Method for monitoring dissolved oxygen in biological membrane, optimized oxygen supply method, system and equipment

Abstract

The invention discloses a monitoring method for dissolved oxygen in a biological membrane and an optimized oxygen supply method, system and equipment, and relates to the technical field of wastewater treatment. The monitoring method comprises the following steps: acquiring image data of a biological membrane of a sewage treatment device of a membrane aeration bio-membrane reactor (MABR) in a sewage treatment process; performing feature extraction on the image data to obtain image feature parameters; inputting the image characteristic parameters into a biological membrane dissolved oxygen prediction model to obtain the effective concentration of dissolved oxygen in the biological membrane output by the biological membrane dissolved oxygen prediction model; the biofilm dissolved oxygen prediction model is obtained based on training of a machine learning model. According to the method for monitoring the dissolved oxygen in the biological membrane, the machine learning model is used for carrying out image recognition, the defects that a microelectrode is high in cost, prone to damage and large in maintenance difficulty are overcome, and low-cost effective monitoring of the dissolved oxygen in the biological membrane is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of wastewater treatment, and in particular to a method for monitoring dissolved oxygen in a biofilm, a method for optimizing oxygen supply, a system and equipment. Background Art

[0002] Membrane Aerated Biofilm Reactor (MABR) is a new type of sewage treatment process that combines gas separation membrane technology with biofilm sewage treatment technology. This process uses MABR as a microbial carrier and provides bubble-free aeration for microorganisms, forming aerobic, anoxic, and anaerobic biological environments from close to the membrane layer to far away from the membrane layer, achieving the effect of simultaneous denitrification and carbon removal. Compared with traditional aeration processes, it has the advantages of low energy consumption, biofilm is not easy to fall off, and high oxygen utilization rate.

[0003] In MABR, oxygen is supplied from the lumen of the membrane fibers and diffuses through the membrane into the biofilm. Therefore, the DO (Dissolved Oxygen) concentration in the biofilm decreases from the inner layer of the biofilm (close to the membrane) along the biofilm thickness to the biofilm-liquid boundary layer; at the same time, nutrients and other pollutants diffuse into the biofilm from the bulk liquid. Compared with traditional biofilm reactors with co-diffusion biofilms, electron acceptors (such as nitrate and nitrite) and electron donors (ammonia nitrogen and chemical oxygen demand) diffuse into the MABR biofilm from opposite directions, forming a special counter-diffusion biofilm.

[0004] Since oxygen diffuses from the inside, it is difficult to monitor the dissolved oxygen in the solution to reflect the oxygen situation in the biofilm. Even if the dissolved oxygen in the solution is zero, the biofilm can still achieve good water purification due to the formation of an aerobic inner layer and an anaerobic outer layer biological environment. The dissolved oxygen situation in the biofilm can be monitored through microelectrodes and other technologies, but microelectrodes are expensive, easy to damage, and difficult to maintain, making them difficult to use in actual working conditions. Summary of the invention

[0005] The purpose of this application is to provide a method for monitoring dissolved oxygen in biofilms, a method, system and equipment for optimizing oxygen supply, so as to overcome the defects of microelectrodes such as high cost, easy damage and difficult maintenance, and to achieve low-cost and effective monitoring of dissolved oxygen in biofilms.

[0006] To achieve the above objectives, this application provides the following solutions.

[0007] In a first aspect, the present application provides a method for monitoring dissolved oxygen in a biofilm, which is applied to a membrane aerated biofilm reactor, wherein the aerated biofilm reactor includes a biochemical reaction unit, wherein a heterogeneous mass transfer biofilm component is arranged in the biochemical reaction unit, wherein the heterogeneous mass transfer biofilm component includes a biofilm and a MABR membrane component, and the method for monitoring dissolved oxygen in the biofilm includes: when using a membrane aerated biofilm reactor for sewage treatment, obtaining image data of the biofilm during the sewage treatment process; performing feature extraction on the image data to obtain image feature parameters; inputting the image feature parameters into a biofilm dissolved oxygen prediction model to obtain the effective concentration of dissolved oxygen in the biofilm output by the biofilm dissolved oxygen prediction model; the biofilm dissolved oxygen prediction model is obtained based on training of a machine learning model.

