Pulse injection adaptive seeking control method, system and computer readable storage medium

Abstract

The present application relates to the technical field of bag type dust collector blowing and cleaning control, and particularly relates to a pulse blowing self-adaptive optimization control method, a system and a computer readable storage medium. The pulse blowing self-adaptive optimization control method defines the blowing parameter combination as the action of an intelligent agent by collecting the running state data of the bag type dust collector in real time, then calculates the energy efficiency reward value according to the pressure difference change and the compressed air consumption before and after blowing, and then performs online learning and optimization based on the state data and the reward value, and dynamically outputs the optimal blowing parameter combination to perform cleaning. The present application upgrades the control target from maintaining the pressure difference to maximizing the cleaning energy efficiency ratio, and through online self-learning, significantly reduces the compressed air consumption under the premise of ensuring the cleaning effect, and has the beneficial effects of energy saving, strong adaptability and prolonging the service life of the filter bag.

Description

Technical Field

[0001] This invention relates to the field of pulse jet cleaning control technology for baghouse dust collectors, and in particular to an adaptive optimization control method, system, and computer-readable storage medium for pulse jet cleaning. Background Technology

[0002] Baghouse dust collectors are the most widely used high-efficiency dust removal equipment in the industrial field. Their core component is the filter bag. When dust-laden gas passes through the filter bag, the dust is trapped on the outer surface of the filter bag, forming a dust layer. As the dust layer thickens, the operating resistance of the dust collector (usually measured by the pressure difference between the inside and outside of the filter bag) will continue to increase. When the resistance reaches a certain level, the filter bag must be cleaned to remove the dust layer and restore the air permeability of the filter bag.

[0003] Pulse jet cleaning is currently the most mainstream dust removal method. It uses high-pressure compressed air injected instantaneously into the filter bags through a jet pipe and venturi tube, causing the filter bags to expand and vibrate rapidly, thus shaking off the adhering dust. The control strategy of the pulse jet system directly determines the operational stability, energy consumption, and filter bag life of the dust collector. Currently, there are two main pulse jet control schemes: 1. Timed Control Scheme: This scheme triggers each pulse valve sequentially for pulse cleaning according to a pre-set fixed time cycle. For example, it is set to pulse clean all filter bag units in turn every 30 minutes. The parameters used for pulse cleaning, such as the pressure and pulse width (i.e., the energizing time of the solenoid valve), are also pre-set fixed values ​​manually. However, timed control does not consider the actual dust load. When the dust concentration is low, the system continues to pulse on schedule, resulting in a large waste of compressed air; when the dust concentration suddenly increases, it cannot respond in time, leading to excessive pressure difference, affecting dust removal efficiency, and even damaging the filter bags.

[0004] 2. Constant Differential Pressure Control Scheme: This scheme uses differential pressure sensors installed at the inlet and outlet of the dust collector to monitor the pressure difference across the filter bags in real time. When the pressure difference rises to a preset upper limit (e.g., 1500 Pa), the controller initiates a pulse-jet cleaning program to clean the filter bags until the pressure difference drops to a preset lower limit (e.g., 1000 Pa). Parameters such as the pulse pressure and pulse width in this scheme are also preset as fixed values. While constant differential pressure control achieves on-demand cleaning, its control logic remains rudimentary.

[0005] In the two schemes described above, parameters such as blowing pressure and width are fixed. However, the optimal cleaning parameters vary significantly depending on the properties of the dust (e.g., different viscosity, humidity, and particle size). Fixed parameters cannot achieve the best energy efficiency ratio under all operating conditions. Furthermore, the existing schemes lack automatic learning and adaptive optimization capabilities, and cannot dynamically adjust for different dust characteristics, humidity, and temperature. Moreover, the control objective is only to maintain the pressure difference within a certain range, without incorporating compressed air consumption into the control objective for optimization. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a pulse jet adaptive optimization control method to address the problems of high operating costs, coarse control strategies, and lack of optimization capabilities in the existing technologies mentioned above.

