Light-Fenton coupled fluidized bed reaction device and wastewater treatment process

Abstract

The invention discloses a light-Fenton coupled fluidized bed reaction device and a wastewater treatment process, and relates to the technical field of wastewater treatment.The wastewater treatment process comprises the steps of wastewater adjustment and agent pre-feeding; the method comprises the following steps: carrying out photocatalysis-fluidized bed coupling reaction, monitoring the flow velocity and pressure drop of fluidized bed reaction, constructing a fluidization identification model and a fluctuation feature vector, judging whether the purification efficiency of the wastewater in a stable fluidized state is in an optimal interval or not, if deviation exists, eliminating interference factors through an event sequence causality method, and determining the purification efficiency of the wastewater in the stable fluidized state after determining that the wastewater is caused by the flow velocity. Constructing a flow velocity control model containing a dynamic buffer boundary, and realizing flow velocity control by using closed-loop control and a hierarchical response mechanism; the reaction device comprises a photocatalytic reaction module, a fluidized bed reactor module, a Fenton reagent adding module, a three-phase separation module, a circulating system module and an auxiliary equipment module, and through dynamic control over the flow speed, the stable wastewater treatment effect can be achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of wastewater treatment, and particularly to a photo-Fenton coupled fluidized bed reaction device and a wastewater treatment process. Background Art

[0002] In the field of industrial wastewater treatment, the coupled technology of photocatalysis and Fenton reaction has attracted much attention because it can efficiently generate hydroxyl radicals to degrade organic matter. In the traditional process, the fluidized bed reactor makes the carrier particles loaded with the catalyst in a fluidized state through liquid flow, which can strengthen the solid-liquid mass transfer. However, its stable operation depends on precise flow rate control. If the flow rate is too low, the carrier will easily settle and the mass transfer efficiency will decrease; if it is too high, particle erosion and loss will occur, and the energy consumption will increase. However, there are deficiencies in the flow rate control of the existing technology.

[0003] During the wastewater treatment process, fluctuations in parameters such as initial COD, pollutant concentration, and pH will significantly affect the purification efficiency. However, the existing technology lacks a systematic analysis of the causal relationship of multiple variables, and it is easy to misjudge the influence of the flow rate on the efficiency. For example, when the influent pollutant concentration suddenly increases, if its interference is not excluded, the flow rate may be wrongly adjusted, resulting in system oscillation or deterioration of the treatment effect. Summary of the Invention

[0004] The purpose of the present invention is to provide a photo-Fenton coupled fluidized bed reaction device and a wastewater treatment process to solve at least one of the above-mentioned problems of the existing technology.

[0005] The photo-Fenton coupled fluidized bed wastewater treatment process includes the following steps: S1. Wastewater adjustment and reagent pre-injection; S2. Photocatalysis-fluidized bed coupled reaction and flow rate monitoring; S3. Solid-liquid-gas three-phase separation and catalyst recovery; S4. Advanced treatment and sludge disposal; The method of flow rate monitoring is as follows: Real-time collect the fluid flow rate and pressure drop in S2, and establish a change curve of the real-time pressure drop and the rising flow rate; By collecting the flow rate and fluid change characteristics in the historical process data, construct a fluidization identification model, and input the change curve of the real-time pressure drop and the rising flow rate into the fluidization identification model to identify the fluidization state of the fluidized bed; Based on the determined fluidization state, construct a fluctuation feature vector and identify the sub-state of the fluid state change. If the sub-state is a stable fluidization state, identify whether the wastewater purification efficiency in the stable fluidization state is in the optimal interval; If not, judge whether the wastewater purification efficiency deviates from the optimal interval due to the flow rate. If so, construct a flow rate control model to control and adjust the flow rate.

[0006] As a further technical solution of the present invention: The method for constructing the fluidization identification model is as follows: Smooth the pressure drop and flow rate in the historical data, identify and process the outliers in the historical data through the 3σ criterion, and construct the change curve of the historical flow rate and pressure drop; Verify the change curve of the historical flow rate and pressure drop according to the Darcy's law, identify the change curve of the historical flow rate and pressure drop through the flow rate demarcation point, and divide the change curve into a laminar flow section and a turbulent flow section; Based on the change curves of the historical flow rate and pressure drop in the laminar flow section and the turbulent flow section, construct a fluidization identification model, and verify and correct the fluidization identification model using the Ergun equation.

[0007] As a further technical solution of the present invention: The method for obtaining the flow rate demarcation point is as follows: Identify the inflection points of the change curve of the historical flow rate and pressure drop, divide the change curve of the historical flow rate and pressure drop into two sections based on the inflection points to obtain a low flow rate section and a high flow rate section; Perform fitting verification on the low flow rate section according to the Darcy's law, and perform polynomial regression fitting verification on the high flow rate section; Analyze the fitting errors of the low flow rate section and the high flow rate section, construct a stage error pair, and select the inflection point with the smallest root mean square error in the stage error pair as the flow rate demarcation point.

[0008] As a further technical solution of the present invention: The method for analyzing the fitting errors of the low flow rate section and the high flow rate section is as follows: If there are multiple inflection points on the change curve of the historical flow rate and pressure drop, obtain the root mean square error of the low flow rate stage fitting verification and the root mean square error of the high flow rate stage verification corresponding to each inflection point; Construct a stage error pair with the root mean square error of the low flow rate stage fitting verification and the root mean square error of the high flow rate stage verification corresponding to each inflection point.

[0009] As a further technical solution of the present invention: The method for identifying the sub-states of the fluid state change is as follows: Based on the determined fluidization state, construct a fluctuation feature vector and identify the sub-states of the fluid state change; Based on the random forest algorithm, input the fluctuation feature vector into the random forest algorithm to realize the identification of the sub-states of the fluidization state.

[0010] As a further technical solution of the present invention: The method for constructing the fluctuation feature vector is as follows: Calculate the peak factor, power spectrum entropy, fractal dimension, and Lyapunov exponent of the pressure drop within the monitoring period, and construct a fluctuation feature vector.

[0011] As a further technical solution of the present invention: The method for identifying whether the wastewater purification efficiency in the stable fluidization state is in the optimal range is as follows: Obtain the wastewater purification efficiency characteristics in the stable fluidization state in historical data, and at the same time extract the fluctuation feature vectors in the historical data; Construct a dynamic interval model by using the quantile regression algorithm for the fluctuation feature vectors in the historical data and the wastewater purification efficiency characteristics in the stable fluidization state in the historical data; Input the fluctuation feature vectors and wastewater purification efficiency of the historical stable fluidization state into the dynamic interval model, and calculate the optimal range of the wastewater purification efficiency; Obtain the wastewater purification efficiency corresponding to the fluctuation characteristics of the real-time fluidization state, compare it with the wastewater purification efficiency in the optimal range, and judge whether the wastewater purification efficiency in the stable fluidization state is in the optimal range.

[0012] As a further technical solution of the present invention: The method for controlling and adjusting the flow rate is as follows: If the wastewater purification efficiency deviates from the optimal range due to the flow rate, determine the optimal range of the flow rate through the optimal range, and set a dynamic buffer boundary for the optimal range of the flow rate; Construct a flow rate control model, input the optimal range of the flow rate and the dynamic buffer boundary into the flow rate control model, and realize the control and adjustment of the flow rate.

[0013] As a further technical solution of the present invention: The method for judging whether the wastewater purification efficiency deviates from the optimal range due to the flow rate is as follows: Through the event sequence causality method, combined with other interference factors of the wastewater treatment process corresponding to the optimal range, judge whether the wastewater purification efficiency deviates from the optimal range due to the flow rate.

