Thermal power denitration catalyst activity gradient regeneration system

Abstract

The invention relates to the technical field of thermal power plant denitration, and discloses a thermal power denitration catalyst activity gradient regeneration system, which comprises a sensing module used for collecting operation state data of different areas of a thermal power denitration catalyst bed layer, including parameters such as temperature, flue gas components, conductivity and pressure difference, and sending the operation state data to a decision module; the decision module is used for receiving the running state data from the sensing module, constructing a catalyst activity distribution model, performing optimization calculation of a regeneration strategy based on the model, generating a control instruction and sending the control instruction to the execution module; and the execution module is used for receiving the control instruction sent by the decision module. A closed-loop control system is formed through the feedback module, a regeneration strategy can be dynamically optimized according to data collected in real time, in addition, the system can adaptively adjust regeneration areas, temperature and medicament injection amount, and it is ensured that a catalyst in each area can be effectively regenerated.

Description

Technical Field

[0001] The present invention relates to the technical field of denitration in thermal power plants, in particular to an activity gradient regeneration system for a thermal power plant denitration catalyst. Background Art

[0002] In the denitrification process of thermal power plants, catalysts are key components responsible for reducing NO x However, as the catalyst ages, its activity gradually decreases, leading to a drop in denitration efficiency. Catalyst deactivation is typically caused by a variety of factors, including carbon deposits, soot accumulation, and sulfur poisoning, which can significantly reduce the catalyst's reactivity and even cause it to fail completely. To maintain the long-term stable operation of the denitration system, catalyst regeneration is an effective means of addressing catalyst deactivation.

[0003] Currently, there are widespread problems with the application of catalyst regeneration technology, especially in terms of accuracy and efficiency. Traditional catalyst regeneration methods often adopt a unified temperature and reagent injection strategy, which cannot be flexibly adjusted according to the degree of catalyst deactivation in different areas. Due to the significant spatial differences in the activity distribution of the catalyst bed, a unified regeneration strategy is difficult to achieve efficient and balanced regeneration effects, often resulting in over-regeneration or under-regeneration in some areas, thereby affecting the overall recovery effect and service life of the catalyst. In addition, the injection amount and injection path of the reagent in traditional methods are usually not optimized, resulting in waste of reagents and excessive consumption of energy.

[0004] More importantly, existing catalyst regeneration processes often lack the ability to monitor and dynamically adjust them in real time. Due to factors such as catalyst deactivation and environmental changes, traditional technologies are often unable to optimize and adjust regeneration strategies based on real-time data. Consequently, catalyst regeneration is often static and pre-set, lacking the flexibility to adapt to changing operating conditions.

[0005] In order to overcome these defects, improve catalyst regeneration efficiency and extend catalyst service life, a more accurate, dynamic and adaptive regeneration control method is needed. Summary of the Invention

[0006] In view of the shortcomings of the existing technology, the present invention provides an activity gradient regeneration system for thermal power denitration catalysts, which solves the problems of the inability to accurately control the regeneration area and the low efficiency of the regeneration process in traditional catalyst regeneration methods.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a thermal power denitration catalyst activity gradient regeneration system, comprising:

[0008] A perception module for collecting the operating state data of different regions of the catalyst bed, including temperature, flue gas composition, conductivity and differential pressure parameters, and sending the operating state data to the decision module;

[0009] A decision module for receiving the operating state data from the perception module, constructing a catalyst activity distribution model, and performing optimization calculation of the regeneration strategy based on the model to generate control instructions and send them to the execution module;

[0010] An execution module for receiving the control instructions sent by the decision module, implementing regeneration operation on the target region of the catalyst bed according to the control instructions, and the regeneration operation including medicament injection and temperature control;

[0011] A feedback module for collecting the catalyst state data after regeneration and sending the post-regeneration state data to the decision module to realize closed-loop re-optimization control based on feedback information.

[0012] Preferably, the perception module comprises:

[0013] A thermal imaging sensor for monitoring the temperature distribution of the catalyst bed;

[0014] A conductivity sensor for detecting the conductivity change of the catalyst;

[0015] A flue gas composition analyzer for real-time collection of NO x Concentration;

[0016] A differential pressure sensor for collecting the airflow resistance change of the catalyst bed.

[0017] Preferably, the decision module comprises:

[0018] A model construction unit for generating a two-dimensional or three-dimensional heat map of catalyst activity distribution based on the data collected by the perception module;

[0019] An optimization calculation unit for executing a multi-objective function-based optimization algorithm according to the heat map to output control parameters for guiding the regeneration operation.