[0007] The technical solution adopted by this invention to solve its technical problem is: a pulse jet adaptive optimization control method, comprising the following steps: S1. Real-time acquisition of operating status data of the bag filter system, wherein the operating status data includes at least the pressure difference value on both sides of the filter bag; S2. The action of the agent is based on a combination of spraying parameters, wherein the spraying parameters include at least spraying pressure and pulse width; S3. Calculate the energy efficiency bonus value of this action based on the pressure difference change before and after the injection and the compressed air consumption of this injection. The energy efficiency bonus value is directly proportional to the pressure difference change and inversely proportional to the compressed air consumption. S4. Based on the operating status data and energy efficiency bonus value, a reinforcement learning algorithm is used for online learning and optimization, dynamically adjusting and outputting the optimal combination of injection parameters; S5. Generate control commands based on the optimal combination of pulse jet parameters to drive the pulse jet actuator to perform dust removal operations; S6. After the reinforcement learning algorithm's policy converges, switch to steady-state control mode, execute the blowing action based on the historical optimal parameters, and periodically trigger the exploration mechanism to cope with changes in working conditions.

[0008] S1 collects status data in real time, providing a data foundation for subsequent intelligent decision-making; S2 defines the injection parameters as actions, treating the parameters as dynamically adjustable actions, providing conditions for refinement and adaptability; S3 calculates the energy efficiency bonus value, combining pressure difference changes and compressed air consumption to form an evaluation index; S4 uses reinforcement learning for online optimization, continuously interacting with the environment through reinforcement learning to automatically search for the optimal injection parameter combination under different operating conditions; S5 executes the optimal command; and S6 avoids energy loss caused by continuous exploration.

[0009] Furthermore, the operating status data in S1 also includes the differential pressure change rate. At least one of the following: gas tank pressure, inlet dust concentration, and ambient humidity.

[0010] Increasing the differential pressure change rate, inlet dust concentration, and ambient humidity makes the system's perception of operating conditions more refined and comprehensive, enhancing the system's ability to make judgments when facing complex and variable operating conditions.

[0011] Furthermore, the injection parameters in S2 also include the injection interval. Furthermore, the blowing parameters are discretely or continuously selected within a preset safety range.

[0012] spray interval By incorporating an optimizable motion space, intelligent control of the dust removal rhythm can be further achieved, making the dust removal strategy more complete and efficient.

[0013] Furthermore, the energy efficiency bonus value for this action in S3 The calculation is as follows: S31, During the execution of the blowing action Record the current differential pressure value. ; S32, Perform the blowing action Record the pressure difference value shortly after the injection. Calculate the dust removal effect E: ; S33. Calculate the compressed air consumption C for this injection: Where k is the correlation coefficient between the nozzle and the valve. It is the blowing pressure at time t. It is the pulse width at time t; S34. Calculate the energy efficiency bonus value for this action. : .

[0014] Using the ratio of cleaning effect (i.e. pressure difference change before and after blowing) to compressed air consumption as an evaluation criterion provides optimization direction for the learning algorithm.

[0015] Furthermore, the calculation of the energy efficiency reward value also includes the introduction of a penalty term and / or a steady-state reward term. The penalty term is triggered when the pressure difference exceeds the safety limit, and the steady-state reward term is triggered when the pressure difference reaches or falls below the target pressure difference.

[0016] Introducing penalty terms and / or steady-state reward terms can guide the system to proactively avoid risks and ensure stable operation.

[0017] Furthermore, the reinforcement learning algorithm in S4 is Q-Learning or a deep Q-network algorithm, and the online learning and optimization steps include: S41. Create a Q-table and initialize all Q values ​​with small random numbers or zero; S42. Perceive the current state Based on the ε-greedy strategy, actions are selected from the current action space. ; S43, Execution Action To obtain a new state Calculate the instant reward value obtained from this action. ; S44. Update the Q value according to the Bellman equation. The specific calculation formula is as follows: , In the formula, Here is the updated Q-value, α is the learning rate, and γ is the discount factor. This is the discount value for the maximum future reward. S45, New Status Transfer to current state Repeat the above steps to gradually converge to the optimal strategy.