[0014] The photocatalytic-Fenton coupled fluidized bed reactor device includes the following modules: Photocatalytic reaction module: Using an ultraviolet mercury lamp as the light source, arranged on the inner wall or central axis of the reactor, generating electron-hole pairs by light energy to stimulate the photocatalyst, promoting the generation of hydroxyl radicals and driving the Fenton cycle; Fluidized bed reactor module: Maintaining the liquid flow rate through a circulation pump, making the filler loaded with the catalyst in a fluidized state, realizing solid-liquid mixing and mass transfer, and the design of an inverted cone hopper and a settling area to promote solid-liquid separation; Fenton reagent dosing module: Including hydrogen peroxide and ferrous ion dosing units, a pH adjustment unit, to create an acidic condition for the Fenton reaction; Three-phase separation module: Separating the treated water, reaction gas and carrier particles through inclined plates and a collecting tank, realizing efficient three-phase separation of solid, liquid and gas; Circulation system module: Controlling the liquid reflux ratio to promote uniform mixing of the reagent and the wastewater; Auxiliary equipment module: It includes an adjustment tank, a sedimentation tank and a sludge treatment unit, which assist the main device to operate stably and treat by-products.

[0015] Advantages of the present invention: 1. By filling a quartz sand carrier loaded with titanium dioxide photocatalyst in the fluidized bed reactor and combining with the irradiation of an ultraviolet mercury lamp, the photocatalyst is excited to generate electron-hole pairs, and hydroxyl radicals with strong oxidizing properties are generated synergistically with the Fenton reaction, realizing the deep oxidation and degradation of organic substances. The coupling mechanism of photocatalysis and Fenton reaction not only drives the Fe 2+ / Fe 3+ cycle, reducing the generation of iron sludge, but also adsorbing Fe 2+ through the γ-FeOOH catalytic film on the carrier surface and promoting the decomposition of hydrogen peroxide, improving the utilization rate of the catalyst and the reaction sustainability, and significantly enhancing the wastewater treatment capacity.

[0016] 2. By collecting the fluid flow rate and pressure drop in real time and combining with Darcy's law and the Ergun equation, a fluidization identification model is constructed to divide the fixed bed, fluidized bed, and transport bed states; based on classical fluid mechanics theory, the fluidization state is scientifically classified, and the flow characteristics in different flow rate intervals are clarified, providing a theoretical basis for flow rate control and reducing the possibility of process failure caused by misjudgment of the flow regime.

[0017] 3. Using fractal and chaotic characteristics such as the peak factor and power spectrum entropy to construct a fluctuation feature vector, splitting sub-states through the random forest algorithm, and dynamically defining the optimal interval of wastewater purification efficiency based on quantile regression. By capturing the complex dynamic characteristics of the pressure drop signal through fractal and chaotic theory and combining with the ensemble learning algorithm to realize the classification of the fluidization state, it can identify the subtle state differences that are difficult to distinguish by traditional single parameters, providing a state criterion for the dynamic evaluation of wastewater purification efficiency.

[0018] 4. When the efficiency deviates, by using the event sequence causality method to exclude interference factors such as the initial COD and pollutant concentration, after confirming that it is a flow rate problem, a dynamic buffer boundary is set, and a hierarchical response mechanism is combined with a closed-loop control model to achieve flow rate control, reducing the possibility of mass transfer failure or carrier loss caused by improper flow rate, and maintaining the optimal degradation efficiency under a stable fluidization state. Description of the Drawings

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings 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.

[0020] Figure 1It is the flow chart of the wastewater treatment process provided by the first embodiment of the present invention; Figure 2 It is the flow chart of a liquid flow rate monitoring method provided by the present invention; Figure 3 It is the module diagram of a liquid flow rate monitoring system provided by the sixth embodiment of the present invention; Figure 4 It is the module diagram of the photo-Fenton coupled fluidized bed reaction device provided by the seventh embodiment of the present invention. Detailed implementation manners

[0021] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0022] Embodiment 1

[0023] As Figure 1 shown, the wastewater treatment process provided by the embodiment of the present invention specifically includes the following steps: S1. Wastewater adjustment and reagent pre-injection; Industrial wastewater enters the regulation tank of the auxiliary equipment module, and the water quality and quantity are balanced through mechanical stirring; The pH monitoring and adjustment system built in the regulation tank initially adjusts the pH of the wastewater to 5, reducing the reagent consumption for subsequent acidic condition adjustment; The wastewater enters the Fenton reagent dosing module, and hydrogen peroxide and ferrous ion solutions are dosed through a metering pump. At the same time, sulfuric acid is dosed to adjust the pH of the reaction solution system to 3, creating a suitable acidic environment for subsequent reactions; S2. Photocatalysis-fluidized bed coupling reaction and flow rate monitoring; The pretreated wastewater enters from the inverted cone hopper at the bottom of the fluidized bed reactor and fully contacts with the quartz sand carrier loaded with titanium dioxide photocatalyst in the reactor; The circulation pump of the circulation system module drives the wastewater to flow back from the top to the bottom, maintaining the upward liquid flow rate in the fluidized bed at 60 m / h, making the carrier in a fluidized state, ensuring uniform mixing and efficient mass transfer of the solid-liquid two phases; The ultraviolet mercury lamp arranged on the inner wall or central axis of the reactor emits 254 nm ultraviolet light, exciting the titanium dioxide on the surface of the carrier to generate electron-hole pairs. The holes oxidize hydrogen peroxide to generate hydroxyl radicals (•OH), and the electrons reduce Fe 3+ to Fe 2 +, drive the Fenton reaction to achieve the deep oxidation and degradation of organic matter, and control the residence time of the Fenton reaction at 1.5 hours; It should be noted that the Fenton reaction is Fe 2+ +H 2 O 2 →•OH+Fe 3+ ; S3, solid-liquid-gas three-phase separation and catalyst recovery; The mixed liquid after the S2 reaction rises to the three-phase separation area of the three-phase separation module at the top of the reactor. Through the gravity sedimentation of the inclined plate assembly, the carrier particles loaded with the catalyst fall back to the downflow area and return to the packing layer with the circulating reflux liquid for reuse; The treated supernatant is collected and discharged through the water collection tank, and the gas generated by the reaction is released from the top exhaust port; It should be noted that the gas generated by the reaction includes CO 2 , O 2 etc.; during the solid-liquid-gas three-phase separation and catalyst recovery process, the γ-FeOOH catalytic film formed on the surface of the carrier adsorbs Fe 2+ in water and catalyzes the decomposition of H 2 O 2 , and the photocatalytic synergy promotes the Fe 3+ reduction cycle, reducing the generation of iron sludge and improving the catalyst utilization rate; S4, advanced treatment and sludge disposal; The effluent after the S3 separation enters the sedimentation tank of the auxiliary equipment module, and PAC / PAM is added for coagulation sedimentation to further remove residual iron ions and suspended solids. At the same time, NaOH is added to adjust the pH to 7, so that the total iron concentration of the effluent is 4.0 mg / L; It should be noted that polyaluminum chloride (abbreviated as PAC), also known as basic aluminum chloride or hydroxyaluminum chloride, can quickly form precipitates of colloids in sewage or sludge through it or its hydrolysis products, facilitating the separation of large particle precipitates; Polyacrylamide (abbreviated as PAM), commonly known as flocculant or coagulant, is used to treat fine particles and colloidal substances; The sludge generated by the precipitation is discharged into the thickening tank, dehydrated by a plate and frame filter press and then transported out for disposal, and the filtrate is refluxed to the regulation tank for circular treatment to achieve sludge reduction and water resource recycling.

