Method and system for monitoring water quality of seawater desalination effluent

Abstract

The invention belongs to the technical field of water quality monitoring, and discloses a seawater desalination effluent quality monitoring method and system.The system comprises a monitoring host, and a main control unit, a model library and a communication interface are arranged in the monitoring host; at least one pluggable monitoring module which is installed on the slot of the monitoring host through a standardized mechanical electrical interface; an edge computing gateway and a cloud platform, wherein the monitoring host identifies the accessed monitoring module and the type thereof through an identity identification chip; and calling the exclusive analysis model with the matched type to process the data uploaded by the signal conditioning circuit, and executing online self-learning optimization on the exclusive analysis model. The system is convenient to maintain, high in availability, flexible in configuration, high in expandability and high in long-term monitoring accuracy.

Description

Technical Field

[0001] This invention belongs to the field of water quality monitoring technology, specifically relating to a method and system for monitoring the quality of seawater desalination effluent. Background Technology

[0002] The quality of desalination product water directly affects water safety and equipment lifespan; therefore, real-time, accurate, and multi-parameter online monitoring is crucial. Currently, common monitoring methods fall into two main categories: one involves integrating multiple sensors directly into the pipeline in a fixed installation, and the other uses a separate online analyzer for periodic sampling and analysis.

[0003] The above monitoring methods have the following shortcomings: First, in a fixed integrated sensor system, if a sensor fails or needs calibration, the entire system often needs to be shut down for maintenance, which affects continuous monitoring. In addition, the system functions are fixed, making it difficult to flexibly add or remove monitoring items according to needs. Secondly, whether it is a sensor or an analyzer, its data processing model (such as temperature compensation curve and spectral analysis model) is usually preset during production. It cannot be effectively adjusted later according to the actual water quality changes on site, sensor aging and other factors, which leads to long-term monitoring accuracy drift. Finally, the relationship between the monitoring system and the main control system for seawater desalination is mostly a simple data reporting relationship, lacking intelligent linkage control capabilities based on in-depth analysis.

[0004] For the reasons mentioned above, it is of great practical significance to develop a method and system for monitoring the quality of desalinated water that is easy to maintain, flexible in configuration, can maintain high accuracy over a long period of time, and has intelligent decision-making capabilities. Summary of the Invention

[0005] In view of the shortcomings of existing technologies, the purpose of this invention is to provide a method and system for monitoring the quality of desalinated water. Through the synergistic innovation of "pluggable hardware" and "adaptive software", the monitoring module can be conveniently maintained and flexibly configured. Through automatic model matching and online learning, the accuracy and intelligence of the monitoring system can be guaranteed for long-term operation.

[0006] To achieve the above technical objectives, In a first aspect, the present invention provides a method for monitoring the quality of seawater desalination effluent, comprising the following contents. (1) Identification and loading (hardware identification and model loading) The monitoring host reads the pre-set identity and water quality parameter type information in the monitoring module, and automatically loads or activates a dedicated analysis model that matches the monitoring module from the model library based on the water quality parameter type information. (2) Adaptive monitoring The dedicated analysis model calculates and fuses the raw water quality signals collected by the monitoring module, and outputs the corrected and / or compensated target water quality parameter values. (3) Model optimization The monitoring host triggers online self-learning optimization of the parameters of the dedicated analysis model based on the preset calibration cycle, external calibration instructions, or abnormal fluctuations in the target parameter value, and writes back and saves the optimized model parameters to the model library. (4) Fault self-diagnosis and health management The monitoring module monitors the working status of the sensing unit in real time. When it detects abnormal signal, excessive drift, or lifespan warning, it reports the fault code and health status to the monitoring host. The monitoring host generates maintenance suggestions and updates the module's working status based on the reported information.

[0007] By utilizing the above-described solution, the present invention can achieve the following effects: Easy maintenance and high availability: With its pluggable hardware design, a single monitoring module can be hot-swapped when it fails or needs calibration, without system downtime, which greatly improves maintenance efficiency and system availability. Flexible configuration and strong scalability: Users can flexibly select different monitoring modules according to changes in monitoring needs. The system automatically identifies and loads the corresponding model, realizing "plug and play" functionality and smooth expansion. High accuracy in long-term monitoring: Through software adaptive mechanism, the system can optimize and calibrate the dedicated analysis model online according to changes in on-site water quality, sensor drift and other factors, effectively combating performance degradation and ensuring long-term reliability and accuracy of data.

[0008] In some optional instances, when multiple monitoring modules are set in content (2), the raw water quality signals collected include temperature, flow rate, turbidity, pH, boron ion concentration, etc.