[0018] By employing an exploration-utilization strategy for decision-making and by iteratively updating the Q-table through the Bellman equation, the system ensures its self-learning and adaptive capabilities.

[0019] Furthermore, S4 employs an evolutionary algorithm for online learning and optimization. The evolutionary algorithm can be either a genetic algorithm or a particle swarm optimization algorithm. The energy efficiency reward value is used as the fitness function to iteratively evolve the optimal combination of jetting parameters.

[0020] In this invention, the algorithm used for online learning and optimization can be broadly categorized as any algorithm that interacts with the environment and iteratively optimizes based on feedback signals (such as the energy efficiency reward value in this invention). Typical examples include reinforcement learning algorithms (such as Q-Learning), but also evolutionary algorithms (such as genetic algorithms) with similar effects.

[0021] A control system for the pulse jet adaptive optimization control method described in the above scheme is also provided, comprising: Sensor array, used to collect real-time operating status data of bag filter system; The controller is communicatively connected to the sensor group and executes the steps of the pulse jet adaptive optimization control method described in the above scheme. The actuator group is communicatively connected to the controller and is used to receive the control commands and perform the blowing action.

[0022] Furthermore, the controller includes: The data acquisition and preprocessing module is used to process the data from the sensor group; The dynamic energy efficiency model module is used to calculate the energy efficiency bonus value; An adaptive optimization algorithm module is used to run the reinforcement learning algorithm; A jetting command generation module is used to generate the control commands; The actuator group includes at least a pulse valve, a solenoid valve, and a pressure regulating valve.

[0023] A computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the pulse jet adaptive optimization control method as described in the above scheme.

[0024] The beneficial effects of this invention are: This invention upgrades the control objective from simply maintaining the pressure difference to maximizing the dust removal energy efficiency ratio. Through online optimization, the system can always find the blowing parameters that consume the least amount of compressed air and achieve the desired dust removal effect under the current operating conditions, thus avoiding the huge energy waste caused by fixed parameters in the traditional method. This invention employs an online learning mechanism that can automatically adapt to changes in working conditions such as dust concentration, particle size, viscosity, and humidity. When the production process is adjusted or the raw materials are changed, there is no need to manually reset the parameters. The system can learn on its own and obtain the optimal control strategy, which greatly improves the robustness and intelligence of the system. By optimizing the process, this invention avoids the need for prolonged use of excessively high pressure for violent dust removal, thus reducing mechanical impact and wear on the filter bags. At the same time, precise control also prevents irreversible damage to the filter bags caused by high pressure differentials due to untimely dust removal, thereby extending the filter bag replacement cycle and reducing maintenance costs. Intelligent dust removal control makes the operating pressure difference of the dust collector more stable, maintaining it within the economic resistance range, which is conducive to the stable operation of the front-end main fan, reduces the fluctuation of fan energy consumption, and ensures the process stability of the entire production line. Attached Figure Description

[0025] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0026] Figure 1 This is a structural block diagram of the pulse jet adaptive optimization control system of the present invention.

[0027] Figure 2 This is a flowchart of the pulse jet adaptive optimization control method of the present invention.

[0028] In the diagram: 100, bag filter body; 200, sensor group; 201, differential pressure sensor; 202, air tank pressure sensor; 300, controller; 301, data acquisition and preprocessing module; 302, dynamic energy efficiency model module; 303, adaptive optimization algorithm module; 304, pulse jet command generation module; 400, actuator group; 401, pulse valve solenoid valve; 402, pressure regulating valve. Detailed Implementation

[0029] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0030] Example 1 like Figure 1 As shown, a control system for an adaptive optimization control method of pulse jet cleaning includes a bag filter body 100, a sensor group 200, a controller 300, and an actuator group 400. The bag filter body 100 includes filter bags, pulse valves, and an air tank, etc. The sensor group 200 is used to collect the operating status data of the bag filter system in real time, and includes a differential pressure sensor 201 and an air tank pressure sensor 202. The differential pressure sensor 201 is used to detect the pressure difference across the filter bag. The gas tank pressure sensor 202 is used to detect the blowing pressure P; the controller 300 is connected to the sensor group 200 and is a PLC, industrial computer or embedded controller; the actuator group 400 is connected to the controller 300 and is used to receive control commands and execute the blowing action, which includes a pulse valve solenoid valve 401 and a pressure regulating valve 402.