[0024] Example 2

[0025] As Figure 1 shown, the wastewater treatment process provided by the embodiment of the present invention specifically includes the following steps: S1, wastewater regulation and chemical agent pre-injection; Industrial wastewater enters the regulation tank of the auxiliary equipment module, and the water quality and quantity are balanced by mechanical stirring; The pH monitoring and adjustment system built in the regulating tank preliminarily adjusts the pH of the wastewater to 4, reducing the chemical consumption for subsequent acidic condition adjustment; The wastewater enters the Fenton reagent dosing module. Hydrogen peroxide and ferrous ion solutions are dosed through metering pumps. Meanwhile, sulfuric acid is dosed to adjust the pH of the reaction solution system to 2.5, creating a suitable acidic environment for subsequent reactions; S2, photocatalysis-fluidized bed coupling reaction; The pretreated wastewater enters from the inverted cone hopper at the bottom of the fluidized bed reactor and comes into full contact with the quartz sand carrier loaded with titanium dioxide photocatalyst filled in the reactor; The circulation pump of the circulation system module drives the wastewater to flow back from the top to the bottom, maintaining the upward liquid flow velocity in the fluidized bed at 30, making the carrier in a fluidized state, ensuring uniform mixing and efficient mass transfer between the solid and liquid phases; The ultraviolet mercury lamp arranged on the inner wall or central axis of the reactor emits ultraviolet light of 254 nm, exciting the titanium dioxide on the surface of the carrier to generate electron-hole pairs. The holes oxidize hydrogen peroxide to generate hydroxyl radicals (•OH), and the electrons reduce Fe 3+ to Fe 2 + , driving the Fenton reaction to achieve the deep oxidation and degradation of organic matter. The residence time of the Fenton reaction is controlled within 1 hour; It should be noted that the Fenton reaction is Fe 2+ +H 2 O 2 →•OH+Fe 3+ ; S3, solid-liquid-gas three-phase separation and catalyst recovery; The mixed liquid after the S2 reaction rises to the three-phase separation area of the three-phase separation module at the top of the reactor. Through the gravity sedimentation of the inclined plate assembly, the carrier particles loaded with the catalyst fall back to the downflow area and return to the packing layer with the circulating return liquid for reuse; The treated supernatant is collected and discharged through the collection tank, and the gas generated by the reaction is released from the top exhaust port; It should be noted that the gas generated by the reaction includes CO 2 , O 2 , etc.; during the solid-liquid-gas three-phase separation and catalyst recovery process, the γ-FeOOH catalytic film formed on the surface of the carrier adsorbs Fe 2+ in water and catalyzes the decomposition of H 2 O 2 , and the photocatalytic synergy promotes the Fe 3+ reduction cycle, reducing iron sludge generation and improving catalyst utilization rate; S4, advanced treatment and sludge disposal; The effluent after S3 separation enters the sedimentation tank of the auxiliary equipment module, where PAC / PAM is added for coagulation sedimentation to further remove residual iron ions and suspended solids. At the same time, NaOH is added to adjust the pH to neutral 7.5, so that the total iron concentration of the effluent is 4.5 mg / L. The sludge produced by sedimentation is discharged into the thickening tank, dehydrated by a plate and frame filter press and then transported out for disposal. The filtrate is refluxed to the regulation tank for circular treatment to achieve sludge reduction and recycling of water resources.

[0026] Example 3

[0027] As Figure 1 shown, the wastewater treatment process provided by the embodiment of the present invention specifically includes the following steps: S1. Wastewater regulation and reagent pre-injection; Industrial wastewater enters the regulation tank of the auxiliary equipment module, and the water quality and quantity are balanced by mechanical stirring. The pH monitoring and adjustment system built in the regulation tank initially adjusts the pH of the wastewater to 6, reducing the reagent consumption for subsequent acidic condition adjustment. The wastewater enters the Fenton reagent injection module, and hydrogen peroxide and ferrous ion solution are injected through a metering pump. At the same time, sulfuric acid is added to adjust the pH of the reaction solution system to 4, creating a suitable acidic environment for subsequent reactions. S2. Photocatalysis-fluidized bed coupling reaction; The pretreated wastewater enters from the inverted cone hopper at the bottom of the fluidized bed reactor and comes into full contact with the quartz sand carrier loaded with titanium dioxide photocatalyst in the reactor. The circulation pump of the circulation system module drives the wastewater to flow back from the top to the bottom, maintaining the liquid rising velocity in the fluidized bed at 100 m / h, making the carrier in a fluidized state, ensuring uniform mixing and efficient mass transfer of the solid-liquid two phases. The ultraviolet mercury lamp arranged on the inner wall or central axis of the reactor emits ultraviolet light of 254 nm, exciting the titanium dioxide on the surface of the carrier to generate electron-hole pairs. The holes oxidize hydrogen peroxide to generate hydroxyl radicals (•OH), and the electrons reduce Fe 3+ to Fe 2 + , driving the Fenton reaction to achieve deep oxidation and degradation of organic matter. The residence time of the Fenton reaction is controlled within 2 hours. It should be explained that the Fenton reaction is Fe 2+ +H 2 O 2 →•OH+Fe 3+ ; S3. Solid-liquid-gas three-phase separation and catalyst recovery; The mixed solution after the S2 reaction rises to the three-phase separation area of the three-phase separation module at the top of the reactor. Through the gravity sedimentation of the inclined plate assembly, the carrier particles loaded with the catalyst fall back to the downflow area and return to the packing layer with the circulating reflux liquid for reuse; The treated supernatant is collected and discharged through the water collection tank, and the gas generated by the reaction is released from the top exhaust port; It should be noted that the gas generated by the reaction includes CO 2 , O 2 etc.; during the three-phase separation of solid, liquid and gas and the catalyst recovery process, the γ-FeOOH catalytic film formed on the surface of the carrier adsorbs Fe 2+ in water and catalyzes the decomposition of H 2 O 2 . The photocatalytic synergy promotes the Fe 3+ reduction cycle, reduces the formation of iron sludge and improves the catalyst utilization rate; S4, advanced treatment and sludge disposal; The effluent after S3 separation enters the sedimentation tank of the auxiliary equipment module, and PAC / PAM is added for coagulation sedimentation to further remove residual iron ions and suspended solids. At the same time, NaOH is added to adjust the pH to neutral 7.8, so that the total iron concentration of the effluent is 5 mg / L; The sludge generated by the sedimentation is discharged into the thickening tank, dehydrated by a plate and frame filter press and then transported for disposal. The filtrate is refluxed to the regulation tank for cyclic treatment to achieve sludge reduction and water resource recycling.

[0028] Example 4 In the photo-Fenton coupled fluidized bed wastewater treatment process, the flow rate is the core operating parameter that determines the fluidization state of the carrier in the fluidized bed, directly affecting the solid-liquid mass transfer efficiency, catalyst stability and reaction system energy consumption. Specifically: The fluidized bed reactor makes the quartz sand carrier loaded with the catalyst in a fluidized state through the liquid flow rate. If the flow rate is too low, the carrier will settle (fixed bed state), resulting in insufficient mass transfer area and incomplete reaction; if the flow rate is too high, it will cause excessive erosion of the carrier (transport bed state), resulting in catalyst loss and a sharp increase in energy consumption. By real-time monitoring of the flow rate and combining data such as pressure drop, the current fluidization state (fixed bed / fluidized bed / transport bed) is identified, so as to make the reactor in a stable fluidized state with high mass transfer, and reduce the possibility of the purification efficiency decline or equipment operation risk caused by improper flow rate.

