Fuzzy control method for stable operation of electric precipitation system

Abstract

The invention discloses a fuzzy control method for stable operation of an electric precipitation system, and relates to the technical field of fuzzy control, comprising: generating a first factor based on multi-physical field coupling analysis, the multi-physical field comprising temperature field gradient, airflow vorticity and charge field unevenness; gray layer dielectric parameters are synchronously collected, gray layer specific resistance is calculated, and a second factor is generated; smoke concentration deviation and energy consumption indexes are collected, a first factor and a second factor are used for conducting dynamic reconstruction on the fuzzy membership function, and a control rule base containing dielectric compensation items is generated; inputting the flue gas concentration deviation and the energy consumption index into a control rule base, executing a multi-target fuzzy decision, and outputting a cooperative control parameter; and triggering the iteration of the first factor according to the dielectric parameter change of the ash layer, reversely adjusting the second factor according to the dust removal efficiency deviation, and optimizing the rule weight. According to the invention, through combination of multi-physics field coupling analysis and ash layer dielectric parameter dynamic detection, the operation stability of the electric precipitation system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of fuzzy control, and particularly to a fuzzy control method for the stable operation of an electrostatic precipitation system. Background Art

[0002] In the field of industrial flue gas purification, the intelligent control technology of electrostatic precipitation systems has gradually evolved from single-variable regulation to multi-parameter collaborative decision-making in recent years. The traditional fuzzy control method realizes basic regulation by constructing a static rule base of flue gas concentration deviation and energy consumption index. Some studies have attempted to use temperature field zoning monitoring or electric field strength feedback to optimize control parameters, but its input variables are mostly limited to single physical field characterization quantities. In the prior art, although the fuzzy PID algorithm based on the dynamic prediction of corona current can alleviate the influence of operating conditions fluctuations, there are still deficiencies in the modeling under the action of multi-field coupling, especially the lack of quantitative analysis ability for non-linear interaction effects such as the change of dust resistivity caused by temperature gradient and the attenuation of charging efficiency caused by air flow vortices. In addition, the on-line detection of ash layer dielectric parameters mostly relies on contact electrode measurement, and the data update frequency and spatial resolution are difficult to meet the requirements of real-time control.

[0003] The current electrostatic precipitation control field faces three bottlenecks: First, the lack of a dynamic integration mechanism for physical field coupling parameters leads to insufficient adaptability of control rules to complex operating conditions such as air flow disturbance and temperature stratification, and is prone to secondary dust emission or electric field flashover; Second, the correlation model between the dielectric properties and resistivity of the ash layer has not been established, and traditional detection means have hysteresis under low resistivity conditions, making it difficult to suppress back corona phenomenon in a timely manner; Third, the boundaries of the fuzzy membership function are fixed during the multi-objective optimization process, and the domain range cannot be dynamically adjusted according to the game relationship between energy efficiency constraints and dust removal efficiency, which is prone to cause mismatch in the weight distribution of the rule base. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a fuzzy control method for the stable operation of an electrostatic precipitation system to solve the problem of poor stability of traditional electrostatic precipitation systems under the action of multi-physical field coupling.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a fuzzy control method for the stable operation of an electrostatic precipitation system, which includes generating a first factor based on multi-physical field coupling analysis, where the multi-physical field includes temperature field gradient, air flow vorticity, and charge field non-uniformity;

[0008] Synchronously collecting the dielectric parameters of the ash layer, calculating the ash layer resistivity, and generating a second factor;

[0009] Collect the flue gas concentration deviation and energy consumption index, dynamically reconstruct the fuzzy membership function using the first factor and the second factor, and generate a control rule base including a dielectric compensation term;

[0010] Input the flue gas concentration deviation and energy consumption index into the control rule base, perform multi-objective fuzzy decision-making, and output the coordinated control parameters;

[0011] Trigger the iteration of the first factor according to the change of the dielectric parameter of the ash layer, and reversely adjust the second factor according to the deviation of the dust removal efficiency to optimize the rule weight.

[0012] As a preferred scheme of the fuzzy control method for the stable operation of the electrostatic precipitation system described in the present invention, wherein: the first factor is obtained by performing normalization processing on the temperature field gradient, air flow vorticity, and charge field non-uniformity, and using weighted summation calculation, wherein the temperature field gradient is collected in real time by a distributed temperature sensor array, the air flow vorticity is determined by the multi-point measurement values of the eddy current sensor at the electric field cross-section, and the charge field non-uniformity is calculated based on the spatial distribution data of the charged density probe.

[0013] As a preferred scheme of the fuzzy control method for the stable operation of the electrostatic precipitation system described in the present invention, wherein: the second factor is dynamically generated based on the real part and the imaginary part of the dielectric parameter of the ash layer, and when the ash layer specific resistance is lower than the preset back corona risk threshold, the compensation mechanism is activated, and the amplitude of the second factor is adjusted by an exponential function, and the ash layer specific resistance is inversely calculated from the dielectric loss parameter collected by the microwave resonant cavity sensor.

