Desulfurization island bed pressure and material level cooperative control method

Abstract

The invention relates to the technical field of desulfurization island bed pressure and material level control, and discloses a desulfurization island bed pressure and material level cooperative control method. In order to solve the problems of large bed pressure fluctuation, non-uniform material level, poor system stability and high operation energy consumption caused by dependence on manual operation in the prior art, flue gas air volume, bed pressure drop and ash hopper material level signals are collected in real time, an optimal bed pressure target value is calculated through a dynamic bed pressure setting model, a total regulating variable is generated through bed pressure main control, and the total regulating variable is calculated through a dynamic bed pressure control model. According to the control method, the adjusting quantity is intelligently distributed to each ash bucket valve in combination with a material level balanced distribution algorithm, automatic cooperative control of stable bed pressure and uniform material level is realized by adopting nonlinear smooth adjustment and a self-adaptive optimization technology, the system stability is remarkably improved, and meanwhile, the operation energy consumption is reduced by optimizing bed pressure setting.

Description

Technical Field

[0001] This invention relates to the field of desulfurization island bed pressure and material level control technology, and in particular to a method for coordinated control of desulfurization island bed pressure and material level. Background Technology

[0002] In the operation of a circulating fluidized bed desulfurization island, six ash hoppers are typically installed, each with a flow control valve at its bottom. Maintaining stable system operation hinges on precise control of the bed pressure drop. Currently, this control relies primarily on manual operation by personnel. Specifically, operators must monitor seven key parameters in real time, including the total bed pressure drop and the material levels in the six ash hoppers. Based on their experience, they manually adjust the opening of each of the six flow control valves to achieve the dual goals of stabilizing the total bed pressure and balancing the material levels in each ash hopper.

[0003] Regarding the aforementioned and existing related technologies, the inventors believe that the following drawbacks often exist: First, manual operation is inherently lagging and unstable, easily leading to significant fluctuations in bed pressure due to untimely adjustments or improper operation. Excessively low bed pressure disrupts the fluidization state, causing a sharp decline in desulfurization efficiency and even triggering the desulfurization island protection system to trip, directly threatening the safe operation of the main system. Second, for conservative safety considerations, operators typically set the bed pressure at a relatively high fixed value. This results in the system maintaining an unnecessarily high bed pressure drop under most operating conditions, especially at low loads, requiring the booster fan to overcome greater system resistance and causing significant energy waste. Third, manual control mode struggles to effectively coordinate the coupled and even contradictory control objectives of stabilizing the total bed pressure and balancing the ash hopper levels. Operators cannot maintain uniform ash hopper levels, easily leading to blockages due to excessively high levels in some hoppers or gas leakage due to excessively low levels, severely threatening the long-term stable operation of the system. Finally, operators need to continuously monitor numerous parameters and intervene frequently, resulting in high workload and demanding extremely high levels of focus and experience. Summary of the Invention

[0004] The technical problem to be solved by this invention is that the existing technology has shortcomings in the control of desulfurization island bed pressure and material level. To address this, we propose a method for the coordinated control of desulfurization island bed pressure and material level.

[0005] To achieve the above objectives, this application adopts the following technical solution: a method for coordinated control of desulfurization island bed pressure and material level, comprising the following steps: Step 1: Multi-source data acquisition and processing: Real-time acquisition of flue gas flow rate signal, total bed pressure drop signal, and material level signal of the six ash hoppers of the desulfurization island through sensors, and transmission of these signals to the control system, and filtering and signal calibration of the acquired raw data; Step 2: Calculate the dynamic bed pressure setpoint: Input the flue gas flow rate signal processed in Step 1 into the preset piecewise function model, dynamically calculate and output the optimal bed pressure drop setpoint under the current operating conditions; The piecewise function model takes the flue gas flow rate as the input variable and maps the corresponding bed pressure setpoint based on the operation optimization objectives under different loads. Step 3: Generate the main control and total adjustment amount of bed pressure: Compare the dynamic bed pressure setpoint obtained in Step 2 with the real-time measured total bed pressure drop value, and send the generated deviation signal to the main control of bed pressure for calculation. The output result is the total adjustment amount command for the flow valves at the bottom of the six ash hoppers. Step 4: Calculate the material level balance distribution and valve position command: Input the total adjustment command obtained in Step 3 and the real-time material level signals of the six ash hoppers into the material level balance distribution algorithm. This algorithm takes ensuring the stability of the total bed pressure drop as a premise and promoting the material level of each ash hopper to approach the weighted average value as its core principle. It dynamically calculates and outputs the specific opening command of the flow valve of each ash hopper. The algorithm controls the rate and amplitude of material level balance adjustment through an adjustable distribution coefficient to ensure a smooth adjustment process and avoid impacting the main control loop of bed pressure. Step 5: Command Execution and Closed-Loop Feedback: The valve opening commands calculated in Step 4 are sent to the corresponding flow valve actuators to drive the valves to move; the system collects bed pressure and material level data in real time to form closed-loop control and continuously optimize and adjust.

[0006] Furthermore, the calculation of the dynamic bed pressure setpoint in step two is as follows: The piecewise function model is implemented in the control system through a piecewise function generator function block, which has multiple coordinate points pre-stored by air volume and bed pressure setpoints. When the system is running, it calculates between coordinate points based on the real-time air volume using linear interpolation and outputs continuously changing dynamic bed pressure setpoints.

[0007] Furthermore, the coordinate points are set as follows: The coordinate points are set based on historical operating data of the desulfurization island and energy-saving optimization targets, including the following typical operating conditions: when the air volume is ≤200000Nm 3 At a rate of / h, the bed pressure setpoint is 0.80 kPa; when the air volume is 300,000 Nm³ / h... 3 When the air volume is ≥400000 Nm³ / h, the bed pressure setting is 1.00 kPa; 3 At / h, the bed pressure setting is 1.15kPa.

[0008] Furthermore, the material level balancing allocation algorithm in step four is as follows: The material level equalization distribution algorithm is calculated based on the deviation between the material level in each ash hopper and the average material level. The rate and magnitude of the material level equalization adjustment are controlled by an adjustable distribution coefficient to ensure a smooth adjustment process. The adjustable distribution coefficient ranges from 0.1 to 0.5. The balance between the material level equalization speed and the stability of the bed pressure main circuit is achieved through on-site tuning.

[0009] Furthermore, the calculation of the opening instruction in step four is as follows: The calculation of the opening command involves combining a base amount that is evenly distributed across the total adjustment command with a correction amount based on dynamically allocated weights.

[0010] Furthermore, the execution of the instructions in step five is as follows: Before the command is executed, the calculated valve opening command is output limited to ensure that it is within the effective working range of the valve.

[0011] Furthermore, the material level balancing allocation algorithm in step four is as follows: The flow valves of the six ash hoppers adopt a grouped collaborative control strategy, dividing the 1-2 ash hoppers with the highest material level into a high material level group and the 1-2 ash hoppers with the lowest material level into a low material level group. In the material level equalization allocation algorithm, a positive correction weight is assigned to the high material level group and a negative correction weight is assigned to the low material level group, while the remaining ash hoppers maintain the basic discharge rate.

[0012] Furthermore, the material level balancing allocation algorithm in step four is as follows: The algorithm introduces a nonlinear adjustment term and achieves smooth adjustment through the hyperbolic tangent function. The formula for calculating the opening command is as follows: ; in, For the first The valve opening command; This is the total adjustment command; For the first Dynamic weight allocation for each valve; It is the tangent function of a hyperbola; This is a non-linear adjustment factor, with a value range of 2-5; For the first Real-time material level in each ash hopper; This represents the average material level. and These are the upper and lower limits of the reasonable operating range for material levels.

[0013] Furthermore, regarding closed-loop feedback and adaptive optimization, specifically: Establish an evaluation index for the coupling degree between bed pressure and material level based on information entropy, which is used to monitor the coupling correlation strength between bed pressure and material level in real time. When the coupling degree exceeds the preset threshold, the adjustable allocation coefficient is automatically adjusted. The formula for calculating the information entropy evaluation index is: ; in, As an evaluation index for the coupling degree of bed pressure and material level; For the first The coupling probability of each ash hopper is calculated using the following formula: ,in This refers to the actual measured pressure drop value of the bed. Set the dynamic bed pressure value; For the first Real-time material level in each ash hopper; This represents the real-time average material level in the six ash hoppers; when When the system is determined to be in a strongly coupled state, the adjustable allocation parameters are automatically adjusted to 80% of the current value.

[0014] Furthermore, regarding the implementation of the method in a distributed control system, the specific details are as follows: The method is implemented in the control system through configuration and includes the following processing steps: signal processing and verification process for validating and eliminating bad values ​​of the collected flue gas volume, bed pressure drop, and ash hopper level signals; dynamic setpoint calculation process for volume-bed pressure mapping calculation, which has a built-in piecewise function model; main logic process for bed pressure control and level balancing, which sequentially executes the bed pressure main controller calculation and the level balancing allocation algorithm; and instruction allocation and output process for issuing opening commands to each flow valve and monitoring their feedback; each processing step exchanges data through the internal data bus of the control system to form a complete control loop.

[0015] The technical effects and advantages of this invention are as follows: This invention achieves multiple significant technical effects through a control strategy that combines dynamic bed pressure setting with balanced material level distribution. This method replaces traditional manual operation with rapid and precise automatic control, effectively suppressing bed pressure fluctuations and maintaining the bed pressure within a safe range. This fundamentally avoids the risk of decreased desulfurization efficiency and system tripping caused by abnormal bed pressure, ensuring the long-term stable operation of the main equipment and the environmental protection system.

[0016] By establishing a dynamic mapping relationship between air volume and bed pressure, the system can automatically adjust the bed pressure setpoint according to the real-time load. Under low load conditions, it can intelligently reduce the bed pressure, significantly reduce the operating resistance of the booster fan, directly reduce the plant's power consumption, achieve significant energy-saving benefits, overcome the energy waste caused by maintaining unnecessary high bed pressure in traditional operation modes, and generate direct economic benefits.

[0017] This invention innovatively solves the complex control problem of stabilizing total bed pressure and balancing ash hopper material levels. While maintaining stable total bed pressure, the system automatically adjusts the discharge rate of each ash hopper through an intelligent distribution algorithm, so that the material level in the six ash hoppers is kept evenly distributed. This control feature effectively prevents blockage and air leakage caused by uneven material levels, significantly improves the material circulation state, and enhances the overall operating quality of the system.

[0018] This control method automates the entire desulfurization island operation process, freeing operators from heavy manual adjustment work. It not only significantly reduces labor intensity but also effectively avoids human error, promoting the intelligent development of desulfurization island operation management and comprehensively improving production efficiency and automation level. Attached Figure Description

[0019] The disclosure of this invention is illustrated with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. In the drawings, the same reference numerals are used to refer to the same parts: Figure 1 This is a flowchart of the desulfurization island bed pressure and material level control of the present invention. Detailed Implementation

[0020] It is readily understood that, based on the technical solution of this invention, those skilled in the art can propose various interchangeable structural methods and implementations without altering the essential spirit of the invention. Therefore, the following detailed embodiments and accompanying drawings are merely illustrative examples of the technical solution of this invention and should not be considered as the entirety of the invention or as limitations or restrictions on the technical solution of this invention.

[0021] To address the technical problems of large bed pressure fluctuations, uneven material levels, poor system stability, and high energy consumption caused by reliance on manual operation in existing desulfurization island control systems, this paper refers to... Figure 1 As shown, this embodiment provides the following technical solution: A method for coordinated control of bed pressure and material level in a desulfurization island includes the following steps: Step 1: Multi-source data acquisition and processing: Real-time acquisition of flue gas flow rate signal, total bed pressure drop signal, and material level signal of the six ash hoppers from the desulfurization island through sensors, and transmission of these signals to the control system. The acquired raw data is filtered and calibrated to remove outliers and interference signals, ensuring data accuracy and reliability, and providing high-quality input for subsequent control decisions. Step 2: Calculate the dynamic bed pressure setpoint: Input the flue gas flow rate signal processed in Step 1 into the preset piecewise function model, dynamically calculate and output the optimal bed pressure drop setpoint under the current operating conditions; the piecewise function model takes the flue gas flow rate as the input variable, and maps the corresponding bed pressure setpoint based on the operation optimization objectives under different loads, so as to realize the dynamic matching between the bed pressure setpoint and the actual load, and avoid the increase in energy consumption caused by excessively high bed pressure or the fluidization failure caused by excessively low bed pressure; Step 3: Generate the main control and total adjustment amount of bed pressure: Compare the dynamic bed pressure setpoint obtained in Step 2 with the real-time measured total bed pressure drop value, and send the generated deviation signal to the main control of bed pressure for calculation. The output result is the total adjustment amount command for the flow valves at the bottom of the six ash hoppers to ensure that the total bed pressure is stable within the target range. Step 4: Calculate the material level balance distribution and valve position command: Input the total adjustment command obtained in Step 3 and the real-time material level signals of the six ash hoppers into the material level balance distribution algorithm. This algorithm takes ensuring the stability of the total bed pressure drop as a premise and promoting the material level of each ash hopper to approach the weighted average value as its core principle. It dynamically calculates and outputs the specific opening command of the flow valve of each ash hopper. The algorithm controls the rate and amplitude of material level balance adjustment through an adjustable distribution coefficient to ensure a smooth adjustment process and avoid impacting the main control loop of bed pressure. Step 5: Command Execution and Closed-Loop Feedback: The valve opening commands calculated in Step 4 are sent to the corresponding flow valve actuators to drive the valves to move; the system collects bed pressure and material level data in real time to form a closed-loop control and continuously optimizes and adjusts to ensure that the system can maintain stable bed pressure and balanced material level under changing operating conditions.

[0022] In this embodiment, the calculation of the dynamic bed pressure setpoint in step two is specifically implemented by a piecewise function model in the control system through a piecewise function generator function block, which pre-stores multiple coordinate points composed of air volume and bed pressure setpoints. When the system is running, it calculates between coordinate points based on the real-time air volume using linear interpolation and outputs a continuously changing dynamic bed pressure setpoint.

[0023] The coordinate points are set based on historical operating data of the desulfurization island and energy-saving optimization targets, including the following typical operating conditions: when the air volume is ≤200000Nm 3 At a rate of / h, the bed pressure setpoint is 0.80 kPa; when the air volume is 300,000 Nm³ / h... 3 When the air volume is ≥400000 Nm³ / h, the bed pressure setting is 1.00 kPa; 3 At / h, the bed pressure setting is 1.15kPa.

[0024] In this embodiment, the material level equalization allocation algorithm in step four is specifically calculated based on the deviation between the material level in each ash hopper and the average material level. The rate and magnitude of the material level equalization adjustment are controlled by an adjustable allocation coefficient to ensure a smooth adjustment process. The adjustable allocation coefficient ranges from 0.1 to 0.5. The balance between the material level equalization speed and the stability of the bed pressure main circuit is achieved through on-site tuning.

[0025] The calculation of the opening command involves combining a base amount that is evenly distributed across the total adjustment command with a correction amount based on dynamically allocated weights.

[0026] Before the command is executed, the calculated valve opening command is output limited to ensure that it is within the effective working range of the valve.

[0027] In this embodiment, the material level balancing allocation algorithm in step four specifically employs a grouped collaborative control strategy for the flow valves of the six ash hoppers. The 1-2 ash hoppers with the highest material levels are divided into a high material level group, and the 1-2 ash hoppers with the lowest material levels are divided into a low material level group. In the material level balancing allocation algorithm, a positive correction weight is assigned to the high material level group, and a negative correction weight is assigned to the low material level group. The remaining ash hoppers maintain the basic discharge rate to accelerate the material level balancing process.