[0029] As Figure 2 shown, the wastewater treatment process of the photo-Fenton coupled fluidized bed reaction also includes a liquid flow rate monitoring method to realize the monitoring and control of the liquid rising flow rate in the S2 step, including the following steps; Step 1: Real-time collect the fluid flow rate and pressure drop in S2, and establish a change curve of the real-time pressure drop and the rising flow rate; Preferably, an electromagnetic flowmeter is installed in the fluidized bed reactor in S2 to collect the upward flow velocity of the fluid in the main fluidized bed in real time; Pressure sensors are installed at the inlet and outlet of the fluidized bed reactor to measure the pressure drop between the inlet and outlet of the fluidized bed reactor; Based on the upward flow velocity of the fluid and the pressure drop of the packing layer, and smoothing the pressure drop and flow velocity, a change curve of the real-time flow velocity and pressure drop is constructed.

[0030] Step 2: By collecting the flow velocity and fluid change characteristics in the historical process data, a fluidization identification model is constructed, and the change curves of the real-time pressure drop and upward flow velocity are input into the fluidization identification model to identify the fluidization state of the fluidized bed; Obtain the upward flow velocity and pressure drop of the photo-Fenton coupled fluidized bed reaction from the historical process data, smooth the pressure drop and flow velocity in the historical data, identify and process the outliers in the historical data through the 3σ criterion, and construct a change curve of the historical flow velocity and pressure drop; Verify the change curve of the historical flow velocity and pressure drop by Darcy's law, and identify that the change curve of the historical flow velocity and pressure drop is divided into a laminar flow section and a turbulent flow section through the flow velocity demarcation point; Among them, the method for verifying Darcy's law is: Identify the inflection point of the change curve of the historical flow velocity and pressure drop, and divide the change curve of the historical flow velocity and pressure drop into two sections based on the inflection point to obtain a low flow velocity section and a high flow velocity section; Perform a fitting verification of Darcy's law on the low flow velocity section, and the fitting model is: ; Perform a polynomial regression fitting verification on the high flow velocity section, and the fitting model is: ; Among them, is the pressure drop, is the fluid flow velocity, and a, b, c, d, and e are the coefficients of the fitting model respectively; It should be explained that in the present invention, the fitting model coefficients (a, b, c, d, e) of the low flow velocity section and the high flow velocity section are determined by the least squares method: for the low flow velocity data, a linear model of Darcy's law is used, and by minimizing the sum of the squares of the residuals between the measured pressure drop and the model predicted value, the linear coefficient b reflecting the viscous resistance and the fixed resistance intercept a are solved; for the high flow velocity data, a quadratic polynomial model is used. Based on the characteristics dominated by the turbulent inertial resistance, the sum of the squares of the residuals is minimized through multiple linear regression to determine the coefficients including the turbulent inertial resistance (e), the transitional zone mixing resistance (d), and the comprehensive fixed resistance (c). During implementation, first eliminate the abnormal data through the 3σ criterion and smooth the historical flow velocity and pressure drop data, and then identify the inflection point to divide the section to ensure that the model fits the laminar-turbulent flow characteristics and provide a basis for accurate identification of the fluidization state; If there are multiple inflection points on the change curve of historical flow velocity and pressure drop, obtain the root mean square error of fitting verification in the low flow velocity stage and the root mean square error of verification in the high flow velocity stage corresponding to each inflection point; Construct a stage error pair with the root mean square error of fitting verification in the low flow velocity stage and the root mean square error of fitting verification in the high flow velocity stage corresponding to each inflection point; Obtain the stage error pairs of all inflection points, and select the inflection point with the smallest root mean square error in the stage error pair as the flow velocity demarcation point; It should be explained that the inflection point with the smallest root mean square error in the stage error pair refers to the smallest comprehensive error of the root mean square error of fitting verification in the low flow velocity stage and the root mean square error of verification in the high flow velocity stage; The comprehensive error can be obtained by weighted calculation of the root mean square error of fitting verification in the low flow velocity stage and the root mean square error of verification in the high flow velocity stage; Based on the flow velocity demarcation point, divide the change curve of historical flow velocity and pressure drop into a laminar flow section and a turbulent flow section; Based on the change curves of historical flow velocity and pressure drop in the laminar flow section and the turbulent flow section, construct a fluidization identification model, and use the Ergun equation to verify and correct the fluidization identification model; Among them, the Ergun equation is: , L is the bed height, dl is the particle diameter in the fluidized bed, n is the fluid viscosity, is the fluid density, is the fluid flow velocity, is the bed porosity; It should be explained that the Ergun equation is a classical equation describing the relationship between fluid flow pressure drop and flow velocity in a fixed bed reactor. The Ergun equation can be used to verify the accuracy of the model. The Ergun equation comprehensively considers the effects of laminar flow and turbulent flow and can more comprehensively describe the flow characteristics of the fluid in the particle bed layer; The method of correcting the fluidization identification model based on the laminar flow section and the turbulent flow section using the Ergun equation is: match the parameters of the Darcy's law model (laminar flow region) and the polynomial model (turbulent flow region) obtained by piecewise fitting with the theoretical form of the Ergun equation, extract the coefficients corresponding to the laminar term (linear term) and the turbulent term (quadratic term) in the Ergun equation as theoretical constraints, compare the fitting coefficients of the existing model (such as b of the Darcy model, d and e of the polynomial model), and adjust the coefficients and demarcation point position of the piecewise model by minimizing the root mean square error between the model predicted value and the Ergun equation calculated value to ensure the pressure drop continuity and physical consistency of the model in the laminar-turbulent transition region, and finally form a corrected fluidization identification model that conforms to the principles of fluid mechanics; Obtain the change curve of real-time pressure drop and rising flow velocity, input it into the fluidization identification model, and identify the fluidization state of the fluidized bed; It should be explained that the rising velocity and pressure drop of the fluid in the fluidized bed are collected in real time and a change curve is constructed. After smoothing and eliminating outliers using the 3σ criterion, the curve is matched with the fluidization identification model constructed by fitting historical data: first, based on Darcy's law and Ergun equation, the low-velocity laminar flow section (applicable to Darcy's law linear fitting) and the high-velocity turbulent flow section (applicable to polynomial regression fitting) are divided by identifying the inflection point of the curve, and the model parameters are corrected in combination with the Ergun equation to conform to the principles of fluid mechanics. Then, according to the distribution characteristics of the real-time curve in the laminar-turbulent section, it is judged that the fluidized bed is in a fixed bed (the flow velocity is lower than the minimum fluidization velocity, and the pressure drop increases linearly), a fluidized bed (the flow velocity is between the minimum fluidization velocity and the terminal velocity, and the particles are suspended and fluidized) or a transport bed (the flow velocity exceeds the terminal velocity, and the particles are entrained and transported) state, thereby realizing the identification of the fluidization state; It can be understood by those skilled in the art that the fluidization state of the fluidized bed is divided into a fixed bed stage, a fluidized bed stage, and an output bed stage; When the fluid velocity is lower than the minimum fluidization velocity, the bed is in the fixed bed stage. At this time, the particles are tightly packed and motionless, and only the fluid flows in the narrow channels between the particles. The fluid flow is mainly laminar, and the pressure drop increases linearly with the flow rate, which conforms to Darcy's law. Its essence is the result of the fluid overcoming the viscous resistance on the particle surface. The bed height maintains the initial stacking state, there is no macroscopic relative movement between the particles, and the whole presents "solid" characteristics; When the flow rate exceeds the minimum fluidization velocity, the fluid drag is sufficient to lift the particles, and the bed enters the fluidized bed stage. At this time, the particles are out of the stacking state, suspended and moving randomly, the bed expands and the height rises with the increase of flow rate, showing fluidity similar to that of liquid; When the flow rate further increases to exceed the particle terminal velocity, the bed enters the transport bed stage. At this time, the fluid drag force completely overcomes the particle gravity, the particles are entrained by the fluid and continuously transported, the bed structure is destroyed, and it is transformed into a gas-solid / liquid-solid two-phase flow transport state. The pressure drop increases significantly again with the increase in flow rate, mainly due to the accelerated movement of particles and the collision resistance during the transport process. The flow pattern is mainly turbulent, and the particle concentration gradually decreases along the flow direction.

