An integrated intelligent control system for electrochemically enhanced biological treatment of organic wastewater

Abstract

This invention relates to an integrated intelligent control equipment for treating organic wastewater using electrochemically enhanced biodegradation, comprising an electrochemically enhanced biodegradation module and an intelligent control system. The electrochemically enhanced biodegradation module includes a porous membrane electrode assembly, a power control system, an electrolyte dosing system, a fixed-bed biofilm, an aeration system, and a carbon source dosing system. The intelligent control system includes a data acquisition and remote monitoring module and a PLC controller. The data acquisition and remote monitoring module collects parameters such as COD, dissolved oxygen concentration, ammonia nitrogen, total nitrogen, and conductivity from the electrochemically enhanced biodegradation module and transmits them to the PLC controller. The PLC controller receives these parameters and automatically adjusts one or more of the following parameters—voltage, current density, aeration rate, and carbon source dosing—based on a preset model established using support vector regression and neural network machine learning algorithms. This intelligent control equipment can maintain optimal treatment performance under different wastewater characteristics and load conditions, and exhibits strong adaptability to environmental or load fluctuations.

Description

Technical Field

[0001] This invention relates to the field of water treatment technology, specifically to an electrochemically enhanced biological integrated intelligent control equipment for treating organic wastewater. Background Technology

[0002] With the acceleration of industrialization and the increase in human activities, large amounts of wastewater containing organic pollutants are discharged into the natural environment, posing a serious threat to aquatic ecosystems 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 advantages such as high efficiency, good selectivity, and convenient operation. It can directly decompose organic pollutants by controlling the electric field or indirectly promote pollutant degradation by generating redox substances. However, single electrochemical treatment has problems such as limited treatment effect and high energy consumption, especially when the concentration of organic matter 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 combined use of electrochemical enhancement and biological treatment technologies effectively overcomes the limitations of single treatment technologies, such as the existing technology CN117326678B. The electrochemical process generates highly oxidizing free radicals in real time, rapidly degrading organic pollutants that are difficult to biodegrade, thereby significantly improving the biodegradability of complex organic wastewater. Simultaneously, by adjusting reaction conditions such as current and voltage, the reaction rate can be optimized, pollutant concentrations can be rapidly reduced, the treatment cycle shortened, overall treatment capacity enhanced, and stable operation under high load conditions ensured. In this combined system, biological treatment technology removes residual organic matter after electrochemical treatment through the metabolic activity of microorganisms, achieving deep purification, and utilizes intermediate products as a nutrient source to enhance their activity and degradation capacity. Furthermore, the synergistic effect of electrochemical and biological treatment promotes the diversity and stability of the microbial community, enhancing its adaptability to different wastewater characteristics.

[0004] However, current electrochemically enhanced biological treatment technologies still face several technical challenges in wastewater treatment: the impact of dynamic changes in electrochemical processes on microbial metabolic activities is not fully understood, making it difficult to determine optimal operating parameters; furthermore, current monitoring technologies and feedback mechanisms are often insufficient for real-time monitoring and automatic adjustment of key wastewater parameters, resulting in a lack of flexibility and reliability in response to emergencies. For example, the multi-stage through-flow electrochemical-fixed-bed biofilm degradation module designed in field tests of cubic-meter scale microbial electrochemical systems in amunicipal wastewater treatment plants failed to achieve real-time monitoring and adjustment of key operating parameters, leading to slow system response to changes in wastewater characteristics and affecting treatment efficiency. More importantly, existing water treatment equipment typically employs traditional PID control or single optimization algorithms, which struggle to handle system nonlinear characteristics, disturbances, and multivariate coupling problems, resulting in low operating efficiency, high energy consumption, insufficient control precision, and poor adaptability to environmental or load fluctuations. Therefore, an intelligent dynamic optimization control technology is needed to improve system performance.

[0005] Therefore, the design and development of intelligent control devices is particularly necessary, as it can not only improve the performance and reliability of 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 electrochemically enhanced biological integrated intelligent control equipment for treating organic wastewater to solve the above-mentioned technical problems.

[0007] This invention proposes an integrated intelligent control equipment for treating organic wastewater using electrochemical enhancement and biological processes, comprising an electrochemical enhancement biodegradation module and an intelligent control system. The electrochemical enhancement 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 packing material, an aeration system, and a carbon source dosing 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 collects parameters including COD, dissolved oxygen concentration, ammonia nitrogen, total nitrogen, and conductivity. The data processing and transmission module converts the sensor signals into digital signals and transmits them to the PLC controller. The PLC controller is connected to the data acquisition and remote monitoring module and receives the parameters. Based on a preset model, it automatically adjusts one or more of the following parameters: the voltage of the membrane electrode assembly, the current density of the membrane electrode assembly, the aeration rate, and the carbon source dosing rate. The preset model includes:

[0008] The current density model is as follows:

[0009]

[0010] The voltage model is:

[0011]

[0012] Where K(x) i Let x) be the Gaussian kernel function, which is described as follows:

[0013]

[0014] Where, x i Input feature vectors for COD and conductivity, a i Here, b represents the support vector weights, b is the bias term, and σ is the kernel function bandwidth hyperparameter.

[0015] The aeration rate model is as follows:

[0016]

[0017] The carbon source addition model is as follows:

[0018]

[0019] Among them, Q air Q is the volume of air introduced per unit time. carbonWhere 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 dissolved oxygen concentration in the effluent, and COD is the concentration of dissolved oxygen in the effluent. in COD of influent, COD out COD is the effluent COD, TN is the effluent total nitrogen, and NH4+ is the effluent total nitrogen. + This refers to ammonia nitrogen in the effluent.

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

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

[0022] Preferably, the electrochemically enhanced biodegradation module further includes 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 includes a liquid level sensor for detecting the liquid level height of the multi-stage permeable electrochemical-fixed bed biofilm degradation module.

[0025] Preferably, the sensor module further includes a power failure sensor for detecting the power status of the multi-stage permeation 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; 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 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 further includes an industrial control computer and a switch; the industrial control computer is connected to the PLC controller through the switch, receives and stores data transmitted from the PLC controller, and the industrial control computer is a host computer used for complex analysis and remote management.

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

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

[0032] This invention utilizes intelligent dynamic optimization control technology to model and optimize complex nonlinear relationships. Based on a preset intelligent dynamic optimization control algorithm model, it intelligently regulates the treatment of organic wastewater. It can automatically adjust key parameters such as the current density, voltage, dissolved oxygen concentration, and carbon source dosage of the membrane electrode assembly according to real-time monitored water quality parameters. It has adaptive control function, thereby maintaining the optimal treatment state under different wastewater characteristics and load conditions. This improves the robustness, treatment capacity, and response speed of the system, greatly enhances operating efficiency and control accuracy, and has strong adaptability to environmental or load fluctuations.

[0033] Furthermore, the embodiments of the present invention can monitor various parameters such as conductivity, pH value, liquid level, and temperature in real time, and automatically adjust them through a PLC controller, which greatly simplifies the operation process, improves the automation level of the system, and reduces the need for manual intervention. Attached Figure Description

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

[0035] Figure 2 This is a schematic diagram of the multi-level penetrating electrochemical-biodegradation module of the electrochemical-enhanced integrated intelligent regulation equipment according to an embodiment of the present invention.

[0036] Figure 3 This is a schematic diagram of the intelligent control system hardware architecture of the electrochemical-enhanced biological integrated intelligent regulation equipment according to an embodiment of the present invention.

[0037] Figure 4 The image shows the support vector regression (SVR) prediction results, with the horizontal axis representing the sample points and the vertical axis representing the current density of the membrane electrode assembly predicted based on the current model.

[0038] Figure 5 This is a diagram of an artificial neural network structure, with the input layer X = (x1, x2, ... x...). j …x n The figures show the corresponding influent water quality parameters: Chemical Oxygen Demand (COD), Dissolved Oxygen (DO), Total Nitrogen (TN), and Ammonia Nitrogen (NH4). + The output layer Y = (y1, y2, ... y) is calculated by an artificial neural network algorithm. j …y n () represents the effluent water quality parameters COD, TN, and NH4 in the figure. + . Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and the accompanying drawings. However, those skilled in the art will understand that this invention is not limited to the accompanying drawings and the following embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0040] Reference Figure 1 This invention proposes an electrochemically enhanced biodegradation integrated intelligent control equipment for treating organic wastewater, comprising an electrochemically enhanced biodegradation module and an intelligent control system. Preferably, the electrochemically enhanced biodegradation module is a multi-stage permeable electrochemical-fixed bed biofilm degradation module.