[0028] In this embodiment, the material level equalization allocation algorithm in step four specifically introduces a nonlinear adjustment term and achieves smooth adjustment through a hyperbolic tangent function. The formula for calculating the opening command is as follows: ; in, For the first The valve opening command; This is the total adjustment command; For the first Dynamic weight allocation for each valve; It is the tangent function of a hyperbola; This is a non-linear adjustment factor, with a value range of 2-5; For the first Real-time material level in each ash hopper; This represents the average material level. and These are the upper and lower limits of the reasonable operating range for material levels.

[0029] In this embodiment, the execution of the instructions in step five specifically involves performing output limiting processing on the calculated valve opening instructions before execution to ensure that they are within the effective working range of the valve (0% to 100%), thus preventing over-adjustment or damage to the valve.

[0030] In this embodiment, in order to achieve adaptive optimization of the system, a bed pressure-material level coupling degree evaluation index based on information entropy is also established to monitor the coupling correlation strength between bed pressure and material level in real time. When the coupling degree exceeds the preset threshold, the adjustable allocation coefficient is automatically adjusted. The formula for calculating the information entropy evaluation index is: ; in, As an evaluation index for the coupling degree of bed pressure and material level; For the first The coupling probability of each ash hopper is calculated using the following formula: ,in This refers to the actual measured pressure drop value of the bed. Set the dynamic bed pressure value; For the first Real-time material level in each ash hopper; This represents the real-time average material level in the six ash hoppers; when When the system is determined to be in a strongly coupled state, the adjustable allocation parameters are automatically adjusted to 80% of the current value.

[0031] In this embodiment, the implementation of the method in a distributed control system is specifically as follows: The method is implemented in the control system through configuration and includes the following processing steps: signal processing and verification process for validating and eliminating bad values ​​of the collected flue gas volume, bed pressure drop, and ash hopper level signals; dynamic setpoint calculation process for volume-bed pressure mapping calculation, which has a built-in piecewise function model; main logic process for bed pressure control and level balancing, which sequentially executes the bed pressure main controller calculation and the level balancing allocation algorithm; and instruction allocation and output process for issuing opening commands to each flow valve and monitoring their feedback; each processing step exchanges data through the internal data bus of the control system to form a complete control loop.

[0032] Working principle: It starts with multi-source data acquisition. Through a sensor network installed at key locations in the desulfurization island, it collects flue gas flow signals, total bed pressure drop signals, and material level signals of the six ash hoppers in real time, which reflect the operating status of the system. These signals are pre-processed by the control system, including filtering and noise reduction and signal calibration, to ensure the accuracy and reliability of the data, providing a solid foundation for subsequent intelligent decision-making. In the dynamic bed pressure setting stage, the system calculates the optimal bed pressure setting value based on the processed real-time flue gas volume through a preset piecewise function model. This model takes the air volume as the input variable and calculates between pre-stored coordinate points based on the operation optimization objectives under different loads using a linear interpolation method. It outputs a continuously changing dynamic bed pressure setting value, enabling the bed pressure setting value to be automatically adjusted with load changes, achieving energy-saving operation while ensuring desulfurization efficiency. The main bed pressure control circuit compares the dynamic bed pressure setpoint with the actual measured bed pressure drop value. The resulting deviation signal is sent to the main bed pressure controller for calculation. The controller uses a proportional-integral-derivative algorithm to output a total adjustment command for the flow valves at the bottom of the six ash hoppers. This command represents the total amount of material discharged required to maintain a stable total bed pressure. The total adjustment command and the real-time material level signals of the six ash hoppers are input into the material level equalization distribution algorithm. The algorithm takes the stability of the total bed pressure drop as a premise and promotes the material level of each ash hopper to approach the weighted average value as its core principle. The rate and amplitude of material level equalization adjustment are controlled by the adjustable distribution coefficient. Nonlinear adjustment terms are introduced in the algorithm calculation process, and hyperbolic tangent function is used to achieve smooth adjustment, ensuring that the adjustment process is stable and avoiding impact on the main control loop of bed pressure. During the command execution phase, the system sends the calculated valve opening commands to the corresponding flow valve actuators. Before execution, the output of the commands is limited to ensure that they are within the effective working range of the valves. After the valves act according to the commands, the system continues to collect bed pressure and material level data in real time to form a complete closed-loop control. To further improve system performance, a bed pressure-level coupling degree evaluation based on information entropy was established. By monitoring the coupling correlation strength between bed pressure and level in real time, the allocation coefficient is automatically adjusted when the coupling degree exceeds a preset threshold, thereby achieving adaptive optimization of control parameters. This intelligent adjustment mechanism ensures the stable operation of the system under complex working conditions. The entire control process achieves the coordinated unity of stable bed pressure and uniform level through the organic combination of dynamic bed pressure setting and balanced level distribution. This effectively solves the problems of large bed pressure fluctuations, uneven level distribution, and poor system stability in traditional control methods. At the same time, the system energy consumption is significantly reduced by optimizing the bed pressure setting.