[0008] Optionally, the image characteristic parameters include: biofilm surface bubble density, surface bubble size, biofilm thickness, biofilm color and biofilm temperature distribution.

[0009] In the second aspect, the present application provides an image recognition-based method for optimizing oxygen supply, which is applied to a membrane aerated biofilm reactor, wherein the aerated biofilm reactor includes a biochemical reaction unit and a gas delivery unit, wherein a heterogeneous mass transfer biofilm component is arranged in the biochemical reaction unit, wherein the heterogeneous mass transfer biofilm component includes a biofilm and a MABR membrane component, and the gas delivery unit is connected to the heterogeneous mass transfer biofilm component; the image recognition-based method for optimizing oxygen supply includes: when using a membrane aerated biofilm reactor for sewage treatment, obtaining image data of the biofilm during the sewage treatment process and water quality monitoring parameters in the biochemical reaction unit; performing feature extraction on the image data to obtain image feature parameters; inputting the image feature parameters into a biofilm dissolved oxygen prediction model to obtain an effective concentration of dissolved oxygen in the biofilm output by the biofilm dissolved oxygen prediction model; the biofilm dissolved oxygen prediction model is obtained based on training of a machine learning model; generating an oxygen supply control signal according to the effective concentration of dissolved oxygen in the biofilm and the water quality monitoring parameters, and adjusting the oxygen supply of the gas delivery unit.

[0010] Optionally, the water quality monitoring parameters include: inlet ammonia nitrogen, outlet ammonia nitrogen, total nitrogen, COD (Chemical Oxygen Demand) concentration and DO concentration in the biochemical reaction unit; the image feature parameters include: biofilm surface bubble density, surface bubble size, biofilm thickness, biofilm color and biofilm temperature distribution.

[0011] Optionally, the method of adjusting the oxygen supply of the gas delivery unit includes: adjusting one or more of the aeration pressure, adjusting the gas flow rate and adjusting the aeration mode.

[0012] In a third aspect, the present application provides an optimized oxygen supply system based on image recognition, and the optimized oxygen supply system based on image recognition is applied to a membrane aerated biofilm reactor, wherein the aerated biofilm reactor comprises a biochemical reaction unit and a gas delivery unit, wherein a heterogeneous mass transfer biofilm component is arranged in the biochemical reaction unit, wherein the heterogeneous mass transfer biofilm component comprises a biofilm and a MABR membrane component, and wherein the gas delivery unit is connected to the heterogeneous mass transfer biofilm component; the optimized oxygen supply system based on image recognition comprises: an oxygen supply regulating device, an image recognition unit, a dissolved oxygen monitoring device and an intelligent terminal; the oxygen supply regulating device is arranged between the gas delivery unit and the heterogeneous mass transfer biofilm component The image recognition unit and the dissolved oxygen monitoring device are arranged in the biochemical reaction unit; the image recognition unit and the dissolved oxygen monitoring device are both connected to the intelligent terminal, and the intelligent terminal is connected to the control end of the oxygen supply regulating device; the image recognition unit is used to collect image data of the biofilm, and the dissolved oxygen monitoring device is used to obtain water quality monitoring parameters; the intelligent terminal is used to adopt the above-mentioned image recognition-based optimized oxygen supply method according to the image data and the water quality monitoring parameters, obtain the oxygen supply control signal, and control the oxygen supply regulating device according to the oxygen supply control signal to adjust the oxygen distribution amount.

[0013] Optionally, the oxygen supply regulating device includes one or both of a membrane gas supply pressure monitoring component and a flow regulating component.

[0014] Optionally, the image recognition unit includes: one or more of an underwater camera, a laser scanning imager and an infrared camera.