[0014] As a preferred scheme of the fuzzy control method for the stable operation of the electrostatic precipitation system described in the present invention, wherein: the multi-physical field coupling analysis refers to the three-dimensional synchronous collection using a temperature gradient sensor, an eddy current sensor, and a charged density probe, and the real-time output of the maximum value of the temperature field gradient, the integral value of the air flow vorticity, and the relative value of the electric field non-uniformity, and the three are linearly superposed after normalization to generate the first factor.

[0015] As a preferred scheme of the fuzzy control method for the stable operation of the electrostatic precipitation system described in the present invention, wherein: the specific steps for dynamically reconstructing the fuzzy membership function are as follows

[0016] Dynamically adjust the fuzzy domain range of the flue gas concentration deviation according to the first factor, so that the fuzzy domain range linearly expands and contracts with the physical field coupling strength;

[0017] Offset the center point of the membership function of the energy consumption index according to the second factor, and the offset is proportional to the square of the ash layer specific resistance;

[0018] Detect the ash layer specific resistance, add an exponential compensation term in the membership degree calculation, and enhance the control sensitivity under the low specific resistance condition.

[0019] As a preferred embodiment of the fuzzy control method for the stable operation of the ESP system of the present invention, wherein: the execution of multi-objective fuzzy decision-making is specifically carried out as follows.

[0020] Select a control rule group according to the threshold judgment. When the first factor exceeds the first preset threshold, activate the rule group dominated by pulses; when the second factor exceeds the second preset threshold, enable the dielectric compensation rule group.

[0021] Calculate the back corona risk coefficient based on the dielectric parameters of the ash layer.

[0022] Generate a collaborative control parameter by synthesizing the flue gas concentration deviation, energy consumption index, and back corona risk.

[0023] As a preferred embodiment of the fuzzy control method for the stable operation of the ESP system of the present invention, wherein: the triggering of the first factor iteration refers to adjusting the weight coefficient of the first factor using the gradient descent method to match the change trend of the first factor and the dielectric parameters of the ash layer.

[0024] As a preferred embodiment of the fuzzy control method for the stable operation of the ESP system of the present invention, wherein: the reverse adjustment of the second factor and the optimization of the rule weight refer to adjusting the calculation coefficient of the second factor through an error feedback mechanism based on the deviation between the actual dust removal efficiency and the target dust removal efficiency, regenerating the second factor, and updating the weight distribution in the control rule base.

[0025] In a second aspect, the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and wherein: when the computer program is executed by the processor, any step of the fuzzy control method for the stable operation of the ESP system as described in the first aspect of the present invention is implemented.

[0026] In a third aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and wherein: when the computer program is executed by the processor, any step of the fuzzy control method for the stable operation of the ESP system as described in the first aspect of the present invention is implemented.

[0027] The beneficial effects of the present invention are as follows: By combining multi-physical field coupling analysis with dynamic detection of ash layer dielectric parameters, a two-factor collaborative optimization mechanism is generated, realizing adaptive adjustment to complex working conditions; A three-dimensional sensor array is used to collect multi-source data in real time, and dynamic control factors are generated through normalization processing and weighted fusion, improving the response accuracy of the system to the physical field coupling effect; Using the ash layer resistivity compensation mechanism and back corona risk suppression strategy, the membership function and the control rule base are dynamically corrected using the exponential function, effectively reducing energy consumption and corona breakdown risk while ensuring the dust removal efficiency; The iterative optimization of control parameters is realized through the gradient descent method and the error feedback mechanism, enabling the system to maintain stable operation even when the dielectric characteristics of the ash layer change suddenly. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0029] Figure 1 It is a flowchart of the overall fuzzy control method for the stable operation of the electrostatic precipitation system in Embodiment 1.

[0030] Figure 2 It is a flowchart for generating the first factor through multi-physical field coupling analysis in Embodiment 1.

[0031] Figure 3 It is a flowchart for processing the dielectric parameters of the ash layer and generating the second factor in Embodiment 1.

[0032] Figure 4 It is a flowchart for dynamically reconstructing the control rule base and multi-objective decision-making in Embodiment 1. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] In order to make the above objects, features and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention will be made in conjunction with the accompanying drawings of the specification.

[0034] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0035] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure or characteristic that can be included in at least one implementation manner of the present invention. The "in one embodiment" that appears in different places in this specification does not all refer to the same embodiment, nor is it a separate or alternative embodiment that excludes other embodiments.

[0036] Embodiment 1, referring to Figures 1 to 4 , this embodiment provides a fuzzy control method for the stable operation of an electrostatic precipitation system, including the following steps:

[0037] S1: Generate a first factor based on multi-physical field coupling analysis, where the multi-physical fields include temperature field gradient, air flow vorticity, and charge field non-uniformity.

[0038] Specifically, it includes the following steps:

[0039] S1.1: Collect multi - physical - field data and perform feature extraction.

[0040] S1.1.1: Deploy a distributed temperature sensor array to collect the temperature - field gradient.

[0041] Specifically, arrange a distributed temperature sensor array (thermocouple or fiber - optic temperature sensor) within the electric - field region to collect the temperature data of each region in real - time. Calculate the local temperature gradient through the temperature difference between adjacent sensors, and take the maximum temperature - gradient value of the entire field as the temperature - field gradient feature quantity.

[0042] S1.1.2: Measure the air - flow vorticity through an eddy - current sensor.

