Multi-parameter adjusted desulfurization system synergistic regulation method and platform

Abstract

The application discloses a multi-parameter adjusted desulfurization system synergic regulation method and platform, relates to the flue gas treatment technical field, and the method comprises the following steps: deploying an asymmetric regulation mode in a desulfurization system, constructing an embedded desulfurization intelligent agent, collecting real-time working condition data of an absorption tower, performing three-phase field state initialization of gas, liquid and solid, executing two-stage decision making according to desulfurization constraint conditions to generate a desulfurization regulation strategy, and driving the synergic regulation execution of a feed pump and two circuits of a top spraying layer and a sub-top spraying layer by the system under the constraint of a time stamp.The application solves the technical problems that the existing wet desulfurization system has response lag, imbalance of interlayer control and high energy consumption in multi-parameter coupling regulation, achieves the technical effects of realizing the hierarchical synergic control of a multi-parameter desulfurization process, reducing coupling interference and improving system response and energy efficiency level through asymmetric synergic regulation and two-stage decision making of an intelligent agent.

Description

Technical Field

[0001] This invention relates to the field of flue gas treatment technology, specifically to a method and platform for coordinated control of desulfurization systems with multi-parameter adjustment. Background Technology

[0002] In modern thermal power plant desulfurization systems, traditional desulfurization technologies face numerous challenges due to increasingly stringent environmental standards. Existing thermal power plants and coal-fired boilers commonly employ limestone-gypsum wet desulfurization processes for deep desulfurization. However, this process suffers from limited control parameters, slow system response, high energy consumption, and difficulty in balancing reaction rate and absorption efficiency when simultaneously removing SO2 and SO3. With frequent fluctuations in unit load and fuel sulfur content, traditional fixed-ratio dosing and single-loop control methods are insufficient for achieving efficient desulfurization under dynamic operating conditions. Summary of the Invention

[0003] This application provides a method and platform for coordinated control of desulfurization systems with multi-parameter adjustment, which is used to solve the technical problems of response lag, inter-layer control imbalance and high energy consumption in existing wet desulfurization systems with multi-parameter coupled adjustment.

[0004] The first aspect of this application provides a method for coordinated control of a desulfurization system with multi-parameter adjustment. The method includes: for a desulfurization equipment group, establishing a desulfurization intelligent agent by deploying an asymmetric control mode, wherein the desulfurization intelligent agent is a built-in plug-in embedded in the desulfurization system; collecting real-time operating data based on the sensor array deployed on the absorption tower, transmitting it back to the desulfurization system and triggering the desulfurization intelligent agent; determining the desulfurization control strategy by performing field state initialization based on a gas-liquid-solid three-phase system and making two-order decisions under the asymmetric control mode according to the desulfurization constraints; the desulfurization system reads the desulfurization control strategy and executes coordinated control drive of the feed pump, the first control loop based on the top spray layer, and the second control loop based on the sub-top spray layer under time-stamp constraints.

[0005] The second aspect of this application provides a multi-parameter adjustable desulfurization system collaborative control platform, the platform comprising: a desulfurization intelligent agent construction module, used to construct a desulfurization intelligent agent for a desulfurization equipment group by deploying an asymmetric control mode, wherein the desulfurization intelligent agent is a built-in plug-in embedded in the desulfurization system; a desulfurization control decision module, used to collect real-time operating condition data based on the sensor array deployed on the absorption tower, transmit it back to the desulfurization system and trigger the desulfurization intelligent agent, determine the desulfurization control strategy by performing field state initialization based on gas-liquid-solid three-phase, and making two-order decisions under the asymmetric control mode according to the desulfurization constraints; and a collaborative control drive module, used to read the desulfurization control strategy through the desulfurization system and execute the collaborative control drive of the feed pump, the first control loop based on the top spray layer, and the second control loop based on the sub-top spray layer under time-stamp constraints.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] This application provides a multi-parameter adjustable desulfurization system collaborative control method and platform, which relates to the field of flue gas treatment technology. By embedding a desulfurization intelligent agent into the desulfurization system and integrating the dynamic modeling of the gas-liquid-solid three-phase field in the absorption tower with desulfurization constraints, a two-order decision mechanism is established. This realizes a closed-loop logic from real-time operating data acquisition and desulfurization space initialization to control strategy generation, forming a multi-loop collaborative control structure with the feed pump and the top and sub-top spray layers as the core. This solves the technical problems of response lag, inter-layer control imbalance and high energy consumption in the multi-parameter coupled regulation of existing wet desulfurization systems. It achieves hierarchical collaborative control of the multi-parameter desulfurization process through asymmetric collaborative regulation and intelligent agent two-order decision, reducing coupling interference and improving system response and energy efficiency. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 A schematic diagram of the process for the coordinated control method of a desulfurization system with multi-parameter adjustment provided in this application embodiment;

[0010] Figure 2 A schematic diagram of the multi-parameter adjustment desulfurization system collaborative control platform structure provided in the embodiments of this application.

[0011] Figure labeling: Desulfurization intelligent agent construction module 11, desulfurization control decision module 12, collaborative control driving module 13. Detailed Implementation

[0012] This application provides a method and platform for coordinated control of desulfurization systems with multi-parameter adjustment, which is used to solve the technical problems of response lag, inter-layer control imbalance and high energy consumption in existing wet desulfurization systems with multi-parameter coupled adjustment.

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0014] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices.

