Intelligent electrostatic oil fume removing device and control method

Abstract

The application discloses a kind of intelligent electrostatic oil fume removal device and control method, device includes installation box, its inside is equipped with air inlet, negative pressure air suction device and sequentially distributed primary filter screen, ionization module, collection module, collection module below is equipped with oil collection warehouse;Equipped with main oil mist concentration sensor, auxiliary oil mist concentration sensor and ultrasonic sensor, electric control box integrates frequency converter controller, data processing module.Control method is collected oil mist concentration, liquid level etc.data by sensor, after pretreatment, initially based on preset mapping table adjusts fan power, self-learning optimization model calls historical data correction parameter every 24 hours, automatically reduces power and applies reverse voltage cleaning polar plate when low concentration, it can also be based on oil return rate optimization control.The application stable purification efficiency, low energy consumption, strong automation maintenance ability, high control precision.

Description

Technical Field

[0001] This invention relates to the field of oil mist purification equipment technology, and in particular to an intelligent electrostatic oil fume removal device and control method. Background Technology

[0002] In industrial production operations, the generation of oil mist particles, fumes, and dust has become a common problem. Components such as polychlorinated biphenyls contained in oil mist not only form toxic fumes that diffuse into the air and seriously affect workers' health when inhaled, but also pose a fire hazard when they settle on workshop walls and equipment surfaces. Direct emission into the atmosphere can further disrupt the ecological balance and cause environmental pollution.

[0003] With increasingly stringent environmental protection requirements, enterprises have a growing need for oil mist purification equipment. However, existing technologies still have significant limitations: On the one hand, traditional high-voltage electrostatic oil mist purifiers lack real-time detection capabilities for environmental oil mist concentration, requiring manual operation to start and stop the equipment. This often results in situations where the equipment is not started in time when the concentration exceeds the standard, or continues to run even after the concentration has reached the standard, leading to untimely purification or energy waste. On the other hand, the motor power of their negative pressure suction devices is mostly fixed and cannot be dynamically adjusted according to the oil mist concentration. At high concentrations, insufficient suction strength leads to inadequate purification, while at low concentrations, excessive power results in redundant energy consumption, making it difficult to balance purification efficiency and energy costs. Furthermore, existing equipment does not incorporate a learning optimization mechanism, making it unable to adapt to different operating conditions based on historical operating data, such as concentration fluctuations and equipment aging. After long-term operation, problems such as decreased purification effect and insufficient adaptability are likely to occur, making it difficult to meet the refined purification needs of complex industrial environments.

[0004] Even though some existing solutions attempt to introduce control modules, they still fail to solve the aforementioned core defects and fail to achieve an organic combination of intelligent start-stop based on oil mist concentration, dynamic power adjustment, and self-learning optimization, resulting in significant shortcomings in the equipment's intelligence, energy saving, and adaptive capabilities.

[0005] Therefore, developing an intelligent electrostatic oil fume removal device and control method that can detect environmental oil mist concentration and automatically start and stop, adjust motor power based on concentration, and continuously optimize by combining learning models has become an urgent technical problem to be solved. Summary of the Invention

[0006] This invention addresses the problems of failing to intelligently start and stop based on oil mist concentration, being unable to dynamically adjust motor power, and being unable to meet the refined purification needs of complex industrial environments. It provides an intelligent electrostatic oil fume removal device and control method that features environmental oil mist concentration detection and automatic start and stop, can adjust motor power based on concentration, and can continuously optimize by combining a learning model.

