Intelligent chlorine dioxide disinfectant adding system and method for water supply plant

Abstract

The invention discloses an intelligent chlorine dioxide disinfectant adding system and method for a water supply plant, which can accurately predict the chlorine dioxide concentration of two nodes based on the current working condition of a water supply system by constructing a chlorine dioxide concentration prediction model of the outlet of a clean water tank and the end point of a pipe network in the water supply system. The minimum dosing amount capable of ensuring that the pipe network peripheral disinfectant concentration parameter reaches the standard is solved, theoretical guidance and basis are provided for water supply plant disinfectant dosing, the dosing amount of dosing equipment is adjusted in real time, intelligent and accurate dosing is achieved, and the microorganism breeding risk and the disinfection by-product standard exceeding risk of a water supply system are reduced.

Description

Technical Field

[0001] The invention relates to the technical field of water treatment, in particular to an intelligent dosing system for chlorine dioxide disinfectant in a small water supply plant. Background Art

[0002] Chlorine dioxide is a highly effective, broad-spectrum disinfectant that is widely used in drinking water production. Although chlorine dioxide produces fewer organic byproducts during use than other chlorine-based disinfectants, it produces inorganic byproducts, mainly chlorite. The addition of chlorine dioxide in traditional water plants is mostly controlled manually, and the dosage cannot be adjusted in real time according to the working conditions of the pipe network system, resulting in unsatisfactory disinfection effects. When the dosage is too low, there is a risk of pathogenic microorganisms breeding at the end of the pipe network, which poses a health and safety hazard to users; and blindly increasing the dosage of disinfectants in the water plant, or adding disinfectants for a second time in the middle of the pipe network, will bring the risk of excessive disinfection byproducts.

[0003] Therefore, designing an intelligent disinfection system that can accurately add chlorine dioxide is of great significance to improving water supply quality and ensuring people’s water safety. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide an intelligent chlorine dioxide disinfectant dosing system suitable for small water supply plants. The system uses the chlorine dioxide concentration prediction at the outlet of the clear water tank and the end point of the pipeline network to ensure the minimum dosage that meets the standard and implements real-time and accurate dosing.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] The intelligent dosing system of chlorine dioxide disinfectant for water supply plant provided by the present invention comprises chlorine dioxide online monitoring equipment, intelligent management platform and dosing equipment;

[0007] The chlorine dioxide online monitoring device is arranged at the water outlet, and is used to obtain the chlorine dioxide concentration at the water outlet and the water temperature at the end of the pipe network, and the collected data is used to construct a chlorine dioxide concentration prediction model and to control the chlorine addition amount in real time;

[0008] The intelligent management platform calculates the dosage required for the pre-chlorination equipment and the post-chlorination equipment respectively through the chlorine dioxide concentration prediction model, and controls the dosing equipment to add the agent;

[0009] The dosing equipment includes a front chlorination equipment arranged at the front end of the clear water tank and a rear chlorination equipment arranged at the rear end of the clear water tank. The front end of the clear water tank is the clear water tank entrance, and the rear end of the clear water tank is a water collection well, and the front chlorination and the rear chlorination are performed respectively.

[0010] Furthermore, the chlorine dioxide concentration prediction model calculates the required dosage based on the current water temperature and the chlorine dioxide concentration at the outlet of the clean water tank as the target concentration.

[0011] Furthermore, the difference between the minimum chlorine dioxide concentration of the factory water output by the intelligent management platform and the chlorine dioxide concentration value at the outlet of the clear water tank after chlorination by the pre-chlorination equipment is used as the dosage required for the post-chlorination equipment.

[0012] Furthermore, the intelligent management platform includes a chlorine dioxide concentration prediction model for the clear water tank outlet, a clear water tank dosage optimization model, a chlorine dioxide concentration prediction model for the terminal of the pipeline network, a chlorine dioxide concentration optimization model for the factory water, and a dosing control module.

[0013] Further, the chlorine dioxide concentration prediction model at the outlet of the clean water tank is constructed according to the following steps:

[0014] Get the parameters of the water supply system;

[0015] Constructing a hydraulic model of a clear water tank, the hydraulic model of the clear water tank comprising a first node, a second node, a third node, and a fourth node; the first node is used to set the concentration of a chlorine dioxide disinfectant put into the clear water tank; the second node is used to control the water inlet of the clear water tank; the third node is used to set the size parameters and the mixing model of the clear water tank; the fourth node is used to set the water consumption of the outlet of the clear water tank;

[0016] Determine the chlorine dioxide attenuation coefficient;

[0017] A water quality prediction model was constructed, and the chlorine dioxide attenuation coefficient and the amount of chlorine dioxide added at the clear water tank inlet were input into the clear water tank hydraulic model to obtain the predicted value of chlorine dioxide concentration at the clear water tank outlet.

[0018] Furthermore, the clean water tank dosage optimization model is to calculate the minimum dosage that meets a certain target concentration at the clean water tank outlet through the clean water tank outlet chlorine dioxide concentration prediction model, and the calculation is specifically performed according to the following steps:

[0019] First, the chlorine dioxide attenuation coefficient corresponding to the current water temperature is input into EPANET;

[0020] Then, the chlorine dioxide concentration was input into the EPANET model in ascending order within the range of 0 to 1 mg / L to simulate water quality for a certain period of time;

[0021] Determine whether the chlorine dioxide concentration at the outlet of the clean water tank is higher than the target concentration;

[0022] Output the minimum dosage that meets the requirements;

[0023] Furthermore, the chlorine dioxide concentration prediction model at the end of the pipe network is an LSTM neural network model built based on the Self Attention mechanism, which predicts the chlorine dioxide concentration at the end of the pipe network according to the chlorine dioxide concentration of the outgoing water and the water temperature data at the end of the pipe network.