[0015] Example 1, as Figure 1 As shown, this application provides a method for coordinated control of a desulfurization system with multiple parameters, the method comprising:

[0016] P10: For desulfurization equipment groups, a desulfurization intelligent agent is built by deploying an asymmetric control mode, wherein the desulfurization intelligent agent is a built-in plug-in embedded in the desulfurization system.

[0017] Furthermore, in deploying the asymmetric control mode, step P10 of this application embodiment also includes:

[0018] P11: A first control method is set for the top spray layer, wherein the first control method is for the first control loop, with the first Ca(OH)2 condition and the first slurry circulation volume as control elements, and guided by the SO3 capture environment; P12: A second control method is set for the sub-top spray layer, wherein the second control method is for the second control loop, with the second Ca(OH)2 condition and the first pH value as control elements, and guided by the SO2 removal efficiency; P13: The first control method and the second control method constitute the asymmetric control mode. The first control loop consists of a first flow equalizer, a top slurry circulation pump, and a top spray layer; the second control loop consists of a second flow equalizer, a sub-top slurry circulation pump, and a sub-top spray layer. The first and second control loops are externally connected to a feed pump for supplying desulfurizing agent slurry.

[0019] It should be understood that the asymmetric control mode constructed for the desulfurization equipment group is achieved by embedding a desulfurization intelligent agent within the desulfurization system. This intelligent agent, as a built-in plug-in, is embedded in the system control core, forming a closed-loop structure for data interaction and execution control with the absorption tower and its related equipment. Based on the multi-layer spray structure of the absorption tower, this intelligent agent establishes a multi-loop decoupled control model. By identifying the differences in gas-liquid contact characteristics, reaction rates, and mass transfer conditions among different spray layers, it forms differentiated control logic. Asymmetric control refers to the system employing different control targets and methods for different spray layers during operation, enabling the operating states of each layer to complement each other functionally, thereby achieving optimal coordination of overall desulfurization efficiency, energy consumption, and reaction stability.

[0020] First, a first control method is set for the top spray layer in the desulfurization equipment group. This control method mainly targets the first control loop, which consists of a first flow equalizer, a top slurry circulation pump, and the top spray layer. Its focus is on optimizing the SO3 capture environment to achieve effective absorption of fine aerosols and acid mist. Therefore, the first Ca(OH)2 condition and the first slurry circulation rate are used as control elements. Specifically, the first Ca(OH)2 condition refers to injecting Ca(OH)2 slurry into the top spray layer at a lower flow rate while optimizing the Ca(OH)2 concentration to meet the SO3 capture requirements. By adjusting the flow rate of the feed pump, it is ensured that the Ca(OH)2 slurry enters the top spray layer at a suitable concentration and flow rate. Furthermore, the control of the first slurry circulation rate is achieved by adjusting the frequency converter of the top slurry circulation pump to appropriately increase the slurry circulation rate of this layer, thereby optimizing the droplet size distribution and creating a more suitable chemical and physical environment for SO3 capture. The goal of this control method is to extend the gas-liquid contact time and enhance the SO3 capture efficiency by controlling the droplet size distribution and slurry circulation rate. The droplet size distribution is achieved through comprehensive control of nozzle pressure, liquid viscosity, and spray angle. During operation, the system can automatically adjust the spray parameters based on SO3 concentration changes fed back by the sensor array, creating a microenvironment conducive to SO3 absorption in the top spray layer, thereby reducing the acid mist content in the flue gas.

[0021] Secondly, a second control method is implemented for the sub-top spray layer. This method primarily targets the second control loop, which consists of a second flow equalizer, a sub-top slurry circulation pump, and the sub-top spray layer. Its control objective is to improve SO2 removal efficiency. The core of this method is to use a second Ca(OH)2 condition and a first pH value as control factors, i.e., the core control variables. A high flow rate of Ca(OH)2 slurry is injected at the inlet of the circulation pump in the sub-top spray layer to maintain a high and stable pH value in this area, thereby ensuring that the chemical absorption reaction rate of SO2 in the absorbent is at its optimal state. Specifically, the second Ca(OH)2 condition refers to injecting Ca(OH)2 slurry at a high flow rate at the inlet of the circulation pump in the sub-top spray layer, ensuring that a high pH value is maintained in this area. By adjusting the flow rate of the feed pump and the slurry concentration, the pH value of the sub-top spray layer is maintained at a high level, thus providing favorable conditions for efficient SO2 removal. The goal of this control method is to maintain a high pH value to ensure efficient SO2 removal in this area, thereby improving the overall desulfurization system's removal efficiency.

[0022] Finally, the first and second control methods are structurally synergistic and complementary. They constitute independent first and second control loops, respectively, and are uniformly supplied and distributed with desulfurizing agent slurry via an external feed pump. The output of the feed pump is connected to the inlet of each loop, and the intelligent agent dynamically adjusts the diversion ratio based on real-time operating data to ensure that different areas receive the required concentration and flow rate of desulfurizing agent. During operation, the system acquires data on temperature, flow rate, pH, and gas concentration inside the absorption tower through a sensor array. The intelligent agent performs weighted calculations and timestamp-constrained synchronization of the control signals of each loop based on these parameters, ensuring coordinated and consistent response actions of different spray layers. Through the above configuration, a multi-layered desulfurization environment with clearly defined functional zones and a reasonable flow field distribution is formed inside the absorption tower. The top area focuses on SO3 capture and acid mist suppression, while the sub-top area focuses on SO2 absorption and chemical reaction. The two are physically connected and chemically complementary, enabling the synergistic and efficient removal of SO2 and SO3 under complex operating conditions.