[0020] Preferably, the optimization calculation unit adopts a combination of genetic algorithm and dynamic matrix control algorithm, which are respectively used for generating a preliminary solution of the regeneration strategy and for local real-time optimization adjustment of the preliminary solution, and the specific steps include:

[0021] Constructing a multi-objective optimization model based on the catalyst activity heat map, the optimization model taking the catalyst regeneration efficiency function, the regeneration cost function and the catalyst remaining life function as the objective functions, and the construction form being:

[0022] minF(x)=(f1(x),f2(x),f3(x)]T ;

[0023] wherein, f1 is a catalyst regeneration efficiency function; f2 is a regeneration cost function; f3 is a catalyst remaining life function;

[0024] A genetic algorithm is used to perform global search on the above optimization model to obtain a preliminary regeneration strategy solution x0, which includes target region number, regeneration temperature, injection agent concentration and duration;

[0025] A dynamic matrix control model is constructed based on the preliminary solution x0, and real-time feedback data is used to correct prediction model errors to perform rolling optimization on x0 to generate final regeneration control parameters x * for guiding the operation of the execution module.

[0026] Preferably, the generation of final regeneration control parameters includes the following steps:

[0027] A dynamic matrix control model is constructed based on the preliminary solution x0, and real-time feedback data is compared with prediction data to calculate prediction error e(t), which is calculated as follows:

[0028] e(t) = x(t) - x0;

[0029] wherein, x(t) is real-time feedback data; x0 is prediction data;

[0030] According to the error e(t), the preliminary solution x0 is adjusted through a rolling optimization algorithm to generate updated regeneration control parameters x * The adjustment process uses the following update formula:

[0031] x ★ (t+1) = x ★ (t) + a ★ e(t);

[0032] wherein, a is the optimization step; t is the time step; x ★ (t+1) is the updated regeneration control parameters.

[0033] Preferably, the execution module includes multiple independently controlled regeneration units, each of which includes:

[0034] An automatic nozzle for injecting an agent into a target region of a catalyst bed according to control parameters;

[0035] A heating unit for adjusting the temperature required for the regeneration of the region;

[0036] An agent supply unit for providing the required concentration and dosage of regeneration agent according to the optimization results.

[0037] Preferably, the injection path of the automatic nozzle is generated by a path optimization module, which calculates the minimum injection energy consumption path according to the deactivation degree of each region in the catalyst heat map.

[0038] Preferably, the feedback module comprises:

[0039] The state reacquisition unit is configured to reacquire the temperature, conductivity, NO x concentration and pressure difference data of the target region after the execution module completes the regeneration operation.

[0040] The state updating unit is configured to update the catalyst activity heat map based on the reacquired data and return it to the decision module for the next round of optimization.

[0041] Preferably, the state updating unit receives the real-time data and updates the catalyst activity heat map according to the following update formula:

[0042] H(t) = H(t-1) + β·(S(t) - S(t-1));

[0043] wherein H(t) is the catalyst activity heat map at time t; S(t) is the catalyst state data at time t; β is the update step; and S(t-1) is the data of the previous time step.

[0044] The updated heat map is used to reflect the changes in catalyst activity of each region and is fed back to the decision module as the basis for the next round of optimization calculation.

[0045] Preferably, the system is configured with a central control unit or a distributed control system to coordinate the information scheduling, instruction issuance and state synchronization among the modules, ensuring real-time response and efficient operation of the system.

[0046] The present application provides a thermal power denitration catalyst activity gradient regeneration system, which has the following advantages:

[0047] 1. The present application can accurately control the regeneration operation according to the specific state of the catalyst bed through precise real-time data acquisition and dynamic optimization algorithm, and can collect temperature, conductivity, NO x concentration and other data through the sensing module, and can make real-time adjustments based on these data to ensure efficient and targeted regeneration process, avoiding the blindness and unevenness of traditional regeneration methods.

[0048] 2. The present application forms a closed-loop control system through the feedback module, which can dynamically optimize the regeneration strategy according to the real-time collected data, and the system can adaptively adjust the regeneration region, temperature and reagent injection amount to ensure that the catalyst in each region can be effectively regenerated, avoiding excessive or insufficient regeneration, thereby improving the use efficiency and service life of the catalyst.

[0049] 3、The present application adopts path optimization and strategy optimization algorithm to accurately calculate the spraying path and injection volume, thereby minimizing the consumption of energy and reagent, improving the economy of the regeneration process, reducing the environmental burden, and meeting the green environmental protection requirements.

