Method, device, medium and product for comprehensive control of dosing amount of water purification system

Abstract

The application provides a medicament adding amount comprehensive control method and device of a water purification system, a medium and a product, and relates to the technical field of water quality purification. The method comprises the following steps: obtaining water quality parameters and a hydraulic model corresponding to sewage, predicting target water quality parameters and hydraulic retention times corresponding to inlets and outlets of a magnetic coagulation module according to the water quality parameters and the hydraulic model; generating a medicament adding reference curve and a medicament adding correction curve according to the target water quality parameters and preset indexes, wherein the preset indexes comprise discharge indexes and standard reaching indexes; and controlling the medicament adding amount of a medicament adding pump according to the medicament adding reference curve, the medicament adding correction curve and the water quality parameters. The embodiments of the application can automatically adjust the medicament adding amount according to actual water quality parameters, effectively match the medicament adding amount and the phosphorus content of the sewage, reduce medicament consumption, reduce the labor cost and the medicament cost required for sewage purification, and effectively realize the demand for accurate control of the medicament adding amount.

Description

Technical Field

[0001] This application relates to the field of water purification technology, and more specifically, to a method, equipment, medium, and product for comprehensive control of chemical dosage in a water purification system. Background Technology

[0002] Water pollution treatment, a term in water conservancy technology, refers to the process of improving or eliminating water pollution using both engineering and non-engineering methods. Wastewater treatment (also known as sewage treatment) is the process of purifying wastewater to meet the quality requirements for discharge into a water body or for reuse. Classified by source, wastewater treatment is generally divided into industrial wastewater treatment and domestic wastewater treatment. Industrial wastewater includes industrial wastewater, agricultural wastewater, and medical wastewater, while domestic wastewater is wastewater generated during daily life. Wastewater treatment is widely used in various fields such as construction, transportation, energy, petrochemicals, environmental protection, urban landscaping, healthcare, and catering, and is increasingly becoming a part of everyday life for ordinary people.

[0003] In wastewater treatment, phosphorus removal is a crucial process. Current technologies involve manually adding phosphorus removal agents to the wastewater to reduce phosphorus levels. However, this method suffers from drawbacks such as delayed dosage adjustment, high agent consumption, high labor and agent costs, and fails to meet the requirement for real-time, precise control of dosage. Summary of the Invention

[0004] This application provides a method, equipment, medium, and product for comprehensive control of chemical dosing in a water purification system. It addresses the problems of slow dosage adjustment, high chemical consumption, and high labor and chemical costs associated with existing manual dosing methods, making precise dosage control difficult. To achieve this objective, this application provides the following solutions.

[0005] According to one aspect of the embodiments of this application, a method for comprehensive control of chemical dosing in a water purification system is provided. The water purification system includes a magnetic coagulation module equipped with a dosing pump. The method includes:

[0006] The water quality parameters and hydraulic model corresponding to the wastewater are obtained. Based on the water quality parameters and hydraulic model, the target water quality parameters and hydraulic retention time corresponding to the inlet and outlet of the magnetic coagulation module are predicted. The water quality parameters include turbidity, chemical oxygen demand, total phosphorus content, total nitrogen, ammonia nitrogen, temperature, and flow rate corresponding to the inlet and outlet of the water purification system. The hydraulic retention time is determined based on the hydraulic model information of the water purification system.

[0007] Based on the target water quality parameters and preset indicators, a baseline curve and a correction curve for reagent dosing are generated. The preset indicators include emission indicators and compliance indicators.

[0008] The dosage of the dosing pump is controlled according to the dosing baseline curve, the dosing correction curve, and the water quality parameters.

[0009] In one possible implementation, obtaining the hydraulic model includes:

[0010] Obtain the pipeline information of the water purification system, and determine the volume ratio of the pipeline corresponding to the water purification system based on the pipeline information;

[0011] The hydraulic model is calculated based on the volume ratio and pipeline information.

[0012] In one possible implementation, predicting the target water quality parameters and hydraulic retention time corresponding to the inlet and outlet of the magnetic coagulation module based on the water quality parameters and the hydraulic retention model includes:

[0013] The first hydraulic residence time corresponding to the inlet of the magnetic coagulation module and the second hydraulic residence time corresponding to the outlet of the magnetic coagulation module are determined according to the hydraulic model.

[0014] The inlet virtual flow meter corresponding to the inlet of the magnetic coagulation module is determined based on the flow rate corresponding to the inlet of the water purification system and the first hydraulic retention time, and the outlet virtual flow meter corresponding to the outlet of the magnetic coagulation module is determined based on the flow rate corresponding to the outlet of the water purification system and the second hydraulic retention time.

[0015] The target water quality parameters are determined based on the inlet virtual flow meter and the outlet virtual flow meter.