[0023] Furthermore, in constructing the desulfurization intelligent agent, step P10 of this application embodiment also includes:

[0024] P14: The environmental field of the gas-liquid-solid three-phase transfer and reaction process in the absorption tower is integrated and reconstructed as the desulfurization space. The environmental field integration and reconstruction includes at least the field distribution of fluid dynamics, chemical reaction dynamics, thermodynamics and particulate dynamics. P15: Based on the desulfurization space and desulfurization constraints, a desulfurization intelligent body is built.

[0025] Optionally, the construction process of the desulfurization intelligent agent can be further improved. After completing the deployment of the asymmetric control mode, the environmental field of the gas-liquid-solid three-phase transport and reaction processes within the absorption tower is further integrated and reconstructed. This process involves systematically modeling the complex physicochemical phenomena inside the absorption tower, comprehensively considering the field distribution of fluid dynamics, chemical reaction kinetics, thermodynamics, and particulate dynamics to construct a comprehensive and dynamic desulfurization space. This desulfurization space not only encompasses the physical environment within the absorption tower but also includes the dynamic changes of chemical reactions, thus providing a highly simulated virtual environment for the construction of the desulfurization intelligent agent.

[0026] Specifically, the reconstruction of the fluid dynamics field distribution includes real-time monitoring and simulation of parameters such as flue gas velocity, droplet distribution, and slurry circulation flow rate to ensure sufficient contact and mixing of the gas and liquid phases within the absorption tower. The chemical reaction dynamics field distribution involves analyzing the reaction rates and pathways of chemicals such as SO2, SO3, and Ca(OH)2, as well as dynamically controlling key parameters such as slurry pH and Ca / S molar ratio. The reconstruction of the thermodynamics field distribution focuses on temperature distribution, phase transition processes, and heat transfer within the absorption tower to ensure thermal balance in the reaction process. The particulate matter dynamics field distribution focuses on the particle size distribution, settling velocity, and impact on the reaction process of solid particles in the slurry, optimizing the suspension state and reactivity of particulate matter. By fusing and mapping the above multi-field information within a spatial coordinate system, a continuous, dynamically updatable desulfurization spatial model is formed, providing a basis for environmental cognition and decision-making for intelligent agents.

[0027] After constructing the desulfurization space, a desulfurization intelligent agent is built based on its dynamic distribution characteristics and system operating constraints. This agent consists of a sensing layer, an analysis layer, and an execution layer. The sensing layer is responsible for real-time acquisition of operating parameters such as fluid velocity, temperature, pH value, and SO2 and SO3 concentrations within the absorption tower. The analysis layer analyzes and predicts the acquired data using a multi-field coupled calculation model, identifying the reaction state and mass transfer bottlenecks in each region of the tower. The execution layer generates control commands based on the analysis results, synchronizing the feed pump, circulation pump, and spray layer actuators with time and flow rate regulation. During operation, the agent dynamically adjusts weights according to desulfurization constraints, including emission standards, energy consumption limits, and equipment operating safety thresholds, achieving real-time adaptive control. Through this approach, the desulfurization intelligent agent possesses self-learning and self-correction capabilities for the gas-liquid-solid three-phase coupled reaction environment, automatically optimizing the desulfurization process under different loads and fluctuating operating conditions, achieving efficient, stable, and sustainable system operation.

[0028] Furthermore, in constructing the desulfurization intelligent agent, step P15 of this application embodiment also includes:

[0029] P15-1: Deploy the desulfurization constraints with the goal of increasing SO3 removal efficiency, maintaining stable SO2 removal efficiency, and reducing total energy and material consumption; P15-2: Construct an action space using the asymmetric control mode, perform two-order decision training based on the desulfurization space, desulfurization constraints, and action space, generate the desulfurization agent, and embed the desulfurization agent into the desulfurization system.

[0030] Specifically, to enable the desulfurization intelligent system to have adaptive adjustment capabilities for different operating conditions, it is first necessary to clarify and deploy the constraints on the operation of the desulfurization system. Specifically, a multi-objective optimization framework for the desulfurization system should be established with the core constraints of increasing SO3 removal efficiency, maintaining stable SO2 removal efficiency, and reducing total energy and material consumption. Increasing SO3 removal efficiency means that the system should continuously improve the capture ratio of sulfur trioxide during operation, thereby reducing acid mist emissions and the risk of tail gas corrosion. Maintaining stable SO2 removal efficiency means that under different loads and disturbances, the system must ensure that the sulfur dioxide emission concentration remains below the regulatory limit and maintains a high removal rate. Reducing total energy and material consumption requires minimizing power consumption and desulfurizing agent usage by optimizing pump operation, spray flow rate, and Ca(OH)2 feed rate, without reducing desulfurization efficiency.

[0031] Next, based on the asymmetric control mode, the action space of the agent is constructed. The action space refers to the set of all operations that the agent can choose when executing control, including feed pump flow regulation, spray rate control of the top and sub-top spray layers, slurry circulation pump speed variation, and Ca(OH)2 concentration adjustment. The introduction of the asymmetric control mode gives the action space hierarchy and differentiation. The top loop and the sub-top loop are independent yet coupled in terms of control variables and response logic, enabling the agent to make refined decisions under multi-dimensional parameters.

