Electrochemical enhanced biological integrated intelligent regulation and control equipment for treating organic wastewater

Abstract

The embodiment of the invention relates to electrochemical enhanced biological integrated intelligent regulation and control equipment for treating organic wastewater. The electrochemical enhanced biological integrated intelligent regulation and control equipment comprises an electrochemical enhanced biodegradation module and an intelligent control system, the electrochemical enhanced biodegradation module comprises a porous membrane electrode assembly, a power supply control system, an electrolyte adding system, a fixed bed biological membrane, an aeration system and a carbon source adding system; the intelligent control system comprises a data acquisition and remote monitoring module and a PLC (Programmable Logic Controller); the data acquisition and remote monitoring module acquires parameters such as COD (Chemical Oxygen Demand), dissolved oxygen concentration, ammonia nitrogen, total nitrogen and conductivity of the electrochemical enhanced biodegradation module and transmits the parameters to the PLC; and the PLC receives the parameters, and automatically adjusts one or more of voltage, current density, aeration rate and carbon source dosage based on a preset model established by a support vector regression and neural network machine learning algorithm. The intelligent regulation and control equipment can keep an optimal treatment state under different wastewater characteristics and load conditions, and has strong adaptability to environment or load fluctuation.

Description

Technical Field

[0001] The present invention relates to the technical field of water treatment, and in particular to an electrochemical enhanced biological integrated intelligent control equipment for treating organic wastewater. Background Art

[0002] With the acceleration of industrialization and the increase of human activities, a large amount of wastewater containing organic pollutants is discharged into the natural environment, posing a serious threat to the aquatic ecosystem and human health. In recent years, electrochemical enhancement technology combined with biological treatment technology has gradually become a research hotspot in the field of wastewater treatment. Electrochemical treatment has the advantages of high efficiency, good selectivity and convenient operation. It can directly decompose organic pollutants by regulating the electric field or indirectly promote the degradation of pollutants by producing redox substances. However, single electrochemical treatment has the problems of limited treatment effect and high energy consumption, especially when the organic matter concentration in the wastewater is high or the composition is complex. In contrast, biodegradation is widely used due to its low cost and good adaptability, but the biodegradation process is slow and easily affected by environmental conditions.

[0003] The combination of electrochemical enhancement and biological treatment technology effectively overcomes the limitations of a single treatment technology, such as the prior art CN117326678B. The electrochemical process generates strong oxidizing free radicals in real time, rapidly degrading organic pollutants that are difficult to biodegrade, thereby significantly improving the biodegradability of complex organic wastewater. At the same time, by adjusting reaction conditions such as current and voltage, the reaction rate can be optimized and the pollutant concentration can be quickly reduced, the treatment cycle can be shortened, the overall treatment capacity can be improved, and stable operation under high load conditions can be ensured. In this combined system, biological treatment technology removes organic matter remaining after electrochemical treatment through the metabolism of microorganisms, achieves deep purification, and uses intermediate products as a nutrient source to enhance its activity and degradation ability. In addition, the synergistic effect of electrochemical and biological treatment promotes the diversity and stability of microbial communities and enhances their adaptability to different wastewater characteristics.

[0004] However, at present, electrochemical enhanced biological treatment technology still faces some technical problems in the operation process of wastewater treatment: the impact of dynamic changes in electrochemical processes on microbial metabolic activities has not been fully understood, making it difficult to formulate optimal operating parameters; in addition, current monitoring technologies and feedback mechanisms are often insufficient to achieve real-time monitoring and automatic adjustment of key parameters of wastewater treatment, making the system lack flexibility and reliability in dealing with emergencies. For example, the multi-stage penetrating electrochemical-fixed bed biofilm degradation module designed in the field tests of cubic-meter scale microbial electrochemical system in amunicipal wastewater treatment plant failed to achieve real-time monitoring and adjustment of key operating parameters, resulting in a slow response of the system in the face of changes in wastewater characteristics, affecting the treatment effect. More importantly, existing water treatment equipment usually adopts traditional PID control or a single optimization algorithm, which is difficult to deal with the nonlinear characteristics of the system, disturbance changes, and multivariable coupling problems, resulting in low operating efficiency, high energy consumption, insufficient control accuracy, and poor adaptability to environmental or load fluctuations. Therefore, an intelligent dynamic optimization control technology is needed to improve the system operation performance.