[0016] In one possible implementation, the step of calculating the reagent dosing baseline curve and the reagent dosing correction curve based on the water quality parameters and preset indicators includes:

[0017] Based on the inlet virtual flow meter, the total phosphorus content corresponding to the inlet, and the emission indicators, determine the reagent dosing baseline values ​​at different time points, and generate the reagent dosing baseline curve corresponding to the reagent dosing baseline values;

[0018] Using the virtual flow meter at the outlet, the total phosphorus content corresponding to the outlet, and the compliance index, the reagent dosage correction value at different time points is determined, and the reagent dosage correction curve corresponding to the reagent dosage correction value is generated.

[0019] In one possible implementation, determining the reagent dosage baseline value at different time points based on the inlet virtual flow meter, the total phosphorus content corresponding to the inlet, and emission indicators includes:

[0020] The phosphorus removal amount at different time points is determined based on the inlet virtual flow meter at different time points and the total phosphorus content corresponding to the inlet flow rate.

[0021] The baseline value for reagent dosage is calculated based on the phosphorus removal amount, the emission index, and the reaction model information of the reagent.

[0022] In one possible implementation, determining the dosage of the dosing pump based on the reagent dosing reference curve, the reagent dosing correction curve, and the water quality parameters includes:

[0023] A heatmap of correlation coefficients between various water quality parameters is generated using an AI algorithm. Correlation influencing factors are analyzed using the heatmap. Based on the analysis results and the water quality parameters, the influencing factors of the inlet and outlet are obtained.

[0024] The response trend of the total reagent setpoint corresponding to the dosing pump is determined based on the higher-order partial derivatives of the influencing factors of the inlet and the higher-order partial derivatives corresponding to the reagent dosing reference curve. The adjustment trend of the total reagent feedforward value corresponding to the dosing pump is determined based on the higher-order partial derivatives of the influencing factors of the outlet and the higher-order partial derivatives corresponding to the reagent dosing correction curve.

[0025] The control parameters of the dosing pump are dynamically adjusted based on the response trend of the total dosing setpoint and the adjustment trend of the total dosing feedforward value. The dosing pump achieves dosing quantity control based on the variable speed step integral PIDE control algorithm.

[0026] In one possible implementation, obtaining the influencing factors of the inlet and outlet based on the analysis results and the water quality parameters includes:

[0027] A first water quality parameter matrix is ​​generated based on the water quality parameters of the inlet. A first parameter influence factor matrix corresponding to the first water quality parameter matrix is ​​obtained based on the analysis results corresponding to the water quality parameters of the inlet. The influence factor of the inlet is calculated based on the first water quality parameter matrix, the first parameter influence factor matrix, and the first delay coefficient.

[0028] A second water quality parameter matrix is ​​generated based on the water quality parameters of the outlet. A second parameter influence factor matrix corresponding to the second water quality parameter matrix is ​​obtained based on the analysis results corresponding to the water quality parameters of the outlet. The influence factor of the outlet is calculated based on the second water quality parameter matrix, the second parameter influence factor matrix, and the second delay coefficient.

[0029] In one possible implementation, the dosing control includes:

[0030] Wastewater dosing flow data is extracted from manual control experience data. Clustering analysis is performed on the wastewater dosing flow data using a clustering algorithm from an AI platform to obtain the optimal K-value classification. Based on the optimal K-value classification, the response trend of the total reagent setpoint, and the adjustment trend of the total reagent feedforward value, the parameters corresponding to the variable speed step integral PIDE control algorithm are obtained. The parameters include the variable speed gradient and the step step.

[0031] According to one aspect of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described above.

[0032] According to one aspect of the embodiments of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the method described above.

[0033] According to one aspect of the embodiments of this application, a computer program product is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the method described above.

[0034] The beneficial effects of the technical solutions provided in this application are:

[0035] The comprehensive dosing control method for the water purification system provided in this application obtains water quality parameters and a hydraulic model. Based on the water quality parameters and the hydraulic model, it calculates the target water quality parameters and hydraulic retention time at the inlet and outlet of the magnetic coagulation module. Based on the target water quality parameters and preset indicators, it calculates the reagent dosing baseline curve and reagent dosing correction curve. Based on the reagent dosing baseline curve, reagent dosing correction curve, and water quality parameters, it controls the dosing amount of the dosing pump. The embodiments of this application can automatically adjust the dosing amount according to the actual water quality parameters, effectively match the dosing amount with the phosphorus content of the wastewater, reduce reagent consumption, reduce the labor and reagent costs required for wastewater purification, and effectively meet the requirement of precise dosing control. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below.

[0037] Figure 1 A flowchart of the comprehensive control method for chemical dosing in a water purification system provided in this application embodiment;

[0038] Figure 2 This is a structural diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0039] The embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the embodiments described below with reference to the accompanying drawings are exemplary descriptions for explaining the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions of the embodiments of this application.

