Load dynamic adaptation-based pneumatic ash conveying logic optimization control method and system

Abstract

The invention relates to the technical field of pneumatic ash conveying, and particularly discloses a pneumatic ash conveying logic optimization control method and system based on load dynamic adaptation, and the method comprises the steps: collecting a current operation load rate signal of a unit, and obtaining a load rate signal; performing grading processing on the load rate signal to obtain a load grading signal; generating a matched air compressor combined operation strategy and corresponding ash conveying pipeline switching logic to obtain an operation strategy signal; performing'blanking-purging-ash collection 'action control on each bag area of the ash removal system in sequence to obtain a bag area control sequence; dynamically adjusting the time interval between the execution operations of each pocket area to obtain an optimized interval parameter; performing self-adaptive adjustment on the operation strategy and the interval parameter; according to the method, the real-time load of the unit serves as drive, the air compressor and pipeline operation strategy is dynamically generated, and the dust removal rhythm is synchronously adjusted, so that the system can quickly respond to load fluctuation, and the phenomenon of over-blowing or under-blowing generated when the working condition changes in a traditional fixed strategy is avoided.

Description

Technical Field

[0001] This invention belongs to the field of pneumatic ash conveying technology, specifically relating to a logic optimization control method and system for pneumatic ash conveying based on dynamic load adaptation. Background Technology

[0002] In the pneumatic ash conveying system of a coal-fired power plant, ensuring the efficient and stable operation of the ash conveying process is crucial for the normal production of the entire power plant. Traditional pneumatic ash conveying control methods have many shortcomings in responding to changes in unit load and maintaining stable gas grid pressure, making it difficult to meet the requirements of modern power plants for efficient, energy-saving, and stable operation.

[0003] Traditional pneumatic ash conveying systems mostly employ fixed control strategies. Parameters such as the number of operating air compressors, the start and stop of ash conveying pipelines, and the operating intervals of each baghouse are typically preset based on design conditions or empirical values ​​and remain unchanged throughout operation. However, the actual operating load of a coal-fired boiler is constantly changing, and the unit load rate fluctuates within a wide range. When the unit is operating at low load, air compressors operating according to a fixed strategy may over-output, resulting in energy waste; while at high load, insufficient output may occur, leading to poor ash conveying and even pipe blockages. This fixed strategy cannot be dynamically adjusted according to the real-time load of the unit, easily resulting in "over-blowing" or "under-blowing" when operating conditions change. "Over-blowing" not only wastes compressed air resources and increases the operating costs of air compressors but also accelerates equipment wear; while "under-blowing" fails to effectively remove ash accumulation in the ash hopper, affecting the normal combustion and operational safety of the boiler.

[0004] In response to this problem, this application proposes a logic optimization control method and system for pneumatic ash conveying based on dynamic load adaptation, in order to solve the above-mentioned problems. Summary of the Invention

[0005] The purpose of this invention is to provide a logic optimization control method and system for pneumatic ash conveying based on dynamic load adaptation, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A logic optimization control method for pneumatic ash conveying based on dynamic load adaptation includes:

[0008] The current operating load rate signal of the unit is collected to reflect the real-time load status of the coal-fired boiler and obtain the load rate signal;

[0009] The load rate signal is processed into multiple load intervals based on a preset load threshold to obtain a load grading signal.

[0010] Based on the load classification signal, a matching air compressor combination operation strategy and corresponding ash conveying pipeline switching logic are generated to obtain the operation strategy signal;

[0011] Based on the aforementioned operating strategy signal, the "material dropping - blowing - ash collection" actions are sequentially controlled for each bag area of ​​the ash removal system to obtain the bag area control sequence;

[0012] Based on the bag area control sequence, the time interval between the operations performed in each bag area is dynamically adjusted to obtain the optimized interval parameter;

[0013] While executing the optimized interval parameters, the system adaptively adjusts the operating strategy and interval parameters in conjunction with the real-time pressure feedback of the ash removal gas network, thereby achieving dynamic adaptation of system load and closed-loop optimization control of ash conveying logic.

[0014] Preferably, the adaptive adjustment of the operating strategy and interval parameters adopts an adaptive interval optimization method to perform a closed-loop adjustment of the operating strategy signal and the optimized interval parameters to ensure a dynamic balance between the gas network pressure and the unit load.

[0015] The formula for the adaptive interval optimization method is:

[0016]

[0017] in, This is the operation interval (in seconds) for each bag area after the kth ash removal cycle, which is the current value of the "optimized interval parameter".

[0018] The operation interval (in seconds) is updated after the (k+1)th ash removal cycle;

[0019] The actual pressure of the gas network (kPa) at the end of the kth cycle is collected in real time by a pressure sensor.

[0020] The target pressure value for the gas network (kPa) is set at 400 kPa in this scheme.

[0021] The unit load rate (%) at the end of the k-th cycle is obtained from the DCS system or load sensor;

[0022] The unit load reference value (%) can be set to the ideal load or design target load of the previous cycle;

[0023] Pressure regulation gain (seconds / kPa) is used to convert pressure deviation into interval adjustment.

[0024] Load adjustment gain (seconds / %) is used to convert load deviation into interval adjustment amount;

[0025] Dual-objective balance, achieved through the pressure deviation term and load deviation term At the same time, it corrects the deviation between the gas network pressure and the unit load, achieving a balance between gas network stability and minimum output of the air compressor.

[0026] Preferably, the load rate signal is the real-time load percentage of the unit, which is acquired in real time through the DCS system interface or load sensor.

[0027] Preferably, the load grading signal includes:

[0028] Low load range: ≤53.23%; Medium load range: 53.23%–80.65%; High load range: >80.65%.

[0029] Preferably, the operation strategy signal includes the determination of the number of air compressors in operation and the start / stop switching logic of the ash conveying pipeline;

[0030] The air compressors are configured in different load ranges as follows: low load 1 large machine + 3 small machines, medium load 1 large machine + 4 small machines, and high load 1 large machine + 5 small machines.

[0031] Preferably, the bag zone control sequence is performed sequentially according to the bag zone numbers on one side of the boiler, with priority given to the heavy ash bag zone to avoid pipe blockage and pressure fluctuations.

[0032] Preferably, the optimized interval parameter is dynamically calculated by measuring the current ash amount and the air pressure consumption trend. When the material discharge amount is large, the operation interval of the bag area is extended, and when the material discharge amount is small, the interval is shortened.

[0033] Preferably, the adaptive adjustment step adjusts the operating strategy signal and the optimized interval parameter in a proportional-integral control manner according to the changing trend of the gas network pressure and the actual output of the air compressor, so as to maintain the gas network pressure at around 400 kPa.

[0034] Preferably, the method further includes a one-click interlock start / stop step, which is used to automatically switch to single-point control logic to ensure stable system operation in special circumstances such as equipment failure, pipe blockage, or maintenance.

[0035] A logic optimization control system for pneumatic ash conveying based on dynamic load adaptation includes:

[0036] The load rate acquisition and preprocessing module acquires the current operating load rate signal of the unit to reflect the real-time load status of the coal-fired boiler and obtains the load rate signal.

[0037] The load grading and threshold mapping module performs grading processing on the load rate signal, dividing it into multiple load intervals according to a preset load threshold to obtain a load grading signal.

[0038] The operation strategy and pipeline switching logic generation module generates a matching air compressor combination operation strategy and corresponding ash conveying pipeline switching logic based on the load classification signal, thereby obtaining the operation strategy signal.

[0039] The bag zone cycle action control module, according to the operation strategy signal, performs "material dropping - blowing - ash collection" action control on each bag zone of the ash removal system in sequence to obtain the bag zone control sequence;

[0040] The time interval dynamic optimization module dynamically adjusts the time interval between the operations performed in each bag area based on the bag area control sequence to obtain the optimized interval parameter;

[0041] The adaptive closed-loop feedback module, while executing the optimized interval parameters, combines the real-time pressure feedback of the ash removal gas network to adaptively adjust the operating strategy and interval parameters, thereby realizing the dynamic adaptation of system load and closed-loop optimization control of ash conveying logic.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] (1) This invention uses the real-time load of the unit as the driving force to dynamically generate the operation strategy of the air compressor and pipeline, and adjusts the ash removal rhythm in sync, so that the system can respond quickly to load fluctuations and avoid the "over-blowing" or "under-blowing" phenomenon caused by the traditional fixed strategy when the operating conditions change; by participating in the interval adjustment through pressure feedback, a dual-target closed loop is constructed, so that the air network pressure always moves slightly around the target value, suppressing frequent large fluctuations, thereby improving the operational stability of the entire pneumatic ash conveying pipeline network.

