A phenolic wastewater treatment system

Abstract

The application discloses a phenolic wastewater treatment system, and relates to the technical field of sewage treatment.The system comprises a wastewater treatment subsystem and a distributed collaborative control subsystem; the distributed collaborative control system adopting a cloud-edge-end three-level architecture is used to deploy edge computing nodes in each wastewater treatment unit, realize real-time collection, preprocessing and control decision of local data, effectively reduce data transmission delay and bandwidth consumption, realize collaborative control and load optimization across units through interconnection between the edge computing nodes, break the data island between the treatment units, and through deep integration of the cloud platform and multiple management systems of the park, the operation parameters of the treatment units can be dynamically adjusted according to the production plan and energy price of the park, integrated management and control of source reduction, process optimization and end treatment are realized, the operation efficiency of the whole system is improved, and the energy consumption and management difficulty of the system are reduced.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, specifically to a phenol-containing wastewater treatment system. Background Technology

[0002] Wastewater, containing various pollutants, is a major byproduct of industrial production. Untreated or substandard wastewater discharge can severely damage the aquatic environment, affecting ecosystem balance and human life. Phenolic wastewater is a typical type of industrial wastewater containing phenolic compounds, primarily originating from industries such as coal chemical, petrochemical, pharmaceutical, printing and dyeing, and coking. Phenolic compounds are highly toxic, difficult to biodegrade, and cumulative, posing a toxic effect on aquatic organisms. Through bioaccumulation in the food chain, they can also harm human health. Effective treatment of phenolic wastewater can not only reduce environmental pollution and protect the ecological environment and public health, but also achieve water resource recycling, reducing water consumption and production costs for enterprises.

[0003] However, most phenol-containing wastewater treatment systems in large chemical industrial parks currently operate in a decentralized, independent mode. The lack of effective data interaction and collaborative control mechanisms between treatment units has resulted in severe data silos, leading to high overall system management difficulty, low operating efficiency, and high energy consumption. At the same time, existing control systems mostly adopt a centralized architecture, requiring all data to be uploaded to a central server for processing. This results in high data transmission latency, making it impossible to respond promptly to dynamic changes in influent water quality and quantity, and hindering the optimization and refined management of the system. Therefore, developing a phenol-containing wastewater treatment system is of great significance. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a phenol-containing wastewater treatment system that enables collaborative control and data sharing among various treatment units, effectively solves the data silo problem, improves system operating efficiency, and reduces energy consumption and management difficulty.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a phenol-containing wastewater treatment system, which includes a wastewater treatment subsystem and a distributed collaborative control subsystem; The distributed collaborative control subsystem is used to monitor and control the operation of the wastewater treatment subsystem in real time. The distributed collaborative control subsystem adopts a three-level cloud-edge-device architecture and includes a cloud platform, multiple edge computing nodes and multiple terminal sensing and execution units. The terminal sensing and execution unit is deployed in each process stage of the wastewater treatment subsystem. The terminal sensing and execution unit is used to transmit the collected operating parameters to the corresponding edge computing nodes and receive control commands issued by the edge computing nodes. The edge computing nodes are deployed one-to-one with each functional unit of the wastewater treatment subsystem. The edge computing nodes establish communication links with each other through the Industrial Internet of Things. The edge computing nodes are used to upload processed operating data to the cloud platform and receive global optimization instructions issued by the cloud platform.

[0006] Furthermore, the wastewater treatment subsystem includes a pretreatment unit, a biochemical treatment unit, and an advanced treatment unit; The pretreatment unit is used to remove large particulate suspended solids and floating oil from phenol-containing wastewater. The outlet of the pretreatment unit is connected to the inlet of the biochemical treatment unit. The pretreatment unit includes a bar screen, an equalization tank, and an oil separator. The bar screen is located at the inlet of the pretreatment unit and is used to intercept large particulate impurities in the phenol-containing wastewater. The equalization tank is located at the outlet of the bar screen and is used to adjust the water volume and water quality of the phenol-containing wastewater. The oil separator is located at the outlet of the equalization tank and is used to separate floating oil from the phenol-containing wastewater. The biochemical treatment unit is used to degrade organic pollutants in phenol-containing wastewater. The outlet of the biochemical treatment unit is connected to the inlet of the deep treatment unit. The biochemical treatment unit includes an anaerobic tank and an aerobic tank. The anaerobic tank is used for anaerobic biological treatment of phenol-containing wastewater, and the aerobic tank is used for aerobic biological treatment of phenol-containing wastewater. The advanced treatment unit is used to remove residual pollutants from phenol-containing wastewater. The advanced treatment unit includes an activated carbon adsorption tank and a disinfection tank. The activated carbon adsorption tank is used to adsorb residual organic pollutants in the phenol-containing wastewater, and the disinfection tank is used to disinfect the treated phenol-containing wastewater.

