Cloth bag dust cleaning intelligent control system and method based on model prediction

Abstract

The invention discloses a model prediction-based cloth bag dust removal intelligent control system and method, belongs to the technical field of blowing dust removal control, and solves the problems of how to improve the dust removal efficiency of a cloth bag dust remover, prolong the service life of a filter bag and reduce energy consumption. According to the method, the mathematical model is constructed based on the filter bag pressure difference dynamic equation, the running state of the bag-type dust collector in a future period of time is predicted, the optimal injection strategy in the future period of time is solved by using the multi-objective optimization algorithm on the premise that the preset constraint condition is met, and the optimal injection strategy in the future period of time is calculated based on the rolling time domain strategy. Performing blowing operation on the bag-type dust collector according to the instruction, and automatically correcting mathematical model parameters according to long-term operation data; the method overcomes the response hysteresis caused by traditional timing control or constant pressure difference control, effectively improves the dust removal efficiency of the bag-type dust remover, reduces the energy consumption of compressed air, prolongs the service life of the filter bag, and is suitable for environment-friendly island intelligent systems in the industries of electric power, steel, cement, chemical engineering and the like.

Description

Technical Field

[0001] This invention belongs to the field of pulse-jet cleaning control technology, and relates to an intelligent control system and method for bag cleaning based on model prediction. Background Technology

[0002] The denitrification, dust removal, and desulfurization processes in environmental protection islands aim to reduce the environmental impact of harmful industrial emissions (such as nitrogen oxides (NOx), sulfur dioxide (SO2), and particulate matter). They are primarily used for flue gas treatment in industries such as power, steel, cement, and chemicals. The dust removal process aims to remove solid particulate matter (such as coal ash and dust) from flue gas. These particles can affect the environment and human health. Common dust removal technologies include electrostatic precipitators (ESPs) and bag filters. Bag filters filter particulate matter from the flue gas onto the surface of filter bags. When the flue gas passes through the bag filter, the particles are collected on the bags, and the filtered gas flows out through the gaps between the bags.

[0003] With increasingly stringent environmental standards and higher requirements for energy conservation and emission reduction in industrial production, the efficient and stable operation of dust collection equipment has become crucial. Baghouse dust collectors, as a widely used and highly efficient dust collection device, play a vital role in the dust removal process. The physical mechanism of pulse-jet cleaning is as follows: When compressed air is released instantaneously through the pulse valve, a high-pressure airflow is formed inside the blowpipe. After being accelerated by the nozzle, it forms a shock wave. This shock wave propagates at the speed of sound (approximately 340 m / s) and generates two forces inside the filter bag: first, a positive pressure wave perpendicular to the filter bag surface, causing radial expansion and deformation; second, a shear force wave propagating along the filter bag axis. Experimental data shows that a high-quality pulse jet can generate a transient pressure of 300-500 Pa on the filter bag surface within 0.1 seconds. This sudden load can disrupt the van der Waals bond between the dust layer and the filter media.

[0004] The dust removal process follows the principle of "inertial separation": when the filter bag expands rapidly, the attached dust retains its original position due to inertia, while the filter fiber has shifted, resulting in relative motion between the two. If the blowing pressure reaches 0.3~0.6MPa at this time (the blowing pressure range can be adjusted according to the filter material), the dust layer will crack and peel off in flakes; in particular, excessively high pressure (pressure > 0.7MPa) will cause the dust to embed itself deeper into the filter material, producing an "over-blowing effect".

[0005] However, traditional bag filter dust removal control systems have the following limitations: (1) Timed control: The dust removal operation is performed at fixed time intervals. This method is simple and direct, but it fails to consider the changes in actual working conditions, which can easily lead to over-dust removal or under-dust removal, resulting in increased energy consumption or affecting dust removal efficiency. (2) Manual adjustment: The pulse jet parameters are manually adjusted based on experience or on-site observation. This method depends on the experience and skill level of the operator, making it difficult to ensure the consistency and optimization effect of multiple dust removal devices, and it is not suitable for large-scale automated production lines. (3) Constant pressure difference control: When the pressure difference between the inlet and outlet of the filter bag reaches the preset value, the jet cleaning program is started. For example, the invention patent with publication number CN117101293A discloses a PLC-controlled jet cleaning control system for baghouse dust collectors. It discloses that when the jet cleaning demand index corresponding to the current filter bag of the target baghouse dust collector is greater than or equal to the set jet cleaning demand index, the jet air valve of the target baghouse dust collector is activated. Although this method is more flexible than the above method, it is still difficult to achieve optimal control under the conditions of dust properties change and load fluctuations, which may lead to unnecessary compressed air consumption or shortened filter bag life. Therefore, it is particularly important to dynamically adjust the jet cleaning strategy according to the real-time working conditions in order to improve the dust removal efficiency of the baghouse dust collector, extend the service life of the filter bag, and reduce energy consumption. Summary of the Invention

[0006] The technical problem to be solved by this invention is how to improve the dust removal efficiency of bag filters, extend the service life of filter bags, and reduce energy consumption.