[0044] (2) The present invention accurately calculates the bag area operation interval through a closed-loop algorithm. The dust removal action is neither redundant nor too compact, avoiding unnecessary purging, significantly reducing the ineffective running period of the air compressor, and improving air utilization efficiency. Randomly switching the air compressor on and off and the ash conveying pipeline, combined with dynamic interval control, smooths the start and stop of the compressor and the pressure impact of the pipeline, reduces mechanical vibration and system impact load, and helps to extend the service life of the equipment and reduce maintenance costs. Attached Figure Description

[0045] Figure 1 This is a flowchart of a pneumatic ash conveying logic optimization control method based on dynamic load adaptation according to the present invention.

[0046] Figure 2 This is a block diagram of a pneumatic ash conveying logic optimization control system based on dynamic load adaptation according to the present invention. Detailed Implementation

[0047] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0048] Example 1:

[0049] Please see Figure 1 As shown, a logic optimization control method for pneumatic ash conveying based on dynamic load adaptation includes:

[0050] The current operating load rate signal of the unit is collected to reflect the real-time load status of the coal-fired boiler and obtain the load rate signal;

[0051] The load rate signal is the real-time load percentage of the unit, which is acquired in real time through the DCS system interface or load sensor.

[0052] The load rate signal is processed into multiple load intervals based on a preset load threshold to obtain a load grading signal.

[0053] The load grading signal includes:

[0054] Low load range: ≤53.23%; Medium load range: 53.23%–80.65%; High load range: >80.65%.

[0055] Based on the load classification signal, a matching air compressor combination operation strategy and corresponding ash conveying pipeline switching logic are generated to obtain the operation strategy signal;

[0056] The operation strategy signals include the determination of the number of air compressors in operation and the start / stop switching logic of the ash conveying pipeline;

[0057] The air compressors are configured in different load ranges as follows: low load 1 large machine + 3 small machines, medium load 1 large machine + 4 small machines, and high load 1 large machine + 5 small machines.

[0058] Based on the aforementioned operating strategy signal, the "material dropping - blowing - ash collection" actions are sequentially controlled for each bag area of ​​the ash removal system to obtain the bag area control sequence;

[0059] The bag zone control sequence is carried out in the order of the bag zone numbers on one side of the boiler, and priority is given to the execution order of the heavy ash bag zone to avoid pipe blockage and pressure fluctuations.

[0060] Based on the bag area control sequence, the time interval between the operations performed in each bag area is dynamically adjusted to obtain the optimized interval parameter;

[0061] The optimized interval parameter is dynamically calculated by measuring the current ash amount and the air pressure consumption trend. When the material discharge amount is large, the operation interval of the bag area is extended, and when the material discharge amount is small, the interval is shortened.

[0062] While executing the optimized interval parameters, the operating strategy and interval parameters are adaptively adjusted in conjunction with the real-time pressure feedback of the ash removal gas network to achieve dynamic adaptation of system load and closed-loop optimization control of ash conveying logic.

[0063] The adaptive adjustment step adjusts the operating strategy signal and the optimized interval parameter in a proportional-integral control manner according to the changing trend of the gas network pressure and the actual output of the air compressor, so as to maintain the gas network pressure at around 400 kPa.

[0064] The adaptive adjustment of the operation strategy and interval parameters adopts an adaptive interval optimization method to perform a closed-loop adjustment of the operation strategy signal and the optimized interval parameters to ensure a dynamic balance between the dual objectives of gas network pressure and unit load.

[0065] The formula for the adaptive interval optimization method is:

[0066]

[0067] in, This is the operation interval (in seconds) for each bag area after the kth ash removal cycle, which is the current value of the "optimized interval parameter".

[0068] The operation interval (in seconds) is updated after the (k+1)th ash removal cycle;

[0069] The actual pressure of the gas network (kPa) at the end of the kth cycle is collected in real time by a pressure sensor.

