A smart vibration control system and method for electrostatic precipitators

Abstract

This invention discloses an intelligent rapping control system and method for electrostatic precipitators (ESPs), belonging to the field of flue gas dust removal technology. It addresses the problem of improving the control accuracy and operational stability of ESP rapping. The invention collects multi-dimensional operating parameters through a data acquisition module, and uses a dual-model collaborative prediction to output the estimated dust removal efficiency at the current moment, as well as the predicted changes in dust removal efficiency and secondary dust risk after rapping. It constructs an objective function with the optimization goals of maximizing dust removal efficiency, minimizing secondary dust risk, and minimizing energy consumption to solve for the optimal rapping command, effectively reducing energy consumption and improving dust removal efficiency and ESP rapping control accuracy. The control system scans core indicators to determine whether the current scenario belongs to a real-time operating scenario, optimizing the dynamic weight coefficients in the objective function. This effectively improves the adaptability of the ESP under complex working conditions and enhances its operational stability, enabling it to play a crucial role in dust control scenarios.

Description

Technical Field

[0001] This invention belongs to the field of flue gas dust removal technology, and relates to an intelligent vibration control system and method for electrostatic precipitators. 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 negatively impact the environment and human health. Common dust removal technologies include electrostatic precipitators (ESP) and baghouse filters.

[0003] Electrostatic precipitators (ESPs) are core equipment for industrial flue gas purification, and their performance directly affects environmental emission standards. With increasingly stringent environmental standards and higher demands for energy conservation and emission reduction in industrial production, ensuring the efficient and stable operation of ESPs has become crucial. To guarantee the dust removal efficiency of ESPs, rapping is necessary. The physical mechanism is as follows: continuous dust accumulation on the electrode surface significantly increases the electrode resistance, thereby weakening the electric field strength; the attenuation of the electric field strength leads to a sharp increase in the probability of flashover discharge, resulting in a significant decrease in dust removal efficiency; to restore equipment performance, the system is forced to frequently activate rapping for dust removal; after cleaning, dust re-accumulates, thus forming a periodic cycle. Traditional electrostatic precipitator rapping control systems use a fixed time interval rapping mode, which has the following significant drawbacks: (1) energy waste, high-frequency rapping is still performed when the inlet concentration of the electrostatic precipitator is low, resulting in a high proportion of ineffective energy consumption; (2) excessive rapping causes the captured dust to return to the flue gas, posing a risk of secondary dust generation and reducing the net dust removal efficiency; (3) when the electric field experiences flashover or abnormal spark rate, the fixed rapping mode cannot respond quickly, resulting in damage to the electric field stability and a decrease in dust removal efficiency.

[0004] Existing technologies, such as the invention patent with publication number CN118594771A, disclose a mechanical rapping method for an electrostatic precipitator. The rapping strategy is based on a pre-obtained rapping strategy setting principle, which includes the simultaneous rapping of the cathode plate and anode plate in the same electric field within the dust removal chamber. However, the existing improvement scheme relies on adjusting the rapping interval with a single parameter, which cannot adapt to the complex working conditions of multi-variable coupling. There is an urgent need for a solution that integrates multiple parameters and optimizes the rapping control of the electrostatic precipitator in real time. Summary of the Invention

[0005] The technical problem to be solved by this invention is how to improve the control accuracy and operational stability of the rapping mechanism in an electrostatic precipitator.

[0006] The present invention solves the above-mentioned technical problems through the following technical solutions: An intelligent vibration control system for an electrostatic precipitator includes: The data acquisition module is used to collect multi-dimensional operating parameters of the electrostatic precipitator in real time, including dust data, electric field parameters, flue gas parameters, rapping status, and ash hopper status. The model prediction module is used to collaboratively predict and output the estimated value of dust removal efficiency at the current moment, as well as the predicted value of the change in dust removal efficiency after the rapping operation and the predicted value of secondary dust risk through dual model collaboration. The multi-objective optimization module is used to maximize dust removal efficiency, minimize the risk of secondary dust generation, and minimize energy consumption as optimization objectives. It uses model predictive control to solve the optimal combination of rapping frequencies online as the optimal rapping command. The closed-loop execution and feedback module is used to execute the optimal vibration command and collect feedback data. Based on the feedback data, the dual-model parameters are dynamically calibrated to form closed-loop control.

[0007] Furthermore, the dust data in the data acquisition module includes, but is not limited to, inlet dust concentration and outlet dust concentration; electric field parameters include, but are not limited to, secondary voltage and current of each electric field and flashover frequency; flue gas parameters include, but are not limited to, flue gas temperature, flue gas flow rate and flue gas humidity; rapping status includes, but is not limited to, rapping start and stop sequence, rapping frequency and striking force; and ash hopper status includes, but is not limited to, ash hopper material level height and ash discharge frequency.

[0008] Furthermore, the model prediction module includes a dust estimation unit and a vibration prediction unit; The dust removal estimation unit is used to construct a real-time dust removal efficiency estimation model. Based on a dynamic calibration algorithm, it takes dust data and flue gas parameters as input and outputs an estimated value of the current dust removal efficiency. The rapping prediction unit is used to construct a rapping effect-efficiency response prediction model. Based on a dynamic calibration algorithm, it takes electric field parameters, rapping state, ash hopper state and current dust removal efficiency as inputs, and outputs the predicted value of the change in dust removal efficiency after the rapping operation and the predicted value of secondary dust risk.

[0009] Furthermore, the dust estimation unit specifically includes the following: First, calculate the basic dust removal efficiency η(t), using the following logical expression:

[0010] Wherein, η(t) represents the basic dust removal efficiency at time t. This represents the inlet dust concentration at time t. This represents the dust concentration at the outlet at time t; Then, the dust removal efficiency is dynamically calibrated based on the dynamic calibration algorithm. This can be represented using the following logic:

[0011] in, This represents the dust removal efficiency at time t after dynamic calibration. Indicates the flow influencing factor. Indicates the time decay factor. Indicates basic efficiency; Finally, the real-time dust removal efficiency estimation model is delayed, and the final output dust removal efficiency estimate is represented by the following logic. :

[0012] in, , These represent the dust removal efficiency after dynamic calibration at the first two time points, respectively.