[0009] As a preferred technical solution of the present invention, in content (1), if the water quality parameter type information corresponds to the first type of direct measurement parameter, then the corresponding signal standardization processing and temperature compensation model is loaded. If the water quality parameter type information corresponds to the second type of indirect inference parameter, then load the corresponding multi-sensor data fusion soft measurement model or spectral analysis inversion model.

[0010] As a preferred technical solution of the present invention, the monitoring host triggers online self-learning optimization of the parameters of the dedicated analysis model based on a preset calibration cycle, or a received external high-precision calibration command, or based on abnormal fluctuations in the target water quality parameter value. The online self-learning optimization mentioned in content (3) includes, When the triggering condition is met, a movable high-precision analytical instrument, which serves as a reference, is controlled to analyze the current water sample and obtain a standard value. The standard value is compared with the target water quality parameter value currently output by the dedicated analysis model, and the deviation is calculated. Using the aforementioned deviation and the corresponding input data, the internal parameters of the dedicated analysis model are iteratively updated through recursive least squares, Kalman filtering, or neural network backpropagation algorithms.

[0011] As a preferred embodiment of the present invention, it also includes the following: The strategy linkage involves communicating and linking the target water quality parameter value or the predicted trend output by the dedicated analysis model with the control system of the seawater desalination process. When the target water quality parameter value exceeds the preset threshold or the predicted trend deteriorates, process adjustment suggestions or control instructions are automatically generated and sent to the control system of the seawater desalination process. The control instructions include at least one of adjusting the pH value of the reverse osmosis feed water, changing the dosage of scale inhibitor, or starting the membrane cleaning program in advance.

[0012] Secondly, the present invention provides a seawater desalination effluent quality monitoring system for implementing the aforementioned seawater desalination effluent quality monitoring method, and includes, The monitoring host contains a main control unit, a model library, and a communication interface. At least one pluggable monitoring module is connected to the monitoring host; The monitoring module includes, The sensing unit is used to sense one or more water quality characteristics; A signal conditioning circuit, connected to the sensing unit, is used to perform preliminary processing on the original signal; and, The identification chip stores a unique identifier and information about the type of water quality parameters being measured. The monitoring host is configured to identify the accessed monitoring modules and their types through the identification chip; and to process the data uploaded by the signal conditioning circuit by calling a type-matching dedicated analysis model. Perform online self-learning optimization of the proprietary analysis model.

[0013] In some optional instances, the monitoring module connects to the monitoring host via a standardized mechanical-electrical interface, which includes: A self-sealing quick-connect fluid coupling for water sample diversion, which has a valve that automatically switches on and off during insertion and removal; Foolproof electrical connectors used to enable power supply and data communication; Guide rails and latching mechanisms are used to achieve mechanical locking and alignment.

[0014] In some optional examples, the sensing unit is any one or a combination of the following: an electrochemical sensor, an optical sensor, an ion-selective electrode, or a miniature spectroscopic probe.

[0015] In some optional instances, the proprietary analytical model is any one or a combination of the following: a mechanism-based chemical reaction kinetic model, a data-driven machine learning model, or a physical law-based spectroscopic quantitative analysis model.

[0016] As a preferred technical solution of the present invention, it further includes a micro preprocessing unit and / or a micro calibration unit; The micro pretreatment unit is located in the upstream flow path of the sensing unit and is used to filter, stabilize or remove air bubbles from the water sample flowing into the sensing unit. The micro calibration unit stores standard liquid or calibration gas and performs automatic periodic calibration or trigger-based calibration of the sensing unit under the control of the monitoring host.

[0017] As a preferred technical solution of the present invention, it further includes an edge computing gateway. The monitoring host is connected to the cloud platform through the edge computing gateway. The edge computing gateway is used to: receive and temporarily store data uploaded by each monitoring host; perform lightweight data analysis and initial anomaly judgment locally; and upload data requiring in-depth processing or model optimization requests to the cloud platform. The cloud platform is used to: aggregate data from multiple monitoring systems, perform big data analysis and knowledge mining; train and generate better general analysis models or optimization models for specific water quality, and distribute them to the corresponding edge computing gateway or monitoring host.

[0018] Thirdly, the present invention provides a seawater desalination system including a reverse osmosis membrane module, a product water pipeline, a process control system, and the above-mentioned seawater desalination effluent water quality monitoring system. The monitoring module in the seawater desalination effluent water quality monitoring system is installed on the product water pipeline. The monitoring host in the seawater desalination effluent water quality monitoring system is communicatively connected to the process control system and is used to provide real-time water quality data and / or control suggestions to the process control system.