[0070] The target pressure value for the gas network (kPa) is set at 400 kPa in this scheme.

[0071] The unit load rate (%) at the end of the k-th cycle is obtained from the DCS system or load sensor;

[0072] The unit load reference value (%) can be set to the ideal load or design target load of the previous cycle;

[0073] Pressure regulation gain (seconds / kPa) is used to convert pressure deviation into interval adjustment.

[0074] Load adjustment gain (seconds / %) is used to convert load deviation into interval adjustment amount;

[0075] Dual-objective balance, achieved through the pressure deviation term and load deviation term At the same time, it corrects the deviation between the gas network pressure and the unit load, and achieves a balance between gas network stability and minimum output of air compressor;

[0076] Adaptive adjustment, through quantitative processing of feedback after each cycle, enables the bag area operation interval to respond in real time to load and pressure fluctuations, avoiding system "overshoot" or "lag" caused by a single adjustment strategy;

[0077] Reduce energy consumption by precisely controlling the intervals to reduce unnecessary frequent purging or long-term shutdowns, thereby further reducing the compressed air consumption of the ash removal system and thus reducing the plant's power consumption rate.

[0078] It is easy to implement in engineering, and all parameters can be obtained through on-site debugging or online identification. Moreover, the algorithm structure is simple and easy to deploy quickly in DCS or PLC.

[0079] Specifically, the method also includes a one-click interlock start / stop step, which is used to automatically switch to single-point control logic to ensure stable system operation in special circumstances such as equipment failure, pipe blockage, or maintenance.

[0080] As can be seen from the above, the real-time load of the unit drives the dynamic generation of air compressor and pipeline operation strategies, and simultaneously adjusts the dust removal rhythm, so that the system can respond quickly to load fluctuations and avoid the "over-blowing" or "under-blowing" phenomenon caused by the traditional fixed strategy when the operating conditions change.

[0081] By incorporating pressure feedback into interval regulation, a dual-objective closed loop is constructed, ensuring that the gas network pressure always fluctuates slightly around the target value, suppressing frequent large fluctuations, thereby improving the operational stability of the entire pneumatic ash conveying pipeline network.

[0082] Example 2:

[0083] Dynamic optimization under medium load (75%) conditions

[0084] (1) Acquiring load signals

[0085] Equipment and Sampling: Real-time communication with the PT100 load sensor via the ABB 800xA DCS system, with a sampling period of 1 second and a filter window width of 3 seconds;

[0086] Initial value: The measured real-time load rate L0 = 75.2% is stored in the circular buffer, and the load rate signal is output.

[0087] (2) Load grading

[0088] Threshold settings: Low load ≤ 53.23%, Medium load 53.23%–80.65%, High load > 80.65%;

[0089] Judgment logic: 75.2% falls into the medium load range, generating a graded signal of "medium load".

[0090] (3) Generation of operation strategy signals

[0091] Air compressor combination: Select 1 large 630kW compressor (model: Atlas Copco ZH 160) + 4 small 160kW compressors (model: KAESER SM 16).

[0092] Pipeline switching: There are a total of 3 main ash conveying pipes with an inner diameter of 75mm. The strategy is to switch pipelines every 2 bag areas.

[0093] Output: The operation strategy signal includes the [Main unit + Small unit commissioning / discharging] instruction and the [Pipeline 1→2→3] switching sequence.

[0094] (4) Bag area circulation control

[0095] Number of bag zones: 10 bag zones on the boiler side (numbered 1–10);

[0096] Action parameters:

[0097] Material feeding: Pulse valve opens 50ms;

[0098] Purging: The secondary purging valve opens for 200ms;

[0099] Ash collection: The recovery valve opens for 150ms;

[0100] Ash content monitoring: The pressure difference across the filter bag is measured using a Dwyer 640 differential pressure transmitter, with a single discharge amount of approximately 0.12 kg / m².

[0101] Result: A bag control sequence was formed, with a cycle completion time of approximately 11 minutes.