[0013] Furthermore, the flow influencing factor Calculate using the following logic:

[0014] Where Q(t) represents the real-time flue gas flow rate, Indicates the rated design flow rate. This represents the flow correction factor; Calculate the time decay factor using the following logic. :

[0015] in, The efficiency decay coefficient is represented by Δt, which represents the time since the last vibration, in seconds.

[0016] Furthermore, the multi-objective optimization module includes an objective function unit, a weight adjustment unit, and a boundary constraint unit; The objective function unit is used to construct a minimum objective function J, which is represented by the following logic:

[0017] in, The target dust removal efficiency is defined as Energy, the total energy consumption of the system is defined as α, β, and γ, which are dynamic weighting coefficients corresponding to the environmental efficiency, equipment protection, and economic objectives, respectively, and satisfy α+β+γ=1. The weight adjustment unit is used to dynamically adjust the weight coefficients of the objective function according to the real-time operating scenario of the electrostatic precipitator. The operating scenario includes at least environmentally strict control scenario, high-risk equipment scenario, and cost-sensitive scenario. The boundary constraint unit is used to apply at least one constraint condition to the objective function, including rapping frequency constraint, electric field safe operation constraint, and ash hopper level warning constraint.

[0018] Furthermore, under the aforementioned stringent environmental control scenario, a multi-parameter fusion nonlinear function is constructed based on the outlet dust concentration, electric field flashover frequency, target dust removal efficiency, and predicted dust removal efficiency to fit the environmental weight α. The more severe the deterioration of the operating parameters, the higher the environmental weight α, as expressed by the following logic:

[0019] in, Represents the basic weights of α. , , These represent the weighting increment coefficients for excessive dust concentration, excessive flashover frequency, and deviation in emergency response efficiency, respectively. Indicates the dust concentration limit. Indicates the electric field flashover frequency. Indicates the flashover frequency limit. This indicates the target value during environmental emergency response. , , These represent the upper limits of the weighted increments for excessive dust concentration, excessive flashover frequency, and deviation in emergency response efficiency, respectively. This indicates that the weights are adjusted only when the operating parameters deteriorate; otherwise, they are set to 0. This indicates a limit on the maximum increment of a single operating parameter with respect to α, to prevent α from exceeding the safety limit, i=1,2,3; The equipment protection weight β decreases linearly as the environmental protection weight α increases, which can be expressed using the following logic:

[0020] in, express The basic weights, express The lower limit value, express The upper limit; The economic weight γ is obtained by solving the constraint α+β+γ=1.

[0021] Furthermore, in the high-risk scenario of the equipment, a linear fusion function is constructed based on the predicted value of secondary dust risk, the vibration operation time, and the ash hopper level to fit the equipment protection weight β, using the following logical representation:

[0022] in, express The basic weights, , , These represent the weighted increment coefficients for rapping force attenuation, excessive rapping running time, and excessive ash hopper level, respectively. , , These represent the upper limits of the weighted increments for rapping force attenuation, rapping running time exceeding the standard, and ash hopper material level exceeding the standard, respectively. This indicates the design value of the rapping force. This indicates the critical value for the maintenance cycle of the rapper. Indicates the upper limit of the ash hopper level; The environmental protection weight α decreases linearly as the equipment protection weight β increases, which can be expressed using the following logic:

[0023] in, express The lower limit value, express The upper limit; The economic weight γ is obtained by solving the constraint α+β+γ=1.

[0024] Furthermore, in the aforementioned cost-sensitive scenario, an economic weight γ is fitted based on the total system energy consumption (Energy), real-time electricity price, and the proportion of energy consumption of the electrostatic precipitator, using the following logical representation:

[0025] in, express The basic weights, , , These represent the weighted incremental coefficients for exceeding the standards for total energy consumption, electricity price, and the proportion of energy consumption in electrostatic precipitators, respectively. , , These represent the upper limits of the weighted increments for exceeding the standards for total energy consumption, electricity price, and the proportion of energy consumption in electrostatic precipitators. This represents monthly energy consumption, where P represents the real-time electricity price. This indicates the flat electricity price. This indicates the energy consumption ratio of the electrostatic precipitator, specifically the energy consumption of the electrostatic precipitator divided by the total energy consumption of the system. The environmental weight α decreases linearly as the economic weight γ increases, which can be expressed using the following logic:

[0026] in, express The upper limit value; the device protection weight β is represented by the following logic: .

[0027] A method for intelligent vibration control of an electrostatic precipitator includes the following steps: Step 1: Real-time collection of multi-dimensional operating parameters of the electrostatic precipitator, including dust data, electric field parameters, flue gas parameters, rapping status, and ash hopper status; Step 2: Through dual-model collaborative prediction, output the estimated value of dust removal efficiency at the current moment, as well as the predicted value of the change in dust removal efficiency after the rapping operation and the predicted value of secondary dust risk. Step 3: With the optimization objectives of maximizing dust removal efficiency, minimizing the risk of secondary dust generation, and minimizing energy consumption, the optimal combination of rapping frequencies is solved online based on model predictive control as the optimal rapping command. Step 4: Execute the optimal vibration command and collect feedback data. Based on the feedback data, dynamically calibrate the dual-model parameters to form a closed-loop control.