[0031] It should be explained that the role of identifying the fluidization state of the fluidized bed is: Function 1. Ensure the efficiency of solid-liquid mass transfer: clarify whether the current flow state is in the fluidized bed stage of efficient mass transfer, avoid the inefficient or abnormal state of the fixed bed or conveying bed, and ensure uniform fluidization of the carrier and sufficient mixing of the solid and liquid; Function 2: Support precise control of flow velocity: By identifying the inflection point of the laminar-turbulent transition zone, dividing the low-velocity laminar flow zone and the high-velocity turbulent flow zone, combining the fitting model to quantify the relationship between flow velocity and pressure drop, providing a theoretical boundary for the closed-loop control model, and realizing the adjustment of flow velocity in the optimal range; Function 3: Maintaining the stable operation of the system: Combining the fluctuation feature vector with the random forest algorithm to split the sub-states, real-time warning of abnormal flow states, reducing the possibility of efficiency fluctuations or equipment damage caused by misjudgment of flow states, realizing the efficient coupling of photocatalysis and Fenton reaction under stable fluidization state, and improving the robustness and reliability of the wastewater treatment process.

[0032] The technical solution of this embodiment is as follows: Real-time collect the fluid flow rate and pressure drop in S2, and establish the change curve of the real-time pressure drop and the rising flow rate; By collecting the flow rate and fluid change characteristics in the historical process data, construct a fluidization identification model, input the change curve of the real-time pressure drop and the rising flow rate into the fluidization identification model, identify the fluidization state of the fluidized bed, clarify the flow characteristics in different flow rate intervals, provide a theoretical basis for flow rate control, and reduce the possibility of process failure caused by misjudgment of flow states.

[0033] Embodiment 5 As Figure 2 shown, a liquid flow rate monitoring method further includes the following steps: Step 3: Based on the determined fluidization state, construct a fluctuation feature vector and identify the sub-states of the fluid state change. If the sub-state is a stable fluidization state, identify whether the wastewater purification efficiency in the stable fluidization state is in the optimal interval; Based on the determined fluidization state, construct a fluctuation feature vector and identify the sub-states of the fluid state change; Preferably, the sub-states include stable fluidization state, chaotic fluidization state, and critical fluidization state; Among them, the construction method of the fluctuation feature vector is as follows: Calculate the peak factor CF, power spectrum entropy PSE, fractal dimension FD, and Lyapunov exponent LE of the pressure drop within the monitoring period; Preferably, the acquisition method of the peak factor CF is as follows: Through the formula: Obtain the peak factor CF, where is the pressure drop real-time collected by the pressure sensor within the monitoring period, are the maximum pressure drop and the minimum pressure drop within the monitoring period respectively, is the average pressure drop within the monitoring period; Preferably, the acquisition method of the power spectrum entropy PSE is as follows: Through the formula: Obtain the power spectrum entropy PSE; Among them, the acquisition method of PS i is as follows: Perform a fast Fourier transform on the pressure drop within the monitoring period, convert the pressure drop in the time domain into the pressure drop in the frequency domain, and obtain the discrete frequency component f i corresponding power S(f i ); Calculate the ratio of the power of each discrete frequency component to the total power to obtain PS i , where i is the number of the frequency component and n is the total number of frequency components; Calculate the fractal dimension of the pressure drop signal using the box dimension method to obtain the fractal dimension FD; Calculate the maximum Lyapunov exponent of the pressure drop signal as the Lyapunov exponent LE; Those skilled in the art can understand that when calculating the fractal dimension FD of the pressure drop signal using the box dimension method, the pressure drop signal is regarded as a curve in a two-dimensional space, and a square box with a side length of is used to cover the curve, and the number of boxes required for complete coverage is counted . Continuously change and repeat the counting. Through the and fitting relationship curve, the absolute value of the slope of the fitting relationship curve is the fractal dimension FD; The fractal dimension FD is used to represent the complexity and irregularity of the pressure drop signal; Perform phase space reconstruction on the signal, convert the time series into points in a multi-dimensional phase space, find the nearest neighbor points of each point in the phase space, track the distance change of these points as they evolve over time, and calculate the exponential growth rate. The largest exponential growth rate is the maximum Lyapunov exponent LE, and LE can reflect the chaotic degree and dynamic evolution characteristics of the pressure drop signal; Based on the peak factor CF, power spectrum entropy PSE, fractal dimension FD, and Lyapunov exponent LE as fluctuation characteristics, construct a fluctuation feature vector ; Based on the random forest algorithm, input the fluctuation feature vector into the random forest algorithm to realize the identification of the sub-states of the fluidization state; Those skilled in the art can understand that the random forest algorithm, as an ensemble learning method, can realize the splitting of the sub-states of the fluidization state by constructing multiple decision trees and using the Bagging strategy for training, and can effectively capture the complex non-linear relationships and patterns in the data; Use the fluctuation feature vector as the input to train the random forest model. Each decision tree randomly samples the features and samples, optimizes the learning of the mapping relationship from the features to the sub-states through node splitting, and reduces the risk of overfitting; in the prediction stage, after a new fluctuation feature vector is input, multiple decision trees independently predict and determine the final sub-state through a voting mechanism; the "ensemble decision" method can not only mine the subtle differences of different sub-states in the feature space (such as low CF and PSE in the stable fluidization state, high FD and LE in the chaotic fluidization state), but also enhance the robustness of the model to noise and data fluctuations, thus realizing the splitting of the sub-states of the fluidization state; It should be noted that the parameter combination of the number of trees and the maximum depth in the random forest algorithm is determined by grid search cross-validation. By defining the parameter search space, for example, the number of trees is [50, 100, 150, 200], and the maximum depth is [None, 5, 10, 15]. Then, the labeled historical fluctuation feature vector dataset is divided into a training set and a validation set using k-fold cross-validation. GridSearchCV is used to traverse all parameter combinations, and the weighted F1 score of sub-state classification (as the evaluation metric) is used to calculate the average score of each set of parameters on each validation set. The number of trees and the maximum depth in the parameter combination that maximizes the weighted F1 score are selected as the optimal parameters; If the sub-state is the stable fluidization state, the method for identifying whether the wastewater purification efficiency in the stable fluidization state is in the optimal range is as follows: Obtain the wastewater purification efficiency characteristics in the stable fluidization state from historical data, including the COD removal rate and the pollutant degradation rate; At the same time, extract the fluctuation feature vectors from the historical data, and use the quantile regression algorithm to construct a dynamic interval model for the fluctuation feature vectors in the historical data and the wastewater purification efficiency characteristics in the stable fluidization state of the historical data; Through the formula: , establish a quantile model , where X is the fluctuation feature vector in the historical data, is the wastewater purification efficiency characteristic, is the quantile, corresponding to the quantile The regression coefficient vector is obtained through quantile regression training on historical data; It should be noted that quantile regression can fit models for different quantiles to characterize the distribution of data at specific quantiles. For the wastewater purification efficiency in the stable fluidization state, it is affected by fluctuation feature vectors (CF, PSE, FD, LE, etc.), and the data may not follow a normal distribution. Quantile regression does not rely on data distribution assumptions and can flexibly capture the non-linear relationship between efficiency and fluctuation features, providing more practical boundaries for constructing the optimal range; Input the fluctuation feature vectors and wastewater purification efficiency of the historical stable fluidization state into the dynamic interval model to calculate the optimal range of the wastewater purification efficiency; It should be noted that the function of calculating the optimal range of the wastewater purification efficiency is as follows: Function 1: Provide a dynamic benchmark for efficiency evaluation. By using the quantile regression algorithm to associate historical fluctuation feature vectors with purification efficiency, construct a dynamic interval covering specific quantiles, and provide an evaluation benchmark that fits the actual working conditions for real-time efficiency monitoring.