[0033] The technical scope of this invention is not limited to the content described above. Those skilled in the art can make various modifications and variations to the above embodiments without departing from the technical concept of this invention, and all such modifications and variations should fall within the protection scope of this invention.

Claims

1. A method for coordinated control of bed pressure and material level in a desulfurization island, characterized in that, Includes the following steps: Step 1: Multi-source data acquisition and processing: Real-time acquisition of flue gas flow rate signal, total bed pressure drop signal, and material level signal of the six ash hoppers of the desulfurization island through sensors, and transmission of these signals to the control system, and filtering and signal calibration of the acquired raw data; Step 2: Calculate the dynamic bed pressure setpoint: Input the flue gas flow rate signal processed in Step 1 into the preset piecewise function model, dynamically calculate and output the optimal bed pressure drop setpoint under the current operating conditions; the piecewise function model takes the flue gas flow rate as the input variable and maps the corresponding bed pressure setpoint based on the operation optimization objectives under different loads. Step 3: Generate the main control and total adjustment amount of bed pressure: Compare the dynamic bed pressure setpoint obtained in Step 2 with the real-time measured total bed pressure drop value, and send the generated deviation signal to the main control of bed pressure for calculation. The output result is the total adjustment amount command for the flow valves at the bottom of the six ash hoppers. Step 4: Calculate the material level balance distribution and valve position command: Input the total adjustment command obtained in Step 3 and the real-time material level signals of the six ash hoppers into the material level balance distribution algorithm to dynamically calculate and output the specific opening command of the flow valve of each ash hopper. Step 5: Command Execution and Closed-Loop Feedback: The valve opening commands calculated in Step 4 are sent to the corresponding flow valve actuators to drive the valves to move; the system collects bed pressure and material level data in real time and continuously optimizes and adjusts them.

2. The method for coordinated control of bed pressure and material level in the desulfurization island according to claim 1, characterized in that: The calculation of the dynamic bed pressure setpoint in step two is as follows: The piecewise function model is implemented in the control system through a piecewise function generator function block, which has multiple coordinate points pre-stored by air volume and bed pressure setpoints. When the system is running, it calculates between the coordinate points based on the real-time air volume using linear interpolation and outputs a continuously changing dynamic bed pressure setpoint.

3. The method for coordinated control of bed pressure and material level in the desulfurization island according to claim 2, characterized in that: The coordinate points are set as follows: The coordinate points are set based on historical operating data of the desulfurization island and energy-saving optimization targets, including the following typical operating conditions: when the air volume is ≤200000Nm 3 At a rate of / h, the bed pressure setting is 0.80 kPa; when the air volume is 300,000 Nm³ / h... 3 When the air volume is ≥400000 Nm³ / h, the bed pressure setting is 1.00 kPa; 3 At / h, the bed pressure setting is 1.15kPa.