[0015] Optionally, the image recognition unit is spaced apart from the heterogeneous mass transfer biofilm component by a distance of 30 mm to 1000 mm; the dissolved oxygen monitoring device is spaced apart from the heterogeneous mass transfer biofilm component by a distance of 10 mm to 30 mm.

[0016] In a fourth aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned method for monitoring dissolved oxygen in a biofilm or the above-mentioned method for optimizing oxygen supply based on image recognition.

[0017] According to the specific embodiments provided in this application, this application has the following technical effects.

[0018] The present application provides a method for monitoring dissolved oxygen in biofilms, a method for optimizing oxygen supply, a system and equipment, the monitoring method comprising: obtaining image data of biofilms in a sewage treatment process and water quality monitoring parameters in a biochemical reaction unit; extracting features from the image data to obtain image feature parameters; inputting the image feature parameters into a biofilm dissolved oxygen prediction model to obtain an effective concentration of dissolved oxygen in the biofilm output by the biofilm dissolved oxygen prediction model; the biofilm dissolved oxygen prediction model is obtained based on training of a machine learning model. The present application provides a method for monitoring dissolved oxygen in biofilms using a machine learning model for image recognition, overcoming the defects of high cost, easy damage and difficult maintenance of microelectrodes, and realizing low-cost and effective monitoring of dissolved oxygen in biofilms. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 A schematic flow chart of a method for monitoring dissolved oxygen in a biofilm provided in one embodiment of the present application.

[0021] Figure 2 A schematic flow chart of an oxygen supply optimization method based on image recognition provided in one embodiment of the present application.

[0022] Figure 3 A schematic diagram of the structure of an optimized oxygen supply system based on image recognition provided in one embodiment of the present application.

[0023] Figure 4 A schematic diagram of the structure of a computer device provided in one embodiment of the present application.

[0024] Explanation of the reference numerals: 1. Water inlet pool; 2. Gas delivery unit; 201. Gas supply equipment; 202. Gas flow meter; 203. Pressure gauge; 3. Biochemical reaction unit; 301. Heterogeneous mass transfer biofilm component; 302. Image recognition unit; 303. Dissolved oxygen monitoring device; 4. Intelligent terminal; 5. Water outlet pool; 6. Peristaltic pump. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0027] The monitoring method of dissolved oxygen in biofilm, the method for optimizing oxygen supply, the system and the device of the present application are all applied to membrane aeration biofilm reactors, such as Figure 3 As shown, the membrane aerated biofilm reactor comprises: an inlet pool 1 , an outlet pool 5 , a gas transport unit 2 and a biochemical reaction unit 3 .

[0028] The water inlet of the biochemical reaction unit 3 is connected to the water inlet pool 1, and a peristaltic pump 6 is arranged between the water inlet of the biochemical reaction unit 3 and the water inlet pool 1; the water outlet of the biochemical reaction unit 3 is connected to the water outlet pool 5; a heterogeneous mass transfer biofilm component 301 is arranged in the biochemical reaction unit 3, and the heterogeneous mass transfer biofilm component 301 is perpendicular to the water flow direction.

[0029] The gas delivery unit 2 is connected to the gas inlet of the heterogeneous mass transfer biofilm assembly 301 through an air pipe. The gas delivery unit 2 includes a gas supply device 201, a gas flow meter 202 and a pressure gauge 203. The gas flow meter 202 is used to adjust and monitor the gas flow, and the pressure gauge 203 monitors the aeration pressure.

[0030] The working principle of the membrane aerated biofilm reactor is as follows.

[0031] The wastewater to be treated containing organic matter and ammonia nitrogen in the water inlet tank 1 is introduced into the biochemical reaction unit 3 through the peristaltic pump 6, and is discharged from the water outlet to the water outlet tank 5 after staying for a certain period of time.

[0032] Before the gas delivery unit 2 is officially put into operation, biofilm formation needs to be completed. First, a certain amount of nitrifying sludge is added to the biochemical reaction unit 3, and wastewater containing ammonia nitrogen is configured to form a nitrifying biofilm on the surface of the heterogeneous mass transfer biofilm component 301. When a thin layer of yellow-brown biofilm appears on the surface, biofilm formation is considered to be completed.