[0005] Therefore, it is particularly necessary to design and develop intelligent control devices, which can not only improve the performance and reliability of the electrochemical enhancement and biological treatment combined devices, but also help promote the sustainable development of water treatment technology. Summary of the invention

[0006] In view of this, the present invention proposes an electrochemical enhanced biological integrated intelligent control equipment for treating organic wastewater to solve the above technical problems.

[0007] The embodiment of the present invention proposes an electrochemically enhanced biological integrated intelligent control equipment for treating organic wastewater, comprising an electrochemically enhanced biodegradation module and an intelligent control system; the electrochemically enhanced biodegradation module comprises an electrochemical treatment module and a fixed bed biofilm degradation module; the electrochemical treatment module comprises a porous membrane electrode assembly and a power control system; the fixed bed biofilm degradation module comprises a fixed bed biofilm containing microbial filler, an aeration system and a carbon source addition system; the intelligent control system comprises a data acquisition and remote monitoring module and a PLC controller; the data acquisition and remote monitoring module comprises a sensor module and a data processing and transmission module, the sensor module is used to collect parameters, the parameters include COD, dissolved oxygen concentration, ammonia nitrogen, total nitrogen and conductivity, the data processing and transmission module is used to convert the sensor signal into a digital signal and transmit it to the PLC controller; the PLC controller is connected to the data acquisition and remote monitoring module, is used to receive the parameters, and automatically adjusts the voltage of the membrane electrode assembly, the current density of the membrane electrode assembly, the aeration amount and the carbon source addition amount based on a preset model. One or more of the preset model includes:

[0008] The current density model is:

[0009]

[0010] The voltage model is:

[0011]

[0012] Among them, K(x i , x) is the Gaussian kernel function, which is described as follows:

[0013]

[0014] Among them, x i Enter the feature vector for COD and conductivity, a i is the support vector weight, b is the bias term, and σ is the kernel function bandwidth hyperparameter;

[0015] The aeration model is:

[0016]

[0017] The carbon source dosage model is:

[0018]

[0019] Among them, Q air is the volume of air introduced per unit time, Q carbonis the mass of carbon source added per unit time, W1, W2, W3, W4 are weight matrices, b1, b2, b3, b4 are bias vectors, σ is the ReLU activation function, DO is the effluent dissolved oxygen concentration, COD in is the influent COD, COD out is the effluent COD, TN is the effluent total nitrogen, NH4 + Ammonia nitrogen in the effluent.

[0020] Preferably, the electrochemical enhanced biodegradation module is a multi-stage penetrating electrochemical-fixed bed biofilm degradation module.

[0021] Preferably, the electrochemical treatment module further comprises an electrolyte dosing system for adjusting the conductivity of the organic wastewater.

[0022] Preferably, the electrochemical enhanced biodegradation module further comprises a backwashing system for backwashing the porous membrane electrode assembly and the fixed bed biofilm.

[0023] Preferably, the sensor module includes a COD sensor, a dissolved oxygen sensor, an ammonia nitrogen sensor, a total nitrogen sensor and a conductivity sensor.

[0024] Preferably, the sensor module further comprises a liquid level sensor for detecting the liquid level height of the multi-stage penetrating electrochemical-fixed bed biofilm degradation module.

[0025] Preferably, the sensor module further comprises a power-off sensor for detecting the power status of the multi-stage penetrating electrochemical-fixed bed biofilm degradation module.

[0026] Preferably, the electrochemical treatment module further includes an acid-base adjustment system for adjusting the pH value of the organic wastewater; and the sensor module further includes a pH sensor for detecting the pH value of the organic wastewater.

[0027] Preferably, the sensor module further includes an oxidation-reduction potential sensor for detecting the ORP of the organic wastewater.

[0028] Preferably, the sensor module further includes a temperature sensor for detecting the temperature of the organic wastewater.