4. The method for coordinated control of desulfurization island bed pressure and material level according to claim 1, characterized in that: The material level balancing allocation algorithm in step four is as follows: The material level equalization distribution algorithm is calculated based on the deviation between the material level in each ash hopper and the average material level. The rate and magnitude of the material level equalization adjustment are controlled by an adjustable distribution coefficient to ensure a smooth adjustment process. The adjustable distribution coefficient ranges from 0.1 to 0.

5. The balance between the material level equalization speed and the stability of the bed pressure main circuit is achieved through on-site tuning.

5. The method for coordinated control of bed pressure and material level in the desulfurization island according to claim 4, characterized in that: The calculation of the opening instruction in step four is as follows: The calculation of the opening command includes combining a base amount that is evenly distributed across the total adjustment command with a correction amount based on dynamically allocated weights.

6. The method for coordinated control of bed pressure and material level in the desulfurization island according to claim 1, characterized in that: The execution of the instructions in step five is as follows: Before the command is executed, the calculated valve opening command is output limited to ensure that it is within the effective working range of the valve.

7. The method for coordinated control of bed pressure and material level in the desulfurization island according to claim 1, characterized in that: The material level balancing allocation algorithm in step four is as follows: The flow valves of the six ash hoppers adopt a group-based collaborative control strategy, dividing the ash hopper with the highest material level into the high material level group and the ash hopper with the lowest material level into the low material level group. In the material level balancing allocation algorithm, a positive correction weight is assigned to the high material level group and a negative correction weight is assigned to the low material level group, while the remaining ash hoppers maintain the basic discharge rate.

8. The method for coordinated control of bed pressure and material level in a desulfurization island according to claim 1, characterized in that: The material level balancing allocation algorithm in step four is as follows: The algorithm introduces a nonlinear adjustment term and achieves smooth adjustment through the hyperbolic tangent function. The formula for calculating the opening command is as follows: ； in, For the first The valve opening command; This is the total adjustment command; For the first Dynamic weight allocation for each valve; It is the tangent function of a hyperbola; It is a non-linear adjustment factor; For the first Real-time material level in each ash hopper; This represents the average material level. and These are the upper and lower limits of the reasonable operating range for material levels.

9. The method for coordinated control of bed pressure and material level in a desulfurization island according to claim 4, characterized in that: Specifically, regarding closed-loop feedback and adaptive optimization: An evaluation index for bed pressure and material level coupling based on information entropy is established to monitor the coupling correlation strength between bed pressure and material level in real time. When the coupling degree exceeds a preset threshold, the adjustable allocation coefficient is automatically adjusted. The formula for calculating the information entropy evaluation index is as follows: ； in, As an evaluation index for the coupling degree of bed pressure and material level; For the first The coupling probability of each ash hopper is calculated using the following formula: ,in This refers to the actual measured pressure drop value of the bed. Set the dynamic bed pressure value; For the first Real-time material level in each ash hopper; This represents the real-time average material level in the six ash hoppers; when When the system is determined to be in a strongly coupled state, the adjustable allocation parameters are automatically adjusted to 80% of the current value.

10. The method for coordinated control of bed pressure and material level in a desulfurization island according to claim 1, characterized in that: The implementation of the aforementioned method in a distributed control system is specifically as follows: The method is implemented in the control system through configuration and includes the following processing steps: signal processing and verification process for validating and removing bad values ​​of the collected flue gas volume, bed pressure drop, and ash hopper level signals; dynamic setpoint calculation process for volume-bed pressure mapping calculation, which incorporates the piecewise function model; main logic process for bed pressure control and level balancing, which sequentially executes the bed pressure main controller calculation and the level balancing allocation algorithm; and instruction allocation and output process for issuing opening commands to each flow valve and monitoring their feedback. Each processing step exchanges data through the internal data bus of the control system, forming a complete control loop.

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