[0050] 4、The feedback module in the present application monitors the state of the catalyst bed in real time, and adjusts the regeneration strategy in a timely manner according to the feedback data, so as to ensure that the regeneration process is not affected by environmental changes and catalyst state fluctuations, and to ensure that the system always maintains the best operating state, avoiding the performance degradation of the catalyst caused by long-time operation.

[0051] 5、The present application can accurately control the regeneration process, avoid unnecessary over-regeneration or uneven regeneration, and maintain a high activity of the catalyst for a long time, thereby prolonging the service life of the catalyst. In addition, through effective dynamic adjustment and real-time feedback, the system can maintain high-efficiency denitration while maximizing the protection of the catalyst, reducing the replacement frequency and cost of the catalyst. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 The present application provides a system architecture diagram. DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0054] Please refer to the accompanying drawings Figure 1 The present application provides a thermal power denitration catalyst activity gradient regeneration system, which comprises the following modules:

[0055] A perception module is used to collect the operating state data of different regions of the thermal power denitration catalyst bed, including temperature, flue gas composition, electrical conductivity and pressure difference parameters, and to send the operating state data to a decision module;

[0056] A decision module is used to receive the operating state data from the perception module, construct a catalyst activity distribution model, and perform optimization calculation of the regeneration strategy based on the model to generate control instructions and send them to an execution module;

[0057] An execution module is used to receive the control instructions sent by the decision module, and to perform regeneration operation on the target region of the catalyst bed according to the control instructions. The regeneration operation includes reagent injection and temperature control.

[0058] a feedback module, configured to collect post-regeneration catalyst state data after the regeneration operation, and send the post-regeneration state data to the decision module to realize closed-loop re-optimization control based on feedback information.

[0059] The following is a detailed description of each component in the system of the present application.

[0060] For the perception module, in the embodiment, the perception module is configured to collect multi-dimensional information of the operating state of different regions of the thermal power denitration catalyst bed, and serve as a basic data source for catalyst activity modeling and regeneration strategy generation by the decision module. The module is arranged in the upstream and downstream gas channels of the catalyst device, and through the cooperative work of multiple data acquisition subsystems, comprehensive perception of the actual operating state of the denitration catalyst is realized.

[0061] In the embodiment, the perception module preferably includes four types of information acquisition devices, namely, a thermal imaging sensor, a flue gas component analyzer, an electrical conductivity sensor, and a differential pressure sensor, which are used to acquire operating parameters such as the thermal characteristics, chemical reaction characteristics, electrical performance characteristics, and gas flow resistance of the catalyst.

[0062] The thermal imaging sensor is arranged at the upper part or side wall region of the catalyst reactor, and is used to continuously measure the temperature distribution of the surface or interior of the catalyst bed. The two-dimensional thermal image acquired by the sensor can be converted into matrix-form temperature data through a digital processing algorithm, and is expressed as T(x, y), where x and y are the coordinates of the catalyst bed region. The temperature data can not only reflect the local reaction heat effect, but also assist in inferring the active change region, which is used for subsequent thermal map construction and model analysis.

[0063] The flue gas component analyzer is used to measure the NOx concentration changes before and after passing through the catalyst bed in real time, and the sampling points are arranged at the catalyst inlet and outlet. By comparing the NOx concentrations upstream and downstream of the catalyst, the catalyst reaction efficiency of the corresponding region can be inferred, and the denitration conversion capacity index of the local catalyst can be further constructed. The acquired NOx concentration information can be expressed as a function x (x,y,t) for subsequent fusion with temperature and concentration data to construct the activity distribution. x (x,y,t) for subsequent fusion with temperature and concentration data to construct the activity distribution. x (x,y,t) for subsequent fusion with temperature and concentration data to construct the activity distribution. for subsequent activity modeling input.

[0064] The electrical conductivity sensor is preferably embedded into the catalyst support plate or bed bottom structure, and is used to measure the electrical conductivity changes in the catalyst. The electrical conductivity of the catalyst gradually decreases with the carbon deposition, sulfuration, or poisoning of active species, so its change trend can indirectly reflect the deactivation degree of the catalyst. The electrical conductivity measurement data can be normalized and expressed as a relative conductivity function r (x,y,t), which is used for subsequent fusion with temperature and concentration data to construct the activity distribution.

[0065] In addition, a differential pressure sensor is arranged in the gas passage upstream and downstream of the catalyst bed to monitor the resistance of the gas flow through the catalyst in real time, representing whether the bed has running abnormalities such as blockage, dust accumulation or compaction. The trend of the parameter change has auxiliary value for judging the physical state of the catalyst region, which can be expressed as a differential pressure function ΔP(x, y, t).