[0031] Specifically, the controller 300 includes a data acquisition and preprocessing module 301, a dynamic energy efficiency model module 302, an adaptive optimization algorithm module 303, and a spray command generation module 304. The data acquisition and preprocessing module 301 is used to process the data from the sensor group 200 and acquire the pressure difference ΔP and the spray pressure P in real time. The dynamic energy efficiency model module 302 is used to calculate the energy efficiency bonus value and evaluate the energy efficiency of each spray. The adaptive optimization algorithm module 303 is used to run the reinforcement learning algorithm to generate the optimal spray action. The spray command generation module 304 is used to generate control commands to control the actuator group 400 to perform corresponding actions.

[0032] The data acquisition and preprocessing module 301 not only collects data but also provides high-quality, consistent input for subsequent learning algorithms, avoiding noisy data from misleading the learning process. The dynamic energy efficiency model module 302 constructs energy efficiency reward values ​​for dust removal effect (pressure reduction) and energy consumption cost (gas consumption), unifying the originally contradictory performance and energy consumption goals into a single maximizable goal. The adaptive optimization algorithm module 303 models the pulse jet control problem as a sequential decision problem. Through reinforcement learning, the system no longer relies on preset, fixed rules but autonomously discovers the optimal control strategy hidden in complex working conditions through trial and error and experience accumulation with the environment. The jet instruction generation module 304 is responsible for converting intelligent decisions into specific and safe execution instructions to ensure the execution of the algorithm's decisions.

[0033] Example 2 like Figure 2 As shown, an adaptive optimization control method for pulse jet blowing includes the following steps: S1. Real-time acquisition of operating status data of the bag filter system, including at least the pressure difference value on both sides of the filter bag; S2. The action of the agent is based on the combination of jetting parameters, wherein the jetting parameters include at least jetting pressure and pulse width; S3. Calculate the energy efficiency bonus value of this action based on the pressure difference change before and after the injection and the compressed air consumption of this injection. The energy efficiency bonus value is directly proportional to the pressure difference change and inversely proportional to the compressed air consumption. S4. Based on the operating status data and energy efficiency bonus value, a reinforcement learning algorithm is used for online learning and optimization, dynamically adjusting and outputting the optimal combination of injection parameters; S5. Generate control commands based on the optimal combination of pulse jet parameters to drive the pulse jet actuator to perform dust removal operations; S6. After the reinforcement learning algorithm's policy converges, switch to steady-state control mode, execute the blowing action based on the historical optimal parameters, and periodically trigger the exploration mechanism to cope with changes in working conditions.

[0034] After the strategy converges, using the historically optimal parameters can avoid unnecessary performance fluctuations and computational resource consumption caused by continuous exploration, allowing the system to run at a stable and efficient operating point. The periodically triggered exploration mechanism is equivalent to setting up a long-term self-checking and fine-tuning function for the system, which can detect unstable operating conditions caused by slow factors such as filter bag aging and seasonal changes. Once it is detected that the original strategy is no longer optimal, the learning process can be reactivated by changes in the reward signal, ensuring that the system always maintains optimal performance throughout its entire life cycle.

[0035] Specifically, the operating status data in S1 also includes the differential pressure change rate. At least one of the following: gas tank pressure, inlet dust concentration, and ambient humidity. The injection parameters in S2 also include the injection interval. Furthermore, the injection parameters are discretely or continuously set within a preset safety range. The energy efficiency bonus value for this action in S3. The calculation is as follows: S31, During the execution of the blowing action Record the current differential pressure value. ; S32, Perform the blowing action Record the pressure difference value shortly after the injection. Calculate the dust removal effect E: ; S33. Calculate the compressed air consumption C for this injection: Where k is the correlation coefficient between the nozzle and the valve. It is the blowing pressure at time t. It is the pulse width at time t; S34. Calculate the energy efficiency bonus value for this action. : .