[0040] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the terms “comprising” and “including” as used in embodiments of this application mean that the corresponding feature can be implemented as the presented feature, information, data, step, operation, element, and / or component, but do not exclude implementation as other features, information, data, step, operation, element, component, and / or combinations thereof supported by the art. It should be understood that when we say that an element is “connected” or “coupled” to another element, the one element can be directly connected or coupled to the other element, or it can mean that the one element and the other element establish a connection relationship through an intermediate element. Furthermore, “connected” or “coupled” as used herein can include wireless connection or wireless coupling. The term “and / or” as used herein indicates at least one of the items defined by the term; for example, “A and / or B” indicates implementation as “A,” or implementation as “A,” or implementation as “A and B.”

[0041] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0042] The technical solutions of this application and their effects are described below through several exemplary embodiments. It should be noted that the following embodiments can be referenced, borrowed from, or combined with each other. Identical terms, similar features, and similar implementation steps in different embodiments will not be repeated.

[0043] The method, equipment, medium, and product for comprehensive control of chemical dosage in water purification systems provided in this application aim to solve at least one technical problem existing in the prior art.

[0044] This application provides a method for comprehensive control of chemical dosage in a water purification system. The water purification system includes a magnetic coagulation module equipped with a chemical dosing pump. This method can be used to control a control terminal for the chemical dosing pump. The control terminal can be a mobile phone, computer, cloud platform, or other device capable of acquiring water quality parameters and controlling the chemical dosage based on those parameters.

[0045] Optionally, such as Figure 1 As shown, the comprehensive control method for chemical dosage in the water purification system of this application includes:

[0046] S101: Obtain the water quality parameters and hydraulic model corresponding to the wastewater, and calculate the target water quality parameters and hydraulic retention time corresponding to the inlet and outlet of the magnetic coagulation module based on the water quality parameters and hydraulic model.

[0047] Optionally, water quality parameters include turbidity at the inlet and outlet of the water purification system, chemical oxygen demand (COD), total phosphorus (TP, which refers to the result after the water sample has been digested and converted into orthophosphate in various forms (including inorganic and organic phosphorus), measured in milligrams of phosphorus per liter of water sample (mg / L), and total nitrogen (TN, which refers to the total amount of various forms of inorganic and organic nitrogen in water, including NO3). - NO2 - NH4 + Inorganic nitrogen and organic nitrogen such as proteins, amino acids and organic amines (calculated in milligrams of nitrogen per liter of water (mg / L)), ammonia nitrogen, temperature, flow rate, and hydraulic retention time are determined based on the pipeline information of the water purification system.

[0048] In one embodiment, a typical wastewater treatment process at a wastewater treatment plant includes: influent instrument room - pretreatment - biological treatment - secondary sedimentation tank treatment - magnetic coagulation module treatment - disinfection tank treatment - effluent instrument room. The influent and effluent instrument rooms are equipped with monitoring devices for detecting water quality parameters, which are used to obtain relevant water quality parameters for the influent and effluent instrument rooms.

[0049] Optionally, from the inlet to the outlet of the wastewater treatment plant, there is a certain retention time for the wastewater. To achieve precise control of the dosing dosage, it is necessary to obtain a hydraulic model of the wastewater in the wastewater treatment plant. Obtaining the hydraulic model includes: acquiring the pipeline information of the water purification system, determining the volume ratio of the corresponding pipelines of the water purification system based on the pipeline information, and calculating the hydraulic model based on the volume ratio and pipeline information.

[0050] Optionally, the pipeline information includes information related to the sewage holding space, such as the inner diameter, shape, and length of the pipeline, as well as the dimensions of various tanks used to store sewage (such as secondary sedimentation tanks, disinfection tanks, etc.). The volume ratio of the pipeline is calculated based on this pipeline information.

[0051] Optionally, after obtaining the volume ratio, the hydraulic residence time of the wastewater in the pipeline can be calculated using the pipeline information, Bernoulli's equation, and a hydraulic model that describes the flow information of wastewater in the pipeline (such as the residence time in different pipelines). This hydraulic residence time can include the time it takes for wastewater to reach the inlet of the magnetic coagulation module from the inlet and the time it takes for wastewater to reach the outlet from the outlet of the magnetic coagulation module.

[0052] Optionally, flow sensors can be installed at locations in the water purification system where hydraulic retention time needs to be detected (such as the inlet and outlet of the magnetic coagulation module). Alternatively, a hydraulic model can be used to calculate the instantaneous flow rate in the wastewater system's inlet / outlet instrument rooms, the virtual flow meter at the inlet of the magnetic coagulation module, and the virtual flow meter at the outlet of the magnetic coagulation module.

[0053] Optionally, the target water quality parameters and hydraulic retention times corresponding to the inlet and outlet of the magnetic coagulation module are calculated based on water quality parameters and hydraulic retention times, including: determining the first hydraulic retention time corresponding to the inlet of the magnetic coagulation module and the second hydraulic retention time corresponding to the outlet of the magnetic coagulation module based on the hydraulic model; determining the inlet virtual flow meter corresponding to the inlet of the magnetic coagulation module based on the flow rate corresponding to the inlet of the water purification system and the first hydraulic retention time, and determining the outlet virtual flow meter corresponding to the outlet of the magnetic coagulation module based on the flow rate corresponding to the outlet of the water purification system and the second hydraulic retention time; and determining the target water quality parameters based on the inlet virtual flow meter and the outlet virtual flow meter.

