Online system for predicting removal rate of pollutants in sewage based on subsurface infiltration process

Abstract

An online system for predicting the removal rate of pollutants in sewage based on a subsurface infiltration process comprises a plurality of pollutant index front-end pages, and each page is composed of a measured data module, a prediction module, a response curved surface module, a data interaction module and a difference analysis module. The actual measurement data module records site physical parameters and pollutant removal rates in a form, and a pollutant model equation is constructed according to the site physical parameters and the pollutant removal rates; the prediction module is embedded into the model equation and realizes removal rate prediction through a parameter input window; the response curved surface module can adjust physical parameters and update a response curved surface diagram in real time; the data interaction module stores predicted and actually measured data and improves the data utilization rate; the difference analysis module generates an error analysis chart based on the stored data, and visually presents the difference between the actually measured data and the predicted data. According to the method, the pollutant removal rate in subsurface infiltration can be predicted, and the problems of difficulty in sampling and monitoring in actual engineering, imperfection of basic electric power facilities and low utilization rate of water quality monitoring data are solved.

Description

Technical Field

[0001] This invention relates to the field of environmental ecological engineering, and more specifically to an online system for predicting the removal rate of pollutants in wastewater based on underground infiltration technology. Background Technology

[0002] Underground infiltration is a decentralized land-based wastewater treatment process. As wastewater flows through the infiltration system's substrate, it is purified primarily through substrate adsorption and retention, microbial degradation, and absorption by surface plants. This process offers advantages such as high wastewater treatment efficiency and low construction and operating costs, possessing low-carbon ecological attributes. It is suitable for small-scale, decentralized wastewater treatment and reuse in areas without existing wastewater collection networks, providing an effective approach for decentralized wastewater treatment in dispersed areas. However, constructing infiltration systems in remote rural areas, suburbs, and tourist resorts presents the following practical challenges: ① Difficulties in sampling and monitoring: Remote areas suffer from inconvenient transportation, and the distance between sampling areas and water quality monitoring points is significant, resulting in high manpower and time costs. These areas also lack sufficient water quality monitoring equipment and necessary reagents, making water quality monitoring difficult. ② Inadequate basic power infrastructure: On-site monitoring equipment faces operational malfunction issues, leading to reduced water quality monitoring efficiency, inaccurate monitoring data, and increased equipment maintenance costs. ③ Insufficient utilization of water quality monitoring data resources: The existing water quality assessment work has a low utilization rate of historical water quality monitoring data and has failed to fully integrate data resources to form a system as a reference standard and basis for water quality prediction. Summary of the Invention

[0003] The purpose of this invention is to provide an online system for predicting the removal rate of pollutants in wastewater based on underground infiltration technology. This online system is based on underground infiltration technology, uses simple physical parameter monitoring equipment to continuously monitor physical parameters, transmits the signals into the online system, and predicts the removal rate of pollutants in underground infiltration. This solves the problems of difficult sampling and monitoring work, inadequate basic power facilities, and low utilization rate of water quality indicator monitoring data resources in actual engineering.

[0004] The objective of this invention is achieved through the following technical solution: An online system for predicting pollutant removal rates in wastewater based on underground infiltration technology is disclosed. The system consists of several front-end pages, each corresponding to a different pollutant index. The page framework for each index consists of a measured data module, a prediction module, a response surface module, a data interaction module, and a difference analysis module.

[0005] The measured data module sets up a data table to record the physical parameters and pollutant removal rate of the underground infiltration site. The table data is displayed graphically and has import, export, editing and deletion functions. It can integrate pollutant monitoring data of underground infiltration water samples and construct pollutant model equations by measuring physical parameters and pollutant removal rates.

[0006] The prediction module uses computer language functions to write the removal rate model equation. Based on the parameters of the model equation, a physical parameter window for the prediction module is built. The corresponding parameter values ​​are input into the parameter window to predict the pollutant removal rate of groundwater infiltration.

[0007] The response surface module establishes a parameter module window based on the physical parameters of the model equation, and updates the response surface plot by changing the physical parameters of the model equation.

[0008] The data interaction module establishes a data table, storing the data output by the prediction module and the measured data in the system table. By adding data, importing and exporting files, the utilization rate of the data is improved.

[0009] The difference analysis module uses data from the data interaction module to create an error analysis chart, which visually displays the measured data and predicted data in the form of a chart.

[0010] The beneficial effects of this invention are: 1. In the absence of monitoring instruments and chemical reagents, the prediction module of the online system page framework is used to input the monitoring values ​​of physical parameters affecting the pollutant removal rate into the online system to obtain the pollutant removal rate. In areas with inadequate basic power infrastructure, the removal rate of sewage pollutants by underground infiltration can be understood, which solves the problem of high monitoring costs in terms of transportation, manpower and equipment caused by water quality index analysis method. 2. A combination of online forecasting and offline water quality monitoring is employed to fully leverage digital resources. The online system enhances data analysis capabilities and overcomes the challenges of water quality monitoring in remote areas. Monitored physical parameters are transmitted to the online system, which then visually displays the error between predicted and actual values ​​in graphical and tabular formats. Furthermore, as the online system stores more data, it provides data support for subsequent water quality monitoring efforts. Attached Figure Description

[0011] Figure 1 This is the tabular data from the measured data module (taking COD as an example); Figure 2 The bar chart shows the removal rate of the measured data module (taking COD as an example). Figure 3 The prediction module diagram of the online system page framework (taking COD as an example); Figure 4 The responsive surface module diagram of the online system page framework (taking COD as an example); Figure 5 A diagram of the data interaction modules in the online system page framework (taking COD as an example); Figure 6This is a diagram of the difference analysis module in the page framework of an online system (taking COD as an example). Detailed Implementation

[0012] The present invention will now be described in detail with reference to the embodiments. Example

[0013] See the example. Figures 1 to 6 An online system for predicting pollutant removal rates in wastewater based on underground infiltration technology is presented. This system consists of several front-end pages. Taking Chemical Oxygen Demand (COD) as an example, its page framework comprises a measured data module, a prediction module, a response surface module, a data interaction module, and a difference analysis module. The online system is based on web page design, employing Hypertext Markup Language version 5 (HTML5) from the global information system (Web) technology using the HTTP protocol. It incorporates Excel (.xlsx) files for data interaction, enabling the import and export of Excel (.xlsx) files. The system defines semantic tags for documents, selects a user interface (UI) framework to provide responsive layout, and predefined components and styles to construct a consistent system page framework. This online system can realize data input, transformation, storage, and output functions, providing data support for subsequent water quality monitoring work and fully leveraging the value of data analysis.

[0014] In this embodiment, the measured data module uses a SheetJS database to interact with Excel (.xlsx) table data. Data visualization technology employs a lightweight charting library (Chart.js) for creating two-dimensional charts. The core logic is implemented using a native interactive programming language (JavaScript) to perform significance analysis, variance analysis, and linear regression analysis to derive the COD model equation. The table data in this module is based on measured data of groundwater influent and effluent to evaluate the COD removal rate of the groundwater infiltration system. The COD removal rate in this module is... Figure 2 Display in this way Figure 1 The tabular data, using a technology stack that requires no backend support, can be managed and visualized for analysis, serving as a data processing tool; the removal rate graph data can be displayed with a cursor, allowing users to intuitively understand the COD removal rate.

[0015] In this embodiment, the prediction module implements a responsive graphical user interface (GUI) using a front-end development framework (Bootstrap). It employs functions to call soil temperature, soil moisture content, and hydraulic loading parameters from the model equations to construct the prediction module parameter window. See [link to documentation]. Figure 3Taking COD as an example, in the model equation, the pollutant removal rate is represented by RR (Removal Rate, %), soil temperature by T (Temperature, ℃), soil moisture content by WC (Water Content, %), and hydraulic loading by HL (Hydraulic Loading, m³ / s). 3 / (m 2 ▪d)) represents. The removal rate data in the COD model equation comes from underground infiltration at the in-situ soil depth in Northeast China during summer and underground infiltration under laboratory environmental conditions. The removal rate model equation corresponding to this index is as follows: The range of independent variables for COD (RR) removal rate is 25 ≤ T ≤ 27, 18 ≤ WC ≤ 20, 0.073 ≤ HL ≤ 0.11. COD removal rate model equation: RR = -2379.32 + 92.9195T + 127.665WC + 606.356HL + 0.225 T × WC - 9.89412 T × HL + 26.6338WC × HL - 1.84167T 2 - 3.51667 WC 2 - 5407.71 HL 2 An online system was built using Java and PyCharm software. The model equations for water quality indicators were written into the system, allowing users to calculate the COD removal rate through groundwater infiltration by inputting physical parameters, thus enabling COD removal rate prediction. The online system supports simultaneous querying on mobile phones and computers, providing users with convenient access to information on pollutant removal rates.

[0016] In this embodiment, the response surface module uses a response surface plot, interactive charts (Chart.js), and a chart library to visualize the data from the response surface experimental group. A powerful chart library is used as the visualization library to support complex interactions with the 3D response surface chart. According to... Figure 2 The data generates response surface plots within the range of conditional factors. For example, see COD. Figure 4 The monitored underground infiltration physical parameters are input into the online system to update the generated graphs. Users can update the response surface plots corresponding to different physical parameters by changing the physical parameters in the parameter window. When the user clicks on the graph, the removal rate of pollutant COD corresponding to the physical parameter can be displayed. Compared with other response surface analysis software, the data can be viewed with a cursor and the graph can be zoomed in, zoomed out, and downloaded.

[0017] In this embodiment, the data interaction module, this module is with Figure 1Data connectivity is achieved using form elements, container layouts, and cascading style sheets (CSS) to render the module's data window, adjusting page layout, component appearance, and interactive details. See Call of Duty (COD) as an example. Figure 5 It allows users to edit and delete the physical parameters corresponding to COD, supports data import and export, and performs difference analysis between measured and predicted values.

[0018] In this embodiment, the difference analysis module, this module and Figure 5 The data is interconnected, and data processing, Document Object Model (DOM) manipulation, and interactive control are achieved through a programming interaction language. For example, see Call of Duty (COD). Figure 6 The difference analysis module visualizes the underground infiltration monitoring data from the data interaction module, using a combination of bar charts and scatter plots to facilitate error analysis between the actual and predicted data values.

[0019] In this embodiment, the online system can transmit data through various online methods, with a hard disk storage size of less than 1MB. After downloading, it can still be used even without a network connection. The system supports the input and output of Excel (.xlsx) spreadsheet data and the download of response surface graphics.

Claims

1. An online system for predicting pollutant removal rates in wastewater based on underground infiltration technology, characterized in that, The system consists of several front-end pages, each corresponding to different pollutant indicators. The page framework for each indicator consists of a measured data module, a prediction module, a response surface module, a data interaction module, and a difference analysis module.

2. Further, according to claim 1, the measured data module is equipped with a data table to record the physical parameters and pollutant removal rate of the underground infiltration site, the data is displayed intuitively in a graphical manner, and a pollutant model equation based on underground infiltration is constructed through data analysis.

3. Further, according to claims 1 and 2, the prediction module, by writing the model equation, realizes the prediction of pollutant removal rate, and establishes a physical parameter window affecting the pollutant removal rate of wastewater based on the physical parameter values ​​of the model equation; the output module in the prediction module calculates the removal rate of several pollutants based on different physical parameter values, and outputs the value of the pollutant removal rate of wastewater.

4. Further, according to claims 1 and 2, the response surface module inputs the monitored underground infiltration physical parameters into the physical parameter window of the online system, and the generated graph can be updated through the pollutant model equation of the online system.

5. Further, according to claim 1, the data interaction module processes the measured data and predicted data of pollutants and conducts difference analysis of pollutant monitoring data.

6. Further, according to claim 1, the difference analysis module visualizes the underground infiltration monitoring data of the data interaction module.

7. Further, according to claim 1, characterized in that... In the absence of a network, the monitoring data is input into the online system, and the computer executes the pollutant model equation of the system to achieve the prediction of pollutant removal rate in wastewater based on the underground infiltration process as described in any one of claims 1 to 5.

Images