[0007] The present invention solves the above-mentioned technical problems through the following technical solutions: The model-based predictive intelligent control system for baghouse dust removal includes: The data acquisition module is used to collect and monitor the operating parameters of the bag filter in real time, including inlet and outlet pressure difference, flue gas flow rate, dust concentration, temperature and humidity. The dynamic modeling module constructs a mathematical model based on the dynamic equation of filter bag pressure difference to describe the impact of dust accumulation process and pulse-jet cleaning behavior on the pressure difference change of the internal filter bags in the bag filter dust collector. The prediction module, based on a mathematical model and the current operating parameters, predicts the operating status of the bag filter over a period of time in the future. The optimization decision module is used to solve the optimal blowing strategy for a future period of time under the premise of meeting preset constraints using a multi-objective optimization algorithm. The blowing strategy includes blowing pressure, pulse width, blowing interval and timing arrangement. The execution module, based on the rolling time-domain strategy, performs pulse-jet cleaning on the bag filter according to the instructions generated by the optimization decision module, and collects feedback information for optimization in the next cycle. The adaptive update module is used to automatically correct mathematical model parameters based on long-term running data.

[0008] Furthermore, the dynamic modeling module utilizes the following logic to represent the mathematical model:

[0009] In the formula, For a moment The pressure difference of the filter bag Indicates time The rate of change of pressure difference in the filter bag For flue gas flow rate, The concentration of dust at the inlet. The dust deposition coefficient is... For jet control input, , Take 0 or 1, This represents the dust removal efficiency coefficient.

[0010] Furthermore, the prediction module specifically involves: discretizing the time-domain mathematical model constructed in the dynamic modeling module, performing multi-step prediction using the discretized state-space model, and recording the discrete time step size at each interval. For a given moment, the discretized state-space model can be represented using the following logic:

[0011] in, for The pressure difference of the filter bag at any given time for The pressure difference of the filter bag at any given time Represents the discrete first... The sampling point, the first A moment represents time. ; for The amount of smoke at any given time for The concentration of inlet dust at any given time. for The timing of the blow control input, , The natural decay coefficient, , , where a is the natural attenuation factor of the filter bag pressure difference, b is the gain coefficient of dust accumulation, and c is the efficiency factor of pulse-jet cleaning.

[0012] Furthermore, the optimization decision module utilizes a multi-objective optimization algorithm to solve for the optimal spraying strategy over a future period, specifically as follows: By solving a finite-time multi-objective optimization problem in each control cycle to predict the system behavior in the next N steps and optimize the control sequence, the multi-objective optimization problem is represented by a model-predicted control objective function:

[0013] in, For differential pressure tracking error, For the target pressure difference, These are error weight, energy consumption weight, and lifetime weight, respectively. For the future The pressure difference of the filter bag in the step; For the future Energy consumption of steps For the future Step-by-step jet control input; For the future The wear life of the step, , For the future The width of the pulse. For the future Deviation between step injection pressure and target value For the blowing pressure, The width of the pulse.

[0014] Furthermore, the preset constraints in the optimization decision module include minimizing energy consumption, maximizing dust removal efficiency, extending filter bag lifespan, and ensuring emission compliance. These preset constraints are represented using the following logic:

[0015] in, This indicates the maximum number of pulses per unit time. This indicates the maximum differential pressure limit. These represent the minimum and maximum spray intervals, respectively. This indicates the injection interval within the control cycle.

[0016] Furthermore, the rolling time-domain strategy in the execution module is specifically as follows: at time... Based on the current state And predictive models, to solve for the future Optimal control sequence of steps , obtain new The multi-objective optimization problem needs to be solved again.

[0017] Furthermore, the adaptive update module uses recursive least squares (RLS) to update the dust deposition coefficient. This can be represented using the following logic:

[0018] in, for The latest estimate of the dust deposition coefficient at any given time. for The estimated value of the dust deposition coefficient at time (previous time). Here is the gain matrix. Let be the regression vector, denoted as .