[0054] Optionally, the hydraulic retention time information may include the hydraulic retention time corresponding to different pipeline locations in the water purification system. The locations of the inlet and outlet of the magnetic coagulation module in the pipeline system are obtained, and based on these locations and the hydraulic model, the time required for wastewater flowing into the inlet to reach the inlet of the magnetic coagulation module (first hydraulic retention time) and the time required to reach the outlet of the magnetic coagulation module (second hydraulic retention time) are determined.

[0055] Optionally, the hydraulic residence time information may also include the hydraulic residence times at the inlet and outlet of the magnetic coagulation module under different flow rates. When calculating the first hydraulic residence time, the flow rate at the inlet of the water purification system can be obtained, and the first hydraulic residence time can be obtained based on the inlet flow rate and the hydraulic residence time information in the hydraulic model. Alternatively, the flow rate at the outlet can be obtained, and the second hydraulic residence time can be obtained based on the outlet flow rate and the hydraulic residence time information.

[0056] In one embodiment, after obtaining the first hydraulic residence time, the flow data FT1 of the inlet flow meter in the inlet instrument room can be virtualized to the inlet of the magnetic coagulation module using a time axis translation method, and recorded as the inlet flow FT2. The virtualization process is as follows: First, the flow data FT1 is discretized, and FT1[n] is recorded with a period of 1 second. FT2 is calculated based on the first hydraulic residence time and FT1[n], and FT2 is used in subsequent control. After obtaining the second hydraulic residence time, the flow rate of the outlet of the outlet instrument room is recorded as FT4, and this flow rate is virtualized to the outlet of the magnetic coagulation module and recorded as FT3. The virtualization process is as follows: First, FT4 is discretized, and FT4[n] is recorded with a period of 1 second. The flow rate FT3 of the magnetic coagulation module at different time points is obtained based on the second hydraulic residence time from the outlet to the inlet of the magnetic coagulation module and FT4[n], and FT3 is used in subsequent dosing control. Correspondingly, other water quality parameters (such as total phosphorus content) at the inlet and outlet can also be virtually mapped to the inlet and outlet of the magnetic coagulation module based on the hydraulic residence time. By mapping the water quality parameters at the inlet and outlet to the magnetic coagulation module, chemical dosing control can be performed using the water quality parameters mapped to the magnetic coagulation module based on the hydraulic residence time when the instruments at the inlet and outlet malfunction or are being calibrated. This improves the stability of the water purification system and enables the mutual replacement of the total phosphorus content instruments between the magnetic coagulation module and the outlet instrument, thus achieving instrument redundancy.

[0057] S102: Calculate the baseline curve and correction curve for chemical dosing based on the target water quality parameters and preset indicators.

[0058] Optionally, the preset indicators include emission indicators and compliance indicators. The emission indicators are used to calculate the correction value for pesticide dosage, while the compliance indicators are used to calculate the baseline value for pesticide dosage.

[0059] In one embodiment, the emission index may include a threshold value for the total phosphorus content at the time of wastewater discharge, and the compliance index may include a threshold value for the total phosphorus content in the wastewater after phosphorus removal by chemicals. The total phosphorus content threshold value in the emission index is lower than the total phosphorus content threshold value in the compliance index. Specifically, the total phosphorus content threshold value in the emission index can be set arbitrarily, such as 0.15, and the total phosphorus content threshold value in the compliance index can be set arbitrarily, such as 0.3.

[0060] Optionally, before controlling the dosage of the dosing pump, historical dosing information can be collected, and a dosing control curve can be generated based on this information. This historical dosing information can include the dosage and the corresponding water quality parameters. Specifically, historical dosing time periods can be obtained, divided into n segments, and the dosing information for each individual within each segment can be acquired. Here, x can represent any one of time, flow rate, or total phosphorus content at the inlet of the magnetic coagulation module, and y can represent the dosage. The dosing information for each segment is obtained based on a weighted average (with appropriate weights assigned to each individual's dosing information), and this corresponding information is defined as historical dosing information.

[0061] Optionally, after obtaining historical dosing information, a dosing control curve can be obtained by using polynomial fitting.

[0062] Optionally, a baseline curve and a correction curve for chemical dosing are calculated based on water quality parameters and preset indicators, including: determining the baseline values ​​for chemical dosing at different time points based on the inlet flow rate, the total phosphorus content corresponding to the inlet, and the discharge indicators, and generating a baseline curve for chemical dosing corresponding to the baseline values; and determining the correction values ​​for chemical dosing at different time points using the outlet flow rate, the total phosphorus content corresponding to the outlet, and the compliance indicators, and generating a correction curve for chemical dosing corresponding to the correction values.