[0028] The advantages of this invention are: This invention establishes a fusion acquisition framework for multi-source heterogeneous data through a data acquisition module, collecting multi-dimensional operating parameters; it outputs an estimated value of dust removal efficiency at the current moment, as well as a predicted value of the change in dust removal efficiency and a predicted value of secondary dust risk after the rapping operation through dual-model collaborative prediction; and it constructs an objective function with the optimization objectives of maximizing dust removal efficiency, minimizing secondary dust risk, and minimizing energy consumption to solve for the optimal rapping command, effectively reducing energy consumption and improving dust removal efficiency and electrostatic precipitator rapping control accuracy. In addition, the control system determines whether the current scenario belongs to the real-time operation scenario by scanning core indicators, optimizes the value of dynamic weight coefficients in the objective function, effectively improves the adaptability of electrostatic precipitators under complex working conditions, improves the operational stability of electrostatic precipitators, and can play a key role in dust control scenarios.

[0029] The intelligent vibration control method for electrostatic precipitators provided by this invention is particularly suitable for industrial boilers (industries that convert heat energy through fuel combustion, such as thermal power generation, gas power generation, biomass power generation, and waste incineration power generation), industrial kilns (including but not limited to glass, cement, ceramics, and building materials industries that achieve high-temperature processes such as calcination, smelting, and sintering of materials through fuel combustion or electric heating), sintering machines, blast furnaces, pelletizing plants, coking plants, chemical manufacturing plants, marine power systems, metallurgy, and VOC treatment industries, as well as other environmental island flue gas treatment scenarios involving one or more of the following processes: desulfurization, denitrification, and dust removal. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of the execution logic of the intelligent vibration control system for an electrostatic precipitator 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 As shown, specifically, an intelligent vibration control system for an electrostatic precipitator is disclosed, comprising: The data acquisition module is used to collect multi-dimensional operating parameters of the electrostatic precipitator in real time, including dust data, electric field parameters, flue gas parameters, rapping status, and ash hopper status.

[0033] In this embodiment, multi-dimensional operating parameters of the electrostatic precipitator are acquired in real time through a sensor network. Among them, dust data includes, but is not limited to, inlet dust concentration and outlet dust concentration, in mg / Nm³; electric field parameters include, but are not limited to, secondary voltage (in kV) and current (in mA) of each electric field, and flashover frequency; flue gas parameters include, but are not limited to, flue gas temperature (in °C), flue gas flow rate (in m³ / h), and flue gas humidity (in %); rapping status includes, but is not limited to, rapping start-stop sequence, rapping frequency, and rapping force; and ash hopper status includes, but is not limited to, ash hopper level height and ash discharge frequency.

[0034] In this embodiment, the data acquisition module integrates DCS system data and dedicated sensor data through an industrial bus to acquire multi-dimensional operating parameters, and the sampling frequency is set to ≥1Hz.

[0035] In this embodiment, a multi-source heterogeneous data fusion acquisition framework is established through the data acquisition module, providing multi-dimensional data support for subsequent model prediction and multi-objective optimization.

[0036] The model prediction module is used to output the estimated value of dust removal efficiency at the current moment, as well as the predicted value of the change in dust removal efficiency after the rapping operation and the predicted value of secondary dust risk through the collaborative prediction of dual models; the model prediction module includes a dust removal estimation unit and a rapping prediction unit.

[0037] The dust removal estimation unit is used to construct a real-time dust removal efficiency estimation model. Based on a dynamic calibration algorithm, it takes dust data and flue gas parameters as input and outputs an estimated value of the current dust removal efficiency, specifically including the following: First, calculate the basic dust removal efficiency η(t), using the following logical expression:

[0038] Wherein, η(t) represents the basic dust removal efficiency at time t. This represents the inlet dust concentration at time t. This represents the dust concentration at the outlet at time t.

[0039] Then, considering mass balance, the dust removal efficiency is dynamically calibrated based on a dynamic calibration algorithm. This can be represented using the following logic:

[0040] in, This represents the dust removal efficiency at time t after dynamic calibration. Indicates the flow influencing factor. Indicates the time decay factor. This indicates the baseline efficiency, specifically the inherent minimum efficiency of the dust removal equipment, typically... It can be set to 92% to 95%.

[0041] In this embodiment, the flow impact factor is calculated using the following logic. :

[0042] Where Q(t) represents the real-time flue gas flow rate, Indicates the rated design flow rate. This represents the flow correction factor, in this embodiment... It can be -0.1 to -0.3.

[0043] In this embodiment, the time decay factor is calculated using the following logic. :

[0044] in, This represents the efficiency decay coefficient. The value is adjusted according to the specific equipment status. Δt represents the time since the last vibration, and the unit is seconds.

[0045] Finally, to overcome the response delay and ensure that the output response delay of the estimation model is no greater than 3 seconds, a delay processing is applied to the real-time dust removal efficiency estimation model. The final output dust removal efficiency estimate is represented by the following logic. :

[0046] in, , These represent the dust removal efficiency after dynamic calibration at times t-1 and t-2 (the first two times), respectively. This embodiment provides an implementable parameter configuration method, specifically a flow correction coefficient. =-0.15, efficiency attenuation coefficient =0.001, basic efficiency =0.93, data sampling frequency is 1Hz.

[0047] The rapping prediction unit is used to construct a rapping effect-efficiency response prediction model. Based on a dynamic calibration algorithm, it takes electric field parameters, rapping state, ash hopper state, and the current dust removal efficiency estimate as inputs, and outputs the predicted value of the change in dust removal efficiency after the rapping operation and the predicted value of the secondary dust risk. The following logic is used to represent the output of the predicted value of the change in dust removal efficiency after the rapping operation:

[0048] Where Δη represents the predicted change in electrostatic precipitator efficiency after the rapping operation, f(·) represents the functional relationship between the electric field parameters, rapping state, ash hopper state, and the predicted change in dust removal efficiency after the rapping operation, V represents the secondary voltage of the electric field, I represents the secondary current of the electric field, and T represents the flue gas temperature. This indicates the frequency of electrode vibration for removing accumulated ash, measured in times per hour, where H represents the ash hopper height.