[0032] Subsequently, a two-stage decision-making training process was implemented, based on the desulfurization space, desulfurization constraints, and action space. This training process consisted of two phases: first, the physicochemical field distribution within the desulfurization space was initialized, and an initial control strategy was generated by combining real-time monitoring data and historical operating data; second, within the action space, the initial strategy was iteratively adjusted through simulation and optimization to meet the desulfurization constraints. The goal of the two-stage decision-making training was to generate a desulfurization agent capable of adapting to complex operating conditions. This agent could dynamically adjust the control strategy under different operating conditions to achieve efficient removal of SO3 and SO2, while reducing system energy and material consumption.

[0033] Finally, the trained desulfurization intelligent agent is deployed in an embedded manner within the desulfurization system, ensuring seamless integration with the existing system. This allows the agent to receive system operation data in real time and automatically adjust the parameters of the first and second control loops according to preset control strategies. Through this design, the system can maintain efficient desulfurization even under complex and fluctuating operating conditions, achieving high SO3 removal, stable SO2 emission compliance, and comprehensive optimization of energy and material consumption.

[0034] P20: Based on the sensor array deployed in the absorption tower, real-time operating data is collected, transmitted back to the desulfurization system, and the desulfurization intelligent agent is triggered. By performing field state initialization based on the gas-liquid-solid three-phase system, and performing two-order decision-making under the asymmetric control mode according to the desulfurization constraints, the desulfurization control strategy is determined.

[0035] Furthermore, step P20 in this embodiment of the application also includes: taking the slurry injection rate of the feed pump as a first-order decision step, and taking the game-based allocation between the first control loop and the second control loop as a second-order decision step; wherein, the parameter set of the second-order decision includes at least the start-stop combination and frequency adjustment of the slurry circulation pump, and the parameters of the flow equalizer.

[0036] It should be understood that, through a multi-point sensor array deployed inside the absorption tower, real-time operating data such as flue gas temperature, flow rate, pressure, SO2 and SO3 concentrations, slurry pH value, liquid level, and circulating pump operating status are continuously collected and transmitted back to the system's main control module. This data serves as the signal input to trigger the operation of the desulfurization intelligent agent. Upon receiving the data, the intelligent agent initializes the field state of the gas, liquid, and solid phases within the absorption tower, transforming the physicochemical environment within the absorption tower into a manageable dynamic reaction environment model. Based on this model, a two-order decision-making process under desulfurization constraints is initiated to determine the desulfurization control strategy, thereby achieving real-time and precise control of different spray layers and slurry supply equipment.

[0037] In this two-stage decision-making structure, the slurry injection rate of the feed pump is the first-stage decision step. Based on the real-time detected fluctuations in pH, SO2, and SO3 concentrations within the absorption tower, the system calculates the optimal matching relationship between Ca(OH)2 dosage and slurry concentration, dynamically adjusting the feed pump's flow rate output to achieve synchronous response between desulfurizer dosage and changes in flue gas composition. This stage of decision-making is based on material balance and reaction rate balance, ensuring that the slurry supply is coordinated with the chemical absorption rate within the tower. This prevents slurry deposition due to excessive Ca(OH)2 and avoids a decrease in desulfurization efficiency due to insufficient dosage, thus initially establishing a suitable chemical environment for the desulfurization reaction.

[0038] The second-order decision-making process then proceeds, optimizing parameters based on the game-theoretic allocation between the first control loop (top spray layer) and the second control loop (sub-top spray layer). The parameter set for the second-order decision includes at least the start-stop combination and frequency adjustment of the slurry circulation pumps, as well as the parameter adjustment of the flow equalizer. Specifically, the agent allocates operational weights between the two loops using a game-theoretic algorithm. Based on the differences in gas-liquid reaction rates at different heights within the tower, it dynamically adjusts the start-stop combination and frequency settings of the top and sub-top slurry circulation pumps to optimize the slurry circulation volume and droplet distribution, achieving synergistic optimization of the multi-layer spray flow field. Simultaneously, by adjusting the parameters of the flow equalizer, the droplet size distribution and spray uniformity are further optimized, ensuring a suitable environment for SO3 capture in the top spray layer and maintaining efficient SO2 removal conditions in the sub-top spray layer.

[0039] Through this phased decision-making process, the desulfurization agent continuously adjusts its control strategy by comparing desulfurization constraints, including the upper limits of SO2 and SO3 emission concentrations and energy consumption targets, with the output of the prediction model in real time. This ensures that the desulfurization process remains efficient and stable even under dynamic fluctuations. Finally, the agent distributes the generated desulfurization control strategy to the execution layer synchronously using timestamps, driving the feed pumps, top and sub-top circulation pumps, spray layer, and flow equalization device to work in coordination. This ensures that the desulfurization system meets environmental requirements while achieving efficient, stable, and economical operation.

[0040] Furthermore, in the embodiment of this application, step P20 further includes performing field state initialization based on the gas-liquid-solid three-phase system:

[0041] P21: Collect real-time operating data based on the sensor array deployed in the absorption tower, wherein the real-time operating data includes at least load, flue gas volume, temperature, and inlet SO2 and SO3 concentrations; P22: Transmit the real-time operating data back to the desulfurization system, activate the desulfurization intelligent agent, and initialize the field distribution in the desulfurization space.