[0063] Optionally, the baseline and correction values ​​for pesticide dosing can be calculated based on the total phosphorus content data in the emission and compliance indicators. The baseline curve can be used to represent the baseline values ​​at different time points, and the correction curve can be used to represent the correction values ​​at different time points.

[0064] Optionally, the reagent dosage baseline value is determined based on the inlet flow rate, the total phosphorus content corresponding to the inlet, and the emission indicators, including: determining the phosphorus removal amount based on the inlet flow rate and the total phosphorus content corresponding to the inlet; and calculating the reagent dosage baseline value based on the phosphorus removal amount, emission indicators, and the reagent's reaction model information.

[0065] Optionally, the reaction model information may include phosphorus removal reaction model information for the agent or the amount of phosphorus removed per unit of agent. Based on this phosphorus removal reaction model information or the amount of phosphorus removed, the required amount of agent to remove phosphorus from wastewater is calculated, and this amount of agent is determined as the baseline value for agent dosage.

[0066] In one embodiment, after obtaining the inlet flow rate and the corresponding total phosphorus content, the phosphorus content in the wastewater within a specific time period (e.g., one second) can be calculated based on the inlet flow rate and the corresponding total phosphorus content, and this phosphorus content is determined as the phosphorus removal amount. The reagent can be an aluminum salt, which reacts chemically with the phosphorus in the wastewater. The amount of reagent required to remove phosphorus from the wastewater is calculated based on the reaction model of this chemical reaction, and this amount of reagent is determined as the reagent dosage baseline value.

[0067] Optionally, when calculating the reagent dosage correction value, the outlet flow rate and total phosphorus content corresponding to the outlet of the magnetic coagulation module are obtained, the phosphorus removal amount corresponding to the outlet is calculated based on the outlet flow rate and the total phosphorus content corresponding to the outlet, and the reagent dosage correction value is calculated based on the phosphorus removal amount corresponding to the outlet, the emission index, and the reagent reaction model information.

[0068] Optionally, the inlet flow rate and outlet flow rate corresponding to the magnetic coagulation module are obtained based on the hydraulic retention time to ensure that the outlet flow rate and inlet flow rate are the same sewage flow rates at the inlet and outlet of the magnetic coagulation module, thereby ensuring that the sewage corresponding to the inlet flow rate and outlet flow rate are the same sewage.

[0069] S103: Control the dosage of the dosing pump according to the dosing baseline curve, the dosing correction curve and water quality parameters.

[0070] Optionally, the dosage of the dosing pump is determined based on the chemical dosing baseline curve, the chemical dosing correction curve, and water quality parameters. This includes: generating a correlation coefficient heatmap between various water quality parameters using an AI algorithm (machine learning algorithm); performing correlation factor analysis using the correlation coefficient heatmap; obtaining the inlet and outlet influencing factors based on the analysis results and water quality parameters; determining the total chemical setpoint response trend of the dosing pump based on the higher-order partial derivatives of the inlet influencing factors and the corresponding higher-order partial derivatives of the chemical dosing baseline curve; determining the total chemical feedforward adjustment trend of the dosing pump based on the higher-order partial derivatives of the outlet influencing factors and the corresponding higher-order partial derivatives of the chemical dosing correction curve; and dynamically adjusting the control parameters of the dosing pump based on the total chemical setpoint response trend and the total chemical feedforward adjustment trend. The dosing pump uses a variable-speed stepped integral PIDE control algorithm to control the dosage. The higher-order partial derivatives can be second-order or third-order partial derivatives.

[0071] Optionally, the AI ​​algorithm can be a clustering algorithm or other algorithms capable of generating heat maps. Water quality parameters and historical water quality parameters of the inlet and outlet can be obtained, and the correlation coefficient heat maps of the inlet and outlet can be generated using these historical water quality parameters and the AI ​​algorithm, respectively.

[0072] Optionally, obtaining the influencing factors of the inlet and outlet based on water quality parameters includes: obtaining the influencing factors of the inlet and outlet based on analysis results and water quality parameters, including: generating a first water quality parameter matrix based on the water quality parameters of the inlet; obtaining a first parameter influence factor matrix corresponding to the first water quality parameter matrix based on the analysis results corresponding to the water quality parameters of the inlet; calculating the influencing factor of the inlet based on the first water quality parameter matrix, the first parameter influence factor matrix, and a first delay coefficient; generating a second water quality parameter matrix based on the water quality parameters of the outlet; obtaining a second parameter influence factor matrix corresponding to the second water quality parameter matrix based on the analysis results corresponding to the water quality parameters of the outlet; and calculating the influencing factor of the outlet based on the second water quality parameter matrix, the second parameter influence factor matrix, and a second delay coefficient.