[0007] Furthermore, the terminal sensing and execution unit includes a flow sensor, a pH sensor, a dissolved oxygen sensor, a phenol concentration sensor, an electric valve, a variable frequency pump, and an aeration device. The flow sensor is deployed at the inlet and outlet of each functional unit, the pH sensor is deployed inside the equalization tank and the aerobic tank, the dissolved oxygen sensor is deployed inside the aerobic tank, the phenol concentration sensor is deployed at the outlet of the biochemical treatment unit and the outlet of the deep treatment unit, the electric valve is deployed at each pipe connection, the variable frequency pump is deployed in the inlet and outlet pipes of each functional unit, and the aeration device is deployed at the bottom of the aerobic tank.

[0008] Furthermore, the edge computing node performs the following operations when making local control decisions: The terminal sensing execution unit collects the operating parameters and performs data cleaning and format conversion on the operating parameters. The processed operating parameters are compared with preset process parameter thresholds to generate corresponding control commands; Control commands are sent to the corresponding terminal sensing and execution units, and the processed operating parameters and control commands are recorded and uploaded to the cloud platform.

[0009] Furthermore, the edge computing nodes establish point-to-point communication links via the Industrial Internet of Things (IIoT). Each edge computing node sends its own functional unit's operating load data to other edge computing nodes in real time, and each edge computing node receives the operating load data sent by other edge computing nodes. Based on the operating load data of all functional units, the system load balance is calculated. The formula for calculating the system load balance is: Where L is the system load balance degree and n is the total number of functional units. The actual operating load of the i-th functional unit. The design processing capability of the i-th functional unit. The design processing capacity is the average load rate of all functional units. Determined by the equipment's factory parameters, each edge computing node adjusts the operating parameters of its own functional unit based on the calculated system load balance. The operating load data includes influent flow rate, pollutant concentration, and equipment operating power.

[0010] Furthermore, the cloud platform performs the following operations when generating global optimization instructions: Receive operational data uploaded by all edge computing nodes, and summarize and analyze the operational data; Obtain production plan data from the park's production management system, environmental quality data from the environmental monitoring system, and energy price data from the energy management system; The overall system operating cost is calculated by combining operational data and external system data. The formula for calculating the overall system operating cost is as follows: Where C is the overall system operating cost, For system energy consumption costs, For the system's reagent consumption cost, The environmental compliance cost of the system is represented by α, β, and γ, which are the weighting coefficients for energy cost, reagent cost, and environmental cost, respectively. These weighting coefficients are pre-set by the park's operation and management department based on annual operation targets and environmental protection requirements, and are updated quarterly. A global optimization scheme is generated with the goal of minimizing the overall system operating cost. The global optimization scheme is then decomposed into global optimization instructions corresponding to each edge computing node and sent to the corresponding edge computing node.

[0011] Furthermore, the cloud platform interacts with the park's production management system, environmental monitoring system, and energy management system through a standard data interface. The cloud platform obtains the production progress and wastewater discharge plans of each enterprise in the park from the production management system, environmental quality monitoring data of the surrounding area from the environmental monitoring system, and real-time energy prices and energy supply status of the park from the energy management system. The standard data interface adopts the industrial Ethernet protocol.

[0012] Furthermore, the terminal sensing and execution unit performs the following operations when collecting data and executing instructions: The operating parameters of the process step are collected according to the preset sampling frequency, and the collected operating parameters are converted from analog to digital signals. The system transmits digital signals to the corresponding edge computing nodes, receives control commands from the edge computing nodes, and adjusts its own operating status according to the control commands.