[0102] (5) Dynamic interval optimization

[0103] Initial interval: E0 = 100s; gas network pressure measured at the end of the last cycle: P0 = 389.8 kPa;

[0104] Reference values: Pref = 400 kPa, Lref = 80%, gain Kp = 2 s / kPa, Kl = 1 s / %

[0105] Calculation process:

[0106] E1=100+2(400−389.8)+1(80−75.2)=100+20.4+4.8=125.2s

[0107] Rounding and limiting: Round down to 125s, and limit: 90s≤E1≤150s;

[0108] Output: Optimized interval parameter E1 = 125s.

[0109] (6) Closed-loop feedback regulation

[0110] Real-time monitoring: Pressure and load were sampled at a frequency of 0.5s during the purging process, with an average pressure of 398.5±1.5kPa and an average load of 75.0±0.8%.

[0111] Correction logic: If the pressure deviation continues to be >3kPa, immediately adjust the interval parameter E1 by ±5s in the next cycle;

[0112] Operating results: Compressed air consumption was reduced by 3.6% compared to the fixed 120s interval, and the unit's power consumption rate decreased from 3.50% to 3.42%.

[0113] As can be seen from the above, by accurately calculating the bag area operation interval through the closed-loop algorithm, the dust cleaning action is neither redundant nor too compact, avoiding unnecessary purging, significantly reducing the ineffective running period of the air compressor, and improving air utilization efficiency; the random switching of air compressor operation and ash conveying pipeline, combined with dynamic interval control, smooths the start-up and shutdown of the compressor and the pressure impact of the pipeline, reduces mechanical vibration and system impact load, and helps to extend the service life of the equipment and reduce maintenance costs.

[0114] Example 3:

[0115] Dynamic optimization under high load (85%) conditions

[0116] (1) Acquiring load signals

[0117] Equipment and sampling: Same as ABB 800xA DCS and PT100 load sensor, sampling period 1s, filter window 5s;

[0118] Initial value: L0 = 84.7% was measured, and the load rate signal was output.

[0119] (2) Load grading

[0120] The area is identified as a high-load zone, and the classification signal is "high load".

[0121] (3) Generation of operation strategy signals

[0122] Air compressor combination: 1 large 630kW compressor + 5 small 160kW compressors, maintaining pipelines 1-4 (4 lines in total) in operation simultaneously, cycling in the order of "1→2→3→4";

[0123] Pipeline parameters: Each pipeline is 50m long and has an inner diameter of 90mm to reduce pressure loss;

[0124] Output: Generates the running strategy signal.

[0125] (4) Bag area circulation control

[0126] Action parameters:

[0127] Material feeding: Valve opens 60ms;

[0128] Purge: Valve open 180ms;

[0129] Ash collection: Valve open for 140ms;

[0130] Ash content monitoring: Approximately 0.15 kg / m² per single discharge; bag area cycle time approximately 10 minutes;

[0131] (5) Dynamic interval optimization

[0132] Initial interval: E0=90; measured P0=395.3kPa;

[0133] Calculation process:

[0134] E2=90+2(400−395.3)+1(80−84.7)=90+9.4−4.7=94.7s

[0135] Limiting and rounding: Rounding to 95s, with a limit of 80s≤E2≤120s;

[0136] Output: E2=95s.

[0137] (6) Closed-loop feedback regulation

[0138] Monitoring results: Pressure 400.2±1.0 kPa, Load 84.9±0.4%;

[0139] Subsequent adjustments: If load fluctuation >2%, the next cycle will be E2±3s;

[0140] Operating results: Compressed air consumption decreased by 2.1%, and the unit's power consumption rate decreased from 3.80% to 3.72%.

[0141] In contrast, traditional solutions use a fixed purging interval of Etrad=120s under all load conditions, and the air compressor combination and pipeline switching do not change with the load, often leading to:

[0142] Gas network pressure fluctuation: ±5 kPa;

[0143] Compressed air waste: Fixed strategies cannot meet the needs of different loads, often resulting in "over-blowing" or "under-blowing";

[0144] High plant power consumption rate: approximately 3.60%–3.90%;

[0145] A comprehensive comparison of Example 2, Example 3, and the traditional solution is shown in Table 1 below:

[0146] Table 1

[0147] index Traditional solution Example 1 (75% load) Example 2 (85% load) Purging interval (rounded down to the nearest whole number) 120s 125s 95s Gas network pressure fluctuation range ±5kPa ±1.5kPa ±1.0 kPa Changes in compressed air consumption — –3.6% –2.1% Compressor combined power (total output) 5 × 160kW = 800kW 1×630+4×160=1270kW 1×630+5×160=1430kW Plant power consumption rate 3.70% (mean) 3.42% 3.72% Single furnace bag zone cycle (average) 10.5min 11.0min 10.0min

[0148] As can be seen from the above, the steps are closely connected without any obvious breaks in the middle. Combined with the one-click interlocking switching function, it can quickly switch to the safe mode in case of equipment abnormality or maintenance, ensuring that the ash removal system can remain stable and reliable under various operating conditions.

[0149] Example 4:

[0150] like Figure 2 As shown, a logic optimization control system for pneumatic ash conveying based on dynamic load adaptation includes:

[0151] The load rate acquisition and preprocessing module is used to continuously acquire the operating load rate of the coal-fired boiler through real-time communication with the DCS or load sensor; it filters and denoises the raw signal to output an accurate load rate signal as the basis for subsequent logical judgment.

[0152] The load grading and threshold mapping module compares the pre-processed load rate signal with pre-set multi-level thresholds (such as 53.23% and 80.65%), automatically classifying the current load into low, medium, and high ranges to form a load grading signal, providing a clear hierarchy for the selection of operating strategies.

[0153] The operation strategy and pipeline switching logic generation module is used to calculate the optimal air compressor operation and deactivation combination (such as 1 large and 3 small in the low range, 1 large and 4 small in the medium range, and 1 large and 5 small in the high range) based on the graded signals, and synchronously match the corresponding ash conveying pipeline switching rules to output a comprehensive operation strategy signal to ensure optimal coordination between the air source and the ash conveying channel.

[0154] The bag zone cycle action control module receives the operation strategy signal and then executes the "material dropping - blowing - dust collection" process in each bag zone in sequence. The differential pressure sensor monitors the amount of material dropped in a single cycle in real time and adjusts the blowing time to ensure that the dust is completely removed, forming a stable bag zone control sequence.

[0155] The time interval dynamic optimization module is used to automatically calculate and adjust the operation interval of the next round according to the closed-loop regulation formula (as described in the claims) based on the bag zone control sequence and the collected information on the pressure and load deviation of the gas network at the end of the cycle, and output the optimized interval parameters so that the purging frequency and gas network pressure are kept at the most economical and stable level.

[0156] The adaptive closed-loop feedback module continuously reads gas network pressure and unit load data during the optimization of interval parameters. It links real-time feedback with operating strategy signals and interval parameters for correction, forming a dynamic closed loop throughout the entire process. This allows for rapid response to unit load fluctuations and quick switching to a safe mode in case of pipeline or equipment malfunctions, ultimately achieving efficient, energy-saving, and stable operation of pneumatic ash conveying.

[0157] As can be seen from the above, by adjusting the load-oriented strategy and using closed-loop feedback, the optimal dust removal rhythm under each operating condition is fully utilized, achieving dual optimization of compressor energy efficiency and system energy consumption, and providing solid theoretical support for the continuous improvement of power plant power consumption rate.

[0158] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A logic optimization control method for pneumatic ash conveying based on dynamic load adaptation, characterized in that, include: The current operating load rate signal of the unit is collected to reflect the real-time load status of the coal-fired boiler and obtain the load rate signal; The load rate signal is processed into multiple load intervals based on a preset load threshold to obtain a load classification signal. Based on the load classification signal, a matching air compressor combination operation strategy and corresponding ash conveying pipeline switching logic are generated to obtain the operation strategy signal; Based on the operation strategy signal, the material dropping-blowing-ash collection actions of each bag area of ​​the ash removal system are controlled in sequence to obtain the bag area control sequence. Based on the bag area control sequence, the time interval between the operations performed in each bag area is dynamically adjusted to obtain the optimized interval parameter; While executing the optimized interval parameters, the system adaptively adjusts the operating strategy and interval parameters in conjunction with the real-time pressure feedback of the ash removal gas network, thereby achieving dynamic adaptation of system load and closed-loop optimization control of ash conveying logic.