[0029] Preferably, the intelligent control equipment also includes an industrial computer and a switch; the industrial computer is connected to the PLC controller via the switch to receive and store data transmitted from the PLC controller, and the industrial computer is a host computer used for complex analysis and remote management.

[0030] Preferably, the industrial computer is also used to adjust control parameters of the PLC controller through control instructions.

[0031] Beneficial effects of the embodiments of the present invention:

[0032] The embodiment of the present invention models and optimizes complex nonlinear relationships through intelligent dynamic optimization control technology, and intelligently regulates the treatment of organic wastewater based on a preset intelligent dynamic optimization control algorithm model. It can automatically adjust the current density of the membrane electrode assembly, the voltage of the membrane electrode assembly, the dissolved oxygen concentration, the carbon source dosage and other key parameters according to the real-time monitored water quality parameters, and has an adaptive control function, thereby maintaining the optimal treatment state under different wastewater characteristics and load conditions, improving the system's robustness, processing capacity and response speed, greatly improving the operating efficiency and control accuracy, and having a strong ability to adapt to environmental or load fluctuations.

[0033] In addition, the embodiments of the present invention can also monitor various parameters such as conductivity, pH value, liquid level, temperature, etc. in real time, and automatically adjust them through a PLC controller, which greatly simplifies the operating process, improves the automation level of the system, and reduces the need for manual intervention. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a schematic diagram of the overall composition of the electrochemical enhanced biological integrated intelligent control equipment according to an embodiment of the present invention.

[0035] Figure 2 This is a schematic diagram of the composition of the multi-stage penetrating electrochemical-biological degradation module of the electrochemical enhanced biological integrated intelligent control equipment according to an embodiment of the present invention.

[0036] Figure 3 Schematic diagram of the hardware architecture of the intelligent control system of the electrochemical enhanced biological integrated intelligent control equipment according to an embodiment of the present invention.

[0037] Figure 4 It is the prediction result of support vector regression (SVR), the horizontal axis is the sample point, and the vertical axis is the current density of the membrane electrode assembly predicted based on the current model.

[0038] Figure 5 The structure diagram of artificial neural network is shown in Figure 1. The input layer X = (x1, x2, ... x j …x n ) are the corresponding influent water quality parameters in the figure, including chemical oxygen demand (COD), dissolved oxygen (DO), total nitrogen (TN), ammonia nitrogen (NH4 + ), the output layer Y after the data is calculated by the artificial neural network algorithm is (y1, y2, ... y j …y n ) are the effluent water quality parameters COD, TN and NH4 in the figure + . DETAILED DESCRIPTION

[0039] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings. However, those skilled in the art will appreciate that the present invention is not limited to the accompanying drawings and the following embodiments. It should be noted that the embodiments of the present invention and the features in the embodiments may be combined with each other without conflict.

[0040] Reference Figure 1 The embodiment of the present invention proposes an electrochemically enhanced biological integrated intelligent control equipment for treating organic wastewater, including an electrochemically enhanced biodegradation module and an intelligent control system. Preferably, the electrochemically enhanced biodegradation module is a multi-stage penetrating electrochemical-fixed bed biofilm degradation module.

[0041] Reference Figure 2 The multi-stage penetrating electrochemical-fixed bed biofilm degradation module is used for electrochemical enhanced biological treatment of organic wastewater. Specifically, the multi-stage penetrating electrochemical-fixed bed biofilm degradation module includes an electrochemical treatment module, a fixed bed biofilm degradation module and a backwashing system.

[0042] The electrochemical treatment module is used to electrochemically treat organic wastewater. The electrochemical treatment module includes a porous membrane electrode assembly and a power control system. The porous membrane electrode assembly is connected to a power source through a wire, and is used to generate current during the reaction process, provide electrons to assist the reaction process of the biofilm, and the porous membrane electrode assembly is arranged at the front end of the biofilm, so that the oxides or other active substances generated by the current can preferentially act on the water body or pollutants, thereby improving the biodegradability of the organic wastewater. The power control system is used to adjust the current density and voltage of the porous membrane electrode assembly.

