulk density, moisture data, and coal quality parameters of the ash further includes: The ash content parameters of the ash material characteristic spectrum are dynamically adjusted by using boiler load signal, dust collector secondary current signal and ash hopper weighing signal as real-time signal inputs.

[0036] The boiler load signal (in MW) directly reflects the real-time operating load status of the boiler. An increase in boiler load means an increase in coal consumption, and correspondingly, an increase in ash production; conversely, a decrease in load means a decrease in coal consumption and a decrease in ash production. As an important correlated variable of ash content parameters, this signal can capture the overall fluctuation trend of ash content caused by changes in boiler operating intensity in real time, providing a basis for macroscopic adjustments of ash content parameters in the graph.

[0037] The secondary current signal (in A) of the dust collector is closely related to the dust removal efficiency and ash collection status of the dust collector. Changes in the secondary current can indirectly reflect the ash adsorption of the electrodes inside the dust collector. When the secondary current fluctuates abnormally, it may indicate that there is an instantaneous change in the amount of ash entering the ash conveying system (such as a sudden increase in the amount of ash during the electrode cleaning). The system senses the dynamic fluctuation of the amount of ash through this signal and helps to correct the instantaneous value of the ash amount parameter in the spectrum.

[0038] The ash hopper weighing signal (unit: t / h) directly measures the mass flow rate of the ash material to be conveyed in the ash hopper in real time. It is the most direct source of measured data for ash quantity parameters. Its value directly corresponds to the current actual ash material conveying volume of the ash conveying system, providing the core basis for the accurate setting of ash quantity parameters in the graph.

[0039] During the construction of the three-dimensional dynamic ash characteristic map, these real-time signals are continuously input into the system control unit, working in conjunction with the particle size, bulk density, moisture content, and coal quality parameters of the ash. The system first calls the pre-stored basic map based on the coal quality parameters, then predicts the overall trend of ash content through the boiler load signal, obtains the real-time actual ash content by combining the ash hopper weighing signal, and then verifies the ash content fluctuation through the dust collector secondary current signal. After integrating the dynamic changes in ash content reflected by these signals, the parameter value of the "ash content (t / h)" coordinate axis in the map is adjusted in real time to ensure that the ash content parameter in the map is always consistent with the actual ash generation and conveying status of the current ash conveying system. This provides an accurate ash content benchmark for subsequent adaptive gas volume control and avoids unreasonable gas volume configuration caused by lag or deviation in ash content parameters.

[0040] In some embodiments, before optimizing the ash conveying gas volume in real time using a dual-constraint optimization algorithm based on the dynamic ash material characteristic spectrum and pressure gradient data, the method includes: The pressure gradient data is obtained by collecting pressure under different operating conditions based on preset sensors.

[0041] Pre-set sensors refer to pressure detection devices that are pre-deployed at key locations in the ash conveying system according to a specific layout. Specifically, pressure sensors are installed at the outlet of the silo pump and every 3 meters along the ash conveying pipeline. These sensors reuse the interface of a DN20 turbulence valve. This layout comprehensively covers the critical path of ash material from the silo pump discharge to pipeline transport, ensuring full-range monitoring of pressure changes within the pipeline. Sensor sampling and processing are supported by the system control architecture. Each ash pipe is equipped with an independent intelligent host with a built-in FPGA chip, receiving and processing the pressure signals collected by the sensors in real time at a high-frequency sampling frequency of 100Hz, ensuring the timeliness and accuracy of the pressure data.

[0042] The different operating conditions cover the entire operating cycle of the ash conveying system, including the start-up phase (pre-pressurizing the silo pump to 0.15MPa), the stable conveying phase (maintaining a Froude number of 1.25-1.5), the reduction phase (linearly reducing the gas volume according to the ash-to-gas ratio curve), and special operating conditions such as pipe blockage warning. Under each operating condition, pressure sensors continuously collect real-time pressure values ​​at the corresponding locations in the pipeline. The system obtains pressure gradient data by calculating the pressure difference (ΔP) between adjacent sensors. The pressure gradient data directly reflects the trend of resistance changes in ash flow within the pipeline. For example, the pressure gradient is gentle during stable conveying, while when ash agglomerates or locally deposits, the pressure difference between adjacent sensors will increase abnormally, providing a direct basis for subsequent judgment of ash flow status and triggering of gas replenishment or control mechanisms.