[0019] This invention also provides a model-predictive-based intelligent control method for bag filter cleaning, comprising the following steps: S1, Real-time acquisition and monitoring of the operating parameters of the bag filter, including inlet and outlet pressure difference, flue gas flow rate, dust concentration, temperature and humidity; S2, a mathematical model is constructed based on the dynamic equation of filter bag pressure difference to describe the influence of dust accumulation process and pulse-jet cleaning behavior on the pressure difference change of internal filter bags in bag dust collectors; S3, based on the mathematical model and the current operating parameters, predicts the operating status of the bag filter in the future period of time; S4. Under the premise of satisfying the preset constraints, the optimal blowing strategy for a future period of time is solved by a multi-objective optimization algorithm. The blowing strategy includes blowing pressure, pulse width, blowing interval and timing arrangement. S5, based on the rolling time domain strategy, performs pulse-jet cleaning on the bag filter according to the instructions generated by the optimization decision module, and collects feedback information for optimization in the next cycle; S6 is used to automatically correct mathematical model parameters based on long-term operating data.

[0020] Furthermore, the mathematical model in S2 is represented using the following logic:

[0021] In the formula, For a moment The pressure difference of the filter bag Indicates time The rate of change of pressure difference in the filter bag For flue gas flow rate, The concentration of dust at the inlet. The dust deposition coefficient is... For jet control input, , Take 0 or 1, This represents the dust removal efficiency coefficient.

[0022] Furthermore, S3 specifically involves: discretizing the time-domain mathematical model constructed in the dynamic modeling module, performing multi-step prediction using the discretized state-space model, and recording the discrete time step size at each interval. For a given moment, the discretized state-space model can be represented using the following logic:

[0023] in, for The pressure difference of the filter bag at any given time for The pressure difference of the filter bag at any given time Represents the discrete first... The sampling point, the first A moment represents time. ; for The amount of smoke at any given time for The concentration of inlet dust at any given time. for The timing of the blow control input, , The natural decay coefficient, , , where a is the natural attenuation factor of the filter bag pressure difference, b is the gain coefficient of dust accumulation, and c is the efficiency factor of pulse-jet cleaning.

[0024] The advantages of this invention are: (1) This invention describes the dynamic process of the pressure difference of the internal filter bags in a baghouse dust collector changing with dust accumulation and with the pulse-jet cleaning action through differential equations describing the dynamics of the filter bag pressure difference. For the first time, it quantitatively models the key physical processes of the baghouse dust collection system, such as the dust accumulation process, filter bag resistance change, and pulse-jet cleaning effect, and constructs a dynamic prediction model based on the fusion of mechanism and data-driven approaches. This model comprehensively considers multi-dimensional input variables such as flue gas flow rate, dust concentration, temperature, and pressure difference trend, and achieves high-precision prediction of the future evolution trend of filter bag pressure difference. It breaks through the lag of traditional constant pressure difference or timed control and significantly improves the foresight and adaptability of the control.

[0025] (2) This invention introduces a multi-objective optimization algorithm under the model predictive control (MPC) framework, comprehensively considers the three core objectives of dust removal effect, energy consumption minimization and filter bag life extension, dynamically solves the optimal combination of pulse jet parameters, and achieves the best overall performance of the dust collector.

[0026] This invention employs a rolling time-domain optimization strategy. Each cycle, the system updates the state estimate based on the latest sensor data and recalculates the optimal control sequence for a future period. Only the first control action is executed, forming a closed-loop control of "prediction, optimization, execution, and feedback". This architecture has strong robustness, can automatically compensate for model errors and effectively cope with model mismatch and external disturbances (such as flue gas fluctuations), thereby improving system robustness and control accuracy.

[0027] (3) The intelligent control system for bag cleaning based on model prediction provided by the present invention first monitors key parameters such as flue gas flow, dust concentration and filter bag pressure difference in real time through the data acquisition module, and accurately predicts the future pressure difference change trend based on the fusion mechanism and data-driven dynamic model; then, the optimization decision module takes cleaning effect, energy consumption minimization and filter bag life extension as multiple objectives, and solves the optimal injection control sequence in a rolling manner under the condition of satisfying system constraints; finally, the execution module implements the first injection command and collects feedback data, while the adaptive update module corrects the model parameters online to ensure long-term prediction accuracy, thereby forming an intelligent closed-loop control of "prediction-optimization-execution-feedback".

[0028] This invention overcomes the response lag caused by traditional timed control or constant pressure difference control, effectively improving the dust removal efficiency of bag filters; through a multi-objective optimization algorithm, it dynamically balances compressed air energy consumption, filter bag lifespan, and dust removal effect, effectively reducing compressed air energy consumption and increasing filter bag lifespan, while reducing maintenance costs; through a rolling optimization mechanism, the control system has strong robustness, automatically compensating for external disturbances such as flue gas fluctuations and changes in dust characteristics, and the control system can dynamically adjust the pulse jet strategy according to real-time operating conditions.