[0049] This embodiment constructs a predictive model by comprehensively considering electric field parameters, rapping conditions, and ash hopper conditions. The electric field secondary voltage intensity V reflects the dust's charging capacity and migration speed, while the electric field secondary corona current I reflects the charge release intensity. V and I are used to comprehensively characterize the electric field energy input. A low electric field secondary voltage V leads to insufficient charging, while a high electric field secondary current I may trigger back corona, which is particularly evident when processing high resistivity dust. Furthermore, the flue gas temperature T reflects gas viscosity, dust resistivity, and breakdown voltage. At low temperatures, the flue gas viscosity is high, reducing dust migration speed. While high temperatures reduce dust resistivity, which is beneficial for dust removal, the flue gas volume expansion reduces the actual residence time. Characterizing the frequency of electrode rapping to remove accumulated dust (rapping frequency), when When the temperature is too low, dust accumulates on the plates, weakening the electric field strength. When the level is too high, it can easily cause secondary dust generation, leading to a decrease in dust removal efficiency. H represents the height of ash accumulation in the ash hopper. When the ash hopper level is too high, the airflow inside the ash hopper becomes turbulent, causing dust to be mixed back into the electric field. In severe cases, it can short-circuit the rapping system.

[0050] Furthermore, in the actual operation of electrostatic precipitators, the response of Δη is nonlinear and multivariate coupled. Therefore, in this embodiment, Δη is defined as the superposition of the basic efficiency improvement, the efficiency loss caused by secondary dust re-entrainment, and the effect of ash hopper back-mixing, etc., and is expressed by the following logic:

[0051] in, This represents the change in basic efficiency. This indicates the amount of secondary dust loss. This indicates the amount of back-mixing in the ash hopper.

[0052] Changes in basic efficiency To further explain, the change in basic efficiency The electric field energy input is influenced by V, I, T, and Fv, with V and I being the core influencing factors. Higher energy results in higher dust charging and migration efficiency after dust removal. The optimal dust removal range is 80-150℃, where the dust resistivity is moderate and the energy utilization of V and I is highest. Conversely, a lower Fv indicates thicker dust accumulation on the plates, leading to a more significant recovery of the electric field strength after rapping. The higher. Specifically, this embodiment uses the following logic to represent the change in basic efficiency. :

[0053] in, This represents the change in basic efficiency. Represents the electric field energy coefficient. This represents the temperature correction factor and the rapping dust accumulation factor; this embodiment provides an implementable basic efficiency change. The coefficient configuration is as follows: , , , .

[0054] Regarding the amount of secondary dust loss To further explain, the amount of secondary dust loss The effects are influenced by V, I, T, and Fv, with Fv being the dominant factor. A higher Fv indicates more frequent impacts of the rapping on the electrode plates, resulting in greater suspended dust and more severe losses. At lower temperatures, dust exhibits higher viscosity and stronger agglomeration, leading to less suspended dust and smaller losses. Conversely, at higher temperatures, dust has lower viscosity, is more easily dispersed and suspended, increasing losses. Simultaneously, V and I suppress secondary dust generation through the electric field's collection ability; higher V and I values ​​indicate stronger collection capabilities for suspended dust. The decrease is non-linear. Specifically, this embodiment uses the following logic to represent the amount of secondary dust loss. :

[0055] in, Indicates the viscosity coefficient at temperature. This represents the power plant's dust collection coefficient; this embodiment provides an implementable secondary dust loss measure. The coefficient configuration is as follows: 0.1 represents the maximum upper limit of secondary dust pollution loss, which is an empirical value. , .

[0056] Impact of back mixing in ash hopper Further explanation: the impact of back-mixing in the ash hopper Influenced by H, T, V, and I, with H being the core influencing factor, the higher the H, the stronger the obstruction effect of ash accumulation in the ash hopper on airflow, the higher the degree of airflow turbulence, the greater the amount of dust back-mixing, and the more severe the losses. When T is at a high temperature, the flue gas volume expands and the flow velocity increases, exacerbating airflow turbulence and increasing losses. On the other hand, the higher the V and I, the stronger the secondary capture capability of back-mixed dust. The smaller the value. Specifically, this embodiment uses the following logic to represent the impact of ash hopper backmixing. :

[0057] in, This indicates the maximum designed material level in the ash hopper. Indicates the temperature-flow rate coefficient. This represents the secondary collection coefficient; this embodiment provides an implementable measure of the impact of ash hopper backmixing. The coefficient configuration is as follows: 0.08 represents the maximum upper limit of the impact of backmixing in the ash hopper, which is an empirical value. , .

[0058] In this embodiment, the secondary dust risk prediction value is output using the following logical representation:

[0059] Where R represents the predicted risk value of secondary dust pollution, expressed in N·m or a dimensionless exponent. denoted by , g(·) represents the rapping operation time, and g(·) represents the functional relationship between the rapping state, the current estimated dust removal efficiency, the rapping operation time, and the predicted value of secondary dust risk.

[0060] This embodiment constructs a predictive model based on a comprehensive consideration of the rapping state and the current dust removal efficiency. R represents the effective rapping force of the rapping device, which also indicates the actual effect of removing dust accumulation from the electrode plates. A decrease in R signifies rapping failure or weakened dust removal effect, requiring maintenance or adjustment of the rapping device. When R is less than the rapping force limit... The vibration beater is malfunctioning and needs to be stopped for repair. Characterizes the current dust removal efficiency and reflects the dust accumulation state within the electric field. A continuous decrease indicates that the dust accumulation is thickening, at which point the rapping force R needs to be increased, specifically by increasing the vibration intensity. To compensate for the decay of R, when Vibration at excessively low temperatures may cause secondary dust generation; Characterizing the continuous operating time of the rapping equipment, even as the operating time increases... If the equipment remains unchanged, mechanical losses such as bearing wear and hammer fatigue will cause R to decrease.