[0033] During wastewater treatment, wastewater flows from the inlet pool 1 into the biochemical reaction unit 3, and the pollutant monitor is used to monitor the concentration of inlet pollutants; after the wastewater to be treated enters the biochemical reaction unit 3, the gas delivery unit 2 supplies gas to the heterogeneous mass transfer biofilm component 301 through the pipeline, and the gas is transferred to the biofilm through the membrane filaments. Due to the attenuation of oxygen concentration during the gas transfer process, microorganisms at different distances from the surface of the heterogeneous mass transfer biofilm component 301 show different pollutant removal characteristics, such as the inner layer is aerobic bacteria (nitrifying bacteria), and the outer layer is anaerobic (facultative) bacteria (denitrifying bacteria), thereby achieving wastewater purification; the purified water is discharged from the outlet to the outlet pool 5. The outlet pool 5 is provided with a detector for detecting the concentration of pollutants.

[0034] The heterogeneous mass transfer biofilm component 301 includes a biofilm and a MABR membrane component, and the biofilm is attached to the MABR membrane component.

[0035] Due to changes in pollutant levels and biofilm states, the dissolved oxygen level in the biofilm will change accordingly. The biofilm characteristics can be obtained through image recognition, and the oxygen level can be regulated accordingly. Based on this, a method for monitoring dissolved oxygen in biofilms, a method, system and equipment for optimizing oxygen supply are provided.

[0036] In an exemplary embodiment, Figure 1 As shown, a method for monitoring dissolved oxygen in a biofilm is provided, comprising the following steps 101 to 103.

[0037] Step 101, when using a membrane aeration biofilm reactor to treat sewage, obtain image data of the biofilm in the sewage treatment process.

[0038] Step 102: extract features from the image data to obtain image feature parameters.

[0039] Step 103, inputting the image feature parameters into a biofilm dissolved oxygen prediction model to obtain an effective concentration of dissolved oxygen in the biofilm output by the biofilm dissolved oxygen prediction model; the biofilm dissolved oxygen prediction model is obtained based on machine learning model training.

[0040] The implementation of the above steps 101 to 103 can realize low-cost and effective monitoring of dissolved oxygen in the biofilm.

[0041] In another exemplary embodiment, in the above step 102, a deep learning algorithm is used to extract image feature parameters. The extracted image feature parameters and the measured concentration of dissolved oxygen in the biofilm are used to train a machine learning model to obtain a prediction model of dissolved oxygen in the biofilm. The image feature parameters include: surface bubble density of the biofilm, surface bubble size, biofilm thickness, biofilm color, and biofilm temperature distribution. The machine learning model is constructed based on the Open CV library.

[0042] In another exemplary embodiment, the biofilm dissolved oxygen prediction model in step 103 is obtained by training and optimizing a machine learning model according to a predetermined evaluation index. The machine learning model includes an ACO-BP (Ant Colony Optimization-Back Propagation, a multi-layer feedforward neural network pre-trained based on an ant colony algorithm).

[0043] In another exemplary embodiment, the prediction accuracy of the biofilm dissolved oxygen prediction model in step 103 above needs to be verified regularly based on the evaluation index.

[0044] In an exemplary embodiment, Figure 2 As shown, a method for optimizing oxygen supply based on image recognition is provided, including the following steps 201 to 204.

[0045] Step 201, when using a membrane aeration biofilm reactor to treat sewage, obtain image data of the biofilm during the sewage treatment process and water quality monitoring parameters in the biochemical reaction unit.

[0046] Step 202: extract features from the image data to obtain image feature parameters.

[0047] Step 203, inputting the image feature parameters into a biofilm dissolved oxygen prediction model to obtain an effective concentration of dissolved oxygen in the biofilm output by the biofilm dissolved oxygen prediction model; the biofilm dissolved oxygen prediction model is obtained based on machine learning model training.