[0043] Preferably, the electrochemical treatment module further comprises an electrolyte dosing system for adjusting the conductivity of the organic wastewater. When it is detected that the conductivity of the organic wastewater exceeds a preset threshold, the electrolyte dosing system injects electrolyte into the multi-stage penetrating electrochemical-fixed bed biofilm degradation module through a pipeline to optimize the conductivity of the organic wastewater and the current efficiency of the porous membrane electrode assembly, and improve the electron transfer effect.

[0044] Preferably, the electrochemical treatment module also includes an acid-base regulation system, which is used to adjust the pH value of the organic wastewater. The acid-base regulation system is connected to the inlet of the multi-stage penetrating electrochemical-fixed bed biofilm degradation module through pipes and pumps to provide acid-base regulators to the water body, thereby adjusting the pH value of the organic wastewater.

[0045] The fixed bed biofilm degradation module is used for biodegradation of organic wastewater. The fixed bed biofilm degradation module includes a fixed bed biofilm containing microbial fillers, an aeration system and a carbon source addition system. The biofilm uses microorganisms to degrade organic matter in the wastewater treated by the electrochemical treatment module. The aeration system is used to maintain the dissolved oxygen concentration in the biofilm to support the metabolic activities of the microorganisms. The aeration system includes a fan, which is connected to the bottom of the multi-stage penetrating electrochemical-fixed bed biofilm degradation module through an aeration pipe to provide oxygen to assist the aerobic degradation process of the microorganisms; the carbon source addition device is used to supply the carbon source of the biofilm to maintain the biological activity of the microorganisms.

[0046] The backwashing system is used to backwash the porous membrane electrode assembly and the biofilm. The backwashing system includes a pump connected to the downstream of the multi-stage penetrating electrochemical-fixed bed biofilm degradation module, and backwashing is performed by timing or irregular startup to prevent the porous membrane electrode assembly and the biofilm from being blocked, and to maintain the permeability and long-term operation stability of the multi-stage penetrating electrochemical-fixed bed biofilm degradation module; the frequency and pressure of the backwashing system are set by a controller to effectively clean the electrode surface without affecting the activity of the biofilm.

[0047] The electrochemical-biofilm components of the multi-stage penetrating electrochemical-fixed bed biofilm degradation module can be expanded by connecting them in multiple stages in series according to water quality, water volume, treatment efficiency and other conditions.

[0048] Reference Figure 1 The intelligent control system includes a data acquisition and remote monitoring module, a PLC controller and a user operation interface. The hardware architecture of the intelligent control system is as follows: Figure 3 As shown, it includes a data acquisition and remote monitoring module and a PLC controller. Preferably, it also includes an industrial computer and a switch.

[0049] The data acquisition and remote monitoring module includes a sensor module and a data processing and transmission module. The sensor module is used to collect parameters, including COD, dissolved oxygen concentration, ammonia nitrogen, total nitrogen and conductivity. The sensor module includes a chemical oxygen demand sensor (COD sensor), a dissolved oxygen sensor (DO sensor), an ammonia nitrogen sensor (NH4 + ), a total nitrogen sensor (TN) and a conductivity sensor are used to collect COD, dissolved oxygen concentration, ammonia nitrogen, total nitrogen and conductivity parameters of the multi-stage penetrating electrochemical-fixed bed biofilm degradation module respectively. The data processing and transmission module is used to convert the sensor signal into a digital signal and transmit it to the PLC controller.

[0050] Preferably, the sensor module further includes a pH sensor for detecting the pH value of the organic wastewater. When the pH value is too low or too high, it may affect the treatment effect and may damage the equipment. When the pH value of the organic wastewater is outside the threshold range, the pH value of the organic wastewater can be adjusted by the acid-base adjustment system under the control of the intelligent control system.

[0051] Preferably, the sensor module further includes an oxidation-reduction potential sensor (ORP sensor) for detecting the ORP of the organic wastewater. By detecting the oxidation-reduction potential, the degree of oxidation-reduction reaction in the organic wastewater can be obtained, thereby reflecting the degradation of the electrochemically treated organic wastewater, which serves as the control basis of the intelligent control system. By real-time monitoring of the ORP value of the wastewater, the degree of completion of the reaction at each stage of wastewater treatment can be indicated, and the oxidizing or reducing state of the reaction environment can be judged, providing a basis for process optimization, and providing early warning when the system is abnormal (such as membrane electrode failure or decreased microbial activity), further ensuring the stable operation and treatment efficiency of the system.