[0007] This invention provides the following technical solution: an intelligent electrostatic oil fume removal device, comprising a mounting box, an air inlet communicating with the interior at one end of the mounting box, and a negative pressure suction device at the other end of the mounting box. The negative pressure suction device forces outside air into the mounting box through the air inlet. An oil mist purification component is provided between the air inlet and the negative pressure suction device. The oil mist purification component, from the air inlet to the negative pressure suction device, sequentially includes a primary filter, an ionization module, and a collection module. An oil collection chamber is provided inside the mounting box, located below the collection module. A main oil mist concentration sensor is provided at the air inlet, and a secondary oil mist concentration sensor is provided between the collection module and the negative pressure suction device. The system includes an ultrasonic sensor above the oil collection tank for detecting the oil level, and an electrical control box on the side of the mounting box. The electrical control box integrates a frequency converter, a data processing module, and a data storage module. The negative pressure suction device uses a frequency converter fan, which is electrically connected to the frequency converter controller. The signal output terminals of both the main and auxiliary oil mist concentration sensors are connected to the data processing module. The data processing module has a built-in self-learning optimization model, which dynamically adjusts the output power of the fan controlled by the frequency converter based on the oil mist concentration data and optimizes the control strategy based on historical operating data from the data storage module.

[0008] In some embodiments, the collecting module is provided with uniformly distributed plates, and the plates are composed of positive plates and negative plates, which are vertically alternately distributed inside the collecting module.

[0009] In some embodiments, the collection module is connected to a reverse voltage driving unit, which includes a bidirectional relay module and an inverter circuit. The inverter circuit and the bidirectional relay module can convert a 12KV forward voltage into a -5KV reverse voltage.

[0010] In some embodiments, the self-learning optimization model is an incremental PID parameter self-tuning algorithm, which can correct the mapping relationship between oil mist concentration and fan power and collection module voltage based on historical data stored in the data storage module.

[0011] A control method for an intelligent electrostatic oil fume removal device includes the following steps:

[0012] S1. Data Acquisition: The main oil mist concentration sensor, auxiliary oil mist concentration sensor, and ultrasonic sensor collect the oil mist concentration at the air inlet, the oil mist concentration after purification, and the liquid level in the oil collection tank, respectively. The variable frequency fan power, ionization module voltage, and collection module voltage are collected simultaneously. The data is collected once every 10 seconds and stored in the data storage module.

[0013] S2. Preprocessing: Remove outliers from the collected data and classify it by concentration range and add operating condition labels.

[0014] S3. Initial control: Based on the power mapping table corresponding to the preset concentration, the data processing module adjusts the power of the fan through the frequency converter. When the main oil mist concentration is ≥ the start threshold, the equipment is started; when the main oil mist concentration is ≤ the stop threshold, the equipment is stopped.

[0015] S4. Dynamic Optimization: The self-learning optimization model calls historical data from the data storage module every 24 hours and corrects the mapping table parameters through an incremental PID algorithm.

[0016] S5, Low Concentration Energy Saving: When the main oil mist concentration is ≤3mg / m³, the variable frequency fan power is automatically reduced, and the collection module is controlled to apply reverse voltage to clean the plates.

[0017] In some embodiments, in step S4, the self-learning optimization model can also calculate the oil return rate based on the oil level data of the oil collection tank. The oil return rate = oil return volume / initial oil mist volume. When the oil return rate is <60%, the voltage of the collection module is increased by 0.1KV-0.2KV, and the power parameters of the fan are corrected simultaneously.

[0018] In some embodiments, in step S3, the initial parameters of the power mapping table for the preset concentration are: 3mg / m³-5mg / m³ corresponds to 30% of the fan power, 5mg / m³-10mg / m³ corresponds to 50%, 10-20mg / m³ corresponds to 70%, and ≥20mg / m³ corresponds to 100%.

[0019] In some embodiments, in the incremental PID parameter self-tuning algorithm, the control increment Δu(k) at time k satisfies the following formula: Δu(k) = Kp × Δe(k) + Ki × e(k) + Kd × Δ²e(k), where u(k) is the output control quantity at time k, u(k-1) is the output control quantity at time k-1, and Δu(k) = u(k) - u(k-1); Kp is the proportional coefficient, Ki is the integral coefficient, and Ki = Kp / Ti Ti is the integration time, Kd is the differential coefficient and Kd = Kp × T_d, T_d is the differential time; e(k) is the error at time k, and e(k) = target purification concentration (≤1mg / m³) - the value detected by the auxiliary oil mist concentration sensor; Δe(k) is the error increment at time k, and Δe(k) = e(k) - e(k-1); Δ²e(k) is the second-order error increment at time k, and Δ²e(k) = Δe(k) - Δe(k-1).