[0029] The intelligent bag filter cleaning control system provided by this invention is deployed on edge computing devices. It is not only suitable for the design and installation of new equipment, but also for upgrading existing equipment to improve performance. It conforms to the promotion of the Industry 4.0 concept and the development direction of intelligent manufacturing technology, and promotes the technological progress and green development of the entire industry. It is applicable to the treatment of flue gas in environmental islands with one or more of the following processes: industrial boilers, industrial kilns (including but not limited to glass, cement, ceramics, building materials, etc.), sintering machines, blast furnaces, pelletizing, coking, chemical manufacturing, shipbuilding, etc. Attached Figure Description

[0030] Figure 1 This is an execution flowchart of the intelligent control of bag cleaning based on model prediction according to Embodiment 1 of the present invention. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments: Example 1 like Figure 1 Specifically, a model-based predictive intelligent control system for baghouse dust removal is disclosed, comprising: The data acquisition module is used to collect and monitor the operating parameters of the bag filter in real time. These operating parameters include key characteristic data such as inlet and outlet pressure difference, flue gas flow rate, dust concentration, temperature, and humidity.

[0033] In this embodiment, the aforementioned key characteristic data mainly come from various sensors installed on the inlet and outlet flues of the dust removal equipment. Temperature can be obtained through thermocouples or resistance thermometers. Flue gas flow rate is obtained using a thermal mass flow meter or Pitot tube, combined with differential pressure measurement. Flue gas composition (such as oxygen, nitrogen oxides, sulfur dioxide, and dust concentration) is detected online using a continuous emission monitoring system (CEMS) or a dedicated flue gas analyzer. The differential pressure difference between the inlet and outlet of the filter bag, as a core indicator for judging the degree of filter bag clogging and the need for dust removal, is collected by a differential pressure transmitter. The differential pressure transmitter is connected to the dust-laden side and the clean gas side of the filter bag chamber through pressure guide pipes. To prevent the pressure guide pipes from clogging, backflushing or heating devices are often provided.

[0034] Furthermore, the data acquisition module is also used to collect dust removal-related parameters and equipment status parameters in real time. The dust removal-related parameters include data such as blowing pressure, number of pulse valve actions, dust removal cycle and timing, which are obtained from pressure sensors installed on the blowing pipeline or air tank, as well as the counting and status feedback of the solenoid valve actions by the pulse valve controller or PLC. Some control systems can also determine whether the pulse valve is open normally through current detection or valve position switch.

[0035] The equipment status parameters include fan speed, opening and closing status of lift valve and bypass valve, and online or offline status of each compartment. These parameters are usually provided by control signals in DCS or local PLC, or can be obtained through limit switches, proximity switches or frequency converter communication interfaces.

[0036] Regarding sensor hardware deployment, all sensors are rationally arranged according to process locations and environmental conditions. Flue gas sensors are directly mounted to the flue wall via flanges or threaded interfaces to ensure representative measurement points and ease of maintenance. Differential pressure transmitters are installed close to the filter bag chamber with anti-clogging measures in place. Sensors related to the dust removal system are integrated near the blowing pipeline for real-time monitoring of the blowing effect. Equipment status sensors are installed on the actuator body or drive components. All of the above sensors must meet the protection level requirements of industrial sites, typically not lower than IP65. High-temperature areas require high-temperature resistant models or the addition of heat insulation and cooling devices. Signal cables use shielded twisted-pair cables and are properly grounded to suppress electromagnetic interference.

[0037] In terms of communication, the data acquisition module interfaces with the upper-level system through standard industrial communication protocols. Communication with the DCS system can use any of the following communication protocols: Modbus TCP / RTU, Profibus DP, or OPC UA. The acquired data is uploaded to the DCS through I / O modules or communication gateways, while the module receives operating commands from the DCS (such as dust cleaning mode switching, equipment start / stop, etc.).

[0038] For edge computing devices (such as industrial gateways or edge servers), wired connections are primarily established via Ethernet (TCP / IP) or RS485 (Modbus RTU). In areas where cabling is difficult, wireless methods such as LoRa, Wi-Fi, or 5G can also be used. Edge devices are used to preprocess raw data, extract features, and run local prediction models to achieve real-time optimization of dust removal strategies. Simultaneously, the system supports uploading critical data to the cloud platform via protocols such as MQTT or HTTP for model iteration and remote operation and maintenance.

[0039] In this embodiment, to ensure the time consistency of multi-source data, the entire data acquisition module should support Network Time Protocol (NTP or SNTP) for time synchronization, or be uniformly timed by the DCS master clock to ensure the timing accuracy of the model input data.