[0066] In this embodiment, to ensure that the data of the perception module has spatiotemporal consistency and integrability, the data collected by various sensors are uniformly encoded and synchronized at a uniform time step via the local signal processing unit. The synchronized multi-source data is sent to the central control unit through the communication interface and forwarded to the decision module for modeling processing.

[0067] In this embodiment, the multi-source data provided by the perception module constitutes the key input for the decision module to perform the regeneration strategy optimization calculation in terms of spatial accuracy and temporal continuity, especially in the processes of active heat map construction, regional partition identification and regeneration target region determination.

[0068] Through the above configuration and working mode, the perception module can realize online, dynamic and regional perception of the operation state of the thermal power denitration catalyst, form a complete and time-sensitive data closed loop, and provide a stable and accurate data basis for subsequent optimization control.

[0069] For the decision module, in this embodiment, the decision module is one of the core components of the system, responsible for analyzing the activity state of the catalyst based on the real-time data provided by the perception module and generating targeted regeneration optimization strategies. This module not only determines the optimal regeneration operation path, temperature and reagent injection concentration according to the current catalyst operation, but also dynamically adjusts according to real-time feedback data, thereby realizing adaptive catalyst regeneration.

[0070] In this embodiment, the functions of the decision module mainly include two aspects: active heat map construction and regeneration strategy optimization. First, the decision module receives various state data transmitted by the perception module, including temperature, NO x concentration, electrical conductivity and differential pressure, and constructs an active heat map of the catalyst according to these information. The active heat map can intuitively reflect the activity distribution of the catalyst in different regions, providing a basis for subsequent regeneration optimization.

[0071] In the process of heat map construction, the decision module uses an algorithm based on multi-factor weighted fusion to integrate different data sources from the perception module to obtain the activity score of each region. In detail, the active heat map H(x, y, t) is generated by the following formula:

[0072]

[0073] where w1, w2, w3 and w4 are weight coefficients determined empirically to reflect the importance of different parameters in the construction of the heat map; H(x, y, t) is the activity value of the catalyst bed at position (x, y) and time t; T(x, y, t) is the temperature value at that position; the difference between the upstream and downstream NO x concentrations of the catalyst; σ r (x, y, t) is the conductivity data; ΔP(x, y, t) is the pressure difference data.

[0074] After the construction of the heat map, the decision module determines the target regions of the catalyst bed by analyzing the activity levels of different regions in the heat map, and formulates regeneration strategies for these regions. The optimization of the regeneration strategy uses a multi-objective optimization algorithm, aiming to balance multiple objectives such as regeneration efficiency, energy consumption, and catalyst life.

[0075] Specifically, the optimization process of the regeneration strategy includes the following steps:

[0076] In the decision module, first, an optimization objective function of the regeneration strategy is constructed, which is a multi-objective function that considers the catalyst regeneration efficiency, regeneration cost, and remaining life of the catalyst. The objective function can be expressed as:

[0077] F(x) = [f1(x), f2(x), f3(x)] T ;

[0078] where f1(x) is the regeneration efficiency function, representing the recovered activity after catalyst regeneration; f2(x) is the regeneration cost function, representing the energy and reagent consumption required for regeneration; and f3(x) is the catalyst remaining life function, representing the service life of the catalyst after regeneration.

[0079] In the optimization process, the decision module uses a genetic algorithm for global search to find the optimal regeneration control strategy. The genetic algorithm generates an initial solution set x0 and evolves according to the fitness function from generation to generation to find the optimal solution. Through multiple iterations, the genetic algorithm can effectively explore the regeneration path suitable for the current catalyst state.

[0080] To further improve the accuracy and real-time performance of the optimization, the decision module uses dynamic matrix control (DMC) technology. DMC can predict and adjust the regeneration process in real time, optimizing the control parameters by minimizing the prediction error. The prediction error e(t) is expressed as the difference between the actual state and the predicted state:

[0081] e(t) = x(t) - x0;

[0082] where x(t) is the actual control parameter at the current time; and x0 is the predicted data.

[0083] By calculating the error and making adjustments, DMC can continuously optimize the temperature, reagent concentration, and injection path during the regeneration process.

[0084] During each round of optimization, the decision-making module makes rolling adjustments to the optimization results to ensure that real-time feedback data can be quickly and effectively incorporated into the optimization scheme. The process of rolling optimization is adjusted by the following formula:

[0085] x ★ (t+1)=x ★ (t)+α ★ e(t);

[0086] Where α is the optimization step size; t is the time step; x ★ (t+1) is the optimal control parameter at the next time; x ★ (t) is the optimal control parameter at the current time.