[0052] Preferably, the sensor module further comprises a liquid level sensor for detecting the liquid level of the multi-stage penetrating electrochemical-fixed bed biofilm degradation module. When the liquid level is outside a preset threshold range, the intelligent control equipment can control water inlet and / or water outlet to keep the liquid level within a suitable range.

[0053] Preferably, the sensor module further includes a power failure sensor for detecting the power supply status of the multi-stage penetrating electrochemical-fixed bed biofilm degradation module. The power failure sensor can monitor the stability of the system power supply in real time, detect power outages or power failures in a timely manner, and send out an alarm signal to ensure safe operation of the system.

[0054] Preferably, the sensor module also includes a temperature sensor for detecting the temperature of the organic wastewater to maintain the optimal activity and treatment efficiency of the microorganisms in the biodegradation module, ensure that the reaction is carried out at an appropriate temperature, and avoid temperature fluctuations that lead to reduced efficiency or equipment damage.

[0055] The PLC controller is connected to the data acquisition and remote monitoring module, and is used to receive the parameters collected by the sensor module, and automatically adjust one or more of the current density of the membrane electrode assembly, the voltage of the membrane electrode assembly, the aeration amount, and the carbon source dosage based on the preset model. The PLC controller supports modeling based on an advanced intelligent dynamic optimization control algorithm.

[0056] 1. Electrochemical treatment module

[0057] The PLC controller automatically adjusts one or more of the current density of the membrane electrode assembly and the voltage of the membrane electrode assembly through the power control system based on the following preset model:

[0058] The current density model is:

[0059]

[0060] The voltage model is:

[0061]

[0062] Among them, K(x i , x) is the Gaussian kernel function, which is described as follows:

[0063]

[0064] Among them, x i Enter the feature vector for COD and conductivity, a i is the support vector weight, b is the bias term, and σ is the kernel function bandwidth hyperparameter.

[0065] The present invention utilizes the above-mentioned current density model and voltage model to automatically adjust at least one of the current density and the voltage according to the detected effluent COD and conductivity.

[0066] It should be noted that the above current density model and voltage model have the same expression form and use the same training method, but the support vector weight a in the two models is i Different from the bias term b.

[0067] In the embodiment of the present invention, the above model is obtained by fitting the nonlinear relationship based on COD and conductivity through a support vector regression (SVR) machine learning algorithm. Figure 4 This is a current density model predicted based on support vector regression. The error between the predicted current density and the actual current density is small, which shows that the model has high accuracy and can be used to output current density.

[0068] The specific learning process is as follows:

[0069] (1) Data preparation. Input training sample data set: T = {(x1, y1), (x2, y2)...(x N ,y N )}, x is the COD and conductivity input feature vector, the data belongs to the input space, x i ∈X=R n , y is the corresponding target value.

[0070] (2) Objective function selection. The objective function f(x) adopted in the embodiment of the present invention is:

[0071]

[0072] Among them, w is the weight vector; is the feature mapping function, which maps the input feature x to the high-dimensional feature space; b is the bias term.

[0073] (3) Define the error tolerance range. By introducing a parameter ∈, the error tolerance range is defined. If the predicted value f(x i ) Distance to actual y i If it is less than ∈, the prediction is considered accurate and no loss is calculated; the part exceeding ∈ is considered a loss.

[0074] The mathematical expression is:

[0075] |y i -f(x i )|≤∈

[0076] When the error is within the range ∈, no loss is calculated.

[0077] (4) Definition of loss function. In order to fit the data as much as possible within the error range and avoid overfitting, SVR defines the following optimization problem:

[0078]

[0079] Constraints:

[0080] y i -f(x i )≤∈+ξ i

[0081]

[0082] Among them, ‖w‖ 2 is the regularization term; C is the penalty coefficient; It is a slack variable, which is used to indicate the degree to which the error between the predicted value and the true value exceeds the range ∈.