[0043] Specifically, install an eddy - current sensor (ultrasonic eddy - current sensor or hot - wire anemometer) on the cross - section of the electric field to measure the air - flow velocity vector at each point. Integrate the vorticity (curl of the velocity vector) of all measurement points on the cross - section of the electric field to calculate the total vorticity value as the air - flow vorticity feature quantity.

[0044] S1.1.3: Collect charge - density data through a charge - density probe and calculate the charge - field non - uniformity.

[0045] Specifically, use a three - dimensional distributed charge - density probe (electrostatic - probe array) to collect the charge - density data of each spatial point within the electric field in real - time. Calculate the spatial - distribution non - uniformity of the charge density through the standard - deviation formula, and the expression is:

[0046]

[0047] where U is the non - uniformity relative to the mean value of the charge field, C i is the charge density of the i - th probe, is the average charge density, N is the total number of probes, and i is the probe - number index.

[0048] S1.2: Normalize the multi - physical - field feature quantities.

[0049] S1.2.1: Normalize the temperature - field gradient.

[0050] Specifically, determine the maximum temperature gradient based on the temperature resistance of the device material, and normalize the temperature - field gradient feature quantity to:

[0051]

[0052] where G norm is the normalized temperature - field gradient feature quantity, G max is the maximum temperature gradient, and G is the temperature - field gradient feature quantity.

[0053] When G > G max , force G norm= 1.

[0054] S1.2.2: Normalize the vorticity of the air flow.

[0055] Specifically, determine the maximum vorticity integral value based on historical data, and normalize the air flow vorticity characteristic quantity. The expression is:

[0056]

[0057] Among them, V norm is the normalized air flow vorticity characteristic quantity, V max is the maximum vorticity integral value, and V is the air flow vorticity characteristic quantity.

[0058] S1.2.3: Normalize the charge field non-uniformity.

[0059] Specifically, analyze the charge distribution under multiple typical working conditions (such as voltage change, temperature change, material parameter perturbation, geometric deviation, etc.) through finite element simulation, extract the non-uniformity index, and set the threshold U th of the charge field non-uniformity based on the statistical results as the normalization reference. The normalization expression is:

[0060]

[0061] Among them, U norm is the relative mean value of the normalized charge field non-uniformity, U th is the threshold of the charge field non-uniformity, and U is the relative mean value of the charge field non-uniformity.

[0062] S1.3: Calculate the first factor based on the normalized temperature field gradient, air flow vorticity, and charge field non-uniformity.

[0063] Specifically, calculate the first factor through weighted summation. The expression is:

[0064] F1 = w G ·G norm + w V ·V norm + w U ·U norm ;

[0065] Among them, F1 is the first factor (F1 ∈ [0, 1]), which characterizes the multi-physical field coupling state. The larger the value, the more significant the impact of the multi-physical field coupling on the overall stability. w G is the temperature field weight (taking 0.4), w V is the air flow vorticity weight (taking 0.3), w U is the charge field weight (taking 0.3).

[0066] The sum of the temperature field weight, the air flow vorticity weight, and the charge field weight is 1. The temperature field weight is increased in a high-temperature environment, and the specific value is determined by optimizing historical data and actual working conditions.

[0067] S1.3: Perform real-time timestamp synchronization and add a fault tolerance mechanism.

[0068] It should be noted that timestamp alignment is adopted to ensure the synchronous acquisition of temperature field, air flow field, and charge field data.

[0069] If the sensor fails, interpolation compensation is performed based on adjacent data to ensure the continuity of characteristic quantity calculation.

[0070] Preferably, distributed sensors are used to collect temperature gradient, air flow vorticity, and charge field data in real time, and combined with normalization processing, so that each physical quantity has a unified evaluation standard, avoiding deviations caused by dimensional differences. The calculation of the first factor integrates multi-dimensional influencing factors, giving the ability to adaptively adjust weights under different working conditions, thereby optimizing the response to complex environments. The synchronization mechanism and fault tolerance compensation strategy enhance the continuity and reliability of the acquisition of temperature gradient, air flow vorticity, and charge field data, ensuring stable operation even in the case of sensor anomalies or short-term data loss.

[0071] S2: Synchronously collect the dielectric parameters of the ash layer, calculate the ash layer specific resistance, and generate the second factor.

[0072] Specifically, it includes the following steps:

[0073] S2.1: Collect the dielectric parameters of the ash layer and perform preprocessing.

[0074] S2.1.1: Measure the dielectric parameters of the ash layer through a microwave resonator sensor array.

[0075] It should be noted that the dielectric parameters of the ash layer refer to the complex dielectric constant exhibited by the dust deposited on the surface of the ESP plates under the action of electromagnetic waves, including the real part and the imaginary part. Obtaining this parameter through a microwave resonator sensor array can indirectly reflect the conductivity and specific resistance characteristics of the ash layer, providing basic information for ESP control.

[0076] Specifically, a microwave resonator sensor array is uniformly arranged on the surface of the ESP plates. Each sensor emits a microwave signal to penetrate the ash layer, receives the reflected signal, and extracts the real part (representing the polarization ability) and the imaginary part (representing the dielectric loss) of the dielectric parameter.

[0077] Align the timestamps with the temperature field, air flow field, and charge field data to ensure the synchronous acquisition of the dielectric parameters of the ash layer.