[0040] The dynamic modeling module constructs a mathematical model based on the dynamic equation of filter bag pressure difference to describe the impact of dust accumulation process and pulse-jet cleaning behavior on the pressure difference change of the internal filter bags in a baghouse dust collector.

[0041] This embodiment employs a combination of mechanistic modeling and data-driven methods. Based on the dynamic equation of filter bag pressure difference, a mathematical model is constructed. This model describes the relationship between the pressure difference changes and dust accumulation process within the filter bags of the baghouse dust collector, as well as the operating parameters of the baghouse dust collector. Specifically, the mathematical model is represented by the following logic:

[0042] In the formula, For a moment The filter bag pressure difference (in Pa). Indicates time The rate of change of pressure difference in the filter bag This refers to the flue gas flow rate (unit: m³ / s). The inlet dust concentration is expressed in g / m³. The dust deposition coefficient is expressed in (Pa·s) / (m·g). In this embodiment... Calibrated by experiments; For jet control input, , Take 0 or 1, when A value of 0 indicates that no blowing has been performed. A value of 1 indicates that a blowing operation is being performed; The dust removal efficiency coefficient ( (This is used to reflect the ability of the injection to attenuate the pressure difference).

[0043] This invention describes the dynamic process of pressure difference in the internal filter bags of a baghouse dust collector as it changes with dust accumulation and pulse-jet cleaning using the differential equation described above. For the first time, this invention quantitatively models key physical processes in a baghouse dust collection system, such as dust accumulation and pulse-jet cleaning effectiveness, constructing a dynamic prediction model based on a fusion of mechanism and data-driven approaches. This model comprehensively considers multi-dimensional input variables such as flue gas flow rate, dust concentration, and pressure difference trend, achieving high-precision prediction of the future evolution trend of filter bag pressure difference. It overcomes the lag inherent in traditional constant pressure difference or time-based control, significantly improving the foresight and adaptability of the control.

[0044] The prediction module, based on a mathematical model and the current operating parameters, predicts the operating status of the bag filter over a period of time in the future.

[0045] In this embodiment, the time-domain mathematical model constructed in the dynamic modeling module is discretized, and a discretized state-space model is used for multi-step prediction, recording the discrete time step at each interval. For a moment, that is, each interval In this embodiment, one sample can be taken. Discretize the continuous-time mathematical model, and use the following logical representation to represent the discretized state-space model:

[0046] in, for The pressure difference of the filter bag at any given time for The pressure difference of the filter bag at any given time Represents the discrete first... The sampling point, the first A moment represents time. ; for The amount of smoke at any given time for The concentration of inlet dust at any given time. for The timing of the blow control input, , This is the natural decay coefficient, which can be ignored or combined. , a) is the natural attenuation factor of the filter bag pressure difference, used to reflect the self-maintaining characteristic of the pressure difference when there is no pulse-jet cleaning; b) is the gain coefficient of dust accumulation, representing the driving force of flue gas flow and dust concentration on the increase of pressure difference; c) is the efficiency factor of pulse-jet cleaning, reflecting the multiplicative attenuation capability of the pulse-jet cleaning action on the current pressure difference. Together, the physical processes of dust accumulation and cleaning are quantified into key parameters in the discrete state equation.

[0047] Furthermore, the discrete state-space model is rewritten in state-space form:

[0048] in, for The state variable at time t, for The state variable at time t, This is used to reflect the clogging status of the filter bag; Let the disturbance input be denoted as . , This indicates the transpose operation. This is the state-space equation.

[0049] In this embodiment, since industrial control systems commonly employ digital controllers (such as PLCs, DCSs, or edge computing devices), they perform data acquisition and control calculations at fixed time intervals (sampling periods). Therefore, this invention uses a discrete state-space model to predict the pressure difference changes of a bag filter dust collector, which can transform a continuous physical process into a discrete time series, perfectly matching the operating mechanism of existing industrial control systems. This allows the model to run efficiently and stably on a computer for real-time prediction and optimization. Simultaneously, the next optimization decision module adopts a model predictive control (MPC) framework, and the discrete state-space model provides a clear and structured mathematical form, providing a mathematical foundation for MPC rolling optimization. Furthermore, the discrete state-space model in this embodiment integrates the physical mechanisms of dust accumulation and pulse-jet cleaning, using real-time operating conditions such as flue gas flow rate and dust concentration as inputs. It can dynamically reflect the pressure difference change trend under different operating conditions. This model-based prediction method is forward-looking, proactively planning cleaning strategies before the pressure difference reaches a dangerous threshold, thus avoiding the response lag problem of traditional methods.

[0050] The optimization decision module is used to solve the optimal blowing strategy for a future period of time using a multi-objective optimization algorithm, under the premise of meeting preset constraints. The blowing strategy includes, but is not limited to, blowing pressure, pulse width, blowing interval and timing arrangement.