[0034] Function 2: Support the decision-making of flow rate control, and the optimization interval is directly linked to the flow rate control model. If the real-time efficiency deviates from the optimization interval, it provides an adjustment target for the closed-loop control model to ensure that the fine-tuning or gradient adjustment of the flow rate is always oriented towards maintaining the efficiency within the interval, and avoid system oscillations caused by blind adjustment.

[0035] Exemplarily, for example, quantiles are taken as 0.05 and 0.95 respectively, and calculate and , and the interval is covering the fluctuation eigenvectors and wastewater purification efficiency of the historical stable fluidization state from 5% to 95%. The extreme values at both ends of the data can be excluded through the control range; If there are additional constraints in the process, such as the lower limit of the wastewater purification efficiency is 85%, then the adjusted interval is ; Those skilled in the art can understand that by correlating the wastewater purification efficiency with the fluctuation characteristics in the stable fluidization state through quantile regression, the interval boundaries are dynamically adjusted with the boundaries of the fluctuation characteristics. For example, when FD increases, that is, the complexity of the pressure drop signal increases, the quantile model is automatically updated , reflecting the change trend of the wastewater purification efficiency, and making the optimization interval fit the actual working conditions of wastewater purification; Obtain the wastewater purification efficiency corresponding to the fluctuation characteristics of the real-time fluidization state, compare it with the wastewater purification efficiency in the optimization interval, and judge whether the wastewater purification efficiency in the stable fluidization state is within the optimization interval.

[0036] Step 4: If not, judge whether the deviation of the wastewater purification efficiency from the optimization interval is caused by the flow rate. If so, construct a flow rate control model and adjust the flow rate. The method for judging whether the deviation of the wastewater purification efficiency from the optimization interval is caused by the flow rate is: Those skilled in the art can understand that in the wastewater purification fluidized bed reactor, the flow rate is the key factor dominating the pressure drop in the reactor; When the fluid passes through the quartz sand carrier bed layer, the flow rate directly affects the frictional resistance and fluidization state of the solid-liquid two-phase: in the turbulent state, the pressure drop increases approximately quadratically with the increase of the flow rate (in line with fluid mechanics theories such as the Ergun equation), and it is necessary to maintain uniform fluidization of the particles through the flow rate to ensure efficient mass transfer. Although the carrier particle characteristics (particle size, porosity), fluid physical properties (viscosity, density) and reactor structure (inverted cone hopper, packing layer) will affect the pressure drop, the flow rate as the core operating variable, the dynamic adjustment of the flow rate directly determines the magnitude and stability of the bed layer resistance; By means of the event sequence causality method, combined with other interference factors of the wastewater treatment process corresponding to the optimization interval, excluding other interference factors of the current wastewater treatment process, to avoid misjudgment when the wastewater purification efficiency caused by the flow rate in the stable fluidization state is not in the optimization interval, that is, to determine whether the wastewater purification efficiency deviates from the optimization interval due to the flow rate; It should be noted that other interference factors of the wastewater treatment process include: initial COD, pollutant concentration, temperature, pH, etc.; When excluding the misjudgment of unqualified efficiency caused by the flow rate through the event sequence causality method, it is necessary to first align the flow rate C, wastewater purification efficiency D, and interference factors (such as initial COD, pollutant concentration A / B) in time series to construct a multivariate time series model (such as vector autoregression VAR); By using the Granger causality test in the event sequence causality method to control the influence of A / B, verifying the independent prediction ability of C on D; then hierarchically comparing the correlation between D and D under normal and abnormal states of A / B, and using methods such as propensity score matching to quantify the causal effect of C on D, stripping the confounding influence of A / B; at the same time, mining high-frequency event chain patterns (such as the time series pattern of "C abnormal → D unqualified" accompanied by normal A / B), combined with counterfactual tests (the conditional probability of assuming Y is qualified when X is normal), and finally through statistical significance and physical time series logic, determining whether the flow rate is an independent influencing factor, achieving the effective exclusion of other interference factors and ensuring the reduction of the misjudgment rate; Through causal inference and hierarchical control of interference factors, the true influence of the flow rate on the efficiency can be located; If the wastewater purification efficiency deviates from the optimization interval due to the flow rate, determine the optimization range of the flow rate through the optimization interval ; Set dynamic buffer boundaries for the optimization range of the flow rate to reduce the impact of frequent adjustment of the flow rate on the fluidized bed reaction, and mark the optimization range with dynamic buffer boundaries as ; Exemplarily, if the dynamic buffer boundary is 5%, then the optimization range of the flow rate by setting the dynamic buffer boundary ; Construct a flow rate control model, and input the optimization range of the flow rate and the dynamic buffer boundary into the flow rate control model to achieve the control and adjustment of the flow rate; Among them, the construction method of the flow rate control model is as follows: A1. Construct a closed-loop control model based on real-time flow rate, wastewater purification efficiency, and interference factors (initial COD, pollutant concentration); It should be noted that use a model predictive controller to stabilize the flow rate within the optimization range of the flow rate while compensating for the minimum flow rate to reduce the impact of fluctuations in initial COD and pollutant concentration on the wastewater purification efficiency; A2. Establish a hierarchical response mechanism to achieve the control of the flow rate; Preferably, for the primary control: when the wastewater purification efficiency approaches the dynamic buffer boundary of the optimal range (e.g., 90% or 95%) but does not exceed it, gradually pull the flow rate back to the optimal range of the flow rate by finely adjusting the frequency of the inlet / circulation pump (the adjustment amplitude each time ≤ 3%); For the secondary control: when the wastewater purification efficiency continuously deviates from the optimal range for more than a preset time (e.g., 30 minutes), and causal analysis confirms that the flow rate is the main reason, initiate a flow rate gradient adjustment strategy (e.g., adjust 5% - 10% each time), and record the efficiency response curves before and after the adjustment simultaneously to avoid oscillations of the fluidized bed reactor caused by over-adjustment; It should be noted that the primary control is used for finely adjusting the flow rate, and the secondary control is used for quickly controlling the flow rate.

[0037] The technical solution of this embodiment is as follows: Based on the determined fluidization state, construct a fluctuation feature vector and identify the sub-states of the fluid state change. If the sub-state is a stable fluidization state, identify whether the wastewater purification efficiency in the stable fluidization state is within the optimal range; if not, determine whether the wastewater purification efficiency deviates from the optimal range due to the flow rate. If so, construct a flow rate control model, perform control adjustment on the flow rate. After confirming that it is a flow rate problem, set a dynamic buffer boundary, and adopt a hierarchical response mechanism combined with a closed-loop control model to achieve flow rate control, reduce the possibility of mass transfer failure or carrier loss caused by improper flow rate, and maintain the optimal degradation efficiency under the stable fluidization state.