[0041] Reference Figure 2 The multi-stage permeable electrochemical-fixed-bed biofilm degradation module is used for electrochemically enhanced biological treatment of organic wastewater. Specifically, the multi-stage permeable 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 for electrochemical treatment of organic wastewater. The module includes a porous membrane electrode assembly and a power control system. The porous membrane electrode assembly is connected to a power source via wires to generate current during the reaction, providing electrons to assist the biofilm reaction process. The porous membrane electrode assembly is positioned at the front end of the biofilm, allowing the oxides or other active substances generated by the current to preferentially act on the water 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 includes an electrolyte dosing system for adjusting the conductivity of the organic wastewater. When the conductivity of the organic wastewater exceeds a preset threshold, the electrolyte dosing system injects electrolyte through a pipeline into the multi-stage permeable electrochemical-fixed bed biofilm degradation module to optimize the conductivity of the organic wastewater and the current efficiency of the porous membrane electrode assembly, thereby improving electron transfer.

[0044] Preferably, the electrochemical treatment module further includes an acid-base adjustment system, which is used to adjust the pH value of the organic wastewater. The system is connected to the inlet of the multi-stage permeable 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 the biodegradation treatment of organic wastewater. The module includes a fixed-bed biofilm containing microbial packing material, an aeration system, and a carbon source dosing system. The biofilm utilizes microorganisms to degrade organic matter in the wastewater after treatment by the electrochemical treatment module. The aeration system maintains the dissolved oxygen concentration within the biofilm to support microbial metabolic activities. The aeration system includes a blower connected to the bottom of the multi-stage permeable electrochemical-fixed-bed biofilm degradation module via an aeration pipe, providing oxygen to assist the aerobic degradation process of the microorganisms. The carbon source dosing device supplies carbon to 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 downstream of the multi-stage permeate electrochemical-fixed bed biofilm degradation module. It performs backwashing by starting at regular or irregular intervals to prevent clogging of the porous membrane electrode assembly and biofilm, maintaining the permeability and long-term operational stability of the multi-stage permeate 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 biofilm activity.

[0047] The electrochemical-biofilm components of the multi-stage permeable electrochemical-fixed bed biofilm degradation module can be extended by multi-stage series connection according to water quality, water quantity and treatment efficiency.

[0048] Reference Figure 1 The intelligent control system includes a data acquisition and remote monitoring module, a PLC controller, and a user 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 acquire 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), and an ammonia nitrogen sensor (NH4). + The system includes a total nitrogen (TN) sensor and a conductivity sensor, used to collect COD, dissolved oxygen concentration, ammonia nitrogen, total nitrogen, and conductivity parameters from the multi-stage permeation electrochemical-fixed bed biofilm degradation module, respectively. The data processing and transmission module converts the sensor signals into digital signals and transmits them 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 potentially 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 occurring in the organic wastewater can be obtained, thereby reflecting the degradation status of the electrochemically treated organic wastewater and serving as the control basis for the intelligent control system. By monitoring the ORP value of the wastewater in real time, the degree of reaction completion at each stage of wastewater treatment can be indicated, the oxidizing or reducing state of the reaction environment can be determined, providing a basis for process optimization, and providing early warning when system anomalies occur (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 includes a liquid level sensor for detecting the liquid level height of the multi-stage permeable electrochemical-fixed bed biofilm degradation module. When the liquid level height is outside a preset threshold range, the intelligent control equipment can maintain the liquid level height within a suitable range by controlling the influent and / or effluent.

[0053] Preferably, the sensor module further includes a power failure sensor for detecting the power status of the multi-stage permeation electrochemical-fixed bed biofilm degradation module. The power failure sensor can monitor the stability of the system power supply in real time, promptly detect power interruptions or power failures, and issue alarm signals to ensure the safe operation of the system.

[0054] Preferably, the sensor module further includes a temperature sensor for detecting the temperature of the organic wastewater, so as to maintain the optimal activity and treatment efficiency of the microorganisms in the biodegradation module and ensure that the reaction is carried out at a suitable temperature, avoiding temperature fluctuations that could lead to decreased efficiency or equipment damage.