[0036] Reward function for energy efficiency ratio The document explicitly guides reinforcement learning agents to find strategies that achieve the greatest possible pressure reduction using the least amount of compressed air. When operating conditions change, such as increased dust viscosity, the original blowing parameters may not achieve the same dust removal effect E, or may require more air consumption C. This will directly affect the energy efficiency bonus value. Decrease, Energy Efficiency Bonus Value The changes automatically drive the learning algorithm to find new parameter combinations to restore high rewards, achieving adaptive optimization without any human intervention.

[0037] In S4, the reinforcement learning algorithm is either Q-Learning or a deep Q-network algorithm. The online learning and optimization steps include: S41. Create a Q-table and initialize all Q values ​​with small random numbers or zero, i.e., Q(S, A), where S represents all possible states and A represents all possible actions; S42, Controller 300 senses the current state through data acquisition and preprocessing module 301. such as the current pressure difference and pressure difference change rate Based on the ε-greedy strategy (ε being the exploration rate), a random number rand between 0 and 1 is first generated. If rand < ε, then exploration is performed, that is, an action is randomly selected from the current action space. If rand ≥ ε, then it is utilized, that is, the current state is looked up in the Q table. For the corresponding row, select the action with the largest Q value. ; S43, The jetting instruction generation module 304 executes the action according to the selected action. The actuator group 400 completes one spray cycle, and acquires a new status after completion. Calculate the instant reward value obtained from this action. ; S44. Update the Q value according to the Bellman equation. The specific calculation formula is as follows: , In the formula, Here is the updated Q-value, α is the learning rate, and γ is the discount factor. This is the discount value for the maximum future reward. S45, New Status Transfer to current state Repeat the above steps to gradually converge to the optimal strategy.

[0038] By balancing the utilization of known optimal actions with the exploration of unknown possibilities, the system ensures broad search capabilities in the early stages of learning, avoiding getting trapped in local optima, while also ensuring stable utilization of the optimal strategy in the later stages of learning. The update rule based on the Bellman equation essentially approximates the long-term true value of the action corresponding to each state through iteration. The convergence process of the Q(S,A) value is the system's learning process from the control environment. Once the Q-table converges, for any state... Choose to make Q( A) The largest action A is the globally optimal solution under the current working condition; by changing the new state... Continuous feedback to the system forms a closed-loop learning system, enabling the learning process to continue and dynamically track changes in the system state, thereby achieving online self-adaptation.

[0039] As the system continues to operate, the values ​​in the Q-table become increasingly accurate, and the exploration rate ε gradually decreases over time. The system transitions from being primarily exploration-based to primarily utilization-based, meaning it executes more known optimal actions. When operating conditions change (such as changes in dust properties), causing the old optimal strategy to become no longer optimal, the reward value R changes. The Q-learning algorithm automatically relearns and adapts through continuous iteration, finding new optimal combinations of injection parameters, thereby achieving dynamic adaptive adjustment of the control strategy.

[0040] The implementation process of this method will be explained in detail below with reference to a set of preferred initial parameters.