[0048] Step 204: Generate an oxygen supply control signal according to the effective concentration of dissolved oxygen in the biofilm and the water quality monitoring parameters to adjust the oxygen supply of the gas delivery unit.

[0049] In another exemplary embodiment, the water quality monitoring parameters in the above step 201 include: inlet ammonia nitrogen, outlet ammonia nitrogen, total nitrogen, COD concentration and DO concentration in the biochemical reaction unit.

[0050] In another exemplary embodiment, in the above step 201, the image data is further screened and the area with clear shooting is cropped, and the image size, contrast and brightness are adjusted to be uniform.

[0051] In another exemplary embodiment, the specific image feature parameter extraction method in the above step 202 is: install the Open CV library in the Python environment, first use cv2.imread() to read the image, perform Gaussian image filtering and edge detection preprocessing, calculate the number of pixels, and obtain the biofilm color information; fit the edge contour, and calculate the biofilm thickness based on the circumscribed rectangle of the biofilm contour; perform secondary processing such as grayscale, binarization, and background removal on the preprocessed image, and use distance transformation or corrosion expansion to separate and count the microbubbles that may appear on the surface of the biofilm.

[0052] The thermal imaging data of the biofilm was obtained by an infrared detector, the temperature distribution characteristics were visualized using the Open CV library, and the thermal point values ​​were normalized to obtain the thermodynamic parameters of the biofilm.

[0053] In another exemplary embodiment, the machine learning model training process in step 203 is: The obtained image feature parameters and their corresponding measured concentration of dissolved oxygen in the biofilm (exemplarily, the measured concentration is obtained by microelectrode testing) are used to construct a model data set, and the model data set is divided into a training set and a test set. The model data set can be constructed not only using the measured data of the laboratory system, but also using the network open source database and thesis collection data, or by combining multiple methods, which are not limited here. The ACO-BP neural network is trained and tested using the training set and the test set. Furthermore, the performance of the model is evaluated by the mean square error, mean absolute error, and determination coefficient, and the hyperparameters are adjusted to optimize the model fitting effect.

[0054] In another exemplary embodiment, the above step 203 is specifically implemented as follows: intermittently acquired image feature parameters are input into the biofilm dissolved oxygen prediction model to output the effective concentration of dissolved oxygen in the biofilm. The obtained effective concentration of dissolved oxygen in the biofilm and water quality monitoring parameters (including: ammonia nitrogen, total nitrogen, COD measured at the inlet and outlet pools, and DO concentration in the biochemical reaction unit) are transmitted to the smart terminal via a data line. The smart terminal selects an adjustment mode according to the dissolved oxygen guidance value of a specific biochemical reaction, mainly including two schemes of adjusting the output power and adjusting the aeration mode, thereby controlling the aeration volume of the fan and correcting the current dissolved oxygen level of the biofilm.

[0055] In another exemplary embodiment, in the above step 204, the control measures of the gas delivery unit are adjusted according to the effective concentration of dissolved oxygen in the biofilm and the collected and measured water quality monitoring parameters to complete the entire optimized oxygen distribution process. The method of adjusting the oxygen supply of the gas delivery unit includes: adjusting one or more of the aeration pressure, adjusting the gas flow rate, and adjusting the aeration mode.

[0056] The image recognition-based oxygen supply optimization method in the above-mentioned embodiment of the present application introduces image recognition and machine learning modules to predict the dissolved oxygen level of the heterogeneous mass transfer biofilm, providing a new optimization method for the regulation of dissolved oxygen in biofilm treatment, and has the following beneficial effects compared with the prior art.

[0057] 1. Improve treatment efficiency or economy: In the membrane aerated biofilm reactor, the dissolved oxygen monitoring is transferred from the solution to the biofilm. The results obtained are helpful to guide the fine control of the aeration amount and avoid insufficient or over-aeration to a certain extent.

[0058] 2. Provide intelligent decision-making support for biofilm dissolved oxygen monitoring. Input real-time monitoring data into the artificial intelligence model to obtain decision parameters, making the water treatment process more accurate and able to respond quickly to real-time changes in water quality.