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

[0056] I. Electrochemical Treatment Module

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

[0058] The current density model is as follows:

[0059]

[0060] The voltage model is:

[0061]

[0062] Where K(x) i Let x) be the Gaussian kernel function, which is described as follows:

[0063]

[0064] Where, x i Input feature vectors for COD and conductivity, a i Here, b represents the support vector weights, b is the bias term, and σ is the kernel function bandwidth hyperparameter.

[0065] This invention utilizes the aforementioned current density model and voltage model to automatically adjust at least one of the current density and voltage based on the detected effluent COD and conductivity.

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

[0067] The embodiments of the present invention obtain the above model by fitting the nonlinear relationship based on COD and conductivity using the support vector regression (SVR) machine learning algorithm. Figure 4 The current density prediction model is based on support vector regression. The error between the predicted current density and the actual current density is small, indicating that the model has high accuracy. The current density can be output using this model.

[0068] The specific learning process is as follows:

[0069] (1) Data preparation. Input training sample dataset: T = {(x1,y1), (x2,y2)……(x...} N ,y N )}, where x is the input feature vector of COD and conductivity, and 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 this embodiment of the invention is:

[0071]

[0072] Where w is the weight vector; is the feature mapping function that maps the input feature x to a high-dimensional feature space; b is the bias term.

[0073] (3) Define the error tolerance range. The error tolerance range is defined by introducing a parameter ∈. If the predicted value f(x) i Distance from actual y i If the value is less than ∈, the prediction is considered accurate and no loss is calculated; if the value exceeds ∈, the prediction is considered a loss.

[0074] The mathematical expression is:

[0075] |y i -f(x i )|≤∈

[0076] When the error is within the range ∈, the loss is not calculated.

[0077] (4) Loss Function Definition. To fit the data as closely as possible within the error range while avoiding overfitting, SVR defines the following optimization problem:

[0078]

[0079] Constraints:

[0080] y i -f(x i )≤∈+ξ i

[0081]

[0082] Among them, ‖w‖ 2 C is the regularization term; C is the penalty coefficient; This is a slack variable used to represent 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 Let x be the nonlinear original space, and let x be the linear new space after the 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] Where, α i , These are Lagrange multipliers obtained through optimization problems.

[0091] (7) Obtain the prediction function.

[0092]

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

[0094]

[0095] The voltage model is:

[0096]

[0097] Where K(x) i Let x) be the Gaussian kernel function, which is described as follows:

[0098]

[0099] Where, x i Input feature vectors for COD and conductivity, a i Here, b represents the support vector weights, b is the bias term, and σ is the kernel function bandwidth hyperparameter.

[0100] II. Biodegradation Module

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

[0102] The aeration rate model is as follows:

[0103]

[0104] The carbon source addition model is as follows:

[0105]

[0106] Among them, Q air Q is the volume of air introduced per unit time. carbon Where 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 dissolved oxygen concentration in the effluent, and COD is the concentration of dissolved oxygen in the effluent. in COD of influent, COD out COD is the effluent COD, TN is the effluent total nitrogen, and NH4+ is the effluent total nitrogen. + This refers to ammonia nitrogen in the effluent.

[0107] This invention utilizes the aforementioned aeration rate model and carbon source dosage model to automatically adjust at least one of the aeration rate and carbon source dosage based on the detected water quality parameters.

[0108] The embodiments of the present invention are based on DO, COD, and NH4. + Based on the TN water quality parameter data, a nonlinear relationship was fitted using an artificial neural network machine learning algorithm to obtain the aforementioned model. The neural network consists of an input layer, hidden layers, and an output layer, with the hidden layer potentially consisting of one or more layers. In this paper, the input layer comprises the influent water quality parameters DO, COD, and NH4. + After being processed by an artificial neural network algorithm, the predicted water quality parameters COD and NH4 are output at the output layer. + And TN, its structure is as follows Figure 5 As 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 target value for effluent is z, and the actual value is z (i.e., effluent COD, TN and NH4). + The actual output value, i.e., the measured value of the effluent.

[0111] (2) Initialize parameters. Randomly initialize the weight W to a small value; initialize the bias b to zero or a small value.