[0041] The state S of the system is defined as { , }, that is, the current pressure difference and its rate of change, pressure difference The monitoring range is set to 0–1800 Pa, and the differential pressure change rate is... It is obtained by calculating the difference between the current pressure differential and the pressure differential of the previous cycle (e.g., after a 10-second interval); Action A of the agent is the combination of jetting parameters {P, The spray interval will not be optimized for the time being. The injection pressure P is between 0.3 MPa and 0.7 MPa, discrete in 0.1 MPa increments, with five selectable values: {0.3, 0.4, 0.5, 0.6, 0.7} MPa; pulse width... Between 80ms and 200ms, with a step size of 40ms, the options are discretized into four values: {80, 120, 160, 200}ms. There are a total of 5 × 4 = 20 possible combinations of actions. The compressed air consumption calculation coefficient k is set to 0.15 (L / (MPa·ms)) based on the on-site valve and nozzle models; to prevent excessive pressure differential, a safe upper limit for pressure differential is set. =1500 Pa, when > At that time, apply a large negative penalty of -50 to the reward value; Let the learning rate α = 0.1, the discount factor γ = 0.9, and the initial exploration rate be... =0.3, and set it to decay over time. For example, after every 100 spray decisions, the exploration rate is multiplied by the decay coefficient of 0.95, with a minimum of 0.05; the Q table is initialized as a matrix of state number × 20, and all Q values ​​are initially 0; Assume the data acquisition module obtains the current system status as follows: = (1250 Pa, +15 Pa / s), indicating that the current pressure difference is 1250 Pa and is on an upward trend; Controller 300 generates a random number 0.4. Since 0.4 > the current exploration rate ε (e.g., 0.25), it enters the utilization mode and queries the status in the Q table. The corresponding line detects the action. = (P=0.5MPa, The Q value is highest at 120ms, therefore this action is selected for this jetting action; The injection command generation module 304 controls the pressure regulating valve 402 to adjust the pressure to 0.5 MPa and triggers the pulse valve to open for 120 ms. After the injection is completed, wait for the system to stabilize (e.g., wait 3 seconds) and then measure the pressure difference again to obtain the result. =980 Pa; Calculate the dust removal effect E=1250-980=270Pa, compressed air consumption C=0.15×0.5×120=9L, basic energy efficiency bonus value. =270 / 9=30; due to < If there is no penalty, then the total reward for this action is... =30; The system has entered a new state. = (980 Pa, -30 Pa / s), assuming that in the Q-table, the maximum Q value for all actions in the new state is (980 Pa, -30 Pa / s), =35, then update the Q value according to the Bellman equation: Q(S_t, A_t)=0+0.1×[30+0.9×35-0]=0.1×(30+31.5)=6.15, Then, under the condition of (1250Pa, +15Pa / s), the Q value of the action (0.5MPa, 120ms) will be updated from 0 to 6.15.

[0042] Example 3 The difference from Example 2 is that the calculation of the energy efficiency bonus value introduces a penalty term and / or a steady-state bonus term. The penalty term is triggered when the pressure difference exceeds the safety limit, and the steady-state bonus term is triggered when the pressure difference reaches or falls below the target pressure difference.

[0043] The penalty is triggered when the pressure difference exceeds the safety limit: After cleaning, if the pressure difference across the filter bag still exceeds the system's preset safe operating limit (e.g., 1500 Pa), a fixed penalty value (e.g., 100 points) with a large absolute value is subtracted from the total reward value. This forces the learning agent to learn to avoid jet cleaning strategies that may cause excessive system resistance, thus prioritizing the safe operation of the equipment and preventing damage to the filter bag or impact on the main process due to inadequate cleaning.

[0044] The steady-state reward is triggered when the pressure difference reaches or falls below the target pressure difference: After cleaning is completed, if the pressure difference across the filter bag reaches or falls below an ideal target pressure difference value (e.g., 1000 Pa), once triggered, a fixed positive reward score (e.g., 10 points) is added to the total reward value. This positively guides and encourages the agent, prompting the system to actively seek out the blowing parameters that enable the system to maintain ideal operating conditions with low resistance and low energy consumption, thereby ensuring effectiveness while further achieving energy saving, consumption reduction, and extending filter bag life.

[0045] Example 4 The difference from Example 2 is that S4 uses an evolutionary algorithm for online learning and optimization. Specifically, the evolutionary algorithm uses a genetic algorithm, which can treat a set of injection parameters {P, Tw, Ti} as an individual and use the energy efficiency ratio R=E / C as the fitness function. By performing selection, crossover, mutation and other operations on multiple sets of parameters, the individual with the highest fitness, i.e. the optimal parameter combination, is evolved iteratively.