[0061] The multi-objective optimization module is used to maximize dust removal efficiency, minimize the risk of secondary dust generation, and minimize energy consumption as optimization objectives. It solves the optimal combination of rapping frequencies online based on model predictive control as the optimal rapping command. The multi-objective optimization module includes an objective function unit, a weight adjustment unit, and a boundary constraint unit.

[0062] The objective function unit is used to construct a minimum objective function J, which is represented by the following logic:

[0063] in, The target dust removal efficiency is defined as Energy, the total system energy consumption is defined as Energy, and α, β, and γ are dynamic weighting coefficients corresponding to environmental efficiency, equipment protection, and economic objectives, respectively, satisfying α+β+γ=1. This embodiment constructs a multi-objective optimization model for the operation of the electrostatic precipitator (ESP) using model predictive control (MPC), aiming to balance dust removal efficiency, equipment reliability, and energy consumption costs. This is achieved by adjusting control variables (such as voltage V, current I, or rapping frequency). This minimizes the total cost and generates control commands to be issued to the actuators.

[0064] In this embodiment, the total system energy consumption includes high-voltage power supply energy consumption and rapping energy consumption, denoted as . ,in, , These are the first and second conversion coefficients, respectively.

[0065] Furthermore, in this embodiment, the multi-objective optimization controller continuously solves for the optimal control sequence through rolling optimization, with an optimization period of no more than 30 seconds.

[0066] The weight adjustment unit is used to dynamically adjust the weight coefficients of the objective function according to the real-time operating scenario of the electrostatic precipitator.

[0067] In this embodiment, the dynamic weight coefficients α, β, and γ in the objective function J are dynamically adjusted according to the real-time operating scenario of the electrostatic precipitator. The operating scenario includes at least environmentally strict control scenario, high-risk equipment scenario, and cost-sensitive scenario.

[0068] In this embodiment, the above-mentioned operating scenario is further explained. The core characteristic of the strictly controlled environmental protection scenario is to ensure that emission concentrations meet standards at all costs, while the economic efficiency and equipment wear and tear are secondary considerations. Specific situations include: Special government regulatory periods, such as when a country or region holds a major event, or when environmental protection protection periods are implemented during the winter heating season due to poor air quality; alarms from online monitoring systems, such as when the continuous emission monitoring system for flue gas shows that the emission concentration is continuously approaching or exceeding the legal limit; sudden changes in upstream operating conditions, such as when the coal type burned in the boiler suddenly changes to a high-ash coal type, causing a sharp increase in the inlet dust concentration, and in order to deal with the additional dust load, it is necessary to maximize the dust removal efficiency, etc.

[0069] Under the aforementioned stringent environmental control scenario, a multi-parameter fusion nonlinear function is constructed based on the outlet dust concentration, electric field flashover frequency, target dust removal efficiency, and predicted dust removal efficiency to fit the environmental weight α. The more severe the deterioration of the operating parameters, the higher the environmental weight α, as expressed by the following logic:

[0070] in, Represents the basic weights of α. , , These represent the weighting increment coefficients for excessive dust concentration, excessive flashover frequency, and deviation in emergency response efficiency, respectively. Indicates the dust concentration limit. Indicates the electric field flashover frequency. Indicates the flashover frequency limit. This indicates the target value during environmental emergency response (i.e., the value to ensure that emission concentrations meet standards). , , These represent the upper limits of the weighted increments for excessive dust concentration, excessive flashover frequency, and deviation in emergency response efficiency, respectively. This indicates that the weights are adjusted only when the operating parameters deteriorate; otherwise, they are set to 0. This indicates a limit on the maximum increment of a single operating parameter on α, to prevent α from exceeding the safety limit, i=1,2,3.

[0071] Furthermore, the equipment protection weight β decreases linearly as the environmental protection weight α increases, retaining only the basic protection requirements, as expressed by the following logic:

[0072] in, express The basic weights, express The lower limit value, express The upper limit.

[0073] This embodiment provides an implementable parameter configuration method, specifically as follows: Take a value of 0.3~0.5. Take 5 times / minute. The environmental weight is set to 99.5%; to satisfy α+β+γ=1, the environmental weight α in this embodiment does not exceed 0.8. Take 0.8, Take a value of 0.2~0.3. Take 0.1.

[0074] Furthermore, the economic weight γ is obtained by solving the constraint α+β+γ=1.

[0075] The core characteristic of the high-risk equipment scenarios is that the equipment's health condition is at risk, and continued high-load operation could lead to severe and extremely costly damage, thus serving as a means to prevent equipment failure and unplanned downtime. Specific scenarios include: Abnormalities in the rapping system include: abnormally high rapping motor current, cracks or severe wear on the rapping hammers or anvil blocks, and abnormal noises from the rapping transmission device; a high-level alarm in the ash hopper, where the ash hopper level H remains consistently high, indicating a risk of ash blockage. If the ash hopper becomes completely blocked, the electric field will not function properly, requiring an emergency shutdown; deterioration in the performance of insulating components, such as creepage marks on porcelain sleeves or insulator chambers, or heater malfunctions causing internal condensation and reduced insulation, making it highly susceptible to grounding breakdown if excessive voltage is applied; and aging equipment, with the electric field itself or transformer rectifier nearing the design life, leading to decreased reliability over long-term operation.

[0076] In the high-risk scenario of the equipment, a linear fusion function is constructed based on the predicted value of secondary dust emission risk, the vibration operation time, and the ash hopper level to fit the equipment protection weight β. The worse the equipment condition, the higher the equipment protection weight β, which is expressed by the following logic:

[0077] in, express The basic weights, , , These represent the weighted increment coefficients for rapping force attenuation, excessive rapping running time, and excessive ash hopper level, respectively. , , These represent the upper limits of the weighted increments for rapping force attenuation, rapping running time exceeding the standard, and ash hopper material level exceeding the standard, respectively. This indicates the design value of the rapping force. This indicates the critical value for the maintenance cycle of the rapper. This indicates the upper limit of the ash hopper material level.