[0078] S2.1.2: Perform preprocessing on the dielectric parameters of the ash layer.

[0079] Specifically, wavelet denoising is performed on the collected dielectric parameters of the ash layer to eliminate high-frequency electromagnetic interference (such as pulse noise generated by high-voltage electric fields).

[0080] At the same time, outliers are corrected. If the real part of the dielectric parameter of the ash layer collected by the sensor is less than 1 or the imaginary part is less than 0, interpolation replacement is performed based on the data of adjacent sensors to ensure spatial continuity.

[0081] S2.2: Dynamically calculate the specific resistance of the ash layer.

[0082] S2.2.1: Invert the specific resistance of the ash layer based on dielectric loss.

[0083] Specifically, the tangent of the loss angle is calculated according to the real part and the imaginary part of the dielectric parameter of the ash layer. The expression is:

[0084]

[0085] where tanδ is the tangent of the dielectric loss angle, ∈ ′ is the real part of the dielectric parameter of the ash layer, and ∈″ is the imaginary part of the dielectric parameter of the ash layer.

[0086] Calculate the specific resistance of the ash layer. The expression is:

[0087]

[0088] where R dust is the specific resistance of the ash layer, k is the material characteristic constant (calibrated by ash sample test, typical value k = 10 12 Ω·cm·rad), exp is the natural exponential function, α is the temperature correction coefficient (take 0.02), and T dust is the ash layer temperature.

[0089] S2.2.2: Calibrate the specific resistance of the ash layer according to the percentage of flue gas humidity and the ash layer thickness.

[0090] It should be noted that a humidity sensor is used to collect the percentage of flue gas humidity, and a laser thickness gauge is used to measure the ash layer thickness. The specific resistance of the ash layer is calibrated according to the percentage of flue gas humidity and the ash layer thickness to solve the problem of inaccurate calculation of the specific resistance of the ash layer caused by excessive flue gas humidity or too high ash layer thickness.

[0091] Specifically, if the percentage of flue gas humidity > 15%, the specific resistance of the ash layer is corrected according to the percentage of flue gas humidity. The expression is:

[0092] R new = R dust ·(1 - 0.05·(H - 15%));

[0093] where R newis the ash layer specific resistance after humidity correction, H is the percentage of flue gas humidity, and for every 1% increase in humidity, the specific resistance decreases by 5%.

[0094] Water vapor in the flue gas has extremely strong electronegativity. When the humidity exceeds 15%, it will greatly affect the discharge space conductivity and the degree of ash layer polarization, resulting in a relatively high specific resistance measured by dielectric loss. The correction factor of about 5% reduction in resistance for every 1% increase in humidity is a linear empirical parameter obtained by fitting laboratory flue test and simulation data. When the humidity ≤ 15%, the influence of water vapor content on the polarization path is weak and the dielectric response change is not obvious. To avoid overfitting and unnecessary calculations, no correction is made.

[0095] When the ash layer thickness is greater than 5 mm, ash layer thickness compensation is carried out, and the expression is:

[0096] R thick = R dust ·(1 + 0.1·(d dust - 5));

[0097] where d dust is the ash layer thickness, and R thick is the ash layer specific resistance after ash layer thickness compensation. The greater the thickness, the longer the conduction path of charges inside the ash layer, resulting in non-linear growth of the specific resistance. Especially when the thickness exceeds 5 mm, the upward trend of the resistance becomes larger. The correction factor of 10% increase in resistance for every 1 mm increase is obtained by calibrating with thick ash data in the industrial field.

[0098] S2.3: Calculate the second factor and perform dynamic adjustment.

[0099] S2.3.1: Initial calculation of the second factor.

[0100] Specifically, the second factor is generated by combining the real part and the imaginary part of the ash layer dielectric parameter, and the expression is:

[0101] F2 = β·∈′ + γ·∈″;

[0102] where F2 is the second factor, β is the real part weight (taking 0.6), γ is the imaginary part weight (taking 0.4), and β + γ = 1.

[0103] S2.3.2: Perform low specific resistance compensation.

[0104] Specifically, when R dust < R th , the compensation mechanism is activated to adjust the second factor, and the expression is:

[0105]

[0106] where F 2,compis the adjusted second factor, λ is the compensation intensity coefficient (taking 2), and R th is the preset back-corona risk threshold, taking 10 10 Ω·cm (set according to industry experience. When the specific resistance is lower than 10 9 ~10 10 Ω·cm, back-corona is likely to occur).

[0107] S2.3.3: Adjust the weight according to the change rate of the ash layer specific resistance.

[0108] Specifically, the real part weight and the imaginary part weight are dynamically updated according to the change rate of the ash layer specific resistance. The expression is:[[]]

[0109]

[0110] γ = 1 - β;

[0111] where, ΔR dust is the change rate of the ash layer specific resistance, and Δt is the change rate of time.

[0112] When the ash layer specific resistance drops rapidly, increase the imaginary part weight to respond more quickly to the sudden change of dielectric loss.