[0038] Embodiment Six As Figure 3 shown, a liquid flow rate monitoring system further includes the following modules: Curve construction module: Collect the fluid flow rate and pressure drop in S2 in real time, and establish a change curve of the real-time pressure drop and the rising flow rate; Preferably, install an electromagnetic flowmeter in the fluidized bed reactor in S2 to collect the rising flow rate of the fluid in the main fluidized bed in real time; Install pressure sensors at the inlet and outlet of the fluidized bed reactor to measure the pressure drop between the inlet and outlet of the fluidized bed reactor; Based on the rising flow rate of the fluid and the pressure drop of the packing layer, and perform smoothing processing on the pressure drop and the flow rate, and construct a change curve of the real-time flow rate and the pressure drop.

[0039] State identification module: By collecting the flow rate and fluid change characteristics in the historical process data, construct a fluidization identification model, and input the change curve of the real-time pressure drop and the rising flow rate into the fluidization identification model to identify the fluidization state of the fluidized bed; Obtain the upward flow velocity and pressure drop of the photo-Fenton coupled fluidized bed reaction from historical process data, smooth the pressure drop and flow velocity in the historical data, identify and process outliers in the historical data through the 3σ criterion, and construct the change curve of historical flow velocity and pressure drop; Verify the change curve of historical flow velocity and pressure drop by Darcy's law, and identify that the change curve of historical flow velocity and pressure drop is divided into a laminar flow section and a turbulent flow section through the flow velocity demarcation point; Among them, the method of verifying Darcy's law is: Identify the inflection point of the change curve of historical flow velocity and pressure drop, divide the change curve of historical flow velocity and pressure drop into two sections based on the inflection point to obtain a low flow velocity section and a high flow velocity section; Conduct a fitting verification of Darcy's law on the low flow velocity section, and the fitting model is: ; Conduct a polynomial regression fitting verification on the high flow velocity section, and the fitting model is: ; Among them, is the pressure drop, is the fluid flow velocity, and a, b, c, d, and e are the coefficients of the fitting model respectively; If there are multiple inflection points in the change curve of historical flow velocity and pressure drop, obtain the root mean square error of the fitting verification in the low flow velocity stage and the root mean square error of the verification in the high flow velocity stage corresponding to each inflection point; Construct a stage error pair from the root mean square error of the fitting verification in the low flow velocity stage and the root mean square error of the fitting verification in the high flow velocity stage corresponding to each inflection point; Obtain the stage error pairs of all inflection points, and select the inflection point with the smallest root mean square error in the stage error pair as the flow velocity demarcation point; Based on the flow velocity demarcation point, divide the change curve of historical flow velocity and pressure drop into a laminar flow section and a turbulent flow section; Based on the change curves of historical flow velocity and pressure drop in the laminar flow section and the turbulent flow section, construct a fluidization identification model, and use the Ergun equation to verify and correct the fluidization identification model; Among them, the Ergun equation is: , L is the bed height, dl is the particle diameter in the fluidized bed, n is the fluid viscosity, is the fluid density, is the fluid flow velocity, is the bed porosity; Obtain the change curve of real-time pressure drop and upward flow velocity, input it into the fluidization identification model, and identify the fluidization state of the fluidized bed.

[0040] Optimization discrimination module: Based on the determined fluidization state, construct a fluctuation feature vector and identify the sub-states of the fluid state change. If the sub-state is a stable fluidization state, identify whether the wastewater purification efficiency in the stable fluidization state is within the optimization interval; Based on the determined fluidization state, construct a fluctuation feature vector and identify the sub-states of the fluid state change; Preferably, the sub-states include a stable fluidization state, a chaotic fluidization state, and a critical fluidization state; Among them, the construction method of the fluctuation feature vector is as follows: Calculate the peak factor CF, power spectrum entropy PSE, fractal dimension FD, and Lyapunov exponent LE of the pressure drop within the monitoring period; Preferably, the acquisition method of the peak factor CF is as follows: Through the formula: Obtain the peak factor CF, where is the pressure drop real-time collected by the pressure sensor within the monitoring period, are the maximum pressure drop and the minimum pressure drop within the monitoring period respectively, is the average pressure drop within the monitoring period; Preferably, the acquisition method of the power spectrum entropy PSE is as follows: Through the formula: Obtain the power spectrum entropy PSE; Among them, the acquisition method of PS i is as follows: Perform a fast Fourier transform on the pressure drop in the monitoring period to convert the pressure drop in the time domain into the pressure drop in the frequency domain, and obtain the discrete frequency component f i corresponding power S(f i ); Calculate the proportion of the power of each discrete frequency component in the total power to obtain PS i , i is the number of the frequency component, and n is the total number of frequency components; Use the box dimension method to calculate the fractal dimension of the pressure drop signal to obtain the fractal dimension FD; Calculate the maximum Lyapunov exponent of the pressure drop signal as the Lyapunov exponent LE; Based on the peak factor CF, power spectrum entropy PSE, fractal dimension FD, and Lyapunov exponent LE as fluctuation features, construct a fluctuation feature vector ; Based on the random forest algorithm, input the fluctuation feature vector into the random forest algorithm to realize the identification of the sub-states of the fluidization state; If the sub-state is a stable fluidization state, the method for identifying whether the wastewater purification efficiency in the stable fluidization state is within the optimization interval is as follows: Obtain the wastewater purification efficiency characteristics in the stable fluidization state in the historical data, including the COD removal rate and the pollutant degradation rate; Simultaneously extract the fluctuation feature vectors in the historical data, and construct a dynamic interval model by using the quantile regression algorithm for the fluctuation feature vectors in the historical data and the wastewater purification efficiency features in the stable fluidization state of the historical data; Through the formula: , establish a quantile model , where X is the fluctuation feature vector in the historical data, is the wastewater purification efficiency feature, is the quantile, is the regression coefficient vector corresponding to the quantile, and is obtained by performing quantile regression training on the historical data; Input the fluctuation feature vector and wastewater purification efficiency in the historical stable fluidization state into the dynamic interval model, and calculate the optimization interval of the wastewater purification efficiency; Obtain the wastewater purification efficiency corresponding to the fluctuation characteristics of the real-time fluidization state, compare it with the wastewater purification efficiency in the optimization interval, and judge whether the wastewater purification efficiency in the stable fluidization state is within the optimization interval. Control adjustment module: If not, judge whether the deviation of the wastewater purification efficiency from the optimization interval is caused by the flow rate. If so, construct a flow rate control model to control and adjust the flow rate;

[0041] The method for judging whether the deviation of the wastewater purification efficiency from the optimization interval is caused by the flow rate is: By using the event sequence causality method, combined with other interference factors of the wastewater treatment process corresponding to the optimization interval, exclude other interference factors of the current wastewater treatment process, and misjudgment when the flow rate causes the wastewater purification efficiency in the stable fluidization state not to be within the optimization interval, that is, judge whether the deviation of the wastewater purification efficiency from the optimization interval is caused by the flow rate; If the deviation of the wastewater purification efficiency from the optimization interval is caused by the flow rate, determine the optimization range of the flow rate through the optimization interval; Set dynamic buffer boundaries for the optimization range of the flow rate to reduce the impact of frequent flow rate adjustment on the fluidized bed reaction; Construct a flow rate control model, input the optimization range of the flow rate and the dynamic buffer boundaries into the flow rate control model, and realize the control and adjustment of the flow rate.