[0087] Through the above steps, the decision-making module can provide accurate regeneration control parameters for the execution module, including the selection of the regeneration area, the required temperature, reagent concentration, injection path, etc., and make real-time adjustments according to the feedback data, thereby achieving precise and efficient regeneration control of the catalyst bed.

[0088] In summary, the decision-making module in this embodiment combines multi-objective optimization algorithm and dynamic matrix control technology, which not only enables accurate analysis of the activity state of the catalyst bed, but also adjusts the regeneration strategy according to real-time data to ensure the efficiency and sustainability of the regeneration process. This intelligent regeneration control method can effectively improve the regeneration effect of the denitration catalyst and provide a more flexible and efficient operation scheme for the thermal power denitration system.

[0089] For the execution module, in this embodiment, the execution module is responsible for implementing precise regeneration operations on the catalyst bed according to the regeneration strategy output by the decision-making module. The execution module is composed of multiple independently controlled regeneration units, each including an automatic nozzle, a heating unit, and a reagent supply unit, which can achieve precise regeneration operations and flexible adjustment according to the specific needs of the target area.

[0090] First, the execution module receives the control instructions output by the decision-making module, including the selection of the regeneration area, the required temperature, the injection path, the reagent concentration, etc. These control instructions are transmitted by the central control unit to each regeneration unit to ensure precise regeneration operations in the entire catalyst bed area.

[0091] In this embodiment, each regeneration unit is equipped with an automatic spray head for injecting reagent into the target area of the catalyst bed. The spray path and injection volume of the spray head are calculated by the decision module based on the real-time catalyst activity heat map and optimization strategy. To ensure the accuracy of reagent injection, the spray path of the spray head is planned by the path optimization module. According to the degree of deactivation of each region in the catalyst heat map, the path optimization module calculates the minimum energy consumption spray path and generates spray path control instructions.

[0092] The injection volume and injection method (e.g. single injection, continuous injection or pulse injection, etc.) of the automatic spray head are also dynamically adjusted according to the temperature, NO x concentration, conductivity and other parameters of the catalyst region. The spray head can uniformly distribute the reagent in the target area, ensuring the regeneration effect of the catalyst bed.

[0093] The key to spray path optimization is to calculate the optimal injection direction and time of each spray head based on the real-time temperature and NO x concentration data of the catalyst bed to minimize energy consumption and improve injection effect. Path optimization calculation can be described by the following optimization objective:

[0094]

[0095] Where E i is the energy consumption of the i i th spray head; t target is the spray time of the spray head; and N is the total number of spray heads.

[0096] The objective function aims to optimize the efficiency of the injection process by minimizing energy consumption and injection time.

[0097] The heating unit is used to adjust the temperature required for regeneration of the target area. During the regeneration process, the recovery of catalyst activity is often accompanied by an increase in temperature, so accurate temperature control is the key to efficient regeneration. The heating unit can use an electric heater or other types of heating equipment to adjust the temperature of different regions in the catalyst bed according to the temperature instructions provided by the decision module.

[0098] In each regeneration unit, the temperature adjustment is based on real-time catalyst temperature data. The temperature data is provided by the perception module and transmitted to the execution module in real time. During the execution process, if the temperature deviates from the preset range, the heating unit will automatically adjust to ensure that the temperature of the catalyst bed is maintained within the optimal regeneration range.

[0099] The control logic of the heating unit is:

[0100] T target (x,y) = T current (x,y) + ΔT;

[0101] where T target (x, y) is the set temperature of the target region, T current (x, y) is the current measured temperature value; ΔT is the adjustment value.

[0102] The temperature adjustment process ensures that the temperature is stable within the required range for regeneration through feedback control.

[0103] The medicament supply unit provides the required concentration and dosage of the regeneration medicament accurately according to the medicament concentration and dosage control instructions provided by the decision module. The medicament supply unit is usually composed of a medicament storage device and a delivery system, and the medicament concentration and flow rate can be adjusted by precisely controlling the valve and pump.

[0104] During execution, the medicament supply unit sprays a certain amount of regeneration medicament in the target region of each regeneration unit according to the optimized control strategy. The medicament concentration and dosage are adjusted according to the deactivation degree of the catalyst and the required regeneration effect.

[0105] The control formula of the medicament concentration can be expressed as:

[0106] C target (x, y) = C current (x, y) + ΔC;

[0107] where C target (x, y) is the medicament concentration of the target region; C current (x, y) is the current medicament concentration; ΔC is the adjustment value.