2. The pneumatic ash conveying logic optimization control method based on dynamic load adaptation according to claim 1, characterized in that, The adaptive adjustment of the operation strategy and interval parameters adopts an adaptive interval optimization method to perform a closed-loop adjustment of the operation strategy signal and the optimized interval parameters to ensure a dynamic balance between the dual objectives of gas network pressure and unit load. The formula for the adaptive interval optimization method is: in, The interval between each bag area after the kth ash removal cycle, in seconds; The update interval after the (k+1)th ash removal cycle, in seconds; : Actual pressure of the gas network at the end of the kth cycle, in kilopascals (kPa), collected in real time by a pressure sensor; Target pressure value for the gas network, unit: kilopascal (kPa); The unit load rate at the end of the k-th cycle is obtained by the DCS system or load sensor; The unit load reference value is the ideal load or design target load of the previous cycle; Pressure regulation gain, unit: seconds / kPa, used to convert pressure deviation into interval adjustment amount; Load regulation gain, unit: seconds / %, used to convert load deviation into interval adjustment amount.

3. The pneumatic ash conveying logic optimization control method based on dynamic load adaptation according to claim 1, characterized in that, The load rate signal is the real-time load percentage of the unit, which is acquired in real time through the DCS system interface or load sensor.

4. The pneumatic ash conveying logic optimization control method based on dynamic load adaptation according to claim 1, characterized in that, The load grading signal includes: Low load range: ≤53.23%; Medium load range: 53.23%–80.65%; High load range: >80.65%.

5. The pneumatic ash conveying logic optimization control method based on dynamic load adaptation according to claim 1, characterized in that, The operation strategy signals include the determination of the number of air compressors in operation and the start / stop switching logic of the ash conveying pipeline; The air compressors are configured in different load ranges as follows: low load 1 large machine + 3 small machines, medium load 1 large machine + 4 small machines, and high load 1 large machine + 5 small machines.

6. The pneumatic ash conveying logic optimization control method based on dynamic load adaptation according to claim 1, characterized in that, The bag zone control sequence is performed sequentially according to the bag zone numbers on each side of the boiler, with priority given to the heavy ash bag zone.

7. The pneumatic ash conveying logic optimization control method based on dynamic load adaptation according to claim 1, characterized in that, The optimized interval parameter is dynamically calculated by measuring the current ash volume and the trend of air pressure consumption.

8. The pneumatic ash conveying logic optimization control method based on dynamic load adaptation according to claim 1, characterized in that, The adaptive adjustment step adjusts the operating strategy signal and the optimized interval parameter in a proportional-integral control manner according to the changing trend of the gas network pressure and the actual output of the air compressor, so as to maintain the gas network pressure at 400 kPa.

9. The pneumatic ash conveying logic optimization control method based on dynamic load adaptation according to claim 1, characterized in that, The method also includes a one-click interlock start / stop step, which is used to automatically switch to single-point control logic to ensure stable system operation in special circumstances such as equipment failure, pipe blockage, and maintenance.

10. A logic optimization control system for pneumatic ash conveying based on dynamic load adaptation, characterized in that, include: The load rate acquisition and preprocessing module acquires the current operating load rate signal of the unit to reflect the real-time load status of the coal-fired boiler and obtains the load rate signal. The load grading and threshold mapping module performs grading processing on the load rate signal, dividing it into multiple load intervals according to a preset load threshold to obtain a load grading signal. The operation strategy and pipeline switching logic generation module generates a matching air compressor combination operation strategy and corresponding ash conveying pipeline switching logic based on the load classification signal, thereby obtaining the operation strategy signal. The bag zone cycle action control module, according to the operation strategy signal, performs "material feeding - blowing - ash collection" action control on each bag zone of the ash removal system in sequence to obtain the bag zone control sequence; The time interval dynamic optimization module dynamically adjusts the time interval between the operations performed in each bag area based on the bag area control sequence to obtain the optimized interval parameter; The adaptive closed-loop feedback module, while executing the optimized interval parameters, combines the real-time pressure feedback of the ash removal gas network to adaptively adjust the operating strategy and interval parameters, thereby realizing the dynamic adaptation of system load and closed-loop optimization control of ash conveying logic.

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