[0113] Preferably, by collecting and analyzing the dielectric parameters of the ash layer in real time, the accurate calculation and dynamic adjustment of the ash layer specific resistance are realized. The microwave resonant cavity sensor array is used to ensure the spatial coverage of the collection and improve the reliability of the measurement. Wavelet noise reduction and outlier correction are performed on the dielectric parameters of the ash layer to eliminate high-frequency electromagnetic interference. The method of inverting the ash layer specific resistance based on dielectric loss can reflect the conductive characteristics of the ash layer, and through the temperature, humidity and thickness compensation mechanisms, ensure the accuracy of the ash layer specific resistance calculation. The calculation of the second factor combines the real part and the imaginary part of the dielectric characteristics of the ash layer, and adjusts the weight through the specific resistance dynamic change rate, improving the adaptability of the method to the low specific resistance risk. It effectively enhances the real-time perception ability of the electrostatic precipitator to the ash layer accumulation state, helps to early warn the back-corona risk, optimize the operation strategy of the dust removal method, and improve the emission control accuracy and the stability of the equipment operation.

[0114] S3: Collect the flue gas concentration deviation and energy consumption index, and use the first factor and the second factor to dynamically reconstruct the fuzzy membership function to generate a control rule base including the dielectric compensation term.

[0115] Specifically, it includes the following steps:[[]]

[0116] S3.1: Collect the flue gas concentration deviation and energy consumption index through a laser dust sensor.

[0117] Specifically, the concentration of flue gas particles at the smoke outlet is measured by a laser dust sensor and compared with a preset environmental protection limit value to obtain the deviation of the flue gas concentration. The preset environmental protection limit value is the upper limit of the set target emission concentration, which is used to evaluate whether the flue gas purification effect meets the emission standard. It is set according to internal control requirements, and common set values are 10–30 mg / m 3 .

[0118] Collect the real-time current and voltage signals of the high-voltage power supply, calculate the instantaneous input power, and calculate the average value of the power values within a certain time window to obtain the average corona power. Compare this value with the rated power to obtain the normalized energy consumption index, which is used to characterize the power consumption efficiency under the unit dust removal effect and serves as one of the fuzzy control input parameters.

[0119] S3.2: Reconstruct the fuzzy membership function by adjusting the universe of discourse, performing center point offset, and adding a dielectric compensation term.

[0120] S3.2.1: Adjustment of the fuzzy universe of discourse for the flue gas concentration deviation.

[0121] It should be noted that the universe of discourse refers to the domain range of the input variable of the fuzzy membership function (i.e., the deviation of the flue gas concentration), which is used to determine the mapping boundary of the fuzzy quantity.

[0122] Specifically, when the first factor is greater than 0.6 (high multi-physical field coupling intensity), the universe of discourse range linearly expands from the default [-50%, +50%] to [-60%, +60%].[[]END]]

[0123] When the first factor is less than 0.3, the universe of discourse shrinks to [-40%, +40%] to prevent over-control in the low coupling state.

[0124] S3.2.2: Center point offset of the membership of the energy consumption index.

[0125] Specifically, the center point of the membership function of the energy consumption index is adjusted from the default 0.5, and the expression is:

[0126]

[0127] where P center is the center point of the membership of the energy consumption index, and η is the offset coefficient (taking 0.01).

[0128] When R dust < R th (low specific resistance), the center point of the membership of the energy consumption index moves to the left to preferentially reduce energy consumption. When R dust ≥ R th , the center point of the membership of the energy consumption index moves to the right to allow an increase in energy consumption to maintain the dust removal efficiency.

[0129] S3.2.3: Add a dielectric compensation term.

[0130] Specifically, an exponential compensation term is added to the membership degree calculation, and the expression is:

[0131]

[0132] where K comp is the exponential compensation term.

[0133] When R dust is less than R th K comp →0, the control amount is amplified to suppress back corona.

[0134] When R dust ≥R th K comp ≈1, and the compensation term fails.

[0135] S3.3: Reconstruct the control rule base according to the first factor and the second factor.

[0136] S3.3.1: Assign rule weights.

[0137] Specifically, if the first factor is greater than 0.7, the pulse control rule group (weight w pulse = 0.7) is activated to preferentially adjust the corona pulse frequency.

[0138] If the first factor is less than or equal to 0.7, the basic control rule group (weight w base = 0.5) is started.

[0139] When the second factor is greater than 0.8 (ash layer dielectric anomaly), the weight of the dielectric compensation term is increased to w comp = 0.6, and the rapping intensity is forced to decrease.

[0140] Furthermore, the pulse control rule group quickly suppresses the electric field fluctuation through high-frequency and short-time corona pulses to prevent the decrease in dust removal efficiency or the risk of back corona caused by the coupling of multiple physical fields, including:

[0141] When the flue gas concentration deviation is PB, the pulse frequency is increased to enhance the dust removal efficiency;

[0142] When the charge field non-uniformity is high and the ash layer resistivity is low, the pulse width is shortened to reduce the risk of back corona.

[0143] The basic control rule group is responsible for maintaining the balance between dust removal efficiency and energy consumption under normal working conditions and avoiding over-control, including:

[0144] When the flue gas concentration deviation is PM and the energy consumption index is M (medium energy consumption, energy consumption index 0.3 < P norm < 0.7), the corona voltage is slightly increased to optimize the efficiency;

[0145] When the ash layer specific resistance is normal and the back-corona risk is low, extend the rapping cycle to reduce secondary dust emission.