[0042] Specifically, the field state initialization process based on the gas-liquid-solid three-phase system can be further refined.

[0043] First, real-time operating data reflecting the current operating status is continuously collected through a sensor array deployed inside the absorption tower and in related flue gas pipelines. The sensor array includes temperature sensors, flow meters, pressure transmitters, gas composition analyzers, and online pH monitoring devices, distributed at the absorption tower inlet, top spray zone, sub-top spray zone, and key nodes in the slurry circulation pipeline. The collected real-time operating data includes at least the core parameters such as boiler load, flue gas volumetric flow rate, flue gas temperature, inlet SO2 concentration, and inlet SO3 concentration. Among these, boiler load characterizes the current operating power level of the system and directly affects flue gas output and composition ratio; flue gas volume reflects the overall flow intensity of the gas entering the absorption tower and is an important basis for calculating the gas-liquid ratio and spray coverage; flue gas temperature determines the heat transfer rate and reaction kinetics during the gas-liquid contact process; and inlet SO2 and SO3 concentrations serve as initial boundary conditions for the desulfurization reaction, used for subsequent calculations of the reaction rate and removal efficiency in the absorption zone.

[0044] Subsequently, the collected real-time operating data is transmitted back to the desulfurization system. This data transmission activates the desulfurization intelligent agent, enabling it to initialize the field distribution within the desulfurization space. The data initialization process involves matching and calibrating the collected real-time operating data with the physicochemical field distribution within the desulfurization space, thereby constructing a virtual model that accurately reflects the current operating state. This virtual model not only encompasses the field distribution of fluid dynamics, chemical reaction kinetics, thermodynamics, and particulate dynamics within the absorption tower but also dynamically reflects changes in real-time operating conditions.

[0045] During data initialization, the desulfurization agent compares and analyzes real-time operating data with the field distribution within the desulfurization space. For example, by comparing real-time collected flue gas volume and temperature data, the agent can adjust the parameters of the fluid dynamics field to ensure that the gas-liquid flow state in the model is consistent with the actual operating state. Simultaneously, based on the inlet SO2 and SO3 concentration data, the agent initializes the chemical reaction dynamics field, optimizing the reaction rate and path in the model, thereby providing accurate initial conditions for subsequent asymmetric control modes.

[0046] Furthermore, based on the desulfurization constraints, a two-order decision is made under the asymmetric control mode to determine the desulfurization control strategy. In this embodiment, step P20 further includes:

[0047] P23: Based on the initialized desulfurization space, and according to the desulfurization constraints, perform a first-order decision to determine the first strategy point; P24: For the first strategy point, perform a game-theoretic allocation decision based on the first control loop and the second control loop to determine the second strategy point, wherein the second strategy point includes the first loop strategy point and the second loop strategy point; P25: Add the first strategy point and the second strategy point to the desulfurization control strategy.

[0048] Optionally, the specific execution process of the two-order decision under the asymmetric control mode can be further refined to determine the final desulfurization control strategy.

[0049] After initializing the field state based on the gas-liquid-solid three-phase system, the desulfurization agent, according to the initialized desulfurization space and preset desulfurization constraints, begins a two-order decision-making process. This combines global constraints with local loop adjustments, satisfying emission and energy consumption requirements while maintaining the functional differences and synergy between the top and sub-top spray areas. Specifically, the first-order decision is executed. The goal of this stage is to determine a preliminary control direction, i.e., the first strategy point, based on the desulfurization constraints. The first-order decision mainly focuses on the slurry injection rate of the feed pump. By analyzing real-time operating data, such as load, flue gas volume, temperature, and inlet SO2 and SO3 concentrations, and in conjunction with the desulfurization constraints, the flow rate and concentration parameters of the feed pump are dynamically adjusted. For example, the desulfurization constraints include constraints on improving SO3 removal efficiency, constraints on maintaining stable SO2 removal efficiency, energy consumption limits for the circulation pump and feed pump, and material consumption constraints on the amount of desulfurizing agent added. The agent performs a weighted solution to the constraints in this stage, and the output result is the first strategy point. The first strategy point characterizes the overall slurry supply level, Ca(OH)2 dosage intensity range, total spray flow target, and allowable energy consumption limit required by the system under the current operating conditions. It can provide a basic control direction for subsequent control loop allocation, ensuring that the entire system initially establishes a suitable chemical reaction environment while meeting desulfurization constraints.

[0050] Subsequently, a second-order decision is executed for the first strategy point. In this stage, the first strategy point is not directly used as the final execution strategy, but rather as a higher-level reference value for loop-level optimization, further executing a game-theoretic allocation decision based on the first and second control loops. Specifically, the core of this game-theoretic allocation lies in the fact that the first control loop corresponding to the top spray layer mainly undertakes SO3 capture and acid mist suppression functions, while the second control loop corresponding to the sub-top spray layer mainly undertakes efficient SO2 absorption functions. The two differ in their regulation targets, reaction rates, and pH sensitivity; therefore, differentiated allocation of resources and operational intensity is required under the same global strategic objective. To this end, the agent uses the total slurry supply target and allowable energy consumption given in the first strategy point as upper constraints, distributing them between the two loops. Through game theory or dual optimization, the operational intensity of the loop suitable for the top region and the operational intensity of the loop suitable for the sub-top region are determined respectively. In this allocation process, the parameter set for the second-order decision includes at least the start / stop combination of the top and sub-top slurry circulation pumps, the frequency or speed setting of the circulation pumps, the flow distribution parameters of the first and second flow equalizers, and the pressure or opening parameters related to the jet kinetic energy of each spray layer. By jointly solving the above parameters, the agent finally determines the second strategy point, which includes two sub-strategy points: the first loop strategy point and the second loop strategy point, corresponding to the specific operating instructions that the top spray layer and the sub-top spray layer should execute in this cycle, respectively.