[0020] In some embodiments, the scaling factor Kp is dynamically corrected using historical data. The correction formula is Kp(k) = Kp(k-1) + α × |e(k)|, where α is the correction factor and 0 < α < 1. When the absolute value of e(k) increases, Kp(k) increases accordingly, and when the absolute value of e(k) decreases, Kp(k) decreases accordingly.

[0021] Compared with the prior art, the advantages of the present invention are as follows:

[0022] 1. Stable and intelligently adjustable purification efficiency

[0023] This invention monitors the oil mist concentration in the inlet and outlet air in real time using a main oil mist concentration sensor and a secondary oil mist concentration sensor. Combined with a built-in incremental PID parameter self-tuning algorithm, it dynamically adjusts the power of the variable frequency fan and the voltage of the collection module. The algorithm can correct the mapping relationship between oil mist concentration and control variables based on historical operating data, solving the problem of traditional equipment relying on fixed parameter adjustments and being unable to adapt to fluctuating oil fume conditions, thus ensuring that purification efficiency remains stable at a high level.

[0024] II. Energy consumption is significantly reduced.

[0025] For low-concentration oil fume scenarios, this invention automatically triggers an energy-saving mode: reducing the power of the variable frequency fan to 20% and applying a -5KV reverse voltage through the reverse voltage drive unit to clean the plates, ensuring the purification effect under low load while avoiding the energy waste of traditional equipment running at full power; at the same time, the dynamic optimization step adjusts the power parameters in the high energy consumption range by comparing historical energy consumption data, further reducing operating costs.

[0026] III. Strong automated maintenance capabilities

[0027] The reverse voltage drive unit connected to the collection module can automatically switch to -5KV reverse voltage to achieve non-contact cleaning of oil stains on the electrode plates. This solves the problem of frequent manual disassembly and cleaning of electrode plates required by traditional electrostatic oil fume removal equipment, reducing maintenance workload and downtime.

[0028] IV. Control Precision

[0029] In incremental PID algorithm, the proportional coefficient Kp is dynamically corrected by the absolute value of the error. When the error increases, the adjustment sensitivity is increased, and when the error decreases, the coefficient is reduced to avoid oscillation. Compared with traditional fixed parameter PID, the control response is faster and the stability is better.

[0030] V. Optimization of oil mist recovery efficiency

[0031] This invention calculates the oil return rate using the oil level data of the oil collection tank. When the oil return rate is less than 60%, it automatically increases the voltage of the collection module and corrects the fan power to enhance the adsorption capacity of the electrode plate for oil mist particles, thereby improving the oil mist recovery efficiency and meeting the requirements of energy conservation and environmental protection. Attached Figure Description

[0032] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a schematic diagram of the structure of the present invention;

[0034] Figure 2 This is a schematic diagram of the exploded structure of the present invention;

[0035] Figure 3 This is a schematic diagram of the internal structure of the present invention;

[0036] Figure 4 This is a schematic diagram of the ionization module of the present invention;

[0037] Figure 5 This is a schematic diagram of the collection module of the present invention;

[0038] Figure 6 This is a schematic diagram showing the positions of the auxiliary oil mist concentration sensor and the ultrasonic sensor of the present invention;

[0039] Figure 7 This is a schematic flowchart of the control method of the present invention;

[0040] Figure 8 This is a schematic diagram of the low-concentration energy-saving triggering process of the present invention;

[0041] Figure 9 This is the power mapping table of the present invention.

[0042] In the diagram: 1. Installation box; 11. Air inlet; 12. Oil collection tank; 13. Electrical control box; 2. Negative pressure suction device; 21. Fan; 3. Primary filter; 4. Ionization module; 5. Collection module; 51. Positive electrode plate; 52. Negative electrode plate; 6. Main oil mist concentration sensor; 7. Secondary oil mist concentration sensor; 8. Ultrasonic sensor. Detailed Implementation

[0043] The present application will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0044] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. This application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0045] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this application, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number and aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0046] It should also be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. The illustrations only show the components related to this application and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0047] Additionally, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that practice can be carried out without these specific details.

