Water plant process control real-time monitoring system

Abstract

The invention discloses a water plant process control real-time monitoring system, which belongs to the field of water treatment and comprises a sensing layer, a transmission layer, a data processing layer and an application layer. A sensor in the sensing layer collects water quality and equipment operation data in real time and transmits the data to the data processing layer through the transmission layer; the edge computing device and the cloud computing platform process and analyze the data according to a set algorithm, the analysis result is used for controlling the operation of the device, and a notification is displayed at the monitoring center and the mobile terminal; the delay of the whole process from sensor data acquisition to monitoring center display is controlled within a second level, water quality abnormity and equipment faults can be found in time, and water supply accidents are avoided; the multi-parameter sensor fusion and intelligent data analysis technology effectively improves the data accuracy, the water quality monitoring precision reaches the industry leading level, and a reliable basis is provided for accurate operation of a water plant.

Description

Technical Field

[0001] The present invention relates to the technical field of water treatment, and in particular to a real-time monitoring system for process control in a water plant. Background Art

[0002] With the acceleration of urbanization and the improvement of people's requirements for quality of life, higher standards are being put forward for the safety and stability of water supply from water plants. In the operation of traditional water plants, there are many problems with water quality monitoring and equipment control. On the one hand, water quality monitoring mostly relies on manual regular sampling and testing, which cannot reflect water quality changes in real time and continuously, resulting in difficulty in timely detection and treatment of water quality abnormalities, affecting water supply safety. On the other hand, the means of monitoring the operating status of equipment are limited, and equipment failures are often not repaired until they occur, which not only increases maintenance costs, but may also cause production interruptions. In addition, the existing monitoring system's data processing and analysis capabilities are insufficient, making it difficult to accurately control and optimize scheduling based on real-time data, and cannot meet the needs of efficient operation of water plants;

[0003] Therefore, there is a need for a real-time, comprehensive and accurate monitoring system for water plant process control that can monitor the water quality and equipment operating status of water plants in real time, and perform intelligent analysis and automatic control based on monitoring data to improve the safety, stability and efficiency of water plant operations. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a real-time monitoring system for water plant process control to achieve real-time, comprehensive and accurate monitoring of the water quality and equipment operating status of the water plant, and perform intelligent analysis and automatic control based on the monitoring data to improve the safety, stability and efficiency of the water plant operation.

[0005] Technical Solution: To solve the above technical problems, according to one aspect of the present invention, more specifically, a real-time monitoring system for water plant process control includes a perception layer, a transmission layer, a data processing layer, and an application layer;

[0006] The perception layer integrates multiple water quality parameter detection sensors, such as pH, turbidity, residual chlorine, COD, ammonia nitrogen, and heavy metal ion sensors, installed at key locations such as the water plant's raw water inlet, sedimentation tanks, filtration tanks, clear water tanks, and outlet water pipelines. This enables real-time monitoring of multiple water quality parameters. Furthermore, vibration sensors, temperature sensors, current sensors, and pressure sensors are deployed on key equipment such as water pumps, dosing equipment, fans, and valves to monitor equipment operating status parameters.

[0007] The transmission layer utilizes a combination of wireless and wired transmission methods. For widely distributed areas where cabling is difficult, such as remote monitoring points, low-power, long-distance wireless communication technologies like LoRa and NB-IoT are used to transmit sensor data to aggregation nodes. For core areas with high data volumes and extremely high real-time requirements, such as between the central control room and key processing equipment, fiber-optic Ethernet is used as a wired transmission method to ensure high-speed and stable data transmission.

[0008] The data processing layer deploys edge computing devices at aggregation nodes to perform preliminary cleaning, denoising, filtering, and aggregation on the collected data, removing abnormal data and calculating statistical quantities of water quality parameters and equipment operating parameters, such as mean, variance, and rate of change. This reduces data processing pressure on core servers and provides fast data support for real-time control. A cloud computing platform is established in the cloud to receive data uploaded by edge computing nodes and leverage the powerful storage and computing capabilities of cloud computing to conduct in-depth data analysis. By establishing water quality models and equipment operation models, and applying machine learning and deep learning algorithms, the underlying patterns of the data are explored to achieve water quality and equipment failure prediction.

[0009] The application layer: A monitoring center will be established in the water plant's central control room, equipped with a large screen to intuitively display real-time water quality data, equipment operating status, process flow dynamics, and other information. Managers can use the terminal to remotely start and stop equipment, adjust parameters, and perform other operations. A mobile app will also be developed to allow managers to monitor water plant operations anytime, anywhere. The app will feature real-time alert push notifications, instantly notifying relevant personnel when water quality exceeds standards or equipment malfunctions occur, enabling prompt response and resolution.

[0010] The core technology

[0011] Multi-parameter sensor fusion technology: Utilizing data fusion algorithms such as Kalman filtering and weighted averaging, data collected by multiple parameter sensors is integrated and processed. For example, when monitoring turbidity and suspended solids concentration, the Kalman filter algorithm is used to fuse data from an optical turbidity sensor and an ultrasonic suspended solids sensor, eliminating measurement errors from individual sensors and improving data accuracy and reliability. The sensor's built-in self-calibration module automatically compares sensor output parameters with standard samples at regular intervals, adjusting the sensor's output parameters to ensure long-term stable operation. For example, the residual chlorine sensor automatically undergoes electrochemical calibration at regular intervals to ensure accurate residual chlorine measurement.

[0012] This intelligent data analysis technology utilizes machine learning algorithms such as decision trees, support vector machines, and neural networks to analyze water quality and equipment operation data. Using neural networks, it establishes a correlation model between water quality and drug dosage, automatically optimizing drug dosing strategies based on real-time water quality, and improving water treatment effectiveness. It also utilizes anomaly detection algorithms such as isolation forests and one-class support vector machines (SVMs) to detect anomalies in water quality and equipment operation data in real time. Once an anomaly is detected, an alarm is triggered, saving time for fault diagnosis and resolution.

[0013] Real-time control technology: Based on water quality and equipment operation models, model predictive control (MPC) technology is used to provide real-time control of water plant equipment. MPC predicts the system's future state and proactively optimizes control strategies to ensure consistent water quality. For example, based on raw water quality and flow rate forecasts, sedimentation tank desludging cycles and filtration equipment backwash times can be adjusted in advance. The system also possesses adaptive capabilities, automatically adjusting control parameters based on real-time monitoring data. For example, during pump operation, the system adaptively adjusts pump speed based on pipeline pressure fluctuations, achieving energy-efficient and efficient operation.

[0014] The beneficial effects of the water plant process control real-time monitoring system of the present invention are:

[0015] (1) The present invention controls the entire process delay from sensor data collection to monitoring center display within seconds, which can timely detect water quality anomalies and equipment failures and avoid water supply accidents;

[0016] Multi-parameter sensor fusion and intelligent data analysis technology effectively improve data accuracy, and the water quality monitoring accuracy reaches the industry-leading level, providing a reliable basis for the precise operation of water plants.

[0017] (2) The present invention automatically completes data collection, analysis, processing and equipment control, reducing manual intervention. Intelligent prediction and early warning functions prevent potential risks in advance, improve water plant management efficiency and scientific decision-making;

[0018] The modular design allows for easy expansion of new monitoring parameters and equipment. Sensor nodes can be added or software functionality upgraded to meet the plant's development needs, protecting initial investment. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present invention will be further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0020] Figure 1 It is a structural schematic diagram of the present invention;

[0021] Figure 2 This is a data processing flow chart of the present invention. DETAILED DESCRIPTION

[0022] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, unless there is a conflict, the embodiments and features in the embodiments of the present application can be combined with each other.

[0023] In order to make the technical solution of the present invention clearer, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] Reference Figure 1-Figure 2 A real-time monitoring system for water plant process control includes a perception layer, a transmission layer, a data processing layer, and an application layer, and is characterized by:

[0025] The perception layer installs water quality sensors with integrated multiple water quality parameter detection functions at key locations such as the water plant's raw water inlet, sedimentation tank, filtration tank, clear water tank, and outgoing water pipeline. At the same time, equipment sensors are deployed for key equipment such as water pumps, dosing equipment, fans, and valves.

[0026] The transmission layer uses a combination of wireless and wired transmission methods. For areas with wide distribution and difficult wiring, it uses low-power, long-distance wireless communication technologies to transmit sensor data (such as LoRa and NB-IoT) to aggregation nodes. In core areas with large data volumes and high real-time requirements (such as between the central control room and key processing equipment), optical fiber Ethernet is used for high-speed and stable data transmission.

[0027] The data processing layer deploys edge computing devices at the aggregation nodes to clean, denoise, filter, and aggregate the collected data, calculate statistics on water quality and equipment operating parameters, reduce the pressure on the core server, and provide fast data for real-time control. A cloud computing platform is built on the cloud to receive data uploaded by edge computing nodes. The cloud computing capabilities are used to establish water quality models and equipment operation models, and machine learning and deep learning algorithms are used to explore data patterns to achieve water quality prediction and equipment failure prediction.

[0028] The application layer sets up a monitoring center in the water plant's central control room, equipped with a large monitoring screen to intuitively display real-time water quality data, equipment operating status, process flow dynamics and other information. Managers can remotely start and stop equipment and adjust parameters through the operation terminal; develop a mobile APP with real-time alarm push function to promptly notify relevant personnel when water quality exceeds the standard or equipment fails.