[0051] Finally, the first strategy point formed by the first-order decision and the second strategy point formed by the second-order decision are structurally integrated to generate a complete desulfurization control strategy. This control strategy retains the global control of the first-order strategy over the overall slurry supply, desulfurizing agent dosage range, and energy consumption upper limit, ensuring that the overall system operation does not exceed the predetermined emission and economic boundaries. On the other hand, it introduces the refined control commands from the second-order strategy for the top and sub-top loops, enabling the spray layers at different heights to perform differentiated adjustments according to their respective functional positions within the current cycle. The integrated desulfurization control strategy can be directly issued to the feed pump, slurry circulation pump, flow equalizer, and spray layer actuators, achieving closed-loop precise control of the gas-liquid-solid three-phase coupled reaction environment within the absorption tower.

[0052] P30: The desulfurization system reads the desulfurization control strategy and executes the coordinated control drive of the feed pump, the first control loop based on the top spray layer, and the second control loop based on the sub-top spray layer under the time stamp constraint.

[0053] Furthermore, step P30 in this embodiment of the application also includes:

[0054] P31: The desulfurization system uses a three-thread collaborative timestamp identifier for the first strategy point, the first loop strategy point, and the second loop strategy point; P32: The feed pump is driven by parameter control adjustment based on the first strategy point, the first control loop is driven by parameter control adjustment based on the first loop strategy point, and the second control loop is driven by parameter control adjustment based on the second loop strategy point.

[0055] Specifically, after receiving the desulfurization control strategy generated by the desulfurization agent, the desulfurization system first assigns a three-thread coordinated timestamp to the first strategy point, the first loop strategy point, and the second loop strategy point to ensure that each actuator completes the strategy execution under a unified timing reference. For example, the main control unit's clock synchronization module assigns precise timestamps to the three sets of strategy points and calibrates them with microsecond-level time accuracy to ensure that the feed pump's dosing action and the fluid adjustment of the top and sub-top spray layers maintain a strict correspondence in the time dimension. Specifically, the timestamp is used to mark the start time, duration, and end time of each strategy point's execution, thereby avoiding inconsistencies in control caused by equipment response delays or signal transmission time differences.

[0056] Subsequently, the desulfurization system, based on the first strategy point synchronized with the timestamp, sequentially drives the feed pump, the first control loop, and the second control loop to perform parameter control execution. According to the parameter settings of the first strategy point, the feed pump is first regulated and driven to determine the injection rate and concentration output range of the Ca(OH)2 slurry, ensuring a sufficient and stable supply of reactants to the absorber under current operating conditions. During this process, the slurry pipeline pressure and flow rate are monitored in real time to ensure the pump unit operates within its design efficiency range. This regulation and driving process, based on the slurry injection rate and concentration parameters determined in the first strategy point, achieves precise control of the Ca(OH)2 slurry injection volume by adjusting control parameters such as the feed pump speed and valve opening.

[0057] Subsequently, based on the control commands of the first loop strategy point, the first control loop based on the top spray layer is subject to parameter-controlled regulation. This includes adjusting the speed of the top slurry circulation pump, controlling the opening of the top flow equalizer, and the atomization pressure of the spray nozzles to maintain a suitable droplet size distribution and atomization coverage, thereby optimizing the SO3 absorption reaction environment. Simultaneously, based on the second loop strategy point, the system performs parameter-controlled regulation on the second control loop based on the sub-top spray layer, adjusting the frequency and flow rate of the sub-top slurry circulation pump to maintain the pH value in the absorption zone within a high reaction efficiency range, achieving deep SO2 absorption and reaction stability control. The execution signals of each loop are uniformly coordinated through a timestamp constraint mechanism to ensure that each control unit operates collaboratively under the set timing sequence, avoiding fluid disturbances or reaction fluctuations caused by response delays or execution overlaps.

[0058] Through the above-mentioned three-thread coordinated timestamp identification and parameter control adjustment drive, the desulfurization system can achieve synchronous control and precise matching of the gas-liquid-solid three-phase process under complex and fluctuating operating conditions, ensuring that the entire desulfurization system can achieve coordinated and efficient removal of SO3 and SO2 under the premise of meeting the desulfurization constraints, while optimizing system energy consumption and material consumption.

[0059] In summary, the embodiments of this application have at least the following technical effects:

[0060] This application introduces an asymmetric control mode and an embedded desulfurization intelligent agent into the desulfurization system to achieve real-time acquisition, modeling, and intelligent decision-making of multiple parameters within the absorption tower, thereby improving the system's adaptability to complex operating conditions. By implementing differentiated control of the top and sub-top spray layers, the collaborative removal process of SO2 and SO3 is optimized, improving desulfurization efficiency and meeting stringent environmental emission standards. Combined with a timestamp-constrained multi-threaded execution mechanism, synchronous operation of the feed pump and spray circuit is achieved, ensuring stable system operation under different operating conditions. At the same time, a two-order decision mechanism balances desulfurization efficiency, energy consumption, and material consumption, ensuring dynamic stability of system operation and optimal resource utilization.