[0073] Optionally, different influencing factors corresponding to different water quality parameters can be preset. The analysis results can be used to determine the correlation between different water quality parameters and corresponding water quality indicators (such as total phosphorus content). The influencing factors are adjusted based on this correlation (e.g., the greater the correlation, the larger the influencing factor). A parameter influencing factor matrix is ​​generated based on the adjusted influencing factors. Specifically, the first parameter influencing factor matrix is ​​obtained using the analysis results corresponding to the water quality parameters at the inlet, and the second parameter influencing factor matrix is ​​obtained using the analysis results corresponding to the water quality parameters at the outlet.

[0074] In one embodiment, the influence factor of the inlet is λ1, and the influence factor of the outlet is λ2. Here, λ1 = first water quality parameter matrix * first parameter influence factor matrix * first delay coefficient. The first water quality parameter matrix can be represented as [x1x2...xn], and the first parameter influence factor matrix can be represented as [k1 k2...kn], where the parameter influence factor corresponding to x1 is k1, and the parameter influence factor corresponding to xn is kn. Similarly, λ2 = second water quality parameter matrix * second parameter influence factor matrix * second delay coefficient. Based on the calculation formulas for λ1 and λ2, the higher-order partial derivatives of λ1 (denoted as λ′1) and λ2 (denoted as λ′2) can be calculated.

[0075] Optionally, the total reagent setpoint response trend can be calculated using SP = ref′ * λ′1, where SP is the total reagent setpoint response trend and ref′ is the second derivative of the reagent dosing baseline curve. The total reagent feedforward adjustment trend can be calculated using FF = crr′ * λ′2, where FF is the total reagent feedforward adjustment trend and crr′ is the second derivative of the reagent dosing correction curve. Based on these two formulas, the total reagent setpoint response trend and the total reagent feedforward adjustment trend at different time points can be obtained.

[0076] Optionally, the dosage of the dosing pump at different times can be obtained based on the calculation formula corresponding to the dosing control curve. Additionally, the baseline and correction values ​​for pesticide dosing at different times can be obtained, and the sum of these values ​​can be used as the dosage of the dosing pump at different times. Based on this dosage, a PID control algorithm is used to control the dosing pump to adjust the dosage.

[0077] Optionally, the values ​​of Kp, Ki, and Kd related to the PID control algorithm can be adjusted using a polyline method. Specifically, after obtaining the total reagent setpoint response trend, a change curve of the total reagent setpoint response trend of the magnetic coagulation module is obtained based on this trend over a predetermined time period (the length can be 30 minutes, 10 minutes, or other times, with the current time point as the start or end point). The derivative of this change curve at different time points within the predetermined time period is obtained. Based on this derivative, the adjustment range of Kp, Ki, and Kd is determined, or whether the adjustment result meets the requirements is checked. If a derivative greater than a predetermined threshold exists, the adjustment result is determined not to meet the requirements, and further adjustment or an increase in the adjustment range is required. If no derivative greater than the predetermined threshold exists, the adjustment result is determined to meet the requirements, and no further adjustment is needed or the adjustment range is reduced.

[0078] Optionally, when using the variable-speed step-integral PIDE control algorithm for dosing control, the implementation of dosing control includes: extracting wastewater dosing flow data from manual control experience data; performing cluster analysis on the wastewater dosing flow data using a clustering algorithm from an AI platform to obtain the optimal K-value classification; and obtaining the parameters corresponding to the variable-speed step-integral PIDE control algorithm based on the optimal K-value classification, the response trend of the total reagent setpoint, and the adjustment trend of the total reagent feedforward value. The parameters include the variable-speed gradient and the step-level gradient. The AI ​​platform can be Doubao, Zhipu Qingyan, Kimi, or other platforms capable of using clustering algorithms to perform cluster analysis on wastewater dosing flow data.

[0079] Alternatively, based on the optimal K value, relationship curves representing different water quality parameters with the total chemical setpoint and the total chemical feedforward can be obtained. The current total chemical setpoint and total chemical feedforward are then obtained by matching the derivative of these curves with the response trend of the total chemical setpoint and the adjustment trend of the total chemical feedforward. Based on these total chemical setpoints and feedforwards, the parameters of the variable-speed step-integral PIDE control algorithm are adjusted to obtain parameters that match the wastewater currently entering the magnetic coagulation module. The operation of the dosing pump is then controlled based on these parameters, thereby achieving chemical dosing control.

[0080] Optionally, to effectively control the total phosphorus content at the outlet when abnormal operating conditions such as sudden changes in influent water quality, flow rate, production process, or sudden failure of key instruments occur, compensation control is activated when the total phosphorus content at the outlet reaches the warning value, adding the set flow rate to the feedback control of the dosing pump. Since single feedback control is susceptible to adjustment delays due to long dwell times, the input of feedforward and compensation further reduces the impact of shocks from influent water quality and quantity, and composite control can achieve stable control objectives. Chemical dosing control employs a combination of influent / outfluent ratio dosing and rate / amplitude limiting control (flow rate limiting: each dosing pump has a flow range; flow rate limiting: the rate of change of the dosing pump's flow does not exceed a predetermined value, improving anti-interference capabilities and preventing large jumps in the dosing pump's flow due to sudden changes in operating conditions, allowing for smooth transition control). Furthermore, the limits of the dosing pump are...