[0013] Furthermore, the industrial IoT adopts a 5G standalone network architecture, which includes multiple base stations and core network equipment. The base stations are deployed in various locations within the park, and the core network equipment is deployed in the central computer room of the park. The edge computing nodes and the cloud platform are all connected to the core network equipment. The industrial IoT uses network slicing technology to allocate independent network resources for different types of data transmission, including real-time control data and non-real-time monitoring data.

[0014] Furthermore, the cloud platform also includes a fault diagnosis model, which is trained based on historical operating data and equipment fault data. The cloud platform inputs real-time operating data into the fault diagnosis model to obtain diagnostic results for equipment faults and process anomalies. The cloud platform calculates the fault confidence level based on the diagnostic results, and the formula for calculating the fault confidence level is: Where P is the fault confidence level and m is the number of sensors involved in the diagnosis. Let j be the anomaly level value of the j-th sensor. The weighting coefficient for the j-th sensor is determined based on the measurement accuracy of each sensor and the correlation coefficient in historical fault data. The cloud platform generates alarm information based on the calculated fault confidence and sends the alarm information to the terminal device of the management personnel, which includes a computer and a mobile terminal.

[0015] Compared with existing technologies, this phenol-containing wastewater treatment system has the following advantages: This invention employs a distributed collaborative control system with a cloud-edge-device three-tier architecture. Edge computing nodes are deployed in each wastewater treatment unit to enable real-time data acquisition, preprocessing, and control decision-making, effectively reducing data transmission latency and bandwidth consumption. Through industrial IoT interconnection between edge computing nodes, cross-unit collaborative control and load optimization are achieved, breaking down data silos between treatment units. Deep integration with multiple management systems within the park via a cloud platform allows for dynamic adjustment of operating parameters for each treatment unit based on the park's production plan and energy prices. This achieves integrated management of source reduction, process optimization, and end-of-pipe treatment, improving overall system efficiency, reducing energy consumption and management complexity, and enhancing the system's responsiveness to dynamic changes in influent water quality and quantity, ensuring stable compliance of effluent water quality standards.

[0016] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0018] Figure 1 This is a schematic diagram of a phenol-containing wastewater treatment system; Figure 2 This is a flowchart of a phenol-containing wastewater treatment system. Figure 3 This is a flowchart of edge computing nodes making local control decisions. Detailed Implementation

[0019] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0020] This invention provides a phenol-containing wastewater treatment system, which consists of a wastewater treatment subsystem and a distributed collaborative control subsystem. The distributed collaborative control subsystem adopts a three-tier cloud-edge-device architecture, including a cloud platform, edge computing nodes, and terminal sensing and execution units. The terminal sensing and execution units are deployed at each stage of the wastewater treatment process to complete data acquisition and command execution. The edge computing nodes are deployed one-to-one with the wastewater treatment functional units and interconnected through the Industrial Internet of Things to complete local data processing, load balancing calculation, and unit collaborative control. The cloud platform connects to multiple systems such as park production management, environmental monitoring, and energy management to carry out global operating cost optimization, fault diagnosis, and alarm push. The wastewater treatment subsystem completes the purification treatment of phenol-containing wastewater through three stages of pretreatment, biochemical treatment, and deep treatment. The terminal sensing and execution units collect operating parameters such as flow rate, pH, dissolved oxygen, and phenol concentration. The edge computing nodes complete local control decisions and load balancing calculations based on the parameters. The cloud platform combines data from multiple systems to calculate the comprehensive operating cost and generate global optimization commands. At the same time, it relies on a fault diagnosis model to complete equipment and process anomaly diagnosis, realizing intelligent management and control, unit collaboration, and optimal cost control of the entire process of phenol-containing wastewater treatment. The following is a detailed description with reference to specific embodiments.

[0021] This embodiment is applied to the treatment of phenol-containing wastewater in a large coal chemical industrial park. The park is home to many coal chemical production enterprises, which generate a large amount of high-concentration phenol-containing wastewater every day. The wastewater is characterized by high phenol pollutant content, large fluctuations in water quality and quantity, and complex composition. Traditional decentralized treatment mode has problems such as data silos, slow response, high energy consumption, and extensive management. The system of this invention can realize intelligent collaborative treatment of the whole process. The specific implementation process is as follows.

[0022] First, the wastewater treatment subsystem was constructed on-site. Following the process flow, a pretreatment unit, a biological treatment unit, and a deep treatment unit were sequentially arranged, with each unit connected by pipelines to form a continuous wastewater treatment pipeline. The pretreatment unit, located at the system inlet, includes a bar screen, a regulating tank, and an oil separator. The bar screen directly connects to the main wastewater discharge pipe of the park's enterprises, intercepting large particulate impurities and suspended solids in the wastewater. The regulating tank receives the effluent from the bar screen, balancing fluctuations in wastewater volume and quality differences to prevent shock loads from affecting subsequent treatment processes. The oil separator receives the effluent from the regulating tank, removing floating oil pollutants from the wastewater through physical separation, creating stable conditions for biological treatment. The biological treatment unit receives the effluent from the pretreatment unit and includes anaerobic and aerobic tanks. The anaerobic tank degrades large molecular organic pollutants in the wastewater through the metabolism of anaerobic microorganisms, converting phenolic substances into smaller organic molecules. The aerobic tank further decomposes organic pollutants through the aerobic respiration of aerobic microorganisms, completing the deep degradation of phenolic substances. The advanced treatment unit receives the effluent from the biochemical treatment unit and is equipped with an activated carbon adsorption tank and a disinfection tank. The activated carbon adsorption tank removes residual trace organic pollutants and phenolic substances in the wastewater through the adsorption of porous activated carbon, while the disinfection tank kills pathogenic microorganisms in the wastewater with disinfectant. The final product is clean water that meets the standards for reuse or discharge.

[0023] After completing the wastewater treatment subsystem, a distributed collaborative control subsystem is deployed. This system adopts a three-tier cloud-edge-device architecture, such as... Figure 1 As shown, the system is divided into three layers: terminal sensing and execution units, edge computing nodes, and a cloud platform. The terminal sensing and execution units are deployed throughout all process stages of the wastewater treatment subsystem. Various sensors and execution devices are arranged according to monitoring and control requirements. Flow sensors are installed at the inlet and outlet of each functional unit to monitor the influent and effluent flow rates in real time. pH sensors are installed inside the equalization tank and aerobic tank to monitor the acidity and alkalinity of the water in real time. Dissolved oxygen sensors are installed at the bottom of the aerobic tank to monitor the dissolved oxygen content in real time. Phenolic concentration sensors are installed at the outlet of the biochemical treatment unit and the outlet of the advanced treatment unit to monitor the concentration of phenolic pollutants in the effluent in real time. Electric valves are installed at key nodes in the connecting pipes of each unit to control the flow rate and interruption of water flow. Variable frequency pumps are installed in the inlet and outlet pipes of each unit to adjust the water delivery power. Aeration devices are evenly distributed at the bottom of the aerobic tank to introduce oxygen into the tank and ensure the survival of aerobic microorganisms.

[0024] Edge computing nodes are deployed in a one-to-one correspondence with the functional units of the wastewater treatment subsystem. Specifically, independent edge computing nodes are configured for the pretreatment unit, biochemical treatment unit, and deep treatment unit. Each edge computing node establishes a stable communication link through the Industrial Internet of Things (IIoT). The IIoT adopts a 5G standalone network architecture, with communication base stations evenly deployed throughout the park. Core network equipment is installed in the central computer room, and both edge computing nodes and the cloud platform are connected to the core network equipment. Network slicing technology is used to allocate independent network resources for real-time control data and non-real-time monitoring data, ensuring that real-time control command transmission is latency-free and packet-free, while non-real-time monitoring data transmission does not consume core network bandwidth. The cloud platform is built in the park's data center and connects with the park's production management system, environmental monitoring system, and energy management system through standard data interfaces of the industrial Ethernet protocol, enabling real-time interaction and sharing of data across multiple systems.

[0025] After the system starts up, the terminal sensing and execution unit enters the working state, continuously collecting operating parameters of each process step according to the preset sampling frequency. The collected analog signals such as current and voltage are processed by analog-to-digital conversion and converted into digital signals adapted for system transmission. The digital signals are then transmitted to the corresponding edge computing nodes. For example, the flow sensor in the pretreatment unit collects the influent flow rate of the bar screen and the effluent flow rate of the oil separator in real time; the pH sensor collects the pH value of the wastewater in the equalization tank; the dissolved oxygen sensor in the biological treatment unit collects the dissolved oxygen concentration in the aerobic tank; the phenol concentration sensor collects the phenol content of the biological effluent; and the phenol concentration sensor in the deep treatment unit collects the phenol content of the final effluent. All collected data is uploaded to the corresponding edge computing nodes in real time.

[0026] The terminal sensing and execution unit also has command execution capabilities, enabling it to receive control commands from edge computing nodes and adjust its own operating status accordingly. For example, when the pH value in the equalization tank deviates from the process threshold, the edge computing node issues a dosing adjustment command, and the dosing equipment in the terminal sensing and execution unit automatically adjusts the dosage of the chemicals; when the dissolved oxygen content in the aerobic tank is insufficient, the edge computing node issues an aeration command, and the aeration device automatically increases the aeration power; when the influent flow rate of a certain unit is too high, the variable frequency pump automatically adjusts its speed to control the influent flow rate, ensuring that each process link is always in a stable operating state.

[0027] After receiving the operating parameters uploaded by the terminal sensing and execution unit, the edge computing node immediately performs local control decision processing, as follows: Figure 3As shown, the received operating parameters are first cleaned and format-converted to remove noise, missing values, and other invalid information, and to standardize the data format and units of measurement to ensure the accuracy of subsequent calculations. Then, the processed operating parameters are compared with preset process parameter thresholds. If the parameters are within the threshold range, the current equipment operating state is maintained; if the parameters exceed the threshold, a corresponding control command is immediately generated and sent to the terminal sensing and execution unit to complete equipment adjustment. Simultaneously, the processed operating parameters and control commands are recorded and uploaded to the cloud platform, achieving full traceability and cloud backup of local control data.

[0028] In addition to local control, edge computing nodes also need to achieve cross-unit collaborative load balancing regulation. Each edge computing node sends the operating load data of its own functional unit to other nodes in real time through the point-to-point communication link of the Industrial Internet of Things. The operating load data includes three core indicators: water inflow, pollutant concentration, and equipment operating power. At the same time, it receives load data sent by other nodes, forming a load data sharing mechanism for the entire system.

[0029] In the specific implementation of this embodiment, the load state quantitative analysis is completed based on the system load balance calculation formula, which is as follows: Where L is the system load balance degree and n is the total number of functional units. The actual operating load of the i-th functional unit. The design processing capability of the i-th functional unit. This represents the average load factor across all functional units. The design processing capacity of each functional unit. The parameters are directly determined by the equipment's factory specifications, requiring no additional on-site calculations. In this embodiment, the total number of functional units, n, is 3, corresponding to the preprocessing unit, biochemical processing unit, and deep processing unit, respectively. The edge computing node first aggregates the actual operating load of the three units. With design processing capabilities The average load factor of all units was calculated. Then, substitute the values ​​into the formula to obtain the system load balance degree L, and use the value of L to determine whether the system load is balanced.

[0030] like Figure 2As shown, after completing the load balance calculation, the system automatically judges the load balance status. If the load balance is within the preset reasonable range, it indicates that the operating load of each unit is highly matched, and there is no need to adjust the operating parameters. If the load balance exceeds the reasonable range, it indicates that some units are overloaded and some units are underloaded. Each edge computing node immediately coordinates to adjust the operating parameters of its own unit. By adjusting the variable frequency pump speed, electric valve opening, equipment operating power, etc., the system optimizes the influent flow distribution and pollutant treatment load ratio, so that the operating load of each unit tends to be balanced, avoiding the overload operation of a single unit that leads to a decrease in treatment efficiency or equipment damage, and realizing dynamic optimization of the load of the entire system.