[0029] like Figure 2 As shown in the figure, the entire data processing flow starts from sensor data collection, undergoes preliminary processing by edge computing, and then goes to in-depth analysis on the cloud computing platform, and finally is used for real-time control and display.

[0030] Preferably, the water quality sensor in the sensing layer includes a pH sensor, a turbidity sensor, a residual chlorine sensor, a COD sensor, an ammonia nitrogen sensor, and a heavy metal ion sensor, which are used for multi-parameter real-time water quality monitoring;

[0031] The equipment sensors include vibration sensors, temperature sensors, current sensors, and pressure sensors, which are used to monitor equipment operating status parameters.

[0032] The multi-parameter water quality sensor adopts an integrated design. Each sensor works together in the same module, and has a built-in self-calibration module that automatically compares with standard samples and adjusts output parameters regularly.

[0033] Preferably, the wireless communication technology and the wired communication technology of the transmission layer can be automatically switched according to network conditions and data transmission requirements to ensure the stability and continuity of data transmission.

[0034] Preferably, a secure and encrypted data transmission channel is established between the edge computing device of the data processing layer and the cloud computing platform to ensure the security of data during transmission.

[0035] Preferably, the monitoring center operation terminal of the application layer has a rights management function, and managers with different rights can perform operations at different levels to ensure the security of system operations.

[0036] Preferably, the system adopts multi-parameter sensor fusion technology and uses data fusion algorithms such as Kalman filtering and weighted averaging to fuse the data collected by the multi-parameter sensors to improve data accuracy and reliability.

[0037] Preferably, when the multi-parameter sensor fusion technology is applied to monitor turbidity and suspended matter concentration, the Kalman filter algorithm is used to fuse the data of the optical turbidity sensor and the ultrasonic suspended matter sensor.

[0038] Preferably, the system adopts intelligent data analysis technology, uses machine learning algorithms such as decision trees, support vector machines, neural networks, etc. to analyze water quality data and equipment operation data, establishes a correlation model between water quality and dosing dosage, and automatically optimizes dosing strategies.

[0039] Preferably, the intelligent data analysis technology uses anomaly detection algorithms (isolation forest, One-ClassSVM) to detect anomalies in water quality and equipment operation data in real time and trigger an alarm mechanism.

[0040] Preferably, the system adopts real-time control technology, based on the water quality model and the equipment operation model, and uses model predictive control (MPC) technology to control the water plant equipment in real time, and optimize the control strategy in advance to ensure that the water quality is stable and meets the standards;

[0041] On the basis of traditional MPC, the weight coefficient of the control target is increased. Assuming that the control targets include meeting water quality standards, minimizing energy consumption, and maximizing equipment life, they are represented by J1, J2, and J3 respectively, and the weight coefficients are λ1, λ2, and λ3. Then the optimization objective function is J=λ1J1+λ2J2+λ3J3;

[0042] By rationally adjusting the weight coefficients, we can better balance the relationship between different control objectives, while ensuring that water quality is stable and meets standards, achieving the goals of energy saving and extending equipment life, and improving the overall efficiency of water plant operations.

[0043] The steps to build the system are as follows:

[0044] A multi-parameter water quality sensor was installed at the raw water inlet of a water plant. Vibration, temperature, and other equipment status sensors were also deployed around the plant. Wireless transmission modules were used to transmit data to a nearby aggregation node. Sensors were installed at key locations, such as sedimentation tanks and filtration tanks, based on process requirements and connected to the aggregation node via wired connections.

[0045] Deploy edge computing devices at the aggregation nodes to perform preliminary processing on the collected data according to the preset algorithms. Build a cloud computing platform and connect the edge computing nodes to the cloud platform to ensure smooth data upload.

[0046] Install a large monitoring screen and operation terminal in the water plant's central control room, develop and deploy a mobile APP, and complete the construction of the system application layer.

[0047] The system operation and maintenance steps are as follows:

[0048] Once the system is operational, sensors collect real-time data on water quality and equipment operation, which is then transmitted via the transport layer to the data processing layer. Edge computing devices and the cloud computing platform process and analyze the data according to established algorithms, using the results to control equipment operation and display them in the monitoring center and on mobile devices.

[0049] Regularly calibrate and maintain sensors to ensure measurement accuracy. Monitor and optimize the performance of edge computing devices and cloud computing platforms to ensure efficient data processing and analysis. Update system software in a timely manner to improve system functionality and stability.