[0059] 3. Easy to expand and apply to different scenarios. This application sets corresponding sample data for different scenarios to train and update the machine learning model, so as to realize its application in different scenarios.

[0060] In an exemplary embodiment, an optimized oxygen supply system based on image recognition is provided, such as Figure 3 As shown, it includes an oxygen supply regulating device, an image recognition unit 302, a dissolved oxygen monitoring device 303 and an intelligent terminal 4. Among them, the oxygen supply regulating device is arranged between the gas delivery unit 2 and the heterogeneous mass transfer biofilm component 301; the image recognition unit 302 and the dissolved oxygen monitoring device 303 are arranged in the biochemical reaction unit 3; the image recognition unit 302 and the dissolved oxygen monitoring device 303 are both connected to the intelligent terminal 4, and the intelligent terminal 4 is connected to the control end of the oxygen supply regulating device; the image recognition unit 302 is used to collect the image data of the biofilm, and the dissolved oxygen monitoring device 303 is used to obtain water quality monitoring parameters; the intelligent terminal 4 is used to adopt the above-mentioned embodiment based on the image recognition optimization oxygen supply method according to the image data and the water quality monitoring parameters, obtain the oxygen supply control signal, and control the oxygen supply regulating device according to the oxygen supply control signal to adjust the oxygen distribution amount.

[0061] In another exemplary embodiment, the image recognition unit 302 is a combination of one or more underwater cameras, laser scanning imagers, and infrared cameras. The image recognition unit 302 is located at 30mm-1000mm of the heterogeneous mass transfer biofilm component; the dissolved oxygen monitoring device 303 is located at 10mm-30mm of the heterogeneous mass transfer biofilm component. The heterogeneous mass transfer biofilm component 301 includes a biofilm and an MABR membrane component, and the biofilm is attached to the MABR membrane component, and the thickness of the biofilm is greater than 100 microns. The gas in the gas delivery unit 2 is air or oxygen.

[0062] In another exemplary embodiment, the oxygen supply regulating device includes a membrane gas supply pressure monitoring component and a flow regulating component, which are used to adjust the aeration pressure, the gas flow and the aeration mode, thereby adjusting the oxygen supply amount.

[0063] The optimized oxygen supply system in the embodiment of the present application records the real-time image of the biofilm through the image recognition unit during the processing, and uses the deep learning algorithm to extract image features. The extracted image feature parameters and the biofilm dissolved oxygen parameters (i.e., the measured concentration of dissolved oxygen in the biofilm) are input into the machine learning model, and the machine learning model is trained and optimized according to the predetermined evaluation index to obtain the biofilm dissolved oxygen prediction model. Furthermore, according to the effective concentration of dissolved oxygen in the biofilm and the collected and measured water quality data, the control measures of the gas delivery unit are adjusted to complete the entire optimized oxygen distribution process.

[0064] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above-mentioned method for monitoring dissolved oxygen in a biofilm or the above-mentioned method for optimizing oxygen supply based on image recognition is implemented.

[0065] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0066] To illustrate the effects of the methods, systems, and devices of the above embodiments, the present application provides the following specific examples.

[0067] The simulated wastewater with an ammonia nitrogen concentration of 50 mg / L is prepared in the inlet pool 1 as the inlet water source. The simulated wastewater contains 1 g / L sodium bicarbonate, 0.2 g / L glucose, and 1 mL / L of trace elements, and the pH value of the simulated wastewater is between 7-7.5.

[0068] Two identical biochemical reaction units were selected for comparison, one using the optimized oxygen supply method of the present application (hereinafter referred to as R1), and the other using the traditional dissolved oxygen control method (hereinafter referred to as R2). Nitrification sludge from the aerobic tank of the sewage treatment plant was introduced into the two biochemical reaction units, with an MLVSS of 10 g / L, a hydraulic retention time of 24 h, and dissolved oxygen of 1-2 mg / L until the nitrification biofilm was initially formed. At this stage, the effluent pollutant indicators should be stable. In this embodiment, the ammonia nitrogen removal rate is 80-90%, and the total nitrogen removal rate is stable at 40%.