[0061] The technology achieves hierarchical collaborative control of multi-parameter desulfurization processes through asymmetric collaborative regulation and two-order decision-making by intelligent agents, thereby reducing coupling interference and improving system response and energy efficiency.

[0062] Example 2, based on the same inventive concept as the multi-parameter adjustment synergistic control method of the desulfurization system in the foregoing examples, such as... Figure 2 As shown, this application provides a multi-parameter adjustable desulfurization system collaborative control platform. The platform and method embodiments in this application are based on the same inventive concept. The platform includes:

[0063] The desulfurization intelligent agent construction module 11 is used to build a desulfurization intelligent agent for the desulfurization equipment group by deploying an asymmetric control mode. The desulfurization intelligent agent is a built-in plug-in embedded in the desulfurization system.

[0064] The desulfurization control decision module 12 is used to collect real-time operating data based on the sensor array deployed in the absorption tower, transmit it back to the desulfurization system and trigger the desulfurization intelligent agent. By performing field state initialization based on gas-liquid-solid three phases, it performs two-order decision-making under the asymmetric control mode according to the desulfurization constraints to determine the desulfurization control strategy.

[0065] The collaborative control drive module 13 is used to read the desulfurization control strategy through the desulfurization system and execute the collaborative control drive of the feed pump, the first control loop based on the top spray layer and the second control loop based on the sub-top spray layer under the time stamp constraint.

[0066] Furthermore, the desulfurization intelligent agent construction module 11 is also used to perform the following steps:

[0067] A first control method is set for the top spray layer, wherein the first control method is for the first control loop, with the first Ca(OH)2 condition and the first slurry circulation volume as control elements, and the SO3 capture environment as the guide; a second control method is set for the second top spray layer, wherein the second control method is for the second control loop, with the second Ca(OH)2 condition and the first pH value as control elements, and the SO2 removal efficiency as the guide; the first control method and the second control method constitute the asymmetric control mode.

[0068] Furthermore, in the desulfurization intelligent agent construction module 11:

[0069] The first control loop consists of a first flow equalizer, a top slurry circulation pump, and a top spray layer; the second control loop consists of a second flow equalizer, a secondary top slurry circulation pump, and a secondary top spray layer. The first and second control loops are connected to a feed pump for supplying desulfurizing agent slurry.

[0070] Furthermore, the desulfurization intelligent agent construction module 11 is also used to perform the following steps:

[0071] The environmental field of the gas-liquid-solid three-phase transfer and reaction process in the absorption tower is integrated and reconstructed as the desulfurization space. The integrated and reconstructed environmental field includes at least the field distribution of fluid dynamics, chemical reaction dynamics, thermodynamics and particulate dynamics. Based on the desulfurization space and desulfurization constraints, a desulfurization intelligent body is built.

[0072] Furthermore, the desulfurization intelligent agent construction module 11 is also used to perform the following steps:

[0073] The desulfurization constraints are deployed with the goal of increasing SO3 removal efficiency, maintaining stable SO2 removal efficiency, and reducing total energy and material consumption. An action space is constructed using the asymmetric control mode, and two-order decision training based on the desulfurization space, desulfurization constraints, and action space is performed to generate the desulfurization agent. The desulfurization agent is then embedded and deployed in the desulfurization system.

[0074] Furthermore, the desulfurization control decision module 12 is also used to perform the following steps:

[0075] The slurry injection rate of the feed pump is taken as the first-order decision step, and the game-based allocation between the first control loop and the second control loop is taken as the second-order decision step; wherein, the parameter set of the second-order decision includes at least the start-stop combination and frequency regulation of the slurry circulation pump, and the parameters of the flow equalizer.

[0076] Furthermore, the desulfurization control decision module 12 is also used to perform the following steps:

[0077] Based on the sensor array deployed in the absorption tower, real-time operating data is collected, including at least load, flue gas volume, temperature, and inlet SO2 and SO3 concentrations. The real-time operating data is then transmitted back to the desulfurization system to activate the desulfurization intelligent agent and initialize the field distribution within the desulfurization space.

[0078] Furthermore, the desulfurization control decision module 12 is also used to perform the following steps:

[0079] Based on the initialized desulfurization space, and according to the desulfurization constraints, a first-order decision is executed to determine a first strategy point; for the first strategy point, a game-theoretic allocation decision based on the first control loop and the second control loop is executed to determine a second strategy point, wherein the second strategy point includes the first loop strategy point and the second loop strategy point; the first strategy point and the second strategy point are added to the desulfurization control strategy.

[0080] Furthermore, the coordinated control driving module 13 is also used to perform the following steps:

[0081] The coordinated control drive of the feed pump under execution timestamp constraints, the first control loop based on the top spray layer, and the second control loop based on the sub-top spray layer includes:

[0082] The desulfurization system uses a three-thread collaborative timestamp identifier for the first strategy point, the first loop strategy point, and the second loop strategy point; it drives the parameter control adjustment of the feed pump based on the first strategy point, drives the parameter control adjustment of the first control loop based on the first loop strategy point, and drives the parameter control adjustment of the second control loop based on the second loop strategy point.