[0108] The injection amount and frequency of the medicament are dynamically adjusted by the decision module according to the activity state of the catalyst and the required recovery degree.

[0109] Each subunit in the execution module, such as the automatic nozzle, heating unit and medicament supply unit, is coordinated by the central control unit. The central control unit coordinates the working time and state of each execution unit according to the instructions of the decision module, ensuring that the operations are synchronized and avoiding resource waste or operation conflicts.

[0110] The execution module adopts a closed-loop control mechanism in actual operation, not only adjusting according to real-time catalyst bed data, but also optimizing the regeneration strategy for the next time according to the feedback data after each regeneration. The feedback data is provided by the feedback module, which monitors key parameters such as temperature, NO x concentration, conductivity and pressure difference in real time during the regeneration process, and adjusts the control according to these data.

[0111] Through the above control mechanism, the execution module can achieve high-precision regeneration operation, ensure the activity recovery effect of the catalyst bed, and maximize the service life of the catalyst.

[0112] For the feedback module, in this embodiment, the feedback module is responsible for real-time monitoring and data collection of the catalyst regeneration operation completed by the execution module, and dynamically adjusts the regeneration process based on the feedback data. After the regeneration operation, the module can re-evaluate the running state of the catalyst, and provide the newly obtained data to the decision module to complete the closed-loop feedback control, and ensure the continuous optimization of the regeneration process.

[0113] The feedback module mainly includes a state re-collection unit and a state updating unit, which work together to ensure the real-time performance and adaptive ability of the system.

[0114] The state re-collection unit is the front-end part of the feedback module, responsible for real-time collection of multiple running state data of the catalyst bed after the execution module completes the regeneration operation. The re-collected data includes but is not limited to temperature, conductivity, NO x concentration and pressure difference, etc. These data correspond to the data collected by the sensing module in the execution module, and can accurately reflect the actual condition of the catalyst bed after regeneration.

[0115] Temperature data is provided by thermal imaging sensors, which can record the temperature distribution of different areas in the catalyst bed in detail. By analyzing the temperature data, the re-collection unit can determine whether there is overheating or uneven temperature distribution in the regeneration operation, thereby providing a basis for subsequent adjustment.

[0116] Conductivity data is provided by conductivity sensors, which are used to evaluate the activity change of the catalyst. The conductivity of the catalyst is usually related to its activity state, so by monitoring the conductivity, the system can indirectly understand whether the catalyst has recovered to the predetermined activity level.

[0117] NO x Concentration data is obtained by a flue gas component analyzer, which monitors whether the catalyst can effectively reduce NO x emissions after regeneration, reflecting the denitration effect of the catalyst. The change of NO x concentration can reflect the denitration efficiency of the catalyst, further judging the regeneration effect.

[0118] Pressure difference data is obtained by a pressure difference sensor, which reflects the flow resistance of the catalyst bed. If the catalyst bed is blocked or compressed, the pressure difference will increase significantly, affecting the regeneration effect. Therefore, the pressure difference data is helpful to judge the physical state of the catalyst.

[0119] All these data will be collected in real time in the state re-collection unit, and refreshed at a certain time interval to ensure the timeliness of the data. The re-collected data will be transmitted to the state updating unit for further processing and analysis.

[0120] The main task of the state updating unit is to update the activity heat map of the catalyst based on the reacquired state data, and feed back the new heat map information to the decision module. After updating the activity heat map, the decision module can adjust the regeneration strategy based on the new heat map data, so as to realize further optimization regeneration of the catalyst bed.

[0121] The update formula of the activity heat map can be expressed as:

[0122] H(t) = H(t-1) + β·(S(t) - S(t-1));

[0123] Where H(t) is the activity heat map at the current time; S(t) is the acquisition data at the current time; β is the update step, which controls the speed of heat map updating; S(t-1) is the acquisition data at the last time; H(t-1) is the heat map at the last update.

[0124] Through this update mechanism, the state updating unit can dynamically adjust the activity state of the catalyst according to the real-time acquisition data, reflecting the changes of the catalyst in the regeneration process. Especially when the catalyst in local area is insufficient or over-regenerated, the update of the activity heat map can provide effective reference for the decision module, and then adjust the regeneration strategy in time.

[0125] In addition, the state updating unit can also handle data anomalies, such as sensor failure or data fluctuation, to ensure the accuracy of heat map updating and the stability of the system. If the data of a certain sensor is abnormal, the state updating unit can process it through data filtering or data correction algorithm, to ensure that the system will not be affected by inaccurate data.