[0078] Furthermore, the environmental protection weight α decreases linearly as the equipment protection weight β increases, which can be expressed using the following logic:

[0079] in, express The lower limit value, express The upper limit.

[0080] This embodiment provides an implementable parameter configuration method, specifically as follows: Take a value of 0.2~0.3. Take 0.2, The economic weight γ is set to 0.7 and obtained by solving the constraint α+β+γ=1.

[0081] The core characteristic of the cost-sensitive scenarios is minimizing operating costs while meeting minimum environmental protection requirements, which is the norm for most companies during normal operation. Specific scenarios include: During peak electricity price periods, enterprises implement time-of-use pricing, proactively reducing energy consumption to save on electricity costs during peak daytime electricity price periods; enterprises face pressure to reduce costs and increase efficiency, such as when enterprises set specific energy conservation and consumption reduction targets; under low load conditions, such as when boiler load is low and the inlet dust concentration is not high, there is no need to operate at maximum efficiency; the equipment is in good condition, with sufficient emission margin, and the current efficiency is far higher than the legal standard, leaving room for downward optimization.

[0082] In the aforementioned cost-sensitive scenario, an economic weight γ is fitted based on the total system energy consumption (Energy), real-time electricity price, and the proportion of energy consumption of electrostatic precipitators. The higher the cost pressure, the higher the economic weight γ, as expressed by the following logic:

[0083] in, express The basic weights, , , These represent the weighted incremental coefficients for exceeding the standards for total energy consumption, electricity price, and the proportion of energy consumption in electrostatic precipitators, respectively. , , These represent the upper limits of the weighted increments for exceeding the standards for total energy consumption, electricity price, and the proportion of energy consumption in electrostatic precipitators. This represents monthly energy consumption, where P represents the real-time electricity price. This indicates the flat electricity price. This indicates the proportion of energy consumption for electrostatic precipitators, specifically the energy consumption of electrostatic precipitators divided by the total energy consumption of the system.

[0084] Furthermore, the environmental weight α decreases linearly as the economic weight γ increases, which can be expressed logically as follows:

[0085] in, express The upper limit.

[0086] Furthermore, the device protection weight β is represented using the following logic:

[0087] This embodiment provides an implementable parameter configuration method, specifically as follows: Take a value of 0.2~0.3. Take 0.15, Take 0.6, and satisfy α+β+γ=1.

[0088] Furthermore, to prevent the weighting coefficients α, β, and γ from exceeding the safe range, hard boundary constraints are imposed on α, β, and γ, using the following logical representation: α=min(αcalc,0.8), α=max(α,0.05); β=min(βcalc,0.7), β=max(β,0.05); γ=min(γcalc,0.6), γ=max(γ,0.05).

[0089] Wherein, αcalc, βcalc, and γcalc represent the original values ​​of environmental protection weight α, equipment protection weight β, and economic weight γ, respectively.

[0090] The boundary constraint unit is used to apply at least one constraint condition to the objective function. The constraint conditions include rapping frequency constraint, electric field safe operation constraint, and ash hopper level warning constraint, which are represented by the following logic: (1) Vibration frequency constraint: ,in , These represent the upper and lower limits of the vibration frequency, respectively. (2) Constraints for safe operation of the electric field: ,in , These represent the upper and lower limits of the secondary electric field voltage, respectively. (3) Ash hopper level warning constraint: H≤0.85 ,in This is the maximum designed material level for the ash hopper.

[0091] The closed-loop execution and feedback module is used to execute the optimal vibration command and collect feedback data. Based on the feedback data, the dual-model parameters are dynamically calibrated to form closed-loop control.

[0092] The optimized control commands are sent to the PLC controllers of each electric field rapper to execute the commands. The dust concentration at the outlet, the material level in the ash hopper, and the dust concentration at the inlet are monitored in real time. Every preset feedback cycle (5 minutes in this embodiment) is used to calculate the actual dust removal efficiency, the actual total energy consumption of the system, and the actual secondary dust risk value based on the monitoring data. The deviation between the above indicators and the predicted values ​​output by the model prediction module is verified. When the absolute value of the deviation exceeds the preset deviation threshold (5% in this embodiment), the model update mechanism is immediately triggered to update the model parameters of the dual models in the model prediction module and complete the model recalibration.

[0093] In this embodiment, the triggering condition for the dynamic calibration is that the deviation between the actual dust removal efficiency and the predicted value exceeds a preset deviation threshold.

[0094] In this embodiment, the vibration control command includes the independent vibration frequency of each electric field. and the vibration mode selection signal, among which The frequency of the nth electric field is represented by the rapping mode, which includes continuous rapping and intermittent rapping. In this embodiment, the voltage V and current I do not need to be issued as independent control commands. They only need to be limited to a safe range by constraints. The core of the control command is the rapping-related parameters.

[0095] By comparing core indicators with scenario triggering conditions, it is determined whether the current scenario belongs to a real-time operation scenario (such as a strictly controlled environmental scenario, a high-risk equipment scenario, or a cost-sensitive scenario). Based on the rules corresponding to different scenarios, the values ​​of dynamic weight coefficients α, β, and γ are optimized. The adjusted weight values ​​are then substituted into the minimization objective function J. The optimal rapping frequency, secondary electric field strength, and other control variables are solved using a model prediction algorithm to generate the optimal rapping command. After the rapper executes the control parameters, the actual dust removal efficiency is collected every 30 seconds. Actual total system energy consumption and actual secondary dust risk value The deviation from the target value is calculated, and the deviation between the actual value and the corresponding preset target value is calculated respectively. If any deviation exceeds the corresponding preset deviation threshold, the multi-objective optimization module is returned to solve the problem again and the weight coefficient is adjusted. When all deviations calculated in three consecutive cycles are less than the corresponding preset deviation threshold, the current weight is fixed and maintained until the next scenario is triggered, forming a closed-loop control process of perception, decision-making, execution and verification, ensuring the real-time performance and accuracy of the electrostatic precipitator rapping control.