[0146] S3.3.2: Set the back-corona risk suppression rule.

[0147] Combine the ash layer specific resistance and the charge field non-uniformity to calculate the back-corona risk coefficient, and the expression is:

[0148]

[0149] Among them, K risk is the back-corona risk coefficient.

[0150] When K risk > 0.5, it is determined that the back-corona risk is low, and the membership degree of suppressing the rapping intensity is output to 50% of the original value to prevent secondary dust emission.

[0151] Preferably, by collecting the flue gas concentration deviation and the energy consumption index, and dynamically reconstructing the fuzzy membership function in combination with the first factor and the second factor, a more accurate electrostatic precipitation control strategy is realized. The fuzzy universe adjustment based on the coupling strength is adopted to improve the adaptability of the method to different working conditions; through the dynamic offset of the membership center point of the energy consumption, the balance between the dust removal efficiency and the energy consumption cost is achieved; the dielectric compensation term is added to strengthen the control strength under the low specific resistance working condition and effectively suppress the back-corona risk. A hierarchical control rule base is constructed to optimize the pulse control mechanism in the high-strength coupling scenario, and maintain the balanced adjustment of energy consumption and dust removal efficiency under the normal working condition. By calculating the back-corona risk coefficient and dynamically suppressing the rapping intensity, the risk of secondary dust emission is reduced, and the stability and operation efficiency of the method are improved. While ensuring the dust removal effect, the energy consumption is reduced and the intelligent level of electrostatic precipitation is improved.

[0152] S4: Input the flue gas concentration deviation and the energy consumption index into the control rule base, perform multi-objective fuzzy decision-making, and output the coordinated control parameters.

[0153] Specifically, it includes the following steps:

[0154] S4.1: Perform fuzzy processing on the input variables.

[0155] S4.1.1: Fuzzification of the flue gas concentration deviation (E).

[0156] Specifically, according to the reconstructed universe range, it is divided into 5 fuzzy sets:

[0157] NB (Negative Big): E ≤ -40%;

[0158] NM (Negative Medium): -40% < E ≤ -20%;

[0159] ZE (Zero): -20% < E < +20%;

[0160] PM (Centered): +20% ≤ E < +40%;

[0161] PB (Positive Big): E ≥ +40%;

[0162] The triangular membership function is used to map the flue gas concentration deviation to the membership degree of the fuzzy set. For example, the membership degree expression for PM (Centered) is:

[0163]

[0164] where μ PM (E) is the membership degree of the flue gas concentration deviation PM, max is the function to take the maximum value, and E is the flue gas concentration deviation.

[0165] S4.1.2: Fuzzification of the energy consumption index (P norm ).

[0166] Specifically, three fuzzy sets are divided according to the energy consumption index:

[0167] L (Low energy consumption): P norm ≤ 0.3;

[0168] M (Medium energy consumption): 0.3 < P norm < 0.7;

[0169] H (High energy consumption): P norm ≥ 0.7;

[0170] According to the membership center point of the energy consumption index, the trapezoidal membership function is used to map the energy consumption index to the membership degree of the fuzzy set. For example, the membership degree expression for M (Medium energy consumption) is:

[0171]

[0172] where μ M (P norm ) is the membership degree of the energy consumption index M, min is the function to take the minimum value, and P norm is the energy consumption index.

[0173] Furthermore, for the flue gas concentration deviation variable, the triangular membership function is used to enhance the sensitivity of identifying the deviation trend and achieve continuous and smooth control response; while for the energy consumption index variable, the trapezoidal membership function is used, and the platform interval is used to enhance the overall robustness and avoid frequent control adjustments caused by energy consumption fluctuations, thus ensuring the stability and practical adaptability of the fuzzy control rules.

[0174] S4.2: Generate collaborative control parameters.

[0175] It should be noted that the centroid method is used to calculate the final control quantity, and the collaborative control parameters are output by comprehensively considering the flue gas concentration deviation, energy consumption index and back-corona risk. The collaborative control parameters include the corona voltage, pulse frequency and rapping period.

[0176] Preferably, by performing fuzzy processing on the flue gas concentration deviation and energy consumption index, and combining the reconstructed control rule base, more accurate collaborative control of electrostatic precipitation is achieved. By using the optimized fuzzy set partitioning and adaptive membership function, the adaptability to different working conditions is improved. Through multi-objective fuzzy decision-making, comprehensively considering the dust removal efficiency, energy consumption optimization and back-corona risk, it is ensured that the control strategy still has high efficiency and robustness under dynamically changing working conditions. Finally, the centroid method is used to calculate the collaborative control parameters, realizing the adaptive adjustment of the corona voltage, pulse frequency and rapping period, enabling the electrostatic precipitation method to minimize energy consumption and reduce back-corona phenomena while ensuring low emissions, thereby improving the overall operation efficiency and stability.

[0177] S5: Trigger the iteration of the first factor according to the change of the ash layer dielectric parameter, and adjust the second factor in reverse according to the deviation of the dust removal efficiency to optimize the rule weight.

[0178] Specifically, it includes the following steps:

[0179] S5.1: Perform the iteration of the first factor based on the ash layer dielectric parameter.