[0031] The cloud platform continuously receives processed operational data uploaded from all edge computing nodes, summarizes, categorizes, and deeply analyzes the data to generate visualized data reports on the overall system's operational status. Simultaneously, the cloud platform acquires real-time data from external systems within the park through standard data interfaces. This includes production progress and wastewater discharge plans from the production management system, environmental quality monitoring data for the surrounding air and water bodies from the environmental monitoring system, and real-time electricity and gas prices and energy supply from the energy management system. By integrating internal operational data with external system data, the platform provides data support for overall optimization.

[0032] In the specific implementation of this embodiment, the cloud platform takes minimizing the overall system operating cost as its core objective, and completes the cost quantification and accounting based on the overall system operating cost calculation formula, which is as follows: Where C is the overall system operating cost, For system energy consumption costs, For the system's reagent consumption cost, For the system's environmental compliance costs, α, β, and γ are the weighting coefficients for energy costs, reagent costs, and environmental costs, respectively. These weighting coefficients are pre-set by the park's operation and management department based on annual operational targets and environmental requirements. Before setting them, data on the park's average energy procurement price, reagent consumption costs, environmental compliance expenditures, and penalties over the past three years must be collected. A quantitative analysis is performed using the analytic hierarchy process (AHP) to determine the specific values ​​of each coefficient. These coefficients are updated quarterly based on operational conditions. In this embodiment, α is initially set to 0.4, β to 0.3, and γ to 0.3 to ensure that the weighting coefficients align with the actual operational needs of the coal chemical industrial park.

[0033] The cloud platform compares the calculated overall system operating cost with a preset optimal cost threshold, such as... Figure 2As shown, if the current cost is not optimal, a global optimization plan is immediately generated, decomposed into global optimization instructions for each edge computing node, and then issued. For example, during peak energy price periods, the cloud platform issues instructions to reduce the operating power of high-power equipment such as aerobic tank aeration devices and variable frequency pumps, reducing energy consumption while ensuring treatment effectiveness; during off-peak energy price periods, the operating power of the equipment is increased to increase the wastewater treatment volume; the adjustment capacity of the pretreatment unit is adjusted in advance based on the company's wastewater discharge plan to avoid shock loads caused by concentrated wastewater discharge; and the concentration of pollutants in the effluent is strictly controlled based on environmental monitoring data to reduce environmental compliance costs. After receiving the global optimization instructions, the edge computing nodes adjust the terminal device parameters based on their local operating status to achieve global collaborative optimization of the entire system.

[0034] The cloud platform is equipped with a trained fault diagnosis model, which is trained based on historical operating data and equipment fault data of the park's phenol-containing wastewater treatment system. This model can accurately identify equipment faults and process anomalies. The cloud platform inputs real-time operating data into the fault diagnosis model, quickly generating diagnostic results for equipment faults and process anomalies. Subsequently, it performs a quantitative assessment of the fault level based on the fault confidence calculation formula, which is: Where P is the fault confidence level and m is the number of sensors involved in the diagnosis. Let j be the anomaly level value of the j-th sensor. Let be the weighting coefficient for the j-th sensor. Sensor weighting coefficients. Based on the measurement accuracy of each sensor and the correlation coefficient in historical fault data, the sensor with higher measurement accuracy and stronger correlation with equipment failure or process abnormality has a larger weight coefficient. In this embodiment, the correlation between sensor data and fault events is calculated by Pearson correlation analysis, and the weight coefficient is quantitatively allocated by combining the sensor's factory accuracy level.

[0035] The cloud platform determines the severity of a fault based on the calculated fault confidence level P. The higher the confidence level, the greater the risk of the fault. When the confidence level reaches the preset alarm threshold, the corresponding alarm information is immediately generated and sent to the computer and mobile terminal of the management personnel. The alarm information includes the fault location, fault type, confidence level value and handling suggestions, so that the management personnel can rush to the site to handle the fault as soon as possible, avoid the fault from escalating and affecting the system operation, and ensure the continuous and stable treatment process of phenol-containing wastewater.