[0043] By acquiring pressure and calculating gradients under different operating conditions using preset sensors, the system can monitor the dynamic pressure distribution in the ash conveying pipeline in real time. This provides real and continuous pressure gradient data support for the verification and correction of dynamic ash material characteristic maps (such as the judgment of the deviation between measured pressure loss and estimated pressure loss) and the condition adaptation of the dual-constraint optimization algorithm, ensuring that subsequent gas volume optimization and control can accurately respond to the actual flow state of ash material in the pipeline.

[0044] In some embodiments, it also includes: In response to the startup phase, the fluidizing gas flow rate is set to the rated value.

[0045] The start-up phase, as the initial stage of the ash conveying process, involves ash materials in the silo pump in a relatively static or accumulated state. Insufficient fluidization at this time can easily lead to ash material clumping and poor flowability, resulting in blockage at the pump outlet or the beginning of the pipeline. The rated value is the fluidizing gas volume benchmark value preset by the system based on the silo pump design parameters, ash material characteristics, and anti-blocking requirements. In actual operation, this is specifically manifested as the fluidizing gas volume being set to 120% of the design value. This setting is higher than the gas volume during the stable operation phase, aiming to ensure sufficient fluidization of the ash material by enhancing airflow disturbance.

[0046] From a hardware perspective, the system adds an independent fluidizing gas valve assembly (DN40) to each silo pump, coupled with a fluidizing chamber cone angle optimized to 60°±2°. This provides structural assurance for the stable output and uniform action of the rated fluidizing gas volume, enabling the airflow to penetrate the ash layer more evenly, breaking down the agglomeration forces between particles, and transforming the ash into a fluidized state. This ensures that the ash has good flowability to enter the ash conveying pipeline. Simultaneously, the setting of this rated fluidizing gas volume works synergistically with the control strategy of "pre-pressurizing the silo pump to 0.15MPa" during the startup phase. Through the dual guarantee of pressure and gas volume, this lays the foundation for achieving the Froude number (≥1.25) in the subsequent stable conveying phase. It prevents the risk of pipe blockage due to insufficient fluidization from the start-up stage, ensuring a smooth transition of the ash conveying system from standstill to operation.

[0047] Corresponding to the adaptive optimization method of the system described above, this invention also proposes an adaptive optimization device for the system. Since the device embodiments of this invention correspond to the method embodiments described above, details not disclosed in the device embodiments can be referred to in the method embodiments described above, and will not be repeated here.

[0048] Figure 2 This is a schematic diagram of the structure of an adaptive optimization device for a system provided in an embodiment of the present disclosure, as shown below. Figure 2 As shown, it includes: The data acquisition unit 21 is used to collect data on the particle size, bulk density, and moisture content of the ash material in real time. Construction unit 22 is used to construct a three-dimensional dynamic ash material characteristic map based on the particle size, bulk density, moisture data and coal quality parameters of the ash material; The optimization unit 23 is used to optimize the ash conveying gas volume in real time based on the dynamic ash material characteristic spectrum and pressure gradient data using a dual-constraint optimization algorithm.

[0049] Furthermore, in one possible implementation of the embodiments of this disclosure, such as Figure 3 As shown, the device further includes: The adjustment unit 24 is used to optimize the ash conveying gas volume in real time by the optimization unit 23 based on the dynamic ash material characteristic spectrum and pressure gradient data using a dual-constraint optimization algorithm. Then, it calculates the utilization rate of each ash pipe according to the operating status of multiple ash pipes. When the utilization rate is lower than the set threshold, it starts the pipe borrowing mechanism and adjusts the gas volume after pipe borrowing through the gas volume redistribution formula to achieve load balancing and collaborative optimization among multiple ash pipes.

[0050] Furthermore, in one possible implementation of this disclosure embodiment, the construction unit 22 is further configured to: The ash content parameters of the ash material characteristic spectrum are dynamically adjusted by using boiler load signal, dust collector secondary current signal and ash hopper weighing signal as real-time signal inputs.

[0051] Furthermore, in one possible implementation of the embodiments of this disclosure, such as Figure 3 As shown, the device includes: The acquisition unit 25 is used to acquire pressure under different working conditions based on a preset sensor before the optimization unit 23 optimizes the ash conveying gas volume in real time using a dual-constraint optimization algorithm based on the dynamic ash material characteristic spectrum and pressure gradient data, and obtains the pressure gradient data.

[0052] Furthermore, in one possible implementation of the embodiments of this disclosure, such as Figure 3 As shown, it also includes: Setting unit 26 is used to set the fluidizing gas volume to a rated value in response to the startup phase.

[0053] It should be noted that the foregoing explanation of the method embodiments also applies to the apparatus of the embodiments of this disclosure, and the principle is the same. Therefore, the embodiments of this disclosure are not limited thereto.

[0054] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0055] Figure 4 A schematic block diagram of an example electronic device 300 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0056] like Figure 4 As shown, device 300 includes a computing unit 301, which can perform various appropriate actions and processes based on a computer program stored in ROM (Read-Only Memory) 302 or a computer program loaded from storage unit 308 into RAM (Random Access Memory) 303. RAM 303 may also store various programs and data required for the operation of device 300. The computing unit 301, ROM 302, and RAM 303 are interconnected via bus 304. I / O (Input / Output) interface 305 is also connected to bus 304.

[0057] Multiple components in device 300 are connected to I / O interface 305, including: input unit 306, such as keyboard, mouse, etc.; output unit 307, such as various types of monitors, speakers, etc.; storage unit 308, such as disk, optical disk, etc.; and communication unit 309, such as network card, modem, wireless transceiver, etc. Communication unit 309 allows device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0058] The computing unit 301 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, CPUs (Central Processing Units), GPUs (Graphics Processing Units), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSPs (Digital Signal Processors), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as the system's adaptive optimization method. For example, in some embodiments, the system's adaptive optimization method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of the methods described above may be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to perform the adaptive optimization method of the aforementioned system by any other suitable means (e.g., by means of firmware).

[0059] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application-Specific Standard Products), SOCs (System-on-Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0060] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0061] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0062] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0063] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include LANs (Local Area Networks), WANs (Wide Area Networks), the Internet, and blockchain networks.

[0064] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service system that addresses the shortcomings of traditional physical hosts and VPS (Virtual Private Server) services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0065] It's important to note that artificial intelligence (AI) is the study of enabling computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It encompasses both hardware and software technologies. AI hardware technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, and big data processing. AI software technologies primarily include computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graph technologies.

[0066] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0067] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. An adaptive optimization method for a system, characterized in that, include: Real-time acquisition of particle size, bulk density, and moisture content data of ash materials; A three-dimensional dynamic ash characteristic map is constructed based on the particle size, bulk density, moisture content data and coal quality parameters of the ash material. Based on the dynamic ash material characteristic spectrum and pressure gradient data, a dual-constraint optimization algorithm is used to optimize the ash conveying gas volume in real time.

2. The method according to claim 1, characterized in that, After optimizing the ash conveying gas volume in real time using a dual-constraint optimization algorithm based on the dynamic ash material characteristic spectrum and pressure gradient data, the method further includes: The utilization rate of each gray pipe is calculated based on the operating status of multiple gray pipes. When the utilization rate is lower than the set threshold, the pipe borrowing mechanism is activated, and the gas volume after the pipe borrowing is adjusted through the gas volume redistribution formula to achieve load balancing and collaborative optimization among multiple gray pipes.

3. The method according to claim 1, characterized in that, The construction of a three-dimensional dynamic ash characteristic map based on the particle size, bulk density, moisture data, and coal quality parameters of the ash also includes: The ash content parameters of the ash material characteristic spectrum are dynamically adjusted by using boiler load signal, dust collector secondary current signal and ash hopper weighing signal as real-time signal inputs.

4. The method according to claim 1, characterized in that, Before using a dual-constraint optimization algorithm to optimize the ash conveying gas volume in real time based on the dynamic ash material characteristic spectrum and pressure gradient data, the method includes: The pressure gradient data is obtained by collecting pressure under different operating conditions based on preset sensors.

5. The method according to any one of claims 1-4, characterized in that, Also includes: In response to the startup phase, the fluidizing gas flow rate is set to the rated value.

6. An adaptive optimization device for a system, characterized in that, include: The data acquisition unit is used to collect data on particle size, bulk density, and moisture content of the ash material in real time. The construction unit is used to construct a three-dimensional dynamic ash characteristic map based on the particle size, bulk density, moisture data and coal quality parameters of the ash. The optimization unit is used to optimize the ash conveying gas volume in real time based on the dynamic ash material characteristic spectrum and pressure gradient data using a dual-constraint optimization algorithm.

7. The apparatus according to claim 6, characterized in that, The device further includes: The adjustment unit is used to optimize the ash conveying gas volume in real time by the optimization unit based on the dynamic ash material characteristic spectrum and pressure gradient data using a dual-constraint optimization algorithm. Then, it calculates the utilization rate of each ash pipe according to the operating status of multiple ash pipes. When the utilization rate is lower than a set threshold, it activates the pipe borrowing mechanism and adjusts the gas volume after pipe borrowing through the gas volume redistribution formula to achieve load balancing and collaborative optimization among multiple ash pipes.

8. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-5.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1-5.

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