[0046] Specifically, the process is as follows: First, within the safe range allowed by the blowing pressure, pulse width, and blowing interval, multiple different combinations of blowing parameters are randomly generated, for example, 20 combinations. This initial set of parameters is called the initial population, and each set of parameters is called an individual. Each set of parameters (i.e., each individual) in the population is applied sequentially to the actual blowing and cleaning operation. The energy efficiency bonus value defined in Example 2 is used as the evaluation standard to calculate the merits of each set of parameters. This value is called fitness in this algorithm. The higher the fitness, the better the overall performance of the set of parameters in terms of cleaning effect and energy consumption.

[0047] Secondly, based on the calculated fitness, a survival-of-the-fittest selection is carried out. Parameter sets with high fitness are more likely to be retained and participate in the generation of new parameter sets as parents. Parent parameter sets are randomly paired and crossover operations are performed to exchange and combine some values ​​of the two sets of parameters, thereby generating a brand new offspring parameter set. Furthermore, for the parameter group generated by newly, a small, random adjustment is made to one of the parameters (such as the injection pressure) with a small probability (e.g., 10%). Finally, the above steps are repeated to form a continuous evolutionary cycle. After multiple generations (e.g., 100 generations), the overall fitness of the entire population will continuously improve. Ultimately, the set of spray parameters with the highest fitness from all generations is selected as the optimal solution found by the genetic algorithm.

[0048] Example 5 The difference from Example 2 is that S4 uses an evolutionary algorithm for online learning and optimization. Specifically, the evolutionary algorithm is a particle swarm optimization algorithm, which can also be used to find the optimal combination of parameters. Each particle represents a potential solution, and the solution space is searched by following the individual optimum and the global optimum.

[0049] Specifically, within the safe range of the jetting parameters, a large number of candidate jetting parameter combinations are randomly generated, such as 30 groups. Each parameter combination is called a particle. All these particles constitute a particle swarm. Each particle has its own position (i.e., the current parameter value) and flight speed (i.e., the trend and step size of parameter changes). Secondly, the parameter combination represented by each particle is applied to the jet cleaning, and its energy efficiency bonus value is calculated as the fitness of that particle to evaluate the quality of the position; each particle will remember the best historical position it found during the entire flight (i.e., the best set of parameters it has ever found); at the same time, the entire particle swarm will share information and record the best performing position among all particles, i.e., the global best position. Furthermore, in each iteration, each particle adjusts its flight speed (i.e., the next search direction) based on two types of information: moving closer to its own historical best position; or moving closer to the global best position of the entire particle swarm. At the same time, the particle also retains some of its previous flight inertia. Then, the particle moves to a new position based on the updated speed, thus generating a new set of jet parameters. During the update process, if the new parameters calculated by a particle exceed the safe operating range, the system will force it to be corrected to the nearest boundary value to ensure the feasibility of the parameters. Finally, repeating the above steps, the particle swarm gradually gathers towards the optimal parameter region under the guidance of the historical best and the global best. When the preset number of iterations is reached or the performance of the solution no longer improves significantly, the algorithm ends and outputs the currently found global best position, that is, the optimal combination of jetting parameters.

[0050] Example 6 A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the pulse jet adaptive optimization control method of any one of Embodiments 2 to 5.