[0051] In this embodiment, a multi-objective optimization problem in the finite-time domain is solved in each control cycle to predict the system behavior in the next N steps and optimize the control sequence. The multi-objective optimization problem is represented by a model predictive control objective function, and the optimization objective is to minimize the overall cost. The overall minimum can be represented using the following logic:

[0052] in, For differential pressure tracking error, For the target pressure difference, 800 Pa can be used in this embodiment. These are error weight, energy consumption weight, and lifetime weight, respectively. For the future The pressure difference of the filter bag in the step; As an energy consumption item, in this embodiment, energy consumption can be compressed air consumption. For filter bag wear model, For the future The wear life of the step is used to reflect the impact of the jet cleaning on the filter bag under high pressure differential. , For the future Pulse width (blow duration) of the step. For the future The deviation between the step injection pressure and the target value, For the blowing pressure, For pulse width, For the future Step-by-step jet control input.

[0053] Furthermore, the preset constraints include minimizing energy consumption, maximizing dust removal efficiency, extending filter bag lifespan, and ensuring emission compliance. These preset constraints are represented using the following logic:

[0054] in, This indicates the maximum number of pulses per unit time. This indicates the maximum differential pressure limit. These represent the minimum and maximum spray intervals, respectively. This indicates the injection interval within the control cycle.

[0055] In this embodiment, the timing arrangement specifically refers to the planning of the specific time sequence and triggering time for each pulse jet valve or compartment of the bag filter to perform the pulse jet action.

[0056] Furthermore, this includes a gradient-based regional pulse-jet coordinated control strategy, which dynamically plans the pulse-jet pressure gradient and pulse-jet timing for each compartment based on the predicted pressure difference of different compartments in the bag filter. This invention combines the prediction results of a predictive control model to propose a "pressure-timing" dual-dimensional gradient pulse-jet strategy: dynamically planning the pulse-jet pressure gradient and pulse-jet timing based on the predicted pressure difference of each compartment, avoiding system disturbances and peak compressed air consumption caused by synchronous pulse-jet cleaning of all compartments, thus achieving a smoother and more energy-efficient dust removal process.

[0057] This invention introduces a multi-objective optimization algorithm within the Model Predictive Control (MPC) framework, comprehensively considering three core objectives: dust removal effect, energy consumption minimization, and filter bag life extension. It dynamically solves for the optimal combination of pulse jet parameters to achieve optimal overall performance of the dust collector.

[0058] The execution module, based on the rolling time-domain strategy, performs pulse-jet cleaning on the bag filter according to the instructions generated by the optimization decision module, and collects feedback information for optimization in the next cycle.

[0059] In this embodiment, the rolling time-domain strategy specifically refers to: at time... Based on the current state And predictive mathematical models to solve the future Optimal control sequence of steps , obtain new The multi-objective optimization problem needs to be solved again. The optimal control sequence is determined by the injection pressure. Pulse width It consists of multiple parameters, including the blowing interval, blowing action, and blowing sequence of each compartment.

[0060] This invention employs a rolling time-domain optimization strategy. Each cycle, the system updates the state estimate based on the latest sensor data and recalculates the optimal control sequence for a future period. Only the first control action is executed, forming a closed-loop control of "prediction, optimization, execution, and feedback". This architecture has strong robustness, can automatically compensate for model errors and effectively cope with model mismatch and external disturbances (such as flue gas fluctuations), thereby improving system robustness and control accuracy.

[0061] In this embodiment, the execution module receives the optimal blowing command generated by the optimization decision and sends the control signal to the DCS system or directly connects to the field actuator through an industrial communication protocol (such as Modbus TCP / RTU or Profibus). The main controlled object is the electromagnetic pulse valve of each branch of the bag filter, which is used to precisely control its opening sequence and duration (i.e., pulse width). At the same time, it can also adjust the pressure regulating valve of the air tank to realize the dynamic setting of the blowing pressure. In terms of hardware, the execution unit is usually integrated into the local control cabinet or edge computing device to ensure the real-time and reliable execution of the command.

[0062] The adaptive update module is used to automatically adjust the parameters of the mathematical model based on long-term operating data, ensuring the accuracy of the mathematical model's predictions and the effectiveness of its control.

[0063] In this embodiment, to address mathematical model drift, an adaptive parameter update algorithm is used to identify and update model parameters online, ensuring the long-term accuracy of the prediction model and the stability of control performance. Specifically, this embodiment employs recursive least squares (RLS) to update the dust deposition coefficient. This can be represented using the following logic:

[0064] in, for The latest estimate of the dust deposition coefficient at any given time. for The estimated value of the dust deposition coefficient at time (previous time). Here is the gain matrix. Let be the regression vector, denoted as .