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

[0113] Calculate linear combinations:

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

[0115] Choose ReLU as the activation function σ(z).

[0116] a l =σ(z) l )

[0117] (4) Calculate the loss. The loss function L is the Mean Squared Error (MSE) loss function:

[0118]

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

[0120] (5) Backpropagation.

[0121] The gradient δ of the output layer (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] ② Calculate the gradient:

[0127]

[0128] ③ Update parameters:

[0129]

[0130] Among them W (ι)′ b (ι)′ The updated parameters are η, where η is the learning rate (also known as the step size), which is continuously adjusted in experiments to find the most suitable parameters.

[0131] (6) Iterative training.

[0132] Perform the forward and backward propagation steps described above on all training samples; evaluate the model's performance at the end of each epoch, and adjust the learning rate if the performance does not improve.

[0133] The preset model described in this embodiment of the invention is a data-driven model used to optimize one or more of the following parameters in the electrochemical treatment module and the fixed-bed biodegradation module: current density, voltage, aeration rate, and carbon source dosage. When the data acquisition and remote monitoring module detects water quality parameters of the organic wastewater (such as DO, COD, NH4),... + The PLC controller can automatically adjust one or more of the current density, voltage, aeration rate, and carbon source dosage according to a preset model to maintain the multi-stage through-type electrochemical-fixed bed biodegradation module in the optimal operating state, improve the treatment efficiency and effect of organic wastewater, and thus realize the automation and intelligent control of the treatment process.

[0134] For example, the target value for effluent is set as: COD out 50 mg / L, TN: 15 mg / L; NH4+ : 2 mg / L; When the detected water quality parameter is: COD in 500 mg / L, DO: 1.5 mg / L, conductivity: 1300 μS / cm, TN: 30 mg / L; NH4 + The current density calculated using the above-mentioned preset model is 15 A / m³, which is 10 mg / L. 2 Voltage: 4.5V, aeration rate: 5L / min, carbon source dosage: 2g / min. Based on real-time calculations from the aforementioned preset model, the PLC controller, power control system, aeration system, and carbon source dosage system adjust the current density, voltage, aeration rate, and carbon source dosage to ensure the organic wastewater treatment process remains at its optimal state.

[0135] The user interface supports network-based remote access and operation, displays system operating data and equipment status monitoring parameters, and automatically identifies operational anomalies or potential faults. The embedded maintenance guidance module provides operators with step-by-step maintenance guidance, including equipment maintenance tips, common fault diagnosis and repair methods, thereby ensuring continuous and reliable system operation.

[0136] Preferably, the intelligent control equipment further includes an industrial control computer and a switch. The industrial control computer is connected to the PLC controller via the switch, receiving and storing data transmitted from the PLC controller, providing users with convenient data monitoring and operation through a visual interface, and supporting both local and remote access. The industrial control computer is a host computer used for complex analysis and remote management, such as receiving data uploaded by the PLC controller, performing in-depth data analysis, recording, visualization, and storage. Preferably, the industrial control computer is also used to adjust the control parameters of the PLC controller through control commands. Thus, the data acquisition and remote monitoring module performs basic data acquisition and real-time feedback, supporting real-time monitoring of the system. The switch connects the PLC controller, the industrial control computer, and the data acquisition and remote monitoring module, supporting high-speed data exchange and providing 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 for the treatment of organic wastewater, with the target effluent value set as COD. out : 100mg / L, TN: 20mg / L; NH4 + 5 mg / 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 2.5 hours, the effluent water quality has reached the set target value.

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

[0139]

[0140] The intelligent control equipment of this invention operates as follows: After the equipment is powered on, the intelligent control system is activated, confirming that all components are properly connected. Raw water, after acid-base adjustment and electrolyte injection to a suitable pH value and conductivity, enters the multi-stage permeable electrochemical-fixed-bed biodegradation module. The PLC controller periodically acquires parameters transmitted from the sensor module to update the water quality status. The multi-stage permeable electrochemical-fixed-bed biodegradation module is activated, promoting the degradation of organic pollutants. The oxygen supply is adjusted through the aeration system to ensure optimal dissolved oxygen concentration and promote microbial activity. The PLC controller performs real-time analysis of water quality data based on a preset intelligent dynamic optimization control algorithm model. If the preset effluent target value is not reached, the PLC controller automatically adjusts one or more of the following: voltage, current density, aeration rate, and carbon source dosage to maintain balance. Furthermore, the intelligent control system periodically activates the backwashing system to clean the membrane electrodes and biofilm, maintaining their permeability. The cleaning frequency and pressure are automatically adjustable. This workflow ensures efficient synergy between the electrochemical and biodegradation processes, possessing real-time monitoring, automatic control, and remote management capabilities, adapting to different water quality conditions, and maintaining stable organic wastewater treatment effects.