[0083] (5) Specify the kernel function. Use the Gaussian kernel function:

[0084]

[0085] Where σ>0 is the bandwidth of the Gaussian kernel, x i is the nonlinear original space, and x is the linear new space after linear transformation.

[0086] (6) Introduce the Lagrange multiplier α to solve the dual problem. Optimize the following objective function:

[0087]

[0088] The constraints are:

[0089]

[0090] Among them, α i , is the Lagrange multiplier obtained by optimizing the problem.

[0091] (7) Obtain the prediction function.

[0092]

[0093] According to the above identification results, the current density model is obtained as follows:

[0094]

[0095] The voltage model is:

[0096]

[0097] Among them, K(x i , x) is the Gaussian kernel function, which is described as follows:

[0098]

[0099] Among them, x i Enter the feature vector for COD and conductivity, a i is the support vector weight, b is the bias term, and σ is the kernel function bandwidth hyperparameter.

[0100] 2. Biodegradable Module

[0101] The PLC controller automatically adjusts one or more of the aeration amount and the carbon source dosage based on the following preset model:

[0102] The aeration model is:

[0103]

[0104] The carbon source dosage model is:

[0105]

[0106] Among them, Q air is the volume of air introduced per unit time, Q carbon is the mass of carbon source added per unit time, W1, W2, W3, W4 are weight matrices, b1, b2, b3, b4 are bias vectors, σ is the ReLU activation function, DO is the effluent dissolved oxygen concentration, COD in is the influent COD, COD out is the effluent COD, TN is the effluent total nitrogen, NH4 + Ammonia nitrogen in the effluent.

[0107] The present invention utilizes the aeration amount model and the carbon source dosage model to automatically adjust at least one of the aeration amount and the carbon source dosage according to the detected water quality parameters.

[0108] The embodiment of the present invention is based on DO, COD, NH4 + The above model is obtained by fitting the nonlinear relationship between the water quality parameter data and TN through the artificial neural network machine learning algorithm. The neural network consists of an input layer, a hidden layer and an output layer. The hidden layer can be composed of one or more layers. In this paper, the input layer is the influent water quality parameters DO, COD, NH4 + and TN, and after calculation by artificial neural network algorithm, the predicted water quality parameters COD and NH4 are output in the output layer + and TN, whose structure is as Figure 5 shown.

[0109] The specific learning process is as follows:

[0110] (1) Data preprocessing. Normalize the data. The input value is a (i.e., influent DO, COD, NH4 + and TN value), the expected output value is (i.e. effluent COD, TN and NH4 + The output value is z (i.e., the effluent COD, TN and NH4 + The actual output value, i.e. the measured value of the water outlet).

[0111] (2) Initialization parameters: The weight W is randomly initialized to a small value; the bias b is initialized to zero or a small value.

[0112] (3) Forward propagation. For each layer l:

[0113] Compute linear combinations:

[0114] z (l) =W (l) a (l-1) +b l

[0115] Select the activation function σ(z) as ReLU,

[0116] a l =σ(z l )

[0117] (4) Calculate the loss. The loss function L is the mean squared error (MSE):

[0118]

[0119] Among them, z i To output the true value, is the expected output value.

[0120] (5) Back propagation.

[0121] The gradient of the output layer δ (L) :

[0122]

[0123] For each layer l (from back to front):

[0124] ① Calculate the error term:

[0125] δ (l) =(W (l+1) ) T δ l+1 ·σ′(z (l) )

[0126] ②Calculate the gradient:

[0127]

[0128] ③Update parameters:

[0129]

[0130] Where W (ι)′ 、b (ι)′ is the updated parameter, η is the learning rate (also called step size), and the most suitable parameters are continuously adjusted in the experiment.

[0131] (6) Iterative training.

[0132] Perform the above forward propagation and back propagation steps on all training samples; evaluate the performance of the model after each epoch. If the performance does not improve, adjust the learning rate.