[0126] The feedback module forms a closed-loop control system through real-time acquisition and update of the state data after regeneration operation. The execution module continuously adjusts parameters such as temperature, reagent concentration and injection path during the regeneration operation, while the feedback module provides the latest state of the catalyst bed to the decision module through real-time data feedback, ensuring that the regeneration process can be dynamically adjusted according to the actual state of the catalyst.

[0127] This closed-loop control mechanism helps to realize adaptive catalyst regeneration strategy, so that the system can flexibly adjust the regeneration operation according to different operating conditions, catalyst deactivation and environmental conditions, and maximize the recovery of catalyst activity.

[0128] Through real-time monitoring and state updating, the feedback module not only provides accurate data support for the regeneration process of the catalyst, but also discovers and solves potential operation problems in time, such as high temperature or uneven distribution of reagent, to ensure the efficiency and continuity of the regeneration operation.

[0129] The coordination of the feedback module with other modules is the key to the efficient operation of the system. Through close cooperation with the perception module and the execution module, the feedback module realizes the full-process monitoring and dynamic adjustment of the catalyst regeneration operation. After the regeneration operation is completed, the feedback module re-evaluates the activity state of the catalyst and provides a basis for the decision module, ensuring that the system can generate an optimized scheme according to the new catalyst state after each operation, thereby achieving more precise regeneration control.

[0130] In general, the workflow of the system of the present application can be described as follows:

[0131] The system first uses a variety of sensors (such as temperature sensors, conductivity sensors, NO x concentration analyzers, differential pressure sensors, etc.) to collect real-time data on the operating state of the catalyst bed through the perception module. These data include the temperature distribution, conductivity, NO x concentration of the catalyst, and the differential pressure of the bed gas flow, providing the necessary basic data for the subsequent decision module.

[0132] The data transmitted by the perception module enters the decision module. The decision module constructs an activity heat map of the catalyst based on the collected real-time data, reflecting the activity distribution of the catalyst in different regions. Based on this heat map, the decision module calculates the optimal regeneration strategy through a multi-objective optimization algorithm, taking into account factors such as temperature, reagent concentration, injection path, and energy consumption. These strategies include control instructions such as the selection of regeneration regions, the required temperature and reagent injection amount, and the regeneration path.

[0133] The regeneration strategy generated by the decision module is passed to the execution module, which includes multiple independently controlled regeneration units. Each regeneration unit includes an automatic nozzle, a heating unit, and a reagent supply unit. The execution module accurately performs reagent injection, temperature adjustment, and reagent supply for each region of the catalyst bed based on the control parameters provided by the decision module. The nozzle injects reagents according to the optimal path, the heating unit accurately controls the regional temperature, and the reagent supply unit accurately provides reagents as needed.

[0134] After the regeneration operation is completed, the feedback module re-collects the state data of the catalyst bed, such as temperature, conductivity, NO x concentration, and differential pressure, through the state re-collection unit. These data are processed by the state update unit to update the activity heat map of the catalyst, and the updated heat map is fed back to the decision module. The decision module re-optimizes the regeneration strategy based on the new data and adjusts the control parameters of the execution module, thereby realizing closed-loop control.

[0135] After the completion of each regeneration operation, the system continuously updates the state through the feedback module, ensuring that the decision-making module can make optimized adjustments based on the latest catalyst state in subsequent operations. This closed-loop feedback mechanism ensures that the system can adaptively adjust according to the changes of the catalyst in long-term operation, improve the activity recovery effect of the catalyst, and prolong its service life.

[0136] Through this systematic workflow, the present application can achieve precise and efficient regeneration control of thermal power denitration catalyst. The system can dynamically adjust the regeneration strategy according to the real-time operating state of the catalyst, thereby achieving the best regeneration effect and improving the long-term operation efficiency and stability of the catalyst.

[0137] Although embodiments of the present application have been shown and described, it will be understood by those having ordinary skill in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the present application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. Thermal power denitrification catalyst activity gradient regeneration system, characterized by: include: The sensing module is used to collect operating status data of different areas of the thermal power denitration catalyst bed, including temperature, flue gas composition, conductivity and pressure difference parameters, and send the operating status data to the decision module; a decision module, configured to receive the operating status data from the perception module, construct a catalyst activity distribution model, perform optimization calculations on the regeneration strategy based on the model, generate control instructions, and send them to the execution module; an execution module, configured to receive a control instruction sent by the decision module and perform a regeneration operation on a target area of ​​the catalyst bed according to the control instruction, wherein the regeneration operation includes reagent injection and temperature control; The feedback module is used to collect the catalyst status data after the regeneration operation and send the regeneration status data to the decision module to achieve closed-loop re-optimization control based on feedback information.