[0069] Comparative Example 1: The inlet pool is equipped with synthetic wastewater with an ammonia nitrogen concentration of about 30 mg / L and a COD concentration of about 100 mg / L. The aeration pressure of the gas delivery unit is 5 kPa. The effluent indicators are an ammonia nitrogen concentration of about 0, a total nitrogen concentration of about 15 mg / L, and a dissolved oxygen concentration of about 2 mg / L in the biochemical reaction unit. According to the oxygen demand value of the nitrification reaction, the gas delivery method of the heterogeneous mass transfer biofilm component of R2 is not adjusted; while in R1, the effective concentration of dissolved oxygen in the biofilm predicted by the optimized ACO-BP model is 4.5 mg / L, and the optimized aeration pressure is 1 kPa. After optimization, the effluent ammonia nitrogen concentration of R1 is about 0, and the total nitrogen concentration is about 8 mg / L, while saving 80% of the gas generation energy consumption.

[0070] Comparative Example 2: The inlet pool is equipped with synthetic wastewater with an ammonia nitrogen concentration of about 100 mg / L and a COD concentration of about 200 mg / L. The aeration pressure of the gas delivery unit is 2 kPa. The effluent indicators are an ammonia nitrogen concentration of about 30 mg / L and a total nitrogen concentration of about 70 mg / L. The dissolved oxygen concentration in the biochemical reaction unit is about 0 mg / L. According to the oxygen demand value of the nitrification reaction, the aeration pressure is adjusted to 4 kPa to a dissolved oxygen concentration of 2 mg / L in R2. After adjustment, the effluent ammonia nitrogen concentration is 5 mg / L and the effluent total nitrogen concentration is about 70 mg / L. In R1, according to the optimized ACO-BP model, the effective concentration of dissolved oxygen in the biofilm is predicted to be 1.5 mg / L, and the optimized aeration pressure is 3 kPa. After optimization, the effluent ammonia nitrogen concentration of R1 is about 10 mg / L, and the total nitrogen concentration is about 60 mg / L, while saving 25% of the gas generation energy consumption.

[0071] It should be noted that during use, the ACO-BP model embedded in the smart terminal can be replaced later, and the artificial neural network model built into the platform also needs to be calibrated regularly. That is, after the artificial neural network model is simulated and verified, the reliability of the model during use needs to be checked regularly. If the accuracy is reduced, the model needs to be replaced or retrained.

[0072] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0073] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0074] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0075] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0076] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for monitoring dissolved oxygen in a biofilm, characterized in that: The method for monitoring dissolved oxygen in a biofilm is applied to a membrane aerated biofilm reactor, wherein the aerated biofilm reactor comprises a biochemical reaction unit, wherein a heterogeneous mass transfer biofilm component is arranged in the biochemical reaction unit, wherein the heterogeneous mass transfer biofilm component comprises a biofilm and a MABR membrane component, and the method for monitoring dissolved oxygen in a biofilm comprises: When using a membrane aeration biofilm reactor for sewage treatment, image data of the biofilm in the sewage treatment process is obtained; Extracting features from the image data to obtain image feature parameters; The image feature parameters are input into a biofilm dissolved oxygen prediction model to obtain an effective concentration of dissolved oxygen in the biofilm output by the biofilm dissolved oxygen prediction model; the biofilm dissolved oxygen prediction model is obtained based on machine learning model training.

2. The method for monitoring dissolved oxygen in a biofilm according to claim 1, characterized in that: The image characteristic parameters include: biofilm surface bubble density, surface bubble size, biofilm thickness, biofilm color and biofilm temperature distribution.