[0048] The technical solutions provided by the various embodiments of this application are described below with reference to the accompanying drawings.

[0049] Please see Figure 1-6As shown in this embodiment: an intelligent electrostatic oil fume removal device includes a mounting box 1. One end of the mounting box 1 is provided with an air inlet 11 communicating with the interior, and the other end of the mounting box 1 is provided with a negative pressure suction device 2. The negative pressure suction device 2 forces outside air into the mounting box 1 through the air inlet 11. An oil mist purification component is provided between the air inlet 11 and the negative pressure suction device 2. The oil mist purification component consists of a primary filter 3, an ionization module 4, and a collection module 5 arranged sequentially from the air inlet 11 to the negative pressure suction device 2. An oil collection chamber 12 is provided inside the mounting box 1, located below the collection module 5. A main oil mist concentration sensor 6 is provided at the air inlet 11. The collection module 5 and the negative pressure suction device... A secondary oil mist concentration sensor 7 is installed between the two oil collection tanks 12 and 2. An ultrasonic sensor 8 for detecting the oil level is installed above the oil collection tank 12. An electrical control box 13 is installed on the side of the mounting box 1. The electrical control box 13 integrates a frequency converter, a data processing module, and a data storage module. The fan 21 is a frequency converter fan 21 and is electrically connected to the frequency converter. The signal output terminals of the main oil mist concentration sensor 6 and the secondary oil mist concentration sensor 7 are both connected to the data processing module. The data processing module has a built-in self-learning optimization model. The self-learning optimization model can dynamically adjust the output power of the fan 21 controlled by the frequency converter according to the oil mist concentration data, and optimize the control strategy based on the historical operating data of the data storage module.

[0050] In some embodiments, such as Figure 5 As shown, the collection module 5 has uniformly distributed plates inside, and the plates are composed of a positive plate 51 and a negative plate 52. The positive plate 51 and the negative plate 52 are vertically alternately distributed inside the collection module 5.

[0051] In some embodiments, the collection module 5 is connected to a reverse voltage driving unit, which includes a bidirectional relay module and an inverter circuit. The inverter circuit and the bidirectional relay module can convert a 12KV forward voltage into a -5KV reverse voltage.

[0052] It should be noted that when the main oil mist concentration sensor 6 detects a concentration ≤3mg / m³ for 30 minutes, the data processing module can control the reverse voltage drive unit to apply a -5KV reverse voltage to the collection module 5 for a duration of 10-12 seconds.

[0053] In some embodiments, the self-learning optimization model is an incremental PID parameter self-tuning algorithm, which can correct the mapping relationship between oil mist concentration and the power of the fan 21 and the voltage of the collection module 5 based on the historical data stored in the data storage module.

[0054] Please see Figure 7-8 As shown in this embodiment: a control method for an intelligent electrostatic grease removal device includes the following steps:

[0055] S1. Data Acquisition: The main oil mist concentration sensor 6, the auxiliary oil mist concentration sensor 7, and the ultrasonic sensor 8 are used to collect the oil mist concentration at the air inlet 11, the oil mist concentration after purification, and the liquid level data of the oil collection tank 12, respectively. The power of the variable frequency fan 21, the voltage of the ionization module 4, and the voltage of the collection module 5 are collected simultaneously. The data is collected once every 10 seconds and stored in the data storage module.

[0056] S2. Preprocessing: Remove outliers from the collected data and classify it by concentration range and add operating condition labels.

[0057] S3. Initial control: Based on the power mapping table corresponding to the preset concentration, the data processing module adjusts the power of the fan 21 through the frequency converter. When the main oil mist concentration is ≥ the start threshold, the equipment is started; when the main oil mist concentration is ≤ the stop threshold, the equipment is stopped.

[0058] S4. Dynamic Optimization: The self-learning optimization model calls historical data from the data storage module every 24 hours and corrects the mapping table parameters through an incremental PID algorithm: if the purification efficiency in the concentration range is <95%, the corresponding power is increased by 5%-10%; if the energy consumption is 15% higher than the range average, the corresponding power is reduced by 3%-5%.

[0059] S5, Low Concentration Energy Saving: When the main oil mist concentration is ≤3mg / m³ and lasts for 30 minutes, the power of the variable frequency fan 21 is automatically reduced to 20%, and the collection module 5 is controlled to apply a -5KV reverse voltage for 10-12 seconds to clean the plates.

[0060] It should be noted that S4 and S5 operate in parallel and run continuously after the device starts up.

[0061] It should be noted that both the main oil mist concentration sensor 6 and the auxiliary oil mist concentration sensor 7 are laser scattering sensors.

[0062] It should be noted that the start threshold and stop threshold are manually set according to the needs of the scenario.

[0063] In some embodiments, in step S4, the self-learning optimization model can also calculate the oil return rate based on the liquid level data of the oil collection tank 12. The oil return rate = oil return volume / initial oil mist volume. When the oil return rate is <60%, the voltage of the collection module 5 is increased by 0.1KV-0.2KV, and the power parameters of the fan 21 are corrected simultaneously.

[0064] It should be noted that the result is calculated by accumulating the inlet concentration × real-time air volume × time.

[0065] In some embodiments, such as Figure 9As shown, in step S3, the initial parameters of the power mapping table for the preset concentration are as follows: 3mg / m³-5mg / m³ corresponds to 30% power of fan 21 and 6.0KV voltage of collection module 5; 5mg / m³-10mg / m³ corresponds to 50% power and 6.1KV voltage of collection module 5; 10-20mg / m³ corresponds to 70% power of fan 21 and 6.2KV voltage of collection module 5; ≥20mg / m³ corresponds to 100% power of fan 21 and 6.3KV voltage of collection module 5.

[0066] In some embodiments, in the incremental PID parameter self-tuning algorithm, the control increment Δu(k) at time k satisfies the following formula: Δu(k) = Kp × Δe(k) + Ki × e(k) + Kd × Δ²e(k), where u(k) is the output control quantity at time k, u(k-1) is the output control quantity at time k-1, and Δu(k) = u(k) - u(k-1); Kp is the proportional coefficient, Ki is the integral coefficient, and Ki = Kp / Ti Ti is the integration time, Kd is the differential coefficient and Kd = Kp × T_d, T_d is the differential time; e(k) is the error at time k, and e(k) = target purification concentration (≤1mg / m³) - detection value of auxiliary oil mist concentration sensor 7; Δe(k) is the error increment at time k, and Δe(k) = e(k) - e(k-1); Δ²e(k) is the second-order error increment at time k, and Δ²e(k) = Δe(k) - Δe(k-1).

[0067] Algorithm core parameters and initial configuration: Target purification concentration: set to 1 mg / m³, that is, the target detection value of the auxiliary oil mist concentration sensor 7 is ≤ 1 mg / m³;

[0068] Initial parameters: Initial value of proportional coefficient Kp: 0.8 (power regulation of variable frequency fan 21), 0.5 (voltage regulation of collection module 5);

[0069] Integration time Ti: 100s, calculated Ki=Kp / Ti, i.e. 0.008 or 0.005;

[0070] Differential time T_d: 20s, calculated to be Kd=Kp×T_d, i.e. 16 or 10;

[0071] Sampling period: Synchronized with sensor data acquisition, PID calculation is performed every 10 seconds, that is, the interval between time k and time k-1 is 10 seconds.

[0072] Taking the power adjustment of variable frequency fan 21 as an example, the specific steps are as follows:

[0073] Error e(k) calculation: e(k) = target purification concentration − secondary oil mist concentration sensor 7 detection value: For example: when the secondary sensor detection value is 1.5mg / m³, e(k) = 1 - 1.5 = 0.5mg / m³, a negative error indicates insufficient purification;

[0074] Error increment Δe(k) calculation: e(k) = e(k) - e(k-1), for example: at time k-1, e(k-1) = -0.3 mg / m³, then e(k) = -0.5 - (-0.3) = -0.2 mg / m³.

[0075] Calculation of the second-order error increment Δ²e(k): Δ²e(k) = Δe(k) - Δe(k-1): For example: at time k-1, Δe(k-1) = -0.1 mg / m³, then Δ²e(k) = -0.2 - (-0.1) = -0.1 mg / m³;

[0076] Calculation of control increment Δu(k): Substituting into the formula Δu(k)=Kp·Δe(k)+Ki·e(k)+Kd·Δ²e(k), we get: Δu(k)=0.8×(-0.2)+0.008×(-0.5)+16×(-0.1)=-0.16-0.004-1.6=-1.764%. The negative sign indicates that the power of the fan needs to be increased to enhance the purification effect. The actual absolute value of 1.8% is used for adjustment.

[0077] Actual control output: u(k) = u(k-1) + Δu(k). If the power is 50% at time k-1, then the power is adjusted to 51.8% at time k.

[0078] Algorithm application scenarios:

[0079] Variable frequency fan 21 power adjustment: output control range 0-100%, corresponding to 0-50Hz frequency;

[0080] Voltage regulation of collection module 5: The output control range is 5.7-6.3KV, which is achieved through the voltage regulation circuit of the high voltage power supply module;

[0081] The two adjustment modes are automatically switched through the logic judgment of the data processing module: for low concentrations ≤5mg / m³, the voltage is adjusted first, and for high concentrations ≥20mg / m³, the power is adjusted first.

[0082] In some embodiments, the proportional coefficient Kp is dynamically corrected using historical data. The correction formula is Kp(k) = Kp(k-1) + α × |e(k)|, where α is the correction coefficient and 0 < α < 1. When the absolute value of e(k) increases, Kp(k) increases accordingly to improve the adjustment sensitivity. When the absolute value of e(k) decreases, Kp(k) decreases accordingly to avoid adjustment oscillation.

[0083] Kp's correction mechanism is implemented through a timed interrupt routine in the data processing module (executed every 30 seconds), as follows:

[0084] Selection of correction factor α: The value of α is dynamically set according to the operating conditions.

[0085] Stable operating condition: three consecutive e(k) fluctuations ≤ 0.2 mg / m³: α = 0.01, slow correction to avoid oscillation;

[0086] Fluctuation condition: e(k) fluctuation > 0.5 mg / m³: α = 0.1, rapid correction, improve response speed.

[0087] Example of the correction process

[0088] Initially, Kp(k-1) = 0.8. At a certain moment, e(k) = -1.2 mg / m³. As the absolute value of the error increases, α = 0.1: Kp(k) = 0.8 + 0.1 × |-1.2| = 0.8 + 0.12 = 0.92. Kp increases, improving the adjustment sensitivity and accelerating the power / voltage increase.

[0089] If e(k) = -0.3 mg / m³, the absolute value of the error decreases, α = 0.01: Kp(k) = 0.92 + 0.01 × |-0.3| = 0.92 + 0.003 = 0.923, Kp increases slightly to avoid oscillations caused by over-adjustment.

[0090] Correcting constraints:

[0091] The minimum value of Kp is not lower than 0.3 to ensure basic adjustment capability;

[0092] The maximum value of Kp should not exceed 1.5 to prevent excessive adjustment from causing system instability.

[0093] In some embodiments, such as Figure 8 As shown, the concentration continuous detection and timing logic is implemented. The data processing module reads the data from the main oil mist concentration sensor 6 every 10 seconds and performs the following judgment: Initialization: timing variable T=0, status flag = "normal operation";

[0094] Cycle: Read the main sensor concentration C. If C ≤ 3 mg / m³: T = T + 10 seconds, accumulate the time; if T ≥ 1800 seconds and the status flag ≠ "Energy Saving Mode": trigger energy saving mode, execute step S2, status flag = "Energy Saving Mode"; otherwise: T = 0, reset the timer; if the status flag = "Energy Saving Mode": exit energy saving mode and restore normal power; if the status flag = "Normal Operation", energy saving mode execution steps. When the trigger condition is met, T ≥ 1800 seconds, the data processing module controls according to the following timing sequence:

[0095] A1. Power Regulation: Send a command to the frequency converter controller via RS485 communication to reduce the output frequency to 10Hz, corresponding to 20% power. The fan speed 21 will be reduced to maintain low air volume operation.

[0096] A2. Reverse voltage clearing:

[0097] t=0 seconds: The bidirectional relay is switched to the reverse voltage circuit, and a -5KV voltage is applied to the 5-electrode plate of the collection module. The adsorbed oil mist particles fall off due to charge reversal and fall into the oil collection tank.

[0098] t=10 seconds: The relay resets, restoring the positive 6KV voltage, and the plates re-enter the adsorption state;

[0099] A3. Continuous monitoring: In energy-saving mode, the concentration is monitored every 10 seconds. If the concentration rises to >3mg / m³, the energy-saving mode is immediately exited, and the corresponding value in the mapping table is called according to the concentration to restore normal power.

[0100] It should be noted that the main concentration C1 is in mg / m³ and the secondary concentration C2 is in mg / m³.

[0101] Oil collection tank 12 liquid level (L, unit: cm);

[0102] Variable frequency fan 21 power P (unit: %), ionization module 4 voltage U1 (unit: kV), collection module 5 voltage U2 (unit: kV).

[0103] In step S2: Outlier removal: filter data where C1 / C2 > 50 mg / m³ or < 0, indicating the sensor is out of range; filter data where L < 0, indicating an abnormal liquid level.

[0104] Classification labeling: Based on the C1 value, the concentration is divided into low concentration (≤5mg / m³), medium concentration (5-20mg / m³), and high concentration (≥20mg / m³). Combined with the fluctuation of three consecutive collection values, >1mg / m³ is considered fluctuating, otherwise it is considered stable, and an operating condition label is added.

[0105] It should be noted that the power of the variable frequency fan 21 is collected by detecting the input side voltage and current and calculating the active power. The voltage of the ionization module 4 and the voltage of the collection module 5 are respectively converted into low-voltage signals by a high-voltage divider circuit and an isolation optocoupler before being collected.

[0106] The same or similar parts between the various embodiments in this specification can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments.

[0107] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An intelligent electrostatic oil fume removal device, comprising a mounting box (1), wherein one end of the mounting box (1) is provided with an air inlet (11) communicating with the interior, and the other end of the mounting box (1) is provided with a negative pressure suction device (2), wherein the negative pressure suction device (2) forces outside air to enter the mounting box (1) from the air inlet (11), and an oil mist purification component is provided between the air inlet (11) and the negative pressure suction device (2), characterized in that: The oil mist purification assembly is provided with a primary filter (3), an ionization module (4), and a collection module (5) in sequence from the air inlet (11) to the negative pressure suction device (2). The mounting box (1) is provided with an oil collection chamber (12) located below the collection module (5). A main oil mist concentration sensor (6) is provided at the air inlet (11). A secondary oil mist concentration sensor (7) is provided between the collection module (5) and the negative pressure suction device (2). An ultrasonic sensor (8) for detecting the oil level is provided above the oil collection chamber (12). An electrical control box is provided on the side of the mounting box (1). (13) The electrical control box (13) integrates a frequency converter, a data processing module and a data storage module. The fan (21) of the negative pressure suction device (2) is a frequency converter fan (21) and is electrically connected to the frequency converter. The signal output terminals of the main oil mist concentration sensor (6) and the auxiliary oil mist concentration sensor (7) are both connected to the data processing module. The data processing module has a built-in self-learning optimization model. The self-learning optimization model can dynamically adjust the output power of the fan (21) controlled by the frequency converter according to the oil mist concentration data, and optimize the control strategy based on the historical operating data of the data storage module. The collection module (5) is connected to a reverse voltage driving unit, which includes a bidirectional relay module and an inverter circuit. The inverter circuit and the bidirectional relay module can convert 12KV forward voltage into -5KV reverse voltage. The self-learning optimization model is an incremental PID parameter self-tuning algorithm, which can correct the mapping relationship between oil mist concentration and the power of the fan (21) and the voltage of the collection module (5) based on the historical data stored in the data storage module. The control method of the intelligent electrostatic oil fume removal device includes the following steps: S1, data acquisition: the oil mist concentration at the air inlet (11), the oil mist concentration after purification, and the liquid level of the oil collection tank (12) are collected by the main oil mist concentration sensor (6), the auxiliary oil mist concentration sensor (7), and the ultrasonic sensor (8), respectively. The power of the fan (21), the voltage of the ionization module (4), and the voltage of the collection module (5) are collected simultaneously. The data is collected once every 10 seconds and stored in the data storage module; S2, preprocessing: outliers are removed from the collected data, and operating condition labels are added according to the concentration range; S3 S1. Initial control: Based on the power mapping table corresponding to the preset concentration, the data processing module adjusts the power of the fan (21) through the frequency converter. When the main oil mist concentration is ≥ the start threshold, the equipment is started, and when the main oil mist concentration is ≤ the stop threshold, the equipment is stopped; S4. Dynamic optimization: The self-learning optimization model calls the historical data of the data storage module every 24 hours and corrects the mapping table parameters through the incremental PID algorithm; S5. Low concentration energy saving: When the main oil mist concentration is ≤ 3mg / m³, the power of the fan (21) is automatically reduced, and the collection module (5) is controlled to apply reverse voltage to clean the plates; In step S4, the self-learning optimization model can also calculate the oil return rate based on the liquid level data of the oil collection tank (12). The oil return rate = oil return volume / initial oil mist volume. When the oil return rate is <60%, the voltage of the collection module (5) is increased by 0.1KV-0.2KV, and the power parameters of the fan (21) are corrected simultaneously. The two adjustment modes of the fan (21) power adjustment and the collection module (5) voltage adjustment are automatically switched by the logic judgment of the data processing module: for low concentrations ≤5mg / m³, the voltage is adjusted first, and for high concentrations ≥20mg / m³, the power is adjusted first.

2. The intelligent electrostatic oil fume removal device according to claim 1, characterized in that: The collecting module (5) is provided with uniformly distributed electrode plates inside, and the electrode plates are composed of positive electrode plates (51) and negative electrode plates (52). The positive electrode plates (51) and the negative electrode plates (52) are vertically alternately distributed inside the collecting module (5).

3. The intelligent electrostatic oil fume removal device according to claim 1, characterized in that: In step S3, the initial parameters of the power mapping table for the preset concentration are: the main oil mist concentration of 3mg / m³-5mg / m³ corresponds to 30% of the power of the fan (21), 5mg / m³-10mg / m³ corresponds to 50%, 10-20mg / m³ corresponds to 70%, and ≥20mg / m³ corresponds to 100%.

4. The intelligent electrostatic oil fume removal device according to claim 1, characterized in that: In the incremental PID parameter self-tuning algorithm, the control increment Δu(k) at time k satisfies the following formula: Δu(k) = Kp × Δe(k) + Ki × e(k) + Kd × Δ²e(k), where u(k) is the output control quantity at time k, u(k-1) is the output control quantity at time k-1, and Δu(k) = u(k) - u(k-1); Kp is the proportional coefficient, Ki is the integral coefficient, and Ki = Kp / Ti Ti is the integration time, Kd is the differential coefficient and Kd=Kp×T_d, T_d is the differential time; e(k) is the error at time k, and e(k)=target purification concentration-substitute oil mist concentration sensor (7) detection value; Δe(k) is the error increment at time k, and Δe(k)=e(k)-e(k-1); Δ²e(k) is the second-order error increment at time k, and Δ²e(k)=Δe(k)-Δe(k-1).

5. The intelligent electrostatic oil fume removal device according to claim 4, characterized in that: The proportionality coefficient Kp is dynamically corrected based on historical data. The correction formula is Kp(k) = Kp(k-1) + α × |e(k)|, where α is the correction coefficient and 0 < α < 1. When the absolute value of e(k) increases, Kp(k) increases accordingly, and when the absolute value of e(k) decreases, Kp(k) decreases accordingly.

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