[0081] Optionally, when controlling the dosing pump, the flow rate of the agent to be added by the dosing pump can also be detected, and whether the flow rate exceeds the predetermined flow rate. If it does, the flow rate of the dosing pump can be limited to the predetermined flow rate or an early warning can be issued.

[0082] The comprehensive dosing control method for the water purification system of this application acquires water quality parameters and a hydraulic model. Based on the water quality parameters and the hydraulic model, it calculates the target water quality parameters and hydraulic retention time at the inlet and outlet of the magnetic coagulation module. Based on the target water quality parameters and preset indicators, it calculates the reagent dosing baseline curve and reagent dosing correction curve. Based on the reagent dosing baseline curve, reagent dosing correction curve, and water quality parameters, it controls the dosing amount of the dosing pump. The embodiments of this application can automatically adjust the dosing amount according to the actual water quality parameters, achieve effective matching between the dosing amount and the phosphorus content of the sewage, reduce reagent consumption, reduce the labor and reagent costs required for sewage purification, and effectively meet the requirement of precise dosing control.

[0083] In one alternative embodiment, an electronic device is provided, such as Figure 2 As shown, Figure 2 The illustrated electronic device 4000 includes a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, for example, via a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 4004 is not limited to one type, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of this application.

[0084] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), a FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 4001 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0085] Bus 4002 may include a pathway for transmitting information between the aforementioned components. Bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 4002 can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the figure, but this does not indicate that there is only one bus or one type of bus.

[0086] The memory 4003 may be ROM (Read-Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read-Only Memory), CD-ROM (Compact Disc Read-Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media, other magnetic storage devices, or any other medium capable of carrying or storing computer programs and capable of being read by a computer, without limitation herein.

[0087] The memory 4003 stores computer programs that execute embodiments of this application, and its execution is controlled by the processor 4001. The processor 4001 executes the computer programs stored in the memory 4003 to implement the steps shown in the foregoing method embodiments.

[0088] Among them, electronic devices can be any electronic product that can interact with an object, such as personal computers, tablets, smartphones, personal digital assistants (PDAs), game consoles, interactive network television (IPTV), smart wearable devices, etc.

[0089] The electronic device may also include network devices and / or object devices. The network devices include, but are not limited to, a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing consisting of a large number of hosts or network servers.

[0090] The networks in which the electronic devices are located include, but are not limited to, the Internet, wide area networks, metropolitan area networks, local area networks, and virtual private networks (VPNs).

[0091] This application provides a computer-readable storage medium including a computer program that, when executed by a processor, can implement the steps and corresponding content of the aforementioned method embodiments.

[0092] This application provides a computer program product, which includes a computer program that, when executed by a processor, can implement the steps and corresponding content of the aforementioned method embodiments.

[0093] The terms "first," "second," "third," "fourth," "1," "2," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in a sequence other than that shown in the figures or text.

[0094] It should be understood that although arrows indicate various operation steps in the flowcharts of this application's embodiments, the order in which these steps are implemented is not limited to the order indicated by the arrows. Unless explicitly stated herein, in some implementation scenarios of this application's embodiments, the implementation steps in each flowchart can be executed in other orders as required. Furthermore, some or all steps in each flowchart, based on the actual implementation scenario, may include multiple sub-steps or multiple stages. Some or all of these sub-steps or stages can be executed at the same time, and each sub-step or stage can also be executed at different times. In scenarios where execution times differ, the execution order of these sub-steps or stages can be flexibly configured according to requirements, and this application's embodiments do not limit this.

[0095] The above description is only an optional implementation method for some implementation scenarios of this application. It should be noted that for those skilled in the art, other similar implementation methods based on the technical concept of this application without departing from the technical concept of this application also fall within the protection scope of the embodiments of this application.