[0024] Further, the chlorine dioxide concentration optimization model of the factory water is specifically carried out according to the following steps:

[0025] At the beginning, the chlorine dioxide concentration x of the factory water is set as the target concentration at the outlet;

[0026] Determine whether the chlorine dioxide concentration of the factory water satisfies the following relationship: x<Th1. If not, output the chlorine dioxide concentration of the factory water; if yes, proceed to the next step; Th1 represents the preset concentration value of chlorine dioxide of the factory water;

[0027] The prediction results are calculated by using the chlorine dioxide concentration prediction model at the end of the pipe network;

[0028] Output the predicted result y of the terminal chlorine dioxide concentration;

[0029] Determine whether the prediction result satisfies the following relationship: y≥Th2. If not, change the chlorine dioxide concentration of the factory water; if yes, output the chlorine dioxide concentration of the factory water, and then end; Th2 represents the preset predicted concentration value.

[0030] The intelligent dosing method of chlorine dioxide disinfectant for a water supply plant provided by the present invention comprises the following steps:

[0031] Obtain the chlorine dioxide concentration at the outlet of the clean water tank and the water temperature at the end of the pipe network, the outlet of the clean water tank includes the outlet of the clean water tank, the outlet of the water plant, and the end of the pipe network, and use the collected data to build a chlorine dioxide concentration prediction model and control the chlorine addition amount in real time;

[0032] Construct a chlorine dioxide concentration prediction model, calculate the dosage required by the pre-chlorination equipment and the post-chlorination equipment under the current working conditions through the chlorine dioxide concentration prediction model, and control the dosing equipment to automatically add;

[0033] The dosing equipment includes a pre-chlorination device arranged at the front end of the clear water tank and a post-chlorination device arranged at the rear end of the clear water tank. The front end of the clear water tank is the clear water tank entrance, and the rear end of the clear water tank is a water collection well, that is, pre-chlorination and post-chlorination are performed at the clear water tank entrance and the water collection well respectively.

[0034] Furthermore, the dosage required for the post-chlorination equipment is calculated in the following manner: the minimum chlorine dioxide concentration of the factory water is obtained, and the difference between the minimum chlorine dioxide concentration of the factory water and the chlorine dioxide concentration value at the outlet of the clear water tank after chlorination by the pre-chlorination equipment is calculated, and the difference is the dosage required for the post-chlorination equipment.

[0035] The beneficial effects of the present invention are:

[0036] The intelligent dosing system and method of chlorine dioxide disinfectant for a water supply plant provided by the present invention can accurately predict the chlorine dioxide concentrations at the clear water tank outlet and the terminal points of the pipe network in the water supply system based on the current operating conditions of the water supply system, and solve the minimum dosage that can ensure that the concentration parameters of the disinfectant at the terminal of the pipe network meet the standards, thereby providing theoretical guidance and basis for the disinfectant dosage of the water supply plant, adjusting the dosage of the dosing equipment in real time, realizing intelligent and precise dosage, and reducing the risk of microbial growth and excessive disinfection by-products in the water supply system.

[0037] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to make the purpose, technical solution and beneficial effects of the present invention clearer, the present invention provides the following drawings for explanation.

[0039] Figure 1 It is the block diagram of the clear water tank dosage optimization system.

[0040] Figure 2 This is the topological structure diagram of the EPANET clear water tank hydraulic model.

[0041] Figure 3 This is a schematic diagram of the Self Attention-LSTM neural network structure.

[0042] Figure 4 It is a comparison chart between the predicted results of the actual pipeline network terminal chlorine dioxide concentration prediction model and the actual value.

[0043] Figure 5 It is the flow chart of the optimization model of dosage for clear water tank.

[0044] Figure 6 It is the flowchart of the optimization model for chlorine dioxide concentration in factory water.

[0045] Figure 7 It is a real water quality data chart in actual projects.

[0046] Figure 8 It is the simulation result of the water quality prediction module after model optimization. DETAILED DESCRIPTION

[0047] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention.

[0048] Example 1

[0049] like Figure 1 As shown, Figure 1 It is a block diagram of the clear water tank dosage optimization system. The chlorine dioxide intelligent dosing system for the water supply plant provided in this embodiment includes chlorine dioxide online monitoring equipment, dosing equipment, and an intelligent management platform;

[0050] The chlorine dioxide online monitoring device is arranged at the outlet of the clean water tank, and is used to obtain the chlorine dioxide concentration at the outlet and the water temperature at the end of the pipe network. The outlet includes the outlet of the clean water tank, the outlet of the water plant, and the end of the pipe network. The chlorine dioxide concentration at the three points and the water temperature at the end of the pipe network are monitored respectively, and the collected data are used to construct a chlorine dioxide concentration prediction model and to control the chlorine addition amount in real time;

[0051] The intelligent management platform calculates through the constructed chlorine dioxide concentration prediction model, uses the optimization algorithm to solve the dosage required by the front chlorination equipment and the rear chlorination equipment under the current working conditions, and controls the dosing equipment to automatically add;

[0052] The dosing equipment includes a front chlorination equipment arranged at the front end of the clean water tank and a rear chlorination equipment arranged at the rear end of the clean water tank. The front end of the clean water tank is the clean water tank entrance, and the rear end of the clean water tank is the water collection well, that is, the front chlorination and the rear chlorination are respectively performed at the clean water tank entrