A wet dust removal equipment adaptive pollution discharge method and device

Abstract

This invention discloses an adaptive sewage discharge method and device for wet dust collection equipment. First, the dust collection efficiency and corresponding turbidity data of the wet dust collection equipment for particles of different sizes are obtained. A nonlinear regression model is used in conjunction with the above data to establish a quantitative relationship model between turbidity and dust collection efficiency for particles of different sizes. Then, a dust collection efficiency threshold is set, and a curve showing the change in dust collection efficiency with liquid turbidity is generated based on the model, thereby determining the turbidity threshold corresponding to each particle size. During dust collection, the particle size of the main particles in the environment is first measured, and then the corresponding model and turbidity threshold are determined. During the dust collection process, the turbidity value of the liquid inside the equipment is continuously monitored until the turbidity threshold is reached, at which point the liquid inside the equipment is discharged and replaced. If dust collection is subsequently carried out in other environments, only the particle size of the particles in the corresponding environment needs to be determined to achieve adaptive sewage discharge for particles of different sizes. This allows the equipment to operate in the optimal range that balances dust collection efficiency and water conservation.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology for dust control and wet dust removal equipment, specifically an adaptive sewage discharge method and device for wet dust removal equipment based on a turbidity-efficiency dynamic model. Background Technology

[0002] Wet scrubbers are widely used in high-emission industries such as mining, metallurgy, tobacco, and chemicals. Their core principle is to capture dust using droplets, liquid curtains, or bubbles. However, as operating time increases, the turbidity of the liquid within the gas-liquid mixing chamber rises, leading to a decrease in dust removal efficiency. Currently, industry practices often rely on experience to set fixed time intervals for wastewater discharge. This means that after each period of time during dust removal, the liquid in the wet scrubber is discharged and replaced with fresh liquid. While this method allows for periodic wastewater replacement, it lacks a scientific understanding and real-time response mechanism regarding the dynamic relationship between liquid turbidity and dust removal efficiency. This makes it difficult to accurately determine the optimal time for wastewater discharge, easily leading to water waste or failure to provide early warning of declining dust removal efficiency, ultimately resulting in excessive emissions.

[0003] The existing technology has the following shortcomings:

[0004] (a) Relying on fixed time intervals for sewage discharge makes it difficult to accurately grasp the best time for sewage discharge due to the fixed time point for liquid replacement, which can easily lead to waste of water resources or a decrease in dust removal efficiency.

[0005] (b) The wet dust collection field lacks turbidity detection, meaning that turbidity detection is disconnected from dust collection efficiency, making it impossible to achieve intelligent wastewater control based on system performance optimization. Although there are existing devices that use optical principles to detect turbidity, they only reach the level of simple detection and fail to combine the coupling relationship between the collection characteristics of particles of different sizes and turbidity changes during wet dust collection, nor have they established a quantitative model of turbidity and dust collection efficiency, thus failing to achieve intelligent wastewater control based on system performance optimization.

[0006] Therefore, there is an urgent need for a new method and device that can monitor liquid turbidity in real time, establish a quantitative relationship between turbidity and dust removal efficiency, and achieve adaptive sewage discharge accordingly, so as to break through the limitations of traditional timed sewage discharge and improve the overall operational efficiency of wet dust removal equipment. This is the research direction required by this invention. Summary of the Invention

[0007] To address the problems existing in the prior art, the present invention provides an adaptive sewage discharge method and device for wet dust collection equipment, which can monitor liquid turbidity in real time, establish a quantitative relationship between turbidity and dust removal efficiency of particles of different sizes, and thereby achieve adaptive sewage discharge of particles of different sizes, so as to break through the limitations of traditional timed sewage discharge and improve the overall operating efficiency of wet dust collection equipment.

[0008] To achieve the above objectives, the technical solution adopted by the present invention is: an adaptive sewage discharge method for wet dust removal equipment, comprising the following steps:

[0009] Step 1: Wet Dust Collection Equipment Testing: Select fine particles with different particle sizes and test them using a wet dust collection equipment. Calculate the real-time dust collection efficiency. Simultaneously monitor the turbidity of the liquid inside the wet dust collection equipment during the dust collection process. After completion, replace the liquid and select fine particles with different particle sizes, repeating the above steps to obtain the dust collection efficiency and corresponding turbidity data of the wet dust collection equipment for particles of different sizes.

[0010] Step 2: Construct a quantitative relationship model between turbidity and dust removal efficiency: Using a nonlinear regression model, the dust removal efficiency and turbidity data of each particle size range measured in Step 1 are input into the model to establish a quantitative relationship model between turbidity and dust removal efficiency for particles of different sizes.

[0011] Step 3: Determine the turbidity threshold for different particle sizes: Set the dust removal efficiency threshold, and generate curves showing the change of dust removal efficiency with liquid turbidity based on the quantitative relationship model of turbidity-dust removal efficiency for different particle sizes obtained in Step 2. According to the changes in each curve, divide the curves into low turbidity range, medium turbidity range, high turbidity range, and ultra-high turbidity range based on the changes in dust removal efficiency. Select the turbidity value corresponding to the dust removal efficiency threshold in the ultra-high turbidity range of each curve to determine the turbidity threshold corresponding to each particle size.

[0012] Step 4: Preparation before dust removal with wet dust removal equipment: First, determine the particle size range of the dust removal environment, then determine the corresponding quantitative relationship model of turbidity-dust removal efficiency and turbidity threshold, and set the first turbidity detection time interval and the second turbidity detection time interval.

[0013] Step 5: Wet Dust Collector Discharge: The wet dust collector begins dust collection in the environment measured in Step 4. During the dust collection process, turbidity is measured at the first turbidity detection time interval, and the measured value is input into the turbidity-dust collection efficiency quantitative relationship model to determine the interval corresponding to the current turbidity measured value. When the measured value is in the high turbidity interval, subsequent turbidity measurements are performed at the second turbidity detection time interval until the measured value reaches the turbidity threshold. At this point, the liquid in the wet dust collector is discharged and replaced with new liquid. The new liquid continues the dust collection process, and this step is repeated.

[0014] Step Six: Adaptive Sewage Discharge of Wet Dust Collector in Different Environments: When the same wet dust collector is used for dust collection in environments with different particle sizes, steps four and five are repeated respectively. This allows the corresponding turbidity threshold to be determined based on the particle size of the particles in different environments, thus achieving adaptive sewage discharge in different environments.

[0015] Furthermore, the particulate matter used in step one with different particle sizes includes particulate matter with a particle size of PM1 and particulate matter with a particle size of PM2.5. 2.5 Particulate matter, particulate matter with a particle size of PM4, particulate matter with a particle size of PM 10 Particulate matter and total suspended particulate matter (TSP).

[0016] Furthermore, in step three, the interval where the initial dust removal efficiency decreases relatively gradually is divided into a low turbidity interval, indicating that there is still an effective wetting and capture mechanism between particulate matter and droplets.

[0017] The interval where dust removal efficiency decreases rapidly after passing through the low turbidity range is classified as the medium turbidity range, indicating that the active sites on the droplet surface tend to be saturated and the competitive wetting phenomenon of particulate matter is significantly enhanced.

[0018] The interval where the dust removal efficiency tends to stabilize after passing through the medium turbidity range is divided into the high turbidity range, indicating that the wet dust removal equipment has entered the "critical capture state" and the particulate matter resuspension and droplet saturation effect have reached a dynamic equilibrium.

[0019] The section where dust removal efficiency drops rapidly after passing through the high turbidity zone is classified as the ultra-high turbidity zone, indicating that the droplet surface is completely covered by particulate matter, the collection mechanism fails, and forced discharge is required.

[0020] Furthermore, the turbidity value corresponding to the dust removal efficiency threshold in step three is replaced by the dividing point value between the high turbidity range and the ultra-high turbidity range, and used as the turbidity threshold. Since the dust removal efficiency drops rapidly in the ultra-high turbidity range, in order to prevent the turbidity detection from missing the optimal time for sludge discharge, which would lead to a significant drop in dust removal efficiency, the dividing point between the two ranges can be directly used as the turbidity threshold. This allows for sludge discharge and liquid replacement as soon as the dust removal efficiency begins to drop rapidly, thereby ensuring the continuity of dust removal efficiency and conserving water resources as much as possible.

[0021] Furthermore, in step four, the second turbidity detection time interval is shorter than the first turbidity detection time interval. In the low and medium turbidity ranges, since the dust removal efficiency is much higher than the dust removal efficiency threshold, the first turbidity detection time interval is used to reduce data processing, making the time interval between two adjacent detections relatively large. This reduces the number of detections while maintaining stable dust removal efficiency. However, upon entering the high turbidity range, as it approaches the subsequent turbidity threshold, the second turbidity detection time interval is used to ensure real-time detection, making the time interval between two adjacent detections relatively small. This ensures that the liquid turbidity is detected as close to the turbidity threshold as possible, at which point wastewater discharge and liquid replacement begin. This maximizes liquid utilization while maintaining dust removal efficiency, effectively saving water resources.

[0022] The apparatus for the adaptive sewage discharge method of the aforementioned wet dust removal equipment includes wet dust removal equipment, a turbidity monitor, a signal acquisition system, and a host computer.

[0023] The turbidity monitor is installed in the gas-liquid mixing chamber of the wet dust removal equipment, and is located between the liquid surface and the bottom drain pipe, and is used to detect the turbidity of the liquid.

[0024] The signal acquisition system is connected to the turbidity monitor and the host computer, respectively, and is used to transmit the data collected by the turbidity monitor to the host computer.

[0025] The host computer is used to analyze and process the received turbidity values, and control the valves on the sewage pipe to open for sewage discharge and liquid replacement based on the analysis results.

[0026] Furthermore, multiple turbidity monitors are used, all distributed within the gas-liquid mixing chamber below the liquid surface, avoiding areas of air bubbles and sludge. Averaging data from multiple turbidity monitors improves data acquisition accuracy, while avoiding air bubbles and sludge areas further ensures the stability of the data acquisition process.

[0027] Furthermore, the turbidity monitor is a light scattering turbidity monitor. The turbidity monitor can employ ultrasonic or capacitive principles; light scattering is preferred due to its fast response and high accuracy. It monitors the turbidity of the liquid within the gas-liquid mixing chamber in real time by measuring the intensity of scattered light. When light passes through a liquid containing suspended particles, some of the light is scattered by the particles; the higher the intensity of the scattered light, the higher the turbidity of the liquid.

[0028] Furthermore, the turbidity monitor is equipped with an automatic surface cleaning device at the light source for cleaning the surface of the light source. The automatic surface cleaning device is an existing structure, such as a cleaning brush mechanism, which can move and clean the surface of the light source via a connecting rod. This structure can remove sludge adhering to the surface of the light source, ensuring the detection accuracy of the turbidity monitor.

[0029] Compared with existing technologies, this invention first obtains the dust removal efficiency and corresponding turbidity data of wet dust removal equipment for particles of different sizes through experimental measurement; it then uses a nonlinear regression model to input the measured dust removal efficiency and turbidity data for each particle size into the model, thereby establishing a quantitative relationship model between turbidity and dust removal efficiency for particles of different sizes; next, it sets a dust removal efficiency threshold and generates a curve of dust removal efficiency versus liquid turbidity based on the model, while dividing different intervals according to the curve changes; it selects the turbidity value corresponding to the dust removal efficiency threshold and determines it as the turbidity threshold for each particle size; during actual dust removal, it first measures the particle size of the main particles in the current environment, and then determines the corresponding quantitative relationship model between turbidity and dust removal efficiency and the turbidity threshold; during the dust removal process, it continuously monitors the turbidity value of the liquid inside the dust removal equipment. The system determines the range in which the dust collector is located and automatically adjusts the detection frequency based on changes in that range until the detected value reaches the turbidity threshold. At this point, the liquid in the wet scrubbing equipment is discharged and replaced with fresh liquid. If the dust collection environment changes or the particulate matter in the current environment changes, it is only necessary to determine the particle size of the corresponding environment. This allows for the adaptive determination of the corresponding quantitative relationship model between turbidity and dust collection efficiency, as well as the turbidity threshold, thus meeting the dust collection needs for environments with different particle sizes. Through this method, while ensuring dust collection efficiency for different particle sizes, the system can also extend the liquid's usage time as much as possible, achieving adaptive discharge of different particle sizes. This allows the equipment to operate in the optimal range that balances dust collection efficiency and water conservation, overcoming the limitations of traditional timed discharge and improving the overall operational efficiency of the wet scrubbing equipment. Attached Figure Description

[0030] Figure 1 This is a schematic diagram of the device in this invention.

[0031] Figure 2 This is a model showing the relationship between dust removal efficiency and turbidity for particles of different sizes in this invention. Detailed Implementation

[0032] The present invention will be further described below.

[0033] Example: An adaptive sewage discharge method for wet dust collection equipment, comprising the following steps:

[0034] Step 1: Wet Scrubber Equipment Testing: Select fine particulate matter containing different particle size ranges, and conduct a dust removal test using a wet scrubber. Calculate the real-time dust removal efficiency. Simultaneously monitor the turbidity of the liquid inside the wet scrubber during the dust removal process. After completion, replace the liquid and select fine particulate matter containing different particle size ranges, repeating the above steps to obtain the dust removal efficiency and corresponding turbidity data of the wet scrubber for different particle sizes. Different particle sizes include particles with a diameter of PM1 and particles with a diameter of PM2.5. 2.5Particulate matter, particulate matter with a particle size of PM4, particulate matter with a particle size of PM 10 Particulate matter and total suspended particulate matter (TSP).

[0035] Step 2: Constructing a quantitative relationship model between turbidity and dust removal efficiency: A nonlinear regression model is used, inputting the dust removal efficiency and turbidity data for each particle size range measured in Step 1 into the model to establish a quantitative relationship model between turbidity and dust removal efficiency for particles of different sizes. Specifically:

[0036] PM1: η = 187.60 − 2.20×10⁻¹τ + 1.44×10⁻ 4 τ² − 3.17×10⁻ 8 τ³

[0037] PM 2.5 : η = 153.72 − 1.28×10⁻¹τ + 8.27×10⁻ 5 τ² − 1.79×10⁻ 8 τ³

[0038] PM4: η = 126.81 − 6.27×10⁻²τ + 4.00×10⁻ 5 τ² − 8.55×10⁻ 9 τ³

[0039] PM 10 : η = 115.36 − 3.57×10⁻²τ + 2.44×10⁻ 5 τ² − 4.69×10⁻ 9 τ³

[0040] TSP: η = 118.22 − 4.10×10⁻²τ + 2.49×10⁻ 5 τ² − 4.99×10⁻ 9 τ³.

[0041] Step 3: Determine the turbidity threshold for particles of different sizes: Set the dust removal efficiency threshold, and based on the quantitative relationship model of turbidity-dust removal efficiency for particles of different sizes obtained in Step 2, generate curves showing the change in dust removal efficiency with liquid turbidity, as shown below. Figure 1 As shown, based on the changes in each curve, the curves are divided into low turbidity, medium turbidity, high turbidity, and ultra-high turbidity ranges according to the changes in dust removal efficiency. Specifically, the range where the initial dust removal efficiency decreases relatively gradually is designated as the low turbidity range (i.e.,...). Figure 1(742.77–1061.12 NTU, with an average decrease in dust removal efficiency of 0.2%–0.95% for every 100 NTU increase), indicating that an effective wetting and capture mechanism still exists between particulate matter and droplets.

[0042] The interval where dust removal efficiency decreases rapidly after passing through the low turbidity zone is classified as the medium turbidity zone (i.e., Figure 1 The dust removal efficiency decreased rapidly from 1061.12 to 1353.11 NTU for every 100 NTU increase, reaching 1.12% to 8.4%, indicating that the active sites on the droplet surface tended to be saturated and the competitive wetting phenomenon of particulate matter was significantly enhanced.

[0043] The interval where dust removal efficiency tends to stabilize after passing through the medium turbidity range is classified as the high turbidity range (i.e., Figure 1 The dust removal efficiency ranges from 1353.11 to 2183.36 NTU, with an average decrease of less than 0.5% for every 100 NTU increase, indicating that the wet dust removal equipment has entered a "critical capture state," where particulate resuspension and droplet saturation effects have reached a dynamic equilibrium.

[0044] The section where dust removal efficiency drops rapidly after passing through the high turbidity zone is classified as the ultra-high turbidity zone (i.e., Figure 1 The range of 2183.36 to 2476.11 NTU indicates that the droplet surface is completely covered by particulate matter, the trapping mechanism has failed, and forced discharge is required.

[0045] Within the ultra-high turbidity region of each curve, the turbidity value corresponding to the dust removal efficiency threshold was selected to determine the turbidity threshold corresponding to different particle sizes. Based on... Figure 1 It is known that the smaller the particle size, the more sensitive the dust removal efficiency is to changes in turbidity. The efficiency reduction of PM1 is 46.88%, which is 7.9 times that of TSP. Therefore, the dust removal efficiency thresholds for particles of different sizes are set separately, and then their corresponding turbidity thresholds are obtained.

[0046] Step 4: Preparation before dust removal with wet scrubbing equipment: First, determine the particle size range of the dust removal environment, then determine the corresponding quantitative relationship model between turbidity and dust removal efficiency, as well as the turbidity threshold, and set the first and second turbidity detection time intervals; the second turbidity detection time interval should be shorter than the first turbidity detection time interval. In the low and medium turbidity ranges, since the dust removal efficiency is much higher than the dust removal efficiency threshold, the first turbidity detection time interval is used to reduce data processing, making the time interval between two adjacent detections larger. This can maintain the stability of dust removal efficiency while reducing the number of detections. After entering the high turbidity range, since it is close to the subsequent turbidity threshold, the second turbidity detection time interval is used to ensure the real-time nature of the detection, making the time interval between two adjacent detections smaller. This ensures that the liquid turbidity is detected as soon as it reaches the turbidity threshold. At this time, the sewage discharge and liquid replacement begin, maximizing liquid utilization and effectively saving water resources while ensuring dust removal efficiency.

[0047] Step 5: Wet Dust Collector Discharge: The wet dust collector begins dust collection in the environment measured in Step 4. During the dust collection process, turbidity is measured at the first turbidity detection time interval, and the measured value is input into the turbidity-dust collection efficiency quantitative relationship model to determine the interval corresponding to the current turbidity measured value. When the measured value is in the high turbidity interval, subsequent turbidity measurements are performed at the second turbidity detection time interval until the measured value reaches the turbidity threshold. At this point, the liquid in the wet dust collector is discharged and replaced with new liquid. The new liquid continues the dust collection process, and this step is repeated.

[0048] Step Six: Adaptive Sewage Discharge of Wet Dust Collector in Different Environments: When the same wet dust collector is used for dust collection in environments with different particle sizes, steps four and five are repeated respectively. This allows the corresponding turbidity threshold to be determined based on the particle size of the particles in different environments, thus achieving adaptive sewage discharge in different environments.

[0049] As an improvement to the method of this invention, the dividing point between the high turbidity range and the ultra-high turbidity range is used to replace the turbidity value corresponding to the dust removal efficiency threshold in step three, as the turbidity threshold. Since the dust removal efficiency drops rapidly in the ultra-high turbidity range, in order to prevent the turbidity detection from missing the optimal time for sludge discharge, which would lead to a significant drop in dust removal efficiency, the dividing point between the two ranges can be directly used as the turbidity threshold. This allows for sludge discharge and liquid replacement as soon as the dust removal efficiency begins to drop rapidly, thereby ensuring the continuity of dust removal efficiency and conserving water resources as much as possible.

[0050] like Figure 1As shown, the apparatus for the adaptive sewage discharge method of the aforementioned wet dust removal equipment includes wet dust removal equipment, a turbidity monitor, a signal acquisition system, and a host computer. The turbidity monitor is installed in the gas-liquid mixing chamber of the wet dust removal equipment, located between the liquid surface and the bottom sewage discharge pipe, and is used to detect the turbidity of the liquid. The signal acquisition system is connected to both the turbidity monitor and the host computer, and is used to transmit the data collected by the turbidity monitor to the host computer. The host computer is used to analyze and process the received turbidity values, and control the valve on the sewage discharge pipe to open for sewage discharge and liquid replacement based on the analysis results. Multiple turbidity monitors are used, all distributed in the gas-liquid mixing chamber below the liquid surface, avoiding areas of air bubbles and sludge. Averaging the data collected by multiple turbidity monitors improves the accuracy of data acquisition, while avoiding areas of air bubbles and sludge further ensures the stability of the data acquisition process. The turbidity monitor is a light scattering type turbidity monitor. Turbidity monitors can employ ultrasonic or capacitive principles; light scattering is preferred due to its fast response and high accuracy. It monitors the turbidity of the liquid within the gas-liquid mixing chamber in real time by measuring the intensity of scattered light. When light passes through a liquid containing suspended particles, some of the light is scattered by the particles; the higher the intensity of the scattered light, the higher the turbidity of the liquid.

[0051] As an improvement of the present invention, the turbidity monitor is equipped with an automatic surface cleaning device at the light source for cleaning the surface of the light source. The automatic surface cleaning device is an existing structure, such as a cleaning brush mechanism, which can move and clean the surface of the light source via a connecting rod. This structure can remove sludge adhering to the surface of the light source, ensuring the detection accuracy of the turbidity monitor.

[0052] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. An adaptive sewage discharge method for wet dust removal equipment, characterized in that, Includes the following steps: Step 1: Wet Dust Collection Equipment Testing: First, select fine particles with different particle sizes and test them using a wet dust collection equipment, calculating the real-time dust collection efficiency. Simultaneously, monitor the turbidity of the liquid inside the wet dust collection equipment during the dust collection process. After completion, replace the liquid and select fine particles with different particle sizes, repeating the above steps to obtain the dust collection efficiency and corresponding turbidity data of the wet dust collection equipment for particles of different sizes. Step 2: Construct a quantitative relationship model between turbidity and dust removal efficiency: Using a nonlinear regression model, the dust removal efficiency and turbidity data of each particle size range measured in Step 1 are input into the model to establish a quantitative relationship model between turbidity and dust removal efficiency for particles of different sizes. Step 3: Determine the turbidity threshold for different particle sizes: Set the dust removal efficiency threshold, and generate curves showing the change of dust removal efficiency with liquid turbidity based on the quantitative relationship model of turbidity-dust removal efficiency for different particle sizes obtained in Step 2. According to the changes in each curve, divide the curves into low turbidity range, medium turbidity range, high turbidity range, and ultra-high turbidity range based on the changes in dust removal efficiency. Select the turbidity value corresponding to the dust removal efficiency threshold in the ultra-high turbidity range of each curve to determine the turbidity threshold corresponding to each particle size. Step 4: Preparation before dust removal with wet dust removal equipment: First, determine the particle size range of the dust removal environment, then determine the corresponding quantitative relationship model of turbidity-dust removal efficiency and turbidity threshold, and set the first turbidity detection time interval and the second turbidity detection time interval. Step 5, Wet dust removal equipment discharge: The wet dust removal equipment starts dust removal work in the environment after the measurement in step 4. During the dust removal process, turbidity is measured according to the first turbidity detection time interval, and the detection value is brought into the turbidity-dust removal efficiency quantitative relationship model to determine the interval corresponding to the current turbidity detection value. When the detected value is in the high turbidity range, subsequent turbidity tests are performed according to the second turbidity detection time interval until the detected value reaches the turbidity threshold. Then, the liquid in the wet dust collector is drained and replaced with new liquid. The new liquid continues the dust removal work and this step is repeated. Step Six: Adaptive Sewage Discharge of Wet Dust Collector in Different Environments: When the same wet dust collector is used for dust collection in environments with different particle sizes, steps four and five are repeated respectively. This allows the corresponding turbidity threshold to be determined based on the particle size of the particles in different environments, thus achieving adaptive sewage discharge in different environments.

2. The adaptive sewage discharge method for wet dust removal equipment according to claim 1, characterized in that, The particulate matter used in step one includes particles with a particle size of PM1 and particles with a particle size of PM2.

5. 2.5 Particulate matter, particulate matter with a particle size of PM4, particulate matter with a particle size of PM 10 Particulate matter and total suspended particulate matter (TSP).

3. The adaptive sewage discharge method for wet dust removal equipment according to claim 1, characterized in that, In step three, the interval where the initial dust removal efficiency decreases relatively slowly is divided into the low turbidity interval; the interval where the dust removal efficiency decreases rapidly after passing through the low turbidity interval is divided into the medium turbidity interval; the interval where the dust removal efficiency changes tend to stabilize after passing through the medium turbidity interval is divided into the high turbidity interval; and the interval where the dust removal efficiency decreases rapidly after passing through the high turbidity interval is divided into the ultra-high turbidity interval.

4. The adaptive sewage discharge method for wet dust removal equipment according to claim 3, characterized in that, The turbidity values ​​corresponding to the dust removal efficiency threshold in step three are replaced by the dividing point values ​​of the high turbidity range and the ultra-high turbidity range, and are used as the turbidity threshold.

5. The adaptive sewage discharge method for wet dust removal equipment according to claim 1, characterized in that, In step four, the second turbidity detection time interval is shorter than the first turbidity detection time interval.

6. An apparatus utilizing the adaptive sewage discharge method of the wet dust removal equipment according to any one of claims 1 to 5, characterized in that, Includes wet dust collection equipment, turbidity monitor, signal acquisition system and host computer; The turbidity monitor is installed in the gas-liquid mixing chamber of the wet dust removal equipment and is located between the liquid surface and the bottom drain pipe. It is used to detect the turbidity of the liquid. The signal acquisition system is connected to the turbidity monitor and the host computer respectively, and is used to transmit the data collected by the turbidity monitor to the host computer. The host computer is used to analyze and process the received turbidity values, and control the valves on the sewage pipe to open for sewage discharge and liquid replacement based on the analysis results.

7. The apparatus according to claim 6, characterized in that, The turbidity monitors are multiple, and all of them are distributed in the gas-liquid mixing chamber below the liquid surface, avoiding the areas of bubbles and sludge.

8. The apparatus according to claim 6, characterized in that, The turbidity monitor is a light scattering turbidity monitor.

9. The apparatus according to claim 8, characterized in that, The turbidity monitor is equipped with an automatic surface cleaning device at the light source for cleaning the surface of the light source.

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