Claims

1. A method for comprehensive control of chemical dosage in a water purification system, characterized in that, The water purification system includes a magnetic coagulation module equipped with a dosing pump. Wastewater flowing out of the secondary sedimentation tank enters the magnetic coagulation module. The method includes: Obtain the water quality parameters and hydraulic model corresponding to the wastewater, and predict the target water quality parameters and hydraulic retention time corresponding to the inlet and outlet of the magnetic coagulation module based on the water quality parameters and the hydraulic model. The water quality parameters include turbidity, chemical oxygen demand, total phosphorus content, total nitrogen, ammonia nitrogen, temperature and flow rate corresponding to the inlet and outlet of the water purification system. Based on the target water quality parameters and preset indicators, a baseline curve and a correction curve for reagent dosing are generated. The preset indicators include emission indicators and compliance indicators. The dosage of the dosing pump is controlled according to the reagent dosing baseline curve, the reagent dosing correction curve, and the water quality parameters, including: A heatmap of correlation coefficients between various water quality parameters is generated using an AI algorithm. Correlation influencing factors are analyzed using the heatmap. Based on the analysis results and the water quality parameters, the influencing factors of the inlet and outlet are obtained. The response trend of the total reagent setpoint corresponding to the dosing pump is determined based on the higher-order partial derivatives of the influencing factors of the inlet and the higher-order partial derivatives corresponding to the reagent dosing reference curve. The adjustment trend of the total reagent feedforward value corresponding to the dosing pump is determined based on the higher-order partial derivatives of the influencing factors of the outlet and the higher-order partial derivatives corresponding to the reagent dosing correction curve. The control parameters of the dosing pump are dynamically adjusted based on the response trend of the total dosing setpoint and the adjustment trend of the total dosing feedforward value. The dosing pump achieves dosing quantity control based on the variable speed step integral PIDE control algorithm.

2. The method for comprehensive control of chemical dosage in a water purification system according to claim 1, characterized in that, Obtaining the hydraulic model includes: Obtain the pipeline information of the water purification system, and determine the volume ratio of the pipeline corresponding to the water purification system based on the pipeline information; The hydraulic model is calculated based on the volume ratio and pipeline information.

3. The comprehensive control method for chemical dosage in a water purification system according to claim 1, characterized in that, The step of predicting the target water quality parameters and hydraulic retention time corresponding to the inlet and outlet of the magnetic coagulation module based on the water quality parameters and the hydraulic model includes: The first hydraulic residence time corresponding to the inlet of the magnetic coagulation module and the second hydraulic residence time corresponding to the outlet of the magnetic coagulation module are determined according to the hydraulic model. The inlet virtual flow meter corresponding to the inlet of the magnetic coagulation module is determined based on the flow rate corresponding to the inlet of the water purification system and the first hydraulic retention time, and the outlet virtual flow meter corresponding to the outlet of the magnetic coagulation module is determined based on the flow rate corresponding to the outlet of the water purification system and the second hydraulic retention time. The target water quality parameters are determined based on the inlet virtual flow meter and the outlet virtual flow meter.

4. The comprehensive control method for chemical dosage in a water purification system according to claim 3, characterized in that, The calculation of the reagent dosing baseline curve and reagent dosing correction curve based on the water quality parameters and preset indicators includes: Based on the inlet virtual flow meter, the total phosphorus content corresponding to the inlet, and the emission indicators, determine the reagent dosing baseline values ​​at different time points, and generate the reagent dosing baseline curve corresponding to the reagent dosing baseline values; Using the virtual flow meter at the outlet, the total phosphorus content corresponding to the outlet, and the compliance index, the reagent dosage correction value at different time points is determined, and the reagent dosage correction curve corresponding to the reagent dosage correction value is generated.

5. The method for comprehensive control of chemical dosage in a water purification system according to claim 4, characterized in that, The step of determining the reagent dosage baseline value at different time points based on the inlet virtual flow meter, the total phosphorus content corresponding to the inlet, and emission indicators includes: The phosphorus removal rate at different time points is determined based on the virtual flow meter at the inlet and the total phosphorus content corresponding to the inlet. The baseline value for reagent dosage is calculated based on the phosphorus removal amount, the emission index, and the reaction model information of the reagent.

6. The comprehensive control method for chemical dosage in a water purification system according to claim 1, characterized in that, The process of obtaining the influencing factors of the inlet and outlet based on the analysis results and the water quality parameters includes: A first water quality parameter matrix is ​​generated based on the water quality parameters of the inlet. A first parameter influence factor matrix corresponding to the first water quality parameter matrix is ​​obtained based on the analysis results corresponding to the water quality parameters of the inlet. The influence factor of the inlet is calculated based on the first water quality parameter matrix, the first parameter influence factor matrix, and the first delay coefficient. A second water quality parameter matrix is ​​generated based on the water quality parameters of the outlet. A second parameter influence factor matrix corresponding to the second water quality parameter matrix is ​​obtained based on the analysis results corresponding to the water quality parameters of the outlet. The influence factor of the outlet is calculated based on the second water quality parameter matrix, the second parameter influence factor matrix, and the second delay coefficient.

7. The method for comprehensive control of chemical dosage in a water purification system according to claim 1, characterized in that, The implementation of dosage control includes: Wastewater dosing flow data is extracted from manual control experience data. Clustering analysis is performed on the wastewater dosing flow data using a clustering algorithm from an AI platform to obtain the optimal K-value classification. Based on the optimal K-value classification, the response trend of the total reagent setpoint, and the adjustment trend of the total reagent feedforward value, the parameters corresponding to the variable speed step integral PIDE control algorithm are obtained. The parameters include the variable speed gradient and the step step.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-7.

10. A computer program product, the computer program product comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1-7.

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