[0096] The present invention also provides a control method for the above-mentioned intelligent rapping control system of electrostatic precipitator, comprising the following steps: Step 1: Real-time collection of multi-dimensional operating parameters of the electrostatic precipitator, including dust data, electric field parameters, flue gas parameters, rapping status, and ash hopper status; Step 2: Through dual-model collaborative prediction, output the estimated value of dust removal efficiency at the current moment, as well as the predicted value of the change in dust removal efficiency after the rapping operation and the predicted value of secondary dust risk. Step 3: With the optimization objectives of maximizing dust removal efficiency, minimizing the risk of secondary dust generation, and minimizing energy consumption, the optimal combination of rapping frequencies is solved online based on model predictive control as the optimal rapping command. Step 4: Execute the optimal vibration command and collect feedback data. Based on the feedback data, dynamically calibrate the dual-model parameters to form a closed-loop control.

[0097] 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. An intelligent vibration control system for an electrostatic precipitator, characterized in that, include: The data acquisition module is used to collect multi-dimensional operating parameters of the electrostatic precipitator in real time, including dust data, electric field parameters, flue gas parameters, rapping status, and ash hopper status. The model prediction module includes a dust removal estimation unit and a rapping prediction unit. It is used to collaboratively predict and output the estimated dust removal efficiency at the current moment, as well as the predicted change in dust removal efficiency after rapping and the predicted risk of secondary dust generation. Specifically, the dust removal estimation unit constructs a real-time dust removal efficiency estimation model based on a dynamic calibration algorithm, using dust data and flue gas parameters as input, and outputs the estimated current dust removal efficiency. The rapping prediction unit constructs a rapping effect-efficiency response prediction model based on a dynamic calibration algorithm, using electric field parameters, rapping status, ash hopper status, and the current estimated dust removal efficiency as input, and outputs the predicted change in dust removal efficiency after rapping and the predicted risk of secondary dust generation. A multi-objective optimization module is used to maximize dust removal efficiency, minimize the risk of secondary dust generation, and minimize energy consumption as optimization objectives. Based on model predictive control, the optimal combination of rapping frequencies is solved online as the optimal rapping command. The multi-objective optimization module includes an objective function unit, a weight adjustment unit, and a boundary constraint unit. The objective function unit is used to construct a minimum objective function J, which is represented by the following logic: in, To achieve the target dust removal efficiency, R represents the estimated dust removal efficiency of the final output, Energy represents the predicted value of secondary dust risk, and Energy represents the total energy consumption of the system. α, β, and γ are the dynamic weight coefficients corresponding to the environmental efficiency, equipment protection, and economic objectives, respectively. α represents the environmental weight, β represents the equipment protection weight, and γ represents the economic weight, and they satisfy α+β+γ=1. The weight adjustment unit is used to dynamically adjust the weight coefficients of the objective function according to the real-time operating scenario of the electrostatic precipitator. The operating scenario includes at least environmentally strict control scenario, high-risk equipment scenario, and cost-sensitive scenario. The boundary constraint unit is used to apply at least one constraint condition to the objective function, the constraint condition including rapping frequency constraint, electric field safe operation constraint and ash hopper level warning constraint; Under the aforementioned stringent environmental control scenario, a multi-parameter fusion nonlinear function is constructed based on the outlet dust concentration, electric field flashover frequency, target dust removal efficiency, and predicted dust removal efficiency to fit the environmental weight α. The more severe the deterioration of the operating parameters, the higher the environmental weight α, as expressed by the following logic: in, Represents the basic weights of α. , , These represent the weighting increment coefficients for excessive dust concentration, excessive flashover frequency, and deviation in emergency response efficiency, respectively. Indicates the dust concentration limit. Indicates the electric field flashover frequency. Indicates the flashover frequency limit. This indicates the target value during environmental emergency response. , , These represent the upper limits of the weighted increments for excessive dust concentration, excessive flashover frequency, and deviation in emergency response efficiency, respectively. This indicates that the weights are adjusted only when the operating parameters deteriorate; otherwise, they are set to 0. This indicates a limit on the maximum increment of a single operating parameter with respect to α, to prevent α from exceeding the safety limit, i=1,2,3; The equipment protection weight β decreases linearly as the environmental protection weight α increases, which can be expressed using the following logic: in, express The basic weights, express The lower limit value, express The upper limit; The economic weight γ is obtained by solving the constraint α+β+γ=1; The closed-loop execution and feedback module is used to execute the optimal vibration command and collect feedback data. Based on the feedback data, the dual-model parameters are dynamically calibrated to form closed-loop control.

2. The intelligent vibration control system for an electrostatic precipitator according to claim 1, characterized in that, The data acquisition module includes dust data such as inlet dust concentration and outlet dust concentration; electric field parameters such as secondary voltage and current of each electric field and flashover frequency; flue gas parameters such as flue gas temperature, flue gas flow rate and flue gas humidity; rapping status such as rapper start-stop sequence, rapping frequency and impact force; and ash hopper status such as ash hopper material level height and ash discharge frequency.

3. The intelligent vibration control system for an electrostatic precipitator according to claim 1, characterized in that, The dust removal estimation unit specifically includes the following: First, calculate the basic dust removal efficiency η(t), using the following logical expression: Wherein, η(t) represents the basic dust removal efficiency at time t. This represents the inlet dust concentration at time t. This represents the dust concentration at the outlet at time t; Then, the dust removal efficiency is dynamically calibrated based on the dynamic calibration algorithm. This can be represented using the following logic: in, This represents the dust removal efficiency at time t after dynamic calibration. Indicates the flow influencing factor. Indicates the time decay factor. Indicates basic efficiency; Finally, the real-time dust removal efficiency estimation model is delayed, and the final output dust removal efficiency estimate is represented by the following logic. : in, , These represent the dust removal efficiency after dynamic calibration at the first two time points, respectively.