[0036] In summary, this embodiment applies the system of the present invention to the treatment of phenol-containing wastewater in a large-scale coal chemical industrial park. Through a distributed collaborative control architecture with a cloud-edge-device three-tier architecture, it completely solves the data silo problem of traditional treatment systems, achieving data sharing and collaborative control among processing units. Local real-time decision-making by edge computing nodes significantly reduces data transmission latency and bandwidth consumption, improving the system's response speed to dynamic changes in water quality and quantity. Load balancing calculation and regulation ensure that the operating load of each functional unit remains optimal, avoiding resource waste caused by equipment overload or idle operation. The cloud platform combines data from multiple systems in the park—production, environment, and energy—to conduct comprehensive cost optimization, achieving synergistic minimization of energy consumption, reagent consumption, and environmental compliance costs, effectively reducing the operating costs of wastewater treatment in the park. Fault diagnosis and intelligent alarm mechanisms identify equipment and process anomalies in advance, improving the safety and stability of system operation. The effluent quality consistently meets standards, while also achieving water resource recycling, significantly reducing the difficulty of park management. This fully meets the green, low-carbon, intelligent, and refined operation and development needs of chemical industrial parks, providing a replicable and scalable implementation plan for the efficient treatment of industrial phenol-containing wastewater.

[0037] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A phenol-containing wastewater treatment system, characterized in that, The system includes a wastewater treatment subsystem and a distributed collaborative control subsystem; The distributed collaborative control subsystem is used to monitor and control the operation of the wastewater treatment subsystem in real time. The distributed collaborative control subsystem adopts a three-level cloud-edge-device architecture and includes a cloud platform, multiple edge computing nodes and multiple terminal sensing and execution units. The terminal sensing and execution unit is deployed in each process stage of the wastewater treatment subsystem. The terminal sensing and execution unit is used to transmit the collected operating parameters to the corresponding edge computing nodes and receive control commands issued by the edge computing nodes. The edge computing nodes are deployed one-to-one with each functional unit of the wastewater treatment subsystem. The edge computing nodes establish communication links with each other through the Industrial Internet of Things. The edge computing nodes are used to upload processed operating data to the cloud platform and receive global optimization instructions issued by the cloud platform.

2. The phenol-containing wastewater treatment system according to claim 1, characterized in that, The wastewater treatment subsystem includes a pretreatment unit, a biochemical treatment unit, and an advanced treatment unit. The pretreatment unit is used to remove large particulate suspended solids and floating oil from phenol-containing wastewater. The outlet of the pretreatment unit is connected to the inlet of the biochemical treatment unit. The pretreatment unit includes a bar screen, an equalization tank, and an oil separator. The bar screen is located at the inlet of the pretreatment unit and is used to intercept large particulate impurities in the phenol-containing wastewater. The equalization tank is located at the outlet of the bar screen and is used to adjust the water volume and water quality of the phenol-containing wastewater. The oil separator is located at the outlet of the equalization tank and is used to separate floating oil from the phenol-containing wastewater. The biochemical treatment unit is used to degrade organic pollutants in phenol-containing wastewater. The outlet of the biochemical treatment unit is connected to the inlet of the deep treatment unit. The biochemical treatment unit includes an anaerobic tank and an aerobic tank. The anaerobic tank is used for anaerobic biological treatment of phenol-containing wastewater, and the aerobic tank is used for aerobic biological treatment of phenol-containing wastewater. The advanced treatment unit is used to remove residual pollutants from phenol-containing wastewater. The advanced treatment unit includes an activated carbon adsorption tank and a disinfection tank. The activated carbon adsorption tank is used to adsorb residual organic pollutants in the phenol-containing wastewater, and the disinfection tank is used to disinfect the treated phenol-containing wastewater.

3. The phenol-containing wastewater treatment system according to claim 2, characterized in that, The terminal sensing and execution unit includes a flow sensor, a pH sensor, a dissolved oxygen sensor, a phenol concentration sensor, an electric valve, a variable frequency pump, and an aeration device. The flow sensor is deployed at the inlet and outlet of each functional unit. The pH sensor is deployed inside the equalization tank and the aerobic tank. The dissolved oxygen sensor is deployed inside the aerobic tank. The phenol concentration sensor is deployed at the outlet of the biochemical treatment unit and the outlet of the deep treatment unit. The electric valve is deployed at each pipe connection. The variable frequency pump is deployed in the inlet and outlet pipes of each functional unit. The aeration device is deployed at the bottom of the aerobic tank.