[0141] The electrochemical-enhanced biological integrated intelligent control equipment of this 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 embodiments of the present invention have been described above. However, the present invention is not limited to the above embodiments. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An electrochemically enhanced biological integrated intelligent control equipment for treating organic wastewater, characterized in that, The system includes 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 includes a porous membrane electrode assembly and a power control system. The fixed-bed biofilm degradation module includes a fixed-bed biofilm containing microbial packing material, 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 collects parameters including COD, dissolved oxygen concentration, ammonia nitrogen, total nitrogen, and conductivity. The data processing and transmission module converts the sensor signals into digital signals and transmits them to the PLC controller. The PLC controller is connected to the data acquisition and remote monitoring module and receives the parameters. Based on a preset model, it automatically adjusts the voltage, current density, aeration rate, and carbon source addition of the membrane electrode assembly. The preset model includes: The current density model is as follows: ； The voltage model is: ； in, The Gaussian kernel function is described as follows: ； in, Input feature vectors for COD and conductivity. Here, b represents the support vector weights, and b is the bias term. This refers to the kernel function bandwidth hyperparameter. The aeration rate model is as follows: ； The carbon source addition model is as follows: ； Among them, Q air Q is the volume of air introduced per unit time. carbon Let W1, W2, W3, and W4 represent the mass of carbon source added per unit time, and let b1, b2, b3, and b4 represent the weight matrices and bias vectors, respectively. The ReLU activation function is used, where DO is the dissolved oxygen concentration in the effluent, and COD is the concentration in the effluent. in COD of influent, COD out COD is the effluent COD, TN is the effluent total nitrogen, and NH4+ is the effluent total nitrogen. + This refers to ammonia nitrogen in the effluent.

2. The intelligent control equipment as described in claim 1, characterized in that, The electrochemically enhanced biodegradation module is a multi-stage permeable electrochemical-fixed bed biofilm degradation module.

3. The intelligent control equipment as described in claim 2, characterized in that, The sensor module also includes a liquid level sensor for detecting the liquid level height of the multi-stage permeable electrochemical-fixed bed biofilm degradation module.

4. The intelligent control equipment as described in claim 2, characterized in that, The sensor module also includes a power failure sensor for detecting the power status of the multi-stage permeation electrochemical-fixed bed biofilm degradation module.

5. The intelligent control equipment as described in claim 1, characterized in that, The electrochemical treatment module also includes an electrolyte dosing system for adjusting the conductivity of the organic wastewater.

6. The intelligent control equipment as described in claim 1, characterized in that, The electrochemically enhanced biodegradation module also includes a backwashing system for backwashing the porous membrane electrode assembly and the fixed-bed biofilm.

7. The intelligent control equipment as described in 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.

8. The intelligent control equipment as described in claim 1, characterized in that, The electrochemical treatment module also includes an acid-base adjustment system for adjusting the pH value of the organic wastewater; the sensor module also includes a pH sensor for detecting the pH value of the organic wastewater.

9. The intelligent control equipment as described in claim 1, characterized in that, The sensor module also includes an oxidation-reduction potential sensor for detecting the ORP of organic wastewater.

10. The intelligent control equipment as described in claim 1, characterized in that, The sensor module also includes a temperature sensor for detecting the temperature of the organic wastewater.

11. The intelligent control equipment as described in claim 1, characterized in that, The intelligent control equipment also includes an industrial control computer and a switch; the industrial control computer is connected to the PLC controller through the switch, receives and stores data transmitted from the PLC controller, and the industrial control computer is a host computer used for complex analysis and remote management.

12. The intelligent control equipment as described in claim 11, characterized in that, The industrial computer is also used to adjust the control parameters of the PLC controller through control commands.

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