[0051] Based on the above-described preferred embodiments of the present invention, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A pulse jet adaptive optimization control method, characterized in that, Includes the following steps: S1. Real-time acquisition of operating status data of the bag filter system, wherein the operating status data includes at least the pressure difference value on both sides of the filter bag; S2. The action of the agent is based on a combination of spraying parameters, wherein the spraying parameters include at least spraying pressure and pulse width; S3. Based on the pressure difference change before and after the injection and the compressed air consumption of this injection, calculate the energy efficiency bonus value of this action, where the energy efficiency bonus value of this action is... The calculation is as follows: S31, During the execution of the blowing action Record the current differential pressure value. ; S32, Perform the blowing action Record the pressure difference value shortly after the injection. Calculate the dust removal effect E: ; S33. Calculate the compressed air consumption C for this injection: Where k is the correlation coefficient between the nozzle and the valve. It is the blowing pressure at time t. It is the pulse width at time t; S34. Calculate the energy efficiency bonus value for this action. : ; The calculation of the energy efficiency bonus value also includes the introduction of a penalty term and / or a steady-state bonus term. The penalty term is triggered when the pressure difference exceeds the safety limit, and the steady-state bonus term is triggered when the pressure difference reaches or falls below the target pressure difference. S4. Based on operational status data and energy efficiency bonus values, the Q-Learning reinforcement learning algorithm is used for online learning and optimization, dynamically adjusting and outputting the optimal combination of injection parameters; the steps of online learning and optimization include: S41. Create a Q-table and initialize all Q values ​​with small random numbers or zero; S42. Perceive the current state Based on the ε-greedy strategy, actions are selected from the current action space. ; S43, Execution Action To obtain a new state Calculate the instant reward value obtained from this action. ; S44. Update the Q value according to the Bellman equation. The specific calculation formula is as follows: ， In the formula, Here is the updated Q-value, α is the learning rate, and γ is the discount factor. This is the discount value for the maximum future reward. S45, New State Transfer to current state Repeat the above steps to gradually converge to the optimal strategy; S5. Generate control commands based on the optimal combination of pulse jet parameters to drive the pulse jet actuator to perform dust removal operations; S6. After the reinforcement learning algorithm's policy converges, switch to steady-state control mode, execute the blowing action based on the historical optimal parameters, and periodically trigger the exploration mechanism to cope with changes in working conditions.

2. The pulse jet adaptive optimization control method according to claim 1, characterized in that: The operating status data in S1 also includes the differential pressure change rate. At least one of the following: gas tank pressure, inlet dust concentration, and ambient humidity.

3. The pulse jet adaptive optimization control method according to claim 1, characterized in that: The blowing parameters in S2 also include the blowing interval. Furthermore, the blowing parameters are discretely or continuously selected within a preset safety range.

4. The pulse jet adaptive optimization control method according to claim 1, characterized in that: In S4, an evolutionary algorithm is used for online learning and optimization. The evolutionary algorithm is either a genetic algorithm or a particle swarm optimization algorithm. The energy efficiency reward value is used as the fitness function to iteratively evolve the optimal combination of jetting parameters. The genetic algorithm treats a set of spraying parameters as an individual, randomly generates an initial population within a preset safety range of the spraying parameters, performs selection, crossover, and mutation operations based on fitness, repeats until fitness converges or a preset number of iterations is reached, and selects the individual with the highest fitness as the optimal combination of spraying parameters. The particle swarm optimization algorithm treats a set of spray parameters as the position of a particle. An initial particle swarm is randomly generated within a preset safe range of the spray parameters. Each particle has a position and velocity. The energy efficiency reward value is used as the fitness of the particle. The algorithm records the individual historical best position and the global best position of the particle swarm. In each iteration, the particle's velocity is updated based on the individual historical best position and the global best position, and the particle moves to a new position. Positions that exceed the preset safe range are corrected to the boundary value. This process is repeated until the fitness converges or the preset number of iterations is reached. The spray parameter combination corresponding to the global best position is taken as the optimal spray parameter combination.

5. A control system for implementing the pulse jet adaptive optimization control method as described in any one of claims 1 to 3, characterized in that, include: Sensor group (200) is used to collect real-time operating status data of bag filter system; The controller (300) is communicatively connected to the sensor group (200) and executes the steps of the pulse jet adaptive optimization control method as described in any one of claims 1 to 3; The actuator assembly (400) is communicatively connected to the controller (300) and is used to receive the control commands and perform the blowing action; The controller (300) includes: The data acquisition and preprocessing module (301) is used to process the data from the sensor group (200); The dynamic energy efficiency model module (302) is used to calculate the energy efficiency bonus value; An adaptive optimization algorithm module (303) is used to run the reinforcement learning algorithm; The jetting instruction generation module (304) is used to generate the control instruction; The actuator group (400) includes at least a pulse valve solenoid valve (401) and a pressure regulating valve (402).

6. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the pulse jet adaptive optimization control method as described in any one of claims 1 to 4.

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