[0050] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A real-time monitoring system for water plant process control, comprising a perception layer, a transmission layer, a data processing layer, and an application layer, characterized by: The perception layer is equipped with water quality sensors with integrated multiple water quality parameter detection functions at key locations such as the water plant's raw water inlet, sedimentation tank, filtration tank, clear water tank, and outlet water pipeline. At the same time, equipment sensors are deployed for key equipment such as water pumps, dosing equipment, fans, and valves. The transmission layer uses a combination of wireless and wired transmission methods. For areas with wide distribution and difficult wiring, it uses low-power, long-distance wireless communication technology to transmit sensor data to the aggregation node. In core areas with large data volumes and high real-time requirements, optical fiber Ethernet is used for high-speed and stable data transmission. The data processing layer deploys edge computing devices at the aggregation nodes to clean, denoise, filter, and aggregate the collected data, calculate statistics on water quality and equipment operating parameters, reduce the pressure on the core server, and provide fast data for real-time control. A cloud computing platform is built on the cloud to receive data uploaded by edge computing nodes. The cloud computing capabilities are used to establish water quality models and equipment operation models, and machine learning and deep learning algorithms are used to explore data patterns to achieve water quality prediction and equipment failure prediction. The application layer sets up a monitoring center in the water plant's central control room, equipped with a large monitoring screen to intuitively display real-time water quality data, equipment operating status, and process dynamics. Managers can remotely start and stop equipment and adjust parameters through the operation terminal; a mobile app is developed with a real-time alarm push function to promptly notify relevant personnel when water quality exceeds standards or equipment malfunctions; The sensors in the perception layer collect water quality and equipment operation data in real time and transmit the data to the data processing layer via the transmission layer; The edge computing device and the cloud computing platform process and analyze the data according to a predetermined algorithm, use the analysis results to control the operation of the device, and display notifications in the monitoring center and on the mobile terminal.

2. The water plant process control real-time monitoring system according to claim 1 is characterized by: The water quality sensors in the perception layer include pH sensors, turbidity sensors, residual chlorine sensors, COD sensors, ammonia nitrogen sensors, and heavy metal ion sensors, which are used for multi-parameter real-time water quality monitoring; The equipment sensors include vibration sensors, temperature sensors, current sensors, and pressure sensors, which are used to monitor equipment operating status parameters. The multi-parameter water quality sensor adopts an integrated design. Each sensor works together in the same module and has a built-in self-calibration module. It automatically compares with the standard sample regularly and adjusts the output parameters.

3. The water plant process control real-time monitoring system according to claim 1 is characterized in that: The wireless communication technology and wired communication technology of the transmission layer can automatically switch according to network conditions and data transmission requirements to ensure the stability and continuity of data transmission.

4. The water plant process control real-time monitoring system according to claim 1 is characterized in that: A secure and encrypted data transmission channel is established between the edge computing devices of the data processing layer and the cloud computing platform to ensure the security of data during transmission.

5. The water plant process control real-time monitoring system according to claim 1 is characterized by: The monitoring center operation terminal of the application layer has the authority management function, and managers with different authority can perform operations at different levels to ensure the security of system operation.

6. The water plant process control real-time monitoring system according to claim 1 is characterized by: The system adopts multi-parameter sensor fusion technology and uses Kalman filtering and weighted average data fusion algorithms to fuse the data collected by multi-parameter sensors to improve data accuracy and reliability.

7. The water plant process control real-time monitoring system according to claim 6, characterized in that: When the multi-parameter sensor fusion technology is applied to monitor turbidity and suspended solids concentration, the Kalman filter algorithm is used to fuse the data of the optical turbidity sensor and the ultrasonic suspended solids sensor.

8. The water plant process control real-time monitoring system according to claim 1, characterized in that: The system adopts intelligent data analysis technology, uses decision tree, support vector machine and neural network machine learning algorithms to analyze water quality data and equipment operation data, establishes a correlation model between water quality and dosing dosage, and automatically optimizes dosing strategy.

9. The water plant process control real-time monitoring system according to claim 8, characterized in that: The intelligent data analysis technology uses an anomaly detection algorithm to detect anomalies in water quality and equipment operation data in real time and trigger an alarm mechanism.

10. The water plant process control real-time monitoring system according to claim 1, characterized in that: The system adopts real-time control technology, based on water quality model and equipment operation model, and uses model predictive control technology to control water plant equipment in real time, optimizing control strategies in advance to ensure stable water quality. On the basis of traditional MPC, the weight coefficient of the control target is increased. Assuming that the control targets include meeting water quality standards, minimizing energy consumption, and maximizing equipment life, they are represented by J1, J2, and J3 respectively, and the weight coefficients are λ1, λ2, and λ3. Then the optimization objective function is J=λ1J1+λ2J2+λ3J3; By rationally adjusting the weight coefficients, we can better balance the relationship between different control objectives, while ensuring that water quality is stable and meets standards, achieving the goals of energy saving and extending equipment life, and improving the overall efficiency of water plant operations.

Images