3. A method for optimizing oxygen supply based on image recognition, characterized in that: The image recognition-based method for optimizing oxygen supply is applied to a membrane aerated biofilm reactor, wherein the aerated biofilm reactor comprises a biochemical reaction unit and a gas delivery unit, wherein a heterogeneous mass transfer biofilm component is arranged in the biochemical reaction unit, wherein the heterogeneous mass transfer biofilm component comprises a biofilm and a MABR membrane component, and wherein the gas delivery unit is connected to the heterogeneous mass transfer biofilm component; the image recognition-based method for optimizing oxygen supply comprises: When using a membrane aeration biofilm reactor for sewage treatment, image data of the biofilm during the sewage treatment process and water quality monitoring parameters in the biochemical reaction unit are obtained; Extracting features from the image data to obtain image feature parameters; Inputting the image feature parameters into a biofilm dissolved oxygen prediction model to obtain an effective concentration of dissolved oxygen in the biofilm output by the biofilm dissolved oxygen prediction model; the biofilm dissolved oxygen prediction model is obtained based on machine learning model training; An oxygen supply control signal is generated according to the effective concentration of dissolved oxygen in the biofilm and the water quality monitoring parameters, and the oxygen supply of the gas delivery unit is adjusted.

4. The method for optimizing oxygen supply based on image recognition according to claim 3, characterized in that: The water quality monitoring parameters include: inlet ammonia nitrogen, outlet ammonia nitrogen, total nitrogen, COD concentration and DO concentration in the biochemical reaction unit; The image characteristic parameters include: biofilm surface bubble density, surface bubble size, biofilm thickness, biofilm color and biofilm temperature distribution.

5. The method for optimizing oxygen supply based on image recognition according to claim 3, characterized in that: The way to adjust the oxygen supply of the gas delivery unit includes: adjusting one or more of the aeration pressure, adjusting the gas flow rate and adjusting the aeration mode.

6. An optimized oxygen supply system based on image recognition, characterized in that: The image recognition-based optimized oxygen supply system is applied to a membrane aerated biofilm reactor, wherein the aerated biofilm reactor comprises a biochemical reaction unit and a gas delivery unit, wherein a heterogeneous mass transfer biofilm component is arranged in the biochemical reaction unit, wherein the heterogeneous mass transfer biofilm component comprises a biofilm and a MABR membrane component, and wherein the gas delivery unit is connected to the heterogeneous mass transfer biofilm component; The image recognition-based optimized oxygen supply system comprises: an oxygen supply regulating device, an image recognition unit, a dissolved oxygen monitoring device and an intelligent terminal; The oxygen supply regulating device is arranged between the gas delivery unit and the heterogeneous mass transfer biofilm assembly; The image recognition unit and the dissolved oxygen monitoring device are arranged in the biochemical reaction unit; the image recognition unit and the dissolved oxygen monitoring device are both connected to the intelligent terminal, and the intelligent terminal is connected to the control end of the oxygen supply regulating device; The image recognition unit is used to collect image data of the biofilm, and the dissolved oxygen monitoring device is used to obtain water quality monitoring parameters; The intelligent terminal is used to adopt the image recognition-based oxygen supply optimization method described in any one of claims 3-5 according to the image data and the water quality monitoring parameters, obtain the oxygen supply control signal, and control the oxygen supply regulating device according to the oxygen supply control signal to adjust the oxygen distribution amount.

7. The image recognition-based optimized oxygen supply system according to claim 6, characterized in that: The oxygen supply regulating device includes one or both of a membrane gas supply pressure monitoring component and a flow regulating component.

8. The image recognition-based optimized oxygen supply system according to claim 6, characterized in that: The image recognition unit includes: one or more of an underwater camera, a laser scanning imager and an infrared camera.

9. The image recognition-based optimized oxygen supply system according to claim 6, characterized in that: The distance between the image recognition unit and the heterogeneous mass transfer biofilm component is 30 mm to 1000 mm; the distance between the dissolved oxygen monitoring device and the heterogeneous mass transfer biofilm component is 10 mm to 30 mm.

10. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for monitoring dissolved oxygen in a biofilm as described in any one of claims 1 to 2 or the method for optimizing oxygen supply based on image recognition as described in any one of claims 3 to 5.

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