[0042] Example Seven Figure 4 As shown, the photo-Fenton coupled fluidized bed reaction device includes the following modules: Photocatalytic reaction module: Use an ultraviolet mercury lamp as the light source, arrange it on the inner wall or central axis of the reactor, and match it with the packing of the photocatalyst. Generate electron-hole pairs by exciting the photocatalyst with light energy, promote the generation of hydroxyl radicals, and drive the Fenton cycle; Fluidized bed reactor module: It includes an inverted conical hopper, a packing reaction zone and a sedimentation separation zone. The liquid flow rate is maintained by a circulation pump to make the packing loaded with catalyst in a fluidized state, realizing solid-liquid mixing and mass transfer. The design of the inverted conical hopper and the sedimentation zone promotes solid-liquid separation; Fenton reagent dosing module: It includes a hydrogen peroxide and ferrous ion dosing unit and a pH adjustment unit to create an acidic condition for the Fenton reaction; Three-phase separation module: Located at the top of the reactor, it separates the treated water, reaction gas and carrier particles through inclined plates and a collecting tank, realizing efficient three-phase separation of solid, liquid and gas; Circulation system module: It consists of a circulation pump, a reflux pipeline and a flowmeter, controlling the liquid reflux ratio and promoting uniform mixing of the reagent and the wastewater; Auxiliary equipment module: It includes an adjustment tank, a sedimentation tank and a sludge treatment unit, assisting the main device to operate stably and treating by-products.

[0043] One embodiment of the present invention has been described in detail above, but the content described is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the application of the present invention should still fall within the scope covered by the patent of the present invention.

Claims

1. Photo-Fenton coupled fluidized bed wastewater treatment process, characterized in that: The following steps are involved: Wastewater conditioning and pre-dosing of chemicals; Photocatalytic-fluidized bed coupled reaction and flow rate monitoring; Solid-liquid-gas three-phase separation and catalyst recovery; Advanced treatment and sludge disposal; The flow rate monitoring method is: Collect fluid flow rate and pressure drop, and establish real-time pressure drop and rising flow rate change curve; By collecting the flow rate and fluid change characteristics in the historical process data, a fluidization identification model is constructed, and the real-time pressure drop and rising flow rate change curves are input into the fluidization identification model to identify the fluidization state of the fluidized bed; Based on the determined fluidization state, a fluctuation characteristic vector is constructed and the sub-state of the fluid state change is identified. If the sub-state is a stable fluidization state, it is identified whether the wastewater purification efficiency of the stable fluidization state is in the optimal range; If not, determine whether the wastewater purification efficiency deviates from the optimal interval due to the flow rate. If so, build a flow rate control model to control and adjust the flow rate.

2. The photo-Fenton coupled fluidized bed wastewater treatment process according to claim 1, characterized in that: The method of constructing the fluidization recognition model is: Smooth the pressure drop and flow rate in historical data, identify and process abnormal values ​​in historical data, and construct the change curve of historical flow rate and pressure drop; The change curve of historical flow velocity and pressure drop is verified by Darcy's law, the change curve of historical flow velocity and pressure drop is identified by the flow velocity cutoff point, and the change curve is divided into laminar flow section and turbulent flow section; Based on the historical flow velocity and pressure drop change curves in the laminar section and turbulent section, a fluidization identification model was constructed, and the Ergun equation was used to verify the fluidization identification model and make corrections.

3. The photo-Fenton coupled fluidized bed wastewater treatment process according to claim 2, characterized in that: The flow rate cutoff point is obtained as follows: Identify the inflection point of the historical flow rate and pressure drop change curve, and divide the historical flow rate and pressure drop change curve into two sections based on the inflection point, thereby obtaining a low flow rate section and a high flow rate section; The Darcy's law was used to fit and verify the low flow velocity section, and the polynomial regression fitting was used to verify the high flow velocity section; The fitting errors of the low flow velocity section and the high flow velocity section are analyzed, the stage error pair is constructed, and the inflection point with the smallest root mean square error in the stage error pair is selected as the flow velocity demarcation point.

4. The photo-Fenton coupled fluidized bed wastewater treatment process according to claim 3, characterized in that: The method for analyzing the fitting errors of the low flow rate section and the high flow rate section is: If there are multiple inflection points in the historical flow rate and pressure drop change curve, obtain the root mean square error of the low flow rate stage fitting verification and the root mean square error of the high flow rate stage fitting verification corresponding to each inflection point; The root mean square error of the low flow rate stage fitting verification and the root mean square error of the high flow rate stage verification corresponding to each inflection point are used to construct a stage error pair.

5. The photo-Fenton coupled fluidized bed wastewater treatment process according to claim 1, characterized in that: The method of identifying the sub-states of the fluid state change is: Based on the determined fluidization state, a fluctuation characteristic vector is constructed and the sub-states of the fluid state change are identified; Based on the random forest algorithm, the fluctuation feature vector is input into the random forest algorithm to realize the identification of the sub-state of the fluidization state.

6. The photo-Fenton coupled fluidized bed wastewater treatment process according to claim 5, characterized in that: The method of constructing the fluctuation characteristic vector is: The peak factor, power spectrum entropy, fractal dimension and Lyapunov exponent of the voltage drop during the monitoring period are calculated, and the fluctuation characteristic vector is constructed.

7. The photo-Fenton coupled fluidized bed wastewater treatment process according to claim 1, characterized in that: The method for identifying whether the wastewater purification efficiency in the stable fluidized state is in the optimal range is: Obtain wastewater purification efficiency characteristics of stable fluidized state in historical data, and extract fluctuation feature vectors in historical data; The dynamic interval model is constructed by combining the fluctuation characteristic vector in the historical data with the wastewater purification efficiency characteristics of the stable fluidized state in the historical data through the quantile regression algorithm; The fluctuation characteristic vector of the historical stable fluidization state and the wastewater purification efficiency are input into the dynamic interval model to calculate the optimal interval of the wastewater purification efficiency; The wastewater purification efficiency corresponding to the real-time fluctuation characteristics of the fluidized state is obtained, and compared with the wastewater purification efficiency in the optimal range to determine whether the wastewater purification efficiency in the stable fluidized state is in the optimal range.

8. The photo-Fenton coupled fluidized bed wastewater treatment process according to claim 1, characterized in that: The method for controlling and adjusting the flow rate is: If the wastewater purification efficiency deviates from the optimal range due to the flow velocity, the optimal range of the flow velocity is determined through the optimal range, and a dynamic buffer boundary is set for the optimal range of the flow velocity; Construct a flow rate control model, input the optimal range of flow rate and dynamic buffer boundary into the flow rate control model, and realize the control and adjustment of flow rate.

9. The photo-Fenton coupled fluidized bed wastewater treatment process according to claim 8, characterized in that: The judgment method of if the wastewater purification efficiency deviates from the optimal interval due to the flow rate is: Through the event sequence causal method, combined with other interference factors of the wastewater treatment process corresponding to the optimal interval, it is determined whether the wastewater purification efficiency deviates from the optimal interval due to the flow rate.

10. A photo-Fenton coupled fluidized bed reaction device, characterized in that: Includes the following modules: Photocatalytic reaction module: uses ultraviolet mercury lamp as light source, arranged on the inner wall or central axis of the reactor, and uses light energy to excite the photocatalyst to produce electron-hole pairs, promote the generation of hydroxyl free radicals and drive the Fenton cycle; Fluidized bed reactor module: The liquid flow rate is maintained by a circulating pump to make the catalyst-loaded filler in a fluidized state; Fenton reagent dosing module: includes hydrogen peroxide and ferrous ion dosing unit and pH adjustment unit to create acidic conditions for Fenton reaction; Three-phase separation module: The treated water, reaction gas and carrier particles are separated by inclined plates and water collecting tanks to achieve solid-liquid-gas three-phase separation; Circulation system module: controls the liquid reflux ratio and promotes uniform mixing of reagents and wastewater; Auxiliary equipment module: including regulating tank, sedimentation tank and sludge treatment unit, assisting the main device and processing by-products.

Images