[0133] The above-mentioned preset model in the embodiment of the present invention is a data-driven model, which is used to optimize one or more of the current density, voltage, aeration amount and carbon source dosage of the electrochemical treatment module and the fixed bed biodegradation module. When the data acquisition and remote monitoring module detects the water quality parameters of the organic wastewater (such as DO, COD, NH4 + and TN), the PLC controller can automatically adjust one or more of the current density, voltage, aeration amount, and carbon source dosage according to the preset model to maintain the multi-stage penetrating electrochemical-fixed bed biodegradation module in the optimal operating state, improve the treatment efficiency and effect of organic wastewater, thereby realizing automated and intelligent control of the treatment process.

[0134] For example, the target water value is set as: COD out :50mg / L, TN:15mg / L; NH4+ : 2mg / L; when the water quality parameters detected are: COD in : 500mg / L, DO: 1.5mg / L, conductivity: 1300μS / cm, TN: 30mg / L; NH4 + :10mg / L;The calculated result using the above preset model is current density: 15A / m 2 , voltage: 4.5V, aeration volume: 5L / min, carbon source dosage: 2g / min. Through the PLC controller, power control system, aeration system and carbon source dosage system, the current density, voltage, aeration volume and carbon source dosage are adjusted according to the real-time calculation results of the above preset model to ensure that the treatment process of organic wastewater continues to remain in the best state.

[0135] The user operation interface is used to support network-based remote access and operation, display system operation data and equipment status monitoring parameters, and automatically identify operation anomalies or potential failures; the embedded maintenance guidance module provides operators with step-by-step maintenance guidance, including equipment maintenance prompts, common fault troubleshooting and repair methods, so as to ensure continuous and reliable operation of the system.

[0136] Preferably, the intelligent control equipment also includes an industrial computer and a switch. The industrial computer is connected to the PLC controller through the switch, receives and stores data transmitted from the PLC controller, provides users with the convenience of data monitoring and operation with a visual interface, and supports local and remote access. The industrial computer is a host computer used for complex analysis and remote management, for example, receiving data uploaded by the PLC controller, and performing in-depth data analysis, recording, visualization and storage. Preferably, the industrial computer is also used to adjust the control parameters of the PLC controller through control instructions. Thus, the data acquisition and remote monitoring module performs basic data acquisition and real-time feedback to provide support for real-time monitoring of the system. The switch connects the PLC controller, the industrial computer, and the data acquisition and remote monitoring module together, supports high-speed data exchange, and provides a remote access interface for external devices to achieve data synchronization and sharing functions.

[0137] In one embodiment, a three-stage series electrochemical enhanced biological integrated intelligent control equipment is used to treat organic wastewater, and the effluent target value is set to COD out : 100mg / L, TN: 20mg / L; NH4 + : 5mg / L: The water quality parameters detected by the sensor module and the equipment operation status are shown in Table 1. After the equipment has been running for 2.5h, the outlet water quality reaches the set target value.

[0138] Table 1 Actual water quality parameters and equipment operation status

[0139]

[0140] The working process of the intelligent control equipment of the present invention is as follows: after the equipment is powered on, the intelligent control system is started to confirm that the connections of each component are normal. The raw water enters the multi-stage penetrating electrochemical-fixed bed biodegradation module after acid-base adjustment and electrolyte injection to the appropriate pH value and conductivity. The PLC controller regularly obtains the parameters transmitted by the sensor module to update the water quality status; the multi-stage penetrating electrochemical-fixed bed biodegradation module is started to promote the degradation of organic pollutants, and the oxygen supply is adjusted through the aeration system to ensure the optimal dissolved oxygen concentration and promote microbial activity; the PLC controller analyzes the water quality data in real time based on the preset intelligent dynamic optimization control algorithm model. If the preset water outlet target value is not reached, the PLC controller automatically adjusts one or more of the voltage, current density, aeration volume, and carbon source dosage to maintain balance. In addition, the intelligent control system regularly starts the backwash system to clean the membrane electrode and biofilm to keep it permeable, and the cleaning frequency and pressure can be automatically adjusted. This workflow ensures efficient coordination of the electrochemical and biodegradation processes, has real-time monitoring, automatic control and remote management capabilities, adapts to different water quality conditions, and maintains a stable organic wastewater treatment effect.

[0141] The electrochemical enhanced biological integrated intelligent control equipment of the embodiment of the present invention can improve the treatment effect of organic wastewater, realize intelligent control, ensure system safety and reliability, and promote green and sustainable development, and has broad application prospects and market value.