4. The intelligent vibration control system for an electrostatic precipitator according to claim 3, characterized in that, The flow influencing factors Calculate using the following logic: Where Q(t) represents the real-time flue gas flow rate, Indicates the rated design flow rate. This represents the flow correction factor; Calculate the time decay factor using the following logic. : in, The efficiency decay coefficient is represented by Δt, which represents the time since the last vibration, in seconds.

5. The intelligent vibration control system for an electrostatic precipitator according to claim 1, characterized in that, In the high-risk scenario of the equipment, a linear fusion function is constructed based on the predicted value of secondary dust emission risk, the vibration operation time, and the ash hopper level to fit the equipment protection weight β, using the following logical representation: in, express The basic weights, , , These represent the weighted increment coefficients for rapping force attenuation, excessive rapping running time, and excessive ash hopper level, respectively. , , These represent the upper limits of the weighted increments for rapping force attenuation, rapping running time exceeding the standard, and ash hopper material level exceeding the standard, respectively. This indicates the design value of the rapping force. This indicates the critical value for the maintenance cycle of the rapper. This indicates the upper limit of the ash hopper level, where H represents the ash hopper height. Indicates the vibration running time; The environmental protection weight α decreases linearly as the equipment protection weight β increases, which can be expressed using the following logic: in, express The lower limit value, express The upper limit; The economic weight γ is obtained by solving the constraint α+β+γ=1.

6. The intelligent vibration control system for an electrostatic precipitator according to claim 1, characterized in that, In the cost-sensitive scenario, an economic weight γ is fitted based on the total system energy consumption (Energy), real-time electricity price, and the proportion of energy consumption of electrostatic precipitators, using the following logical representation: in, express The basic weights, , , These represent the weighted incremental coefficients for exceeding the standards for total energy consumption, electricity price, and the proportion of energy consumption in electrostatic precipitators, respectively. , , These represent the upper limits of the weighted increments for exceeding the standards for total energy consumption, electricity price, and the proportion of energy consumption in electrostatic precipitators. This represents monthly energy consumption, where P represents the real-time electricity price. This indicates the flat electricity price. This indicates the energy consumption ratio of the electrostatic precipitator, specifically the energy consumption of the electrostatic precipitator divided by the total energy consumption of the system. The environmental weight α decreases linearly as the economic weight γ increases, which can be expressed using the following logic: in, express The upper limit value; the device protection weight β is represented by the following logic: 。 7. A method for intelligent vibration control of an electrostatic precipitator, characterized in that, Includes the following steps: Step 1: Real-time collection of multi-dimensional operating parameters of the electrostatic precipitator, including dust data, electric field parameters, flue gas parameters, rapping status, and ash hopper status; Step 2 involves using a dual-model collaborative prediction method to output the estimated dust removal efficiency at the current moment, as well as the predicted changes in dust removal efficiency and the predicted risk of secondary dust generation after the rapping operation. This includes: constructing a real-time dust removal efficiency estimation model based on a dynamic calibration algorithm, using dust data and flue gas parameters as inputs, and outputting the estimated current dust removal efficiency; and constructing a rapping effect-efficiency response prediction model based on a dynamic calibration algorithm, using electric field parameters, rapping status, ash hopper status, and the current estimated dust removal efficiency as inputs, and outputting the predicted changes in dust removal efficiency and the predicted risk of secondary dust generation after the rapping operation. Step 3, with the optimization objectives of maximizing dust removal efficiency, minimizing the risk of secondary dust generation, and minimizing energy consumption, uses model predictive control to solve online for the optimal combination of rapping frequencies as the optimal rapping command. This includes: constructing a minimization objective function J, which is represented by the following logic: in, To achieve the target dust removal efficiency, R represents the estimated dust removal efficiency of the final output, Energy represents the predicted value of secondary dust risk, and Energy represents the total energy consumption of the system. α, β, and γ are the dynamic weight coefficients corresponding to the environmental efficiency, equipment protection, and economic objectives, respectively. α represents the environmental weight, β represents the equipment protection weight, and γ represents the economic weight, and they satisfy α+β+γ=1. The weight coefficients of the objective function are dynamically adjusted according to the real-time operating scenario of the electrostatic precipitator. The operating scenario includes at least environmentally strict control scenario, high-risk equipment scenario, and cost-sensitive scenario. At least one constraint is imposed on the objective function, including rapping frequency constraint, electric field safe operation constraint, and ash hopper level warning constraint. Under the aforementioned stringent environmental control scenario, a multi-parameter fusion nonlinear function is constructed based on the outlet dust concentration, electric field flashover frequency, target dust removal efficiency, and predicted dust removal efficiency to fit the environmental weight α. The more severe the deterioration of the operating parameters, the higher the environmental weight α, as expressed by the following logic: in, Represents the basic weights of α. , , These represent the weighting increment coefficients for excessive dust concentration, excessive flashover frequency, and deviation in emergency response efficiency, respectively. Indicates the dust concentration limit. Indicates the electric field flashover frequency. Indicates the flashover frequency limit. This indicates the target value during environmental emergency response. , , These represent the upper limits of the weighted increments for excessive dust concentration, excessive flashover frequency, and deviation in emergency response efficiency, respectively. This indicates that the weights are adjusted only when the operating parameters deteriorate; otherwise, they are set to 0. This indicates a limit on the maximum increment of a single operating parameter with respect to α, to prevent α from exceeding the safety limit, i=1,2,3; The equipment protection weight β decreases linearly as the environmental protection weight α increases, which can be expressed using the following logic: in, express The basic weights, express The lower limit value, express The upper limit; The economic weight γ is obtained by solving the constraint α+β+γ=1; Step 4: Execute the optimal vibration command and collect feedback data. Based on the feedback data, dynamically calibrate the dual-model parameters to form a closed-loop control.

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