[0180] Specifically, when the change rate of the ash layer resistivity is greater than 10 9 Ω·cm / min or the real and imaginary parts of the ash layer dielectric parameter mutate by more than 20%, trigger the weight iteration of the first factor.

[0181] Specifically, a loss function is constructed based on the correlation between the real and imaginary parts of the ash layer dielectric parameter and the first factor, and the expression is:

[0182] L F1 =α1·(∈′ - ∈′ ref ) 2 +α2·(∈″ - ∈″ ref ) 2 ;

[0183] Among them, L F1 is the iteration loss function of the first factor, α1 is the real part iteration weight (taking 0.6), α2 is the imaginary part iteration parameter (taking 0.4), ∈′ ref is the real part reference value, ∈″ ref is the imaginary part reference value, which is obtained by statistical analysis of historical stable working condition data.

[0184] Use the gradient descent method to update w G , w V , w U, first, take the partial derivatives of the weights \(w\) of the three physical fields with respect to the loss function, and use the gradient descent method to calculate their update amounts. After each round of update, normalize the new weight values so that the sum of the three remains 1, thereby ensuring the interpretability and stability of the weighted combination of the first factor. G , \(w\) V , \(w\) U respectively, and use the gradient descent method to calculate its update amount. After each round of update, normalize the new weight values so that the sum of the three remains 1, thereby ensuring the interpretability and stability of the weighted combination of the first factor.

[0185] The expression is:

[0186]

[0187] where \(\eta\) GD is the learning rate (take 0.01), is the partial derivative identifier, \(\Delta w\) i is the change value of \(w\) G , \(w\) V , \(w\) U .

[0188] S5.2: Perform reverse adjustment on the second factor based on the dust removal efficiency deviation.

[0189] Specifically, calculate the dust removal efficiency deviation, and the expression is:

[0190]

[0191] where \(E\) η is the dust removal efficiency deviation, \(\eta\) target is the target dust removal efficiency (take 99.5%), \(C\) out is the outlet flue gas concentration, \(C\) in is the inlet flue gas concentration.

[0192] Correct the calculation coefficients \(\beta\), \(\gamma\), \(\lambda\) of the second factor through error feedback to optimize the ash layer state characterization. The PID feedback expression is:

[0193]

[0194] where \(K\) p is the PID proportional coefficient (take 0.5), \(K\) q is the PID integral coefficient (take 0.2), \(K\) d is the PID derivative coefficient (take 0.1), \(sign(E\) η ) is the deviation sign function (+1 for positive deviation, -1 for negative), \(\Delta\beta\) is the change amount of \(\beta\), \(\Delta\gamma\) is the change amount of \(\gamma\), and \(\Delta\lambda\) is the change amount of \(\lambda\).

[0195] Limit \(\beta\in[0.3,0.7]\), \(\lambda\in[1,3]\) to prevent overshoot.

[0196] S5.3: Optimize the rule weights according to the adjusted first factor and second factor.

[0197] Specifically, the rule weights are adjusted based on the updated first factor and second factor, and the expression is:

[0198]

[0199] where the constant 0.1 is used to avoid a zero denominator, w pulse is the weight of the pulse control rule group, w comp is the weight of the dielectric compensation term, w base is the weight of the basic control rule group.

[0200] When the weight change rate is adopted, first-order low-pass filtering is used for smoothing.

[0201] If E η > 5% lasts for 5 minutes, the weight is forced to be reset to the default value and an alarm is triggered.

[0202] Preferably, through the iteration of the first factor, the reverse adjustment of the second factor, and the optimization of the rule weights, the dynamic correction of the dielectric state of the ash layer and the intelligent optimization of the control strategy are realized. Based on the mutation of the ash layer specific resistance and dielectric parameters, the weight adjustment of the first factor is triggered, and the coupling strength of multiple physical fields is optimized by the gradient descent method, making the method more sensitive to the change of the dielectric environment. According to the deviation of the dust removal efficiency, the calculation coefficient of the second factor is dynamically corrected by using the PID feedback mechanism to ensure the accuracy of the ash layer state representation and improve the stability of the dust removal efficiency. According to the first factor and the second factor, the weight distribution of the control rules is dynamically adjusted to ensure that the control strategy under different working conditions can be adaptively adjusted, avoiding over-control or response lag. By smoothing the weight change through low-pass filtering and triggering alarms and resets when abnormal deviations persist, the overall robustness and reliability are enhanced.

[0203] This embodiment also provides a computer device, which is applicable to the case of the fuzzy control method for the stable operation of the electrostatic precipitator system, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the fuzzy control method for the stable operation of the electrostatic precipitator system as proposed in the above embodiment.

[0204] The computer device may be a terminal, which includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner. The wireless manner can be implemented through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, a trackball, or a touchpad provided on the outer shell of the computer device. It can also be an external keyboard, touchpad, or mouse, etc.

[0205] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements the fuzzy control method for realizing the stable operation of the electrostatic precipitation system as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM for short), Electrically Erasable Programmable Read-Only Memory (EEPROM for short), Erasable Programmable Read Only Memory (EPROM for short), Programmable Red-Only Memory (PROM for short), Read-Only Memory (ROM for short), magnetic memory, flash memory, a magnetic disk, or an optical disc.