2. The thermal power denitration catalyst activity gradient regeneration system according to claim 1, characterized in that: The perception module includes: Thermal imaging sensor to monitor the temperature distribution of the catalyst bed; Conductivity sensor, used to detect changes in the conductivity of the catalyst; Flue gas composition analyzer for real-time collection of NO x concentration; The differential pressure sensor is used to collect changes in the airflow resistance of the catalyst bed.

3. The thermal power denitration catalyst activity gradient regeneration system according to claim 1, characterized in that: The decision module includes: a model building unit for generating a two-dimensional or three-dimensional heat map of catalyst activity distribution based on data collected by the sensing module; An optimization calculation unit is used to execute an optimization algorithm based on a multi-objective function according to the heat map, and output control parameters for guiding the regeneration operation.

4. The thermal power denitration catalyst activity gradient regeneration system according to claim 3, characterized in that: The optimization calculation unit uses a combination of a genetic algorithm and a dynamic matrix control algorithm to generate a preliminary solution for the regeneration strategy and to perform local real-time optimization adjustments on the preliminary solution. The specific steps include: A multi-objective optimization model is constructed based on the catalyst activity heat map. The optimization model takes the catalyst regeneration efficiency function, the regeneration cost function and the catalyst remaining life function as objective functions. The construction form is: minF(x)=[f1(x),f2(x),f3(x)] T ; Among them, f1 is the catalyst regeneration efficiency function; f2 is the regeneration cost function; f3 is the catalyst remaining life function; A genetic algorithm is used to perform a global search on the above optimization model to obtain a preliminary regeneration strategy solution x0, wherein x0 includes the target area number, regeneration temperature, injection agent concentration and duration; Based on the preliminary solution x0, a dynamic matrix control model is constructed, and the prediction model error is corrected using real-time feedback data. Then, a rolling optimization is performed on x0 to generate the final regenerative control parameter x0. * Used to guide the execution of module operations.

5. The thermal power denitration catalyst activity gradient regeneration system according to claim 4, characterized in that: Generating the final regeneration control parameters comprises the following steps: Based on the preliminary solution x0, a dynamic matrix control model is constructed, and the real-time feedback data is compared with the predicted data to calculate the prediction error e(t). The calculation form is: e(t)=x(t)-x0; Among them, x(t) is the real-time feedback data; x0 is the predicted data; According to the error e(t), the preliminary solution x0 is adjusted by the rolling optimization algorithm to generate the updated regeneration control parameter x * , the adjustment process adopts the following update formula: x ＊ (t+1)=x ＊ (t)+α ＊ e(t); Among them, α is the optimization step size; t is the time step size; x ＊ (t+1) is the updated regeneration control parameter.

6. The thermal power denitration catalyst activity gradient regeneration system according to claim 1, characterized in that: The execution module includes a plurality of independently controlled regeneration units, each of which includes: Automatic nozzle, used to inject reagents into target areas of the catalyst bed according to control parameters; a heating unit for adjusting the temperature required for regeneration of the zone; The drug supply unit is used to provide the regeneration drug of required concentration and dosage according to the optimization result.

7. The thermal power denitration catalyst activity gradient regeneration system according to claim 1, characterized in that: The injection path of the automatic nozzle is generated by a path optimization module, and the path optimization module calculates the minimum injection energy consumption path according to the deactivation degree of each area in the catalyst heat map.

8. The thermal power denitration catalyst activity gradient regeneration system according to claim 1, characterized in that: The feedback module includes: The state re-collection unit is used to re-acquire the temperature, conductivity, NO x Concentration and differential pressure data; A state updating unit is used to update the catalyst activity heat map based on the re-collected data and return it to the decision module for the next round of optimization.

9. The thermal power denitration catalyst activity gradient regeneration system according to claim 8, characterized in that: The state updating unit receives the real-time data and updates the catalyst activity heat map according to the following update formula: H(t)=H(t-1)+β·(S(t)-S(t-1)); Where H(t) is the catalyst activity heat map at time t; S(t) is the catalyst state data at time t; β is the update step size; S(t-1) is the data of the previous time step; The updated heat map is used to reflect the change in catalyst activity in each area and is fed back to the decision module as a basis for the next round of optimization calculations.

10. The thermal power denitration catalyst activity gradient regeneration system according to claim 1, characterized in that: The system is configured with a central control unit or a distributed control system for coordinating information scheduling, instruction issuance and status synchronization among modules to ensure real-time response and efficient operation of the system.

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