[0065] Furthermore, the adaptive update module also integrates operating condition recognition functionality, enabling it to dynamically call or correct corresponding sub-model parameters online based on different operating modes. Specifically, it utilizes machine learning algorithms to classify operating modes (such as normal operation, high dust load, shutdown and restart, etc.) and dynamically calls or corrects corresponding predictive control sub-model parameters online, achieving adaptive evolution of the control model, avoiding model aging issues caused by long-term operation, and ensuring long-term stable control performance.

[0066] Furthermore, the control system of the present invention also includes an edge computing unit and a cloud platform collaborative architecture, which is used to achieve local rapid response and continuous optimization supported by cloud big data analysis.

[0067] In this embodiment, the control system adopts a structure of local edge computing device deployment and cloud platform collaboration. The data acquisition module, dynamic modeling module, prediction module, optimization decision module, execution module, and adaptive update module of this invention are all deployed on the local edge computing device to achieve millisecond-level real-time response and closed-loop control. The cloud platform is mainly responsible for receiving the running data uploaded via MQTT or HTTP protocol, performing big data analysis, model iterative optimization, and remote monitoring, and distributing the optimized model parameters or weight configurations to the edge computing device to achieve continuous model evolution, which can effectively improve the system's intelligence level and maintainability.

[0068] In this embodiment, the edge computing device can be an industrial-grade gateway or an embedded industrial control computer such as Advantech UNO series or Siemens SIMATIC IPC, installed in the local control cabinet of the dust removal equipment, and connected to the DCS / PLC via industrial Ethernet to ensure the real-time performance, reliability and security of the control system.

[0069] This invention also discloses a model-predictive-based intelligent control method for bag filter cleaning, comprising the following steps: S1, Real-time acquisition and monitoring of the operating parameters of the bag filter, including inlet and outlet pressure difference, flue gas flow rate, dust concentration, temperature and humidity; S2, a mathematical model is constructed based on the dynamic equation of filter bag pressure difference to describe the influence of dust accumulation process and pulse-jet cleaning behavior on the pressure difference change of internal filter bags in bag dust collectors; S3, based on the mathematical model and the current operating parameters, predicts the operating status of the bag filter in the future period of time; S4. Under the premise of satisfying the preset constraints, the optimal blowing strategy for a future period of time is solved by a multi-objective optimization algorithm. The blowing strategy includes blowing pressure, pulse width, blowing interval and timing arrangement. S5, based on the rolling time domain strategy, performs pulse-jet cleaning on the bag filter according to the instructions generated by the optimization decision module, and collects feedback information for optimization in the next cycle; S6 is used to automatically correct mathematical model parameters based on long-term operating data.

[0070] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A model-based predictive intelligent control system for baghouse dust removal, characterized in that, include: The data acquisition module is used to collect and monitor the operating parameters of the bag filter in real time, including inlet and outlet pressure difference, flue gas flow rate, dust concentration, temperature and humidity. The dynamic modeling module constructs a mathematical model based on the dynamic equation of filter bag pressure difference to describe the impact of dust accumulation process and pulse-jet cleaning behavior on the pressure difference change of the internal filter bags in the bag filter dust collector. The prediction module, based on a mathematical model and the current operating parameters, predicts the operating status of the bag filter over a period of time in the future. The optimization decision module is used to solve the optimal blowing strategy for a future period of time under the premise of meeting preset constraints using a multi-objective optimization algorithm. The blowing strategy includes blowing pressure, pulse width, blowing interval and timing arrangement. The execution module, based on the rolling time-domain strategy, performs pulse-jet cleaning on the bag filter according to the instructions generated by the optimization decision module, and collects feedback information for optimization in the next cycle. The adaptive update module is used to automatically correct mathematical model parameters based on long-term running data.

2. The intelligent control system for baghouse cleaning based on model prediction according to claim 1, characterized in that, The dynamic modeling module uses the following logic to represent the mathematical model: In the formula, For a moment The pressure difference of the filter bag Indicates time The rate of change of pressure difference in the filter bag, For flue gas flow rate, The concentration of dust at the inlet. The dust deposition coefficient is... For jet control input, , Take 0 or 1, This represents the dust removal efficiency coefficient.