[0019] The beneficial effects of this invention are: 1. Convenient maintenance and high availability: With its pluggable hardware design, a single monitoring module can be hot-swapped when it fails or needs calibration, without system downtime, which greatly improves maintenance efficiency and system availability. 2. Flexible configuration and strong scalability: Users can flexibly select different monitoring modules according to changes in monitoring needs. The system automatically identifies and loads the corresponding model, realizing "plug and play" functionality and smooth expansion. 3. High accuracy in long-term monitoring: Through software adaptive mechanism, the system can optimize and calibrate the dedicated analysis model online according to changes in on-site water quality, sensor drift, etc., effectively resisting performance degradation and ensuring long-term reliability and accuracy of data; 4. High level of intelligence: The system not only performs monitoring, but also performs trend prediction and fault diagnosis through model analysis, and intelligently links with the seawater desalination system to achieve closed-loop management from "monitoring" to "early warning" to "control". Attached Figure Description

[0020] The present invention can be further illustrated by the non-limiting embodiments given in the accompanying drawings; Figure 1 This is an architectural diagram of Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the monitoring module in Embodiment 1 of the present invention; Figure 3 This is a diagram of the interface panel of the monitoring host in Embodiment 1 of the present invention; Figure 4 This is a flowchart of Embodiment 2 of the present invention; Figure 5 This is a flowchart of the model optimization process in Embodiment 2 of the present invention; Figure 6 This is a schematic diagram of Embodiment 3 of the present invention; The symbols for the main components are explained below: 100. Monitoring host; 110. Main control unit; 120. Model library; 140. Communication interface; 200. Monitoring module; 210. Module housing; 220. Sensing unit; 230. Miniature preprocessing unit; 240. Signal conditioning circuit; 250. Identification chip; 260. Miniature calibration unit; 300. Edge computing gateway; 400, cloud platform; 500. Mechanical and electrical interface; 510. Quick-connect self-sealing connector; 520. Foolproof multi-pin connector; 530. Guide rail; 600. Process control system. Detailed Implementation

[0021] The technical solutions of the present invention will be described in detail below with reference to specific embodiments and accompanying drawings. The embodiments described herein are specific implementations of the present invention, used to illustrate the concept of the present invention; these descriptions are explanatory and exemplary, and should not be construed as limiting the implementation methods or the scope of protection of the present invention. In addition to the embodiments described herein, those skilled in the art can employ other obvious technical solutions based on the content disclosed in the claims and specification of this application. These technical solutions include those that make any obvious substitutions and modifications to the embodiments described herein. Example 1:

[0022] like Figure 1 , 2 As shown in Figure 3, this embodiment provides a seawater desalination effluent water quality monitoring system, including, The monitoring host 100 is equipped with a main control unit 110, a model library 120 and a communication interface. Several pluggable monitoring modules 200 are installed in slots of the monitoring host 100 via standardized mechanical and electrical interfaces 500. Edge computing gateway 300, and The monitoring host 100 is connected to the cloud platform 400 through the edge computing gateway 300; Among them, the model library contains 120 pre-stored or dynamically downloadable dedicated analysis models for various water quality parameters. Among them, the mechanical and electrical interface 500 includes a quick-connect self-sealing connector 510 for realizing water circuit connection, a foolproof multi-pin connector 520 for realizing power and data connection, and a guide rail 530 and a buckle to ensure proper insertion and removal. The monitoring module 200 includes a module housing 210, a signal conditioning circuit 240, and an identification chip 250. The module housing 210 is equipped with a sensing unit 220. The sensing unit 220 is specifically selected as an ion-selective electrode for measuring boron ions. Upstream of the sensing unit 220 is a micro pretreatment unit 230 with a precision filter and a degassing membrane to stabilize the water sample. It also includes a micro calibration unit 260, which stores standard liquid or calibration gas and performs automatic periodic calibration or trigger calibration of the sensing unit 220 under the control of the monitoring host 100. The signal conditioning circuit 240 amplifies, filters, and performs analog-to-digital conversion on the original signal from the sensing unit 220; The identification chip 250EEPROM stores metadata such as the unique ID of the monitoring module, the monitoring parameter type (boron ion), the measurement range, and the factory calibration coefficient. The monitoring host 100 is configured to identify the access monitoring module 200 and its type through the identification chip 250; call the dedicated analysis model that matches the type to process the data uploaded by the signal conditioning circuit 240, and perform online self-learning optimization of the dedicated analysis model.