[0206] In summary, the present invention combines multi-physical field coupling analysis with dynamic detection of ash layer dielectric parameters to generate a dual-factor collaborative optimization mechanism, realizing adaptive adjustment for complex working conditions; adopts a three-dimensional sensor array to collect multi-source data in real time, generates a dynamic control factor through normalization processing and weighted fusion, and improves the response accuracy of the system to the physical field coupling effect; uses an ash layer specific resistance compensation mechanism and an anti-corona risk suppression strategy, dynamically corrects the membership function and the control rule base using an exponential function, and effectively reduces energy consumption and the risk of corona breakdown while ensuring the dust removal efficiency; realizes iterative optimization of control parameters through the gradient descent method and the error feedback mechanism, enabling the system to still maintain stable operation when the dielectric characteristics of the ash layer change suddenly.

[0207] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A fuzzy control method for the stable operation of an electrostatic precipitation system, characterized in that: including generating a first factor based on multi - physical - field coupling analysis, where the multi - physical fields include temperature - field gradient, air - flow vorticity, and charge - field non - uniformity; synchronously collecting ash - layer dielectric parameters, calculating ash - layer specific resistance, and generating a second factor; collecting flue - gas concentration deviation and energy - consumption index, and dynamically reconstructing a fuzzy membership function using the first factor and the second factor to generate a control - rule base including a dielectric - compensation term; inputting the flue - gas concentration deviation and energy - consumption index into the control - rule base, performing multi - objective fuzzy decision - making, and outputting cooperative control parameters; triggering the iteration of the first factor according to the change of ash - layer dielectric parameters, and reversely adjusting the second factor according to the dust - removal efficiency deviation to optimize the rule weights.

2. The fuzzy control method for stable operation of the electrostatic precipitation system according to claim 1, characterized in that: The first factor is obtained by normalizing the temperature - field gradient, air - flow vorticity, and charge - field non - uniformity and using weighted summation. Among them, the temperature - field gradient is collected in real - time by a distributed temperature - sensor array, the air - flow vorticity is determined by multi - point measurement values of a vortex sensor at the cross - section of the electric field, and the charge - field non - uniformity is calculated based on the spatial - distribution data of a charged - density probe.

3. The fuzzy control method for the stable operation of the electrostatic precipitation system according to claim 1, characterized in that: The second factor is dynamically generated based on the real part and imaginary part of the ash - layer dielectric parameters. When the ash - layer specific resistance is lower than a preset back - corona risk threshold, a compensation mechanism is activated, and the amplitude of the second factor is adjusted by an exponential function. The ash - layer specific resistance is inversely calculated from the dielectric - loss parameters collected by a microwave - resonator sensor.

4. The fuzzy control method for stable operation of the electrostatic precipitation system according to claim 2, characterized in that: The multi - physical - field coupling analysis refers to three - dimensional synchronous collection using a temperature - gradient sensor, a vortex sensor, and a charged - density probe, real - time output of the maximum value of the temperature - field gradient, the integral value of the air - flow vorticity, and the relative value of the electric - field non - uniformity, and linearly superposing the three after normalization to generate the first factor.

5. The fuzzy control method for the stable operation of the electrostatic precipitation system according to claim 1, characterized in that: The dynamic reconstruction of the fuzzy membership function specifically includes the following steps: dynamically adjusting the fuzzy domain range of the flue - gas concentration deviation according to the first factor, so that the fuzzy domain range linearly expands and contracts with the physical - field coupling strength; offsetting the center point of the membership function of the energy - consumption index according to the second factor, and the offset amount is proportional to the square of the ash - layer specific resistance; detecting the ash - layer specific resistance and adding an exponential - compensation term in the membership - degree calculation to enhance the control sensitivity under low - specific - resistance conditions.

6. The fuzzy control method for stable operation of the electrostatic precipitation system according to claim 1, characterized in that: The execution of multi - objective fuzzy decision - making specifically includes the following steps: selecting a control - rule group according to threshold judgment. When the first factor exceeds the first preset threshold, activating the rule group dominated by pulses; when the second factor exceeds the second preset threshold, enabling the dielectric - compensation rule group; calculating the back - corona risk coefficient based on the ash - layer dielectric parameters; generating cooperative control parameters by comprehensively considering the flue - gas concentration deviation, energy - consumption index, and back - corona risk.

7. The fuzzy control method for the stable operation of the electrostatic precipitation system according to claim 1, characterized in that: The triggering of the first - factor iteration refers to using the gradient - descent method to adjust the weight coefficient of the first factor to match the change trend of the first factor and the ash - layer dielectric parameters.

8. The fuzzy control method for stable operation of the electrostatic precipitation system according to claim 1, characterized in that: The reverse adjustment of the second factor and the optimization of the rule weights refer to, based on the deviation between the actual dust - removal efficiency and the target dust - removal efficiency, adjusting the calculation coefficient of the second factor through an error - feedback mechanism, regenerating the second factor, and updating the weight allocation in the control - rule base.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that: When the processor executes the computer program, it realizes the steps of the fuzzy - control method for the stable operation of the electrostatic - precipitation system according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the steps of the fuzzy control method for the stable operation of the electrostatic precipitator system according to any one of claims 1 to 8.

Images