3. The intelligent control system for baghouse dust removal based on model prediction according to claim 2, characterized in that, The prediction module specifically involves: discretizing the time-domain mathematical model constructed in the dynamic modeling module, performing multi-step predictions using the discretized state-space model, and recording the discrete time step size for each interval. For a given moment, the discretized state-space model can be represented using the following logic: in, for The pressure difference of the filter bag at any given time for The pressure difference of the filter bag at any given time Represents the discrete first... The sampling point, the first A moment represents time. ; for The amount of smoke at any given time for The concentration of inlet dust at any given time. for The timing of the blow control input, , The natural decay coefficient, , , where a is the natural attenuation factor of the filter bag pressure difference, b is the gain coefficient of dust accumulation, and c is the efficiency factor of pulse-jet cleaning.

4. The intelligent control system for baghouse dust removal based on model prediction according to claim 3, characterized in that, The optimization decision-making module uses a multi-objective optimization algorithm to solve for the optimal spraying strategy over a future period of time, specifically as follows: By solving a finite-time multi-objective optimization problem in each control cycle to predict the system behavior in the next N steps and optimize the control sequence, the multi-objective optimization problem is represented by a model-predicted control objective function: in, For differential pressure tracking error, For the target pressure difference, These are error weight, energy consumption weight, and lifetime weight, respectively. For the future The pressure difference of the filter bag in the step; For the future Energy consumption of steps For the future Step-by-step jet control input; For the future The wear life of the step, , For the future The width of the pulse. For the future Deviation between step injection pressure and target value For the blowing pressure, The width of the pulse.

5. The intelligent control system for bag cleaning based on model prediction according to claim 4, characterized in that, The preset constraints in the optimization decision module include minimizing energy consumption, maximizing dust removal efficiency, extending filter bag lifespan, and ensuring emission compliance. The preset constraints are represented by the following logic: in, Indicates the maximum number of blows per unit time. This indicates the maximum differential pressure limit. These represent the minimum and maximum spray intervals, respectively. This indicates the injection interval within the control cycle.

6. The intelligent control system for baghouse cleaning based on model prediction according to claim 5, characterized in that, The rolling time-domain strategy in the execution module is specifically as follows: at time... Based on the current state And predictive models, to solve for the future Optimal control sequence of steps , obtain new The multi-objective optimization problem needs to be solved again.

7. The intelligent control system for baghouse dust removal based on model prediction according to claim 2, characterized in that, The adaptive update module uses a recursive least squares method to update the dust deposition coefficient. This can be represented using the following logic: in, for The latest estimate of the dust deposition coefficient at any given time. for The estimated value of the dust deposition coefficient at time (previous time). Here is the gain matrix. Let be the regression vector, denoted as .

8. A model-predictive-based intelligent control method for baghouse dust removal, characterized in that, Includes the following steps: S1, real-time acquisition and monitoring of the operating parameters of the bag filter, including inlet and outlet pressure difference, flue gas flow rate, dust concentration, temperature and humidity; S2, a mathematical model is constructed based on the dynamic equation of filter bag pressure difference to describe the influence of dust accumulation process and pulse-jet cleaning behavior on the pressure difference change of internal filter bags in bag dust collectors; S3, based on the mathematical model and the current operating parameters, predicts the operating status of the bag filter in the future period of time; S4. Under the premise of satisfying the preset constraints, the optimal blowing strategy for a future period of time is solved by a multi-objective optimization algorithm. The blowing strategy includes blowing pressure, pulse width, blowing interval and timing arrangement. S5, based on the rolling time domain strategy, performs pulse-jet cleaning on the bag filter according to the instructions generated by the optimization decision module, and collects feedback information for optimization in the next cycle; S6 is used to automatically correct mathematical model parameters based on long-term operating data.

9. The intelligent control system for baghouse cleaning based on model prediction according to claim 1, characterized in that, The mathematical model in S2 is represented by the following logic: In the formula, For a moment The pressure difference of the filter bag Indicates time The rate of change of pressure difference in the filter bag, For flue gas flow rate, The concentration of dust at the inlet. The dust deposition coefficient is... For jet control input, , Take 0 or 1, This represents the dust removal efficiency coefficient.

10. The intelligent control system for baghouse cleaning based on model prediction according to claim 1, characterized in that, Specifically, S3 involves: discretizing the time-domain mathematical model constructed in the dynamic modeling module, performing multi-step prediction using the discretized state-space model, and recording the discrete time step size for each interval. For a given moment, the discretized state-space model can be represented using the following logic: in, for The pressure difference of the filter bag at any given time for The pressure difference of the filter bag at any given time Represents the discrete first... The sampling point, the first A moment represents time. ; for The amount of smoke at any given time for The concentration of inlet dust at any given time. for The timing of the blow control input, , The natural decay coefficient, , , where a is the natural attenuation factor of the filter bag pressure difference, b is the gain coefficient of dust accumulation, and c is the efficiency factor of pulse-jet cleaning.

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