4. The phenol-containing wastewater treatment system according to claim 1, characterized in that, The edge computing node performs the following operations when making local control decisions: The terminal sensing execution unit collects the operating parameters and performs data cleaning and format conversion on the operating parameters. The processed operating parameters are compared with preset process parameter thresholds to generate corresponding control commands; Control commands are sent to the corresponding terminal sensing and execution units, and the processed operating parameters and control commands are recorded and uploaded to the cloud platform.

5. The phenol-containing wastewater treatment system according to claim 1, characterized in that, The edge computing nodes establish point-to-point communication links via the Industrial Internet of Things (IIoT). Each edge computing node sends its own functional unit's operating load data to other edge computing nodes in real time, and each edge computing node receives the operating load data sent by other edge computing nodes. Based on the operating load data of all functional units, the system load balance is calculated. The formula for calculating the system load balance is: Where L is the system load balance degree and n is the total number of functional units. The actual operating load of the i-th functional unit. The design processing capability of the i-th functional unit. The average load rate of all functional units is used as the basis for each edge computing node to adjust the operating parameters of its own functional unit according to the calculated system load balance. The operating load data includes influent flow rate, pollutant concentration and equipment operating power.

6. The phenol-containing wastewater treatment system according to claim 1, characterized in that, The cloud platform performs the following operations when generating global optimization instructions: Receive operational data uploaded by all edge computing nodes, and summarize and analyze the operational data; Obtain production plan data from the park's production management system, environmental quality data from the environmental monitoring system, and energy price data from the energy management system; The overall system operating cost is calculated by combining operational data and external system data. The formula for calculating the overall system operating cost is as follows: Where C is the overall system operating cost, For system energy consumption costs, For the system's reagent consumption cost, The environmental compliance cost of the system is represented by α, β, and γ, which are the weighting coefficients for energy cost, reagent cost, and environmental cost, respectively. A global optimization scheme is generated with the goal of minimizing the overall system operating cost. The global optimization scheme is then decomposed into global optimization instructions corresponding to each edge computing node and sent to the corresponding edge computing node.

7. The phenol-containing wastewater treatment system according to claim 1, characterized in that, The cloud platform interacts with the park's production management system, environmental monitoring system, and energy management system through a standard data interface. The cloud platform obtains the production progress and wastewater discharge plans of each enterprise in the park from the production management system, environmental quality monitoring data of the surrounding area from the environmental monitoring system, and real-time energy prices and energy supply status of the park from the energy management system. The standard data interface adopts the industrial Ethernet protocol.

8. The phenol-containing wastewater treatment system according to claim 1, characterized in that, The terminal sensing and execution unit performs the following operations when collecting data and executing instructions: The operating parameters of the process step are collected according to the preset sampling frequency, and the collected operating parameters are converted from analog to digital signals. The system transmits digital signals to the corresponding edge computing nodes, receives control commands from the edge computing nodes, and adjusts its own operating status according to the control commands.

9. A phenol-containing wastewater treatment system according to claim 1, characterized in that, The industrial IoT adopts a 5G standalone network architecture, which includes multiple base stations and core network equipment. The base stations are deployed in various locations within the park, and the core network equipment is deployed in the central computer room of the park. The edge computing nodes and cloud platform are all connected to the core network equipment. The industrial IoT uses network slicing technology to allocate independent network resources for different types of data transmission, including real-time control data and non-real-time monitoring data.

10. A phenol-containing wastewater treatment system according to claim 1, characterized in that, The cloud platform also includes a fault diagnosis model, which is trained based on historical operating data and equipment fault data. The cloud platform inputs real-time operating data into the fault diagnosis model to obtain diagnostic results for equipment faults and process anomalies. The cloud platform calculates the fault confidence level based on the diagnostic results, and the formula for calculating the fault confidence level is: Where P is the fault confidence level and m is the number of sensors involved in the diagnosis. Let j be the anomaly level value of the j-th sensor. Let be the weight coefficient of the j-th sensor. The cloud platform generates alarm information based on the calculated fault confidence and sends the alarm information to the terminal device of the management personnel. The terminal device includes a computer and a mobile terminal.

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