[0083] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0084] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0085] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A method for coordinated control of a desulfurization system with multi-parameter adjustment, characterized in that, The method includes: For desulfurization equipment groups, a desulfurization intelligent agent is built by deploying an asymmetric control mode, wherein the desulfurization intelligent agent is a built-in plug-in embedded in the desulfurization system; Based on the sensor array deployed in the absorption tower, real-time operating data is collected, transmitted back to the desulfurization system, and the desulfurization intelligent agent is triggered. By performing field state initialization based on the gas-liquid-solid three-phase system, and performing two-order decision-making under the asymmetric control mode according to the desulfurization constraints, the desulfurization control strategy is determined. The desulfurization system reads the desulfurization control strategy and executes the coordinated control drive of the feed pump, the first control loop based on the top spray layer, and the second control loop based on the sub-top spray layer under the time stamp constraint. Among them, the deployment of asymmetric control modes includes: A first control method is set for the top spray layer, wherein the first control method is for the first control loop, with the first Ca(OH)2 condition and the first slurry circulation volume as control elements, and the SO3 capture environment as the guide. A second control method is set for the sub-top spray layer, wherein the second control method is for the second control loop, with the second Ca(OH)2 condition and the first pH value as control factors, and SO2 removal efficiency as the guide; The asymmetric control mode is constituted by the first control method and the second control method; The first control loop consists of a first flow equalizer, a top slurry circulation pump, and a top spray layer; the second control loop consists of a second flow equalizer, a secondary top slurry circulation pump, and a secondary top spray layer. The first control loop and the second control loop are externally connected to a feed pump for supplying desulfurizing agent slurry. Among them, the slurry injection rate of the feed pump is the first-order decision step, and the game allocation based on the first control loop and the second control loop is the second-order decision step. Among them, the parameter set for the second-order decision includes at least the start-stop combination and frequency regulation of the slurry circulation pump, and the parameters of the flow equalizer.

2. The multi-parameter adjustment and coordinated control method for a desulfurization system as described in claim 1, characterized in that, The construction of a desulfurization intelligent system includes: The environmental field of the gas-liquid-solid three-phase transport and reaction process in the absorption tower is integrated and reconstructed as the desulfurization space. The integration and reconstruction of the environmental field includes at least the field distribution of fluid dynamics, chemical reaction dynamics, thermodynamics and particulate dynamics. Based on the aforementioned desulfurization space and desulfurization constraints, a desulfurization intelligent agent is constructed.

3. The multi-parameter adjustment and coordinated control method for a desulfurization system as described in claim 2, characterized in that, The desulfurization constraints are deployed with the goal of increasing SO3 removal efficiency, maintaining stable SO2 removal efficiency, and reducing total energy and material consumption. An action space is constructed using the asymmetric control mode, and two-order decision training based on the desulfurization space, desulfurization constraints, and action space is performed to generate the desulfurization agent. The desulfurization agent is then embedded and deployed in the desulfurization system.

4. The multi-parameter adjustment and coordinated control method for a desulfurization system as described in claim 3, characterized in that, Perform field state initialization based on gas-liquid-solid three-phase system, including: Based on the sensor array deployed in the absorption tower, real-time operating data is collected, wherein the real-time operating data includes at least load, flue gas volume, temperature, and inlet SO2 and SO3 concentrations; The real-time operating data is transmitted back to the desulfurization system to activate the desulfurization intelligent agent and initialize the field distribution within the desulfurization space.

5. The multi-parameter adjustment and coordinated control method for a desulfurization system as described in claim 4, characterized in that, Based on the desulfurization constraints, a two-order decision is made under the asymmetric control mode to determine the desulfurization control strategy, including: Based on the initialized desulfurization space, and according to the desulfurization constraints, a first-order decision is executed to determine the first strategy point. For the first strategy point, a game-theoretic allocation decision based on the first control loop and the second control loop is executed to determine the second strategy point, wherein the second strategy point includes the first loop strategy point and the second loop strategy point; Add the first strategy point and the second strategy point to the desulfurization control strategy.

6. The multi-parameter adjustment and coordinated control method for a desulfurization system as described in claim 5, characterized in that, The coordinated control drive of the feed pump under execution timestamp constraints, the first control loop based on the top spray layer, and the second control loop based on the sub-top spray layer includes: The desulfurization system uses a three-thread collaborative timestamp identifier for the first strategy point, the first loop strategy point, and the second loop strategy point. The feed pump is controlled and adjusted according to the first strategy point, the first control loop is controlled and adjusted according to the first loop strategy point, and the second control loop is controlled and adjusted according to the second loop strategy point.

7. A multi-parameter adjustable desulfurization system collaborative control platform, characterized in that, The platform for implementing the multi-parameter adjustment method for a desulfurization system as described in any one of claims 1-6 includes: A desulfurization intelligent agent building module is used to build a desulfurization intelligent agent for a desulfurization equipment group by deploying an asymmetric control mode. The desulfurization intelligent agent is a built-in plug-in embedded in the desulfurization system. The desulfurization control decision module is used to collect real-time operating data based on the sensor array deployed in the absorption tower, transmit it back to the desulfurization system and trigger the desulfurization intelligent agent. By performing field state initialization based on gas-liquid-solid three phases, it performs two-order decision-making under the asymmetric control mode according to the desulfurization constraints to determine the desulfurization control strategy. The collaborative control drive module is used to read the desulfurization control strategy through the desulfurization system and execute the collaborative control drive of the feed pump, the first control loop based on the top spray layer and the second control loop based on the sub-top spray layer under the time stamp constraint.

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