[0142] The above is an explanation of the embodiments of the present invention. However, the present invention is not limited to the above embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An electrochemical enhanced biological integrated intelligent control equipment for treating organic wastewater, characterized in that: It includes an electrochemical enhanced biodegradation module and an intelligent control system; the electrochemical enhanced biodegradation module includes an electrochemical treatment module and a fixed bed biofilm degradation module; the electrochemical treatment module includes a porous membrane electrode assembly and a power control system; the fixed bed biofilm degradation module includes a fixed bed biofilm containing microbial filler, an aeration system and a carbon source addition system; the intelligent control system includes a data acquisition and remote monitoring module and a PLC controller; the data acquisition and remote monitoring module includes a sensor module and a data processing and transmission module, the sensor module is used to collect parameters, the parameters include COD, dissolved oxygen concentration, ammonia nitrogen, total nitrogen and conductivity, the data processing and transmission module is used to convert the sensor signal into a digital signal and transmit it to the PLC controller; the PLC controller is connected to the data acquisition and remote monitoring module, is used to receive the parameters, and automatically adjusts the voltage of the membrane electrode assembly, the current density of the membrane electrode assembly, the aeration amount and the carbon source addition amount of one or more based on a preset model, the preset model includes: The current density model is: The voltage model is: Among them, K(x i ,x) is the Gaussian kernel function, which is described as follows: Among them, x i Enter the characteristic vector for COD and conductivity, a i is the support vector weight, b is the bias term, and σ is the kernel function bandwidth hyperparameter; The aeration model is: The carbon source dosage model is: Among them, Q air is the volume of air introduced per unit time, Q carbon is the mass of carbon source added per unit time, W1, W2, W3, W4 are weight matrices, b1, b2, b3, b4 are bias vectors, σ is the ReLU activation function, DO is the effluent dissolved oxygen concentration, COD in is the influent COD, COD out is the effluent COD, TN is the effluent total nitrogen, NH4 + Ammonia nitrogen in the effluent.

2. The intelligent control equipment according to claim 1, characterized in that: The electrochemical enhanced biodegradation module is a multi-stage penetrating electrochemical-fixed bed biofilm degradation module. Preferably, the electrochemical treatment module also includes an electrolyte dosing system for adjusting the conductivity of the organic wastewater.

3. The intelligent control equipment according to claim 1, characterized in that: The electrochemical enhanced biodegradation module also includes a backwashing system for backwashing the porous membrane electrode assembly and the fixed bed biofilm.

4. The intelligent control equipment according to claim 1, characterized in that: The sensor module includes a COD sensor, a dissolved oxygen sensor, an ammonia nitrogen sensor, a total nitrogen sensor and a conductivity sensor.

5. The intelligent control equipment according to claim 1, characterized in that: The sensor module also includes a liquid level sensor for detecting the liquid level height of the multi-stage penetrating electrochemical-fixed bed biofilm degradation module.

6. The intelligent control equipment according to claim 1, characterized in that: The sensor module also includes a power-off sensor for detecting the power status of the multi-stage penetrating electrochemical-fixed bed biofilm degradation module.

7. The intelligent control equipment according to claim 1, characterized in that: The electrochemical treatment module further includes an acid-base adjustment system for adjusting the pH value of the organic wastewater; the sensor module further includes a pH sensor for detecting the pH value of the organic wastewater.

8. The intelligent control equipment according to claim 1, characterized in that: The sensor module also includes an oxidation-reduction potential sensor for detecting the ORP of the organic wastewater.

9. The intelligent control equipment according to claim 1, characterized in that: The sensor module also includes a temperature sensor for detecting the temperature of the organic wastewater.

10. The intelligent control equipment according to claim 1, characterized in that: The intelligent control equipment also includes an industrial computer and a switch; the industrial computer is connected to the PLC controller through the switch to receive and store data transmitted from the PLC controller, and the industrial computer is a host computer for complex analysis and remote management. Preferably, the industrial computer is also used to adjust the control parameters of the PLC controller through control instructions.

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