[0023] In this embodiment, the edge computing gateway 300 is used to receive and temporarily store the data uploaded by each monitoring host 100; perform lightweight data analysis and initial anomaly judgment locally; and upload data requiring in-depth processing or model optimization requests to the cloud platform 400. The cloud platform 400 is used to: aggregate data from multiple monitoring systems, perform big data analysis and knowledge mining; train and generate better general analysis models or optimization models for specific water quality, and distribute them to the corresponding edge computing gateway 300 or monitoring host 100. This embodiment offers convenient maintenance and high availability. Its pluggable hardware design allows for hot-swapping of individual monitoring modules in case of failure or calibration, eliminating system downtime and significantly improving operational efficiency and system availability. It also boasts flexible configuration and strong scalability, enabling users to select different monitoring modules based on changing monitoring needs. The system automatically identifies and loads the corresponding model, achieving "plug-and-play" functionality and smooth expansion. Furthermore, it offers high long-term monitoring accuracy. Through a software adaptive mechanism, the system can optimize and calibrate its proprietary analysis model online based on changes in water quality and sensor drift, effectively combating performance degradation and ensuring long-term data reliability and accuracy. Example 2:

[0024] like Figure 4 , 5 As shown in the figure, this embodiment provides a method for monitoring the quality of seawater desalination effluent, including the following: 1. Identification and Loading When a boron ion monitoring module 200 is inserted into an empty slot of the monitoring host 100, the monitoring host 100 reads the pre-installed identification chip 250 and water quality parameter type information within the monitoring module 200 to identify it as a boron ion monitoring module 200. Based on the identified type, the main control unit 110 automatically loads the corresponding "boron ion concentration soft measurement model" from the model library 120; 2. Adaptive monitoring When the system is running normally, the main control unit 110 continuously acquires data from the monitoring module 200, inputs it into the activated model, and calculates and outputs the boron ion concentration value in real time. 3. Model Optimization Every 24 hours or when the output value fluctuates abnormally, the main control unit 110 triggers the optimization process, which controls a mobile offline high-precision boron analyzer to sample and analyze the water sample in the current pipeline to obtain the standard reference value of boron concentration. Then, the current output value of the model is compared with the standard reference value to generate an error signal. The main control unit 110 uses the recursive least squares algorithm to automatically adjust the weight parameters in the "boron ion concentration soft measurement model" using this error, so that the model output is close to the true value. The optimized model parameters are saved for subsequent monitoring. 4. Fault self-diagnosis and health management The monitoring module 200 monitors the working status of the sensing unit 220 in real time. When it detects abnormal signal, excessive drift or lifespan warning, it reports the fault code and health status to the monitoring host 100. The monitoring host 100 generates maintenance suggestions and updates the module's working status based on the reported information.

[0025] This embodiment demonstrates high long-term monitoring accuracy. Through a software adaptive mechanism, the system can optimize and calibrate the dedicated analysis model online based on changes in on-site water quality, sensor drift, and other factors, effectively combating performance degradation and ensuring long-term data reliability and accuracy. Example 3:

[0026] like Figure 6 As shown, this embodiment provides a seawater desalination system, including a reverse osmosis membrane module, a product water pipeline, a process control system 600, and a seawater desalination effluent quality monitoring system. The monitoring module in the seawater desalination effluent water quality monitoring system is installed on the product water pipeline. The monitoring host in the seawater desalination effluent water quality monitoring system is connected to the process control system to provide real-time water quality data and / or control suggestions to the process control system.

[0027] In this embodiment, when the seawater desalination effluent water quality monitoring system detects that the boron content in the produced water is continuously approaching the upper limit threshold, it not only issues an alarm but also sends a suggestion to the process control system 600 via the communication interface: "It is recommended to fine-tune the pH value of the feed water of the second stage of the reverse osmosis unit to XX to optimize the boron removal rate." Alternatively, when the analysis of multiple parameters predicts that membrane fouling will intensify, it can suggest "starting the chemical cleaning procedure (CIP) 12 hours in advance." The seawater desalination effluent water quality monitoring system and the seawater desalination system are intelligently linked to achieve closed-loop management from "monitoring" to "early warning" and then to "control."

[0028] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the present invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for monitoring the quality of seawater desalination effluent, characterized in that: Includes the following content: (1) Identification and Loading The monitoring host reads the pre-set identity and water quality parameter type information in the monitoring module, and automatically loads or activates a dedicated analysis model that matches the monitoring module from the model library based on the water quality parameter type information. (2) Adaptive monitoring The dedicated analysis model calculates and fuses the raw water quality signals collected by the monitoring module, and outputs the corrected and / or compensated target water quality parameter values. (3) Model optimization When the triggering condition is met, the monitoring host performs online self-learning optimization of the parameters of the dedicated analysis model, and writes back and saves the optimized model parameters to the model library; (4) Fault self-diagnosis and health management The monitoring module monitors the working status of the sensing unit in real time. When it detects abnormal signal, excessive drift, or lifespan warning, it reports the fault code and health status to the monitoring host. The monitoring host generates maintenance suggestions and updates the module's working status based on the reported information.

2. The method for monitoring the quality of desalinated water according to claim 1, characterized in that: The specific content of content (1) includes, If the water quality parameter type information corresponds to the first type of directly measured parameter, then load the corresponding signal standardization processing and temperature compensation model; If the water quality parameter type information corresponds to the second type of indirect inference parameter, then load the corresponding multi-sensor data fusion soft measurement model or spectral analysis inversion model.

3. The method for monitoring the quality of desalinated water according to claim 1, characterized in that: The monitoring host triggers online self-learning optimization of the parameters of the dedicated analysis model based on a preset calibration cycle, a received external high-precision calibration command, or abnormal fluctuations in the target water quality parameter value. The online self-learning optimization mentioned in content (3) includes: When the triggering condition is met, a movable high-precision analytical instrument, which serves as a reference, is controlled to analyze the current water sample and obtain a standard value. The standard value is compared with the target water quality parameter value currently output by the dedicated analysis model, and the deviation is calculated. Using the aforementioned deviation and the corresponding input data, the internal parameters of the dedicated analysis model are iteratively updated through recursive least squares, Kalman filtering, or neural network backpropagation algorithms.

4. The method for monitoring the quality of desalinated seawater according to claim 1, characterized in that: It also includes the following: The strategy linkage involves communicating and linking the target water quality parameter value or the predicted trend output by the dedicated analysis model with the control system of the seawater desalination process. When the target water quality parameter value exceeds the preset threshold or the predicted trend deteriorates, process adjustment suggestions or control instructions are automatically generated and sent to the control system of the seawater desalination process. The control instructions include at least one of adjusting the pH value of the reverse osmosis feed water, changing the dosage of scale inhibitor, or starting the membrane cleaning program in advance.

5. A seawater desalination effluent water quality monitoring system, characterized in that: A method for monitoring the quality of desalinated water as described in any one of claims 1 to 4, comprising, The monitoring host contains a main control unit, a model library, and a communication interface. At least one pluggable monitoring module is connected to the monitoring host; The monitoring module includes, The sensing unit is used to sense one or more water quality characteristics; A signal conditioning circuit, connected to the sensing unit, is used to perform preliminary processing on the raw signal; as well as, The identification chip stores a unique identifier and information about the type of water quality parameters being measured. The monitoring host is configured to identify the accessed monitoring modules and their types through the identification chip; The data uploaded by the signal conditioning circuit is processed by invoking a dedicated analysis model that matches the data type. Perform online self-learning optimization of the proprietary analysis model.

6. The seawater desalination effluent quality monitoring system according to claim 5, characterized in that: The monitoring module is connected to the monitoring host via a standardized mechanical and electrical interface, which includes, A self-sealing quick-connect fluid coupling for water sample diversion, which has a valve that automatically switches on and off during insertion and removal; Foolproof electrical connectors used to enable power supply and data communication; Guide rails and latching mechanisms are used to achieve mechanical locking and alignment.

7. A seawater desalination effluent water quality monitoring system according to claim 5, characterized in that: It also includes a micro preprocessing unit and / or a micro calibration unit; The micro pretreatment unit is located in the upstream flow path of the sensing unit and is used to filter, stabilize or remove air bubbles from the water sample flowing into the sensing unit. The micro calibration unit stores standard liquid or calibration gas and performs automatic periodic calibration or trigger-based calibration of the sensing unit under the control of the monitoring host.

8. A seawater desalination effluent water quality monitoring system according to claim 5, characterized in that: It also includes an edge computing gateway, through which the monitoring host connects to the cloud platform.

9. A seawater desalination effluent water quality monitoring system according to claim 5, characterized in that: The sensing unit is any one or a combination of the following: an electrochemical sensor, an optical sensor, an ion-selective electrode, or a miniature spectrometer.

10. A seawater desalination system, characterized in that: Includes a reverse osmosis membrane module, a product water pipeline, a process control system, and a seawater desalination effluent quality monitoring system as described in any one of claims 5 to 9; The monitoring module in the seawater desalination effluent water quality monitoring system is installed on the product water pipeline. The monitoring host in the seawater desalination effluent water quality monitoring system is communicatively connected to the process control system and is used to provide real-time water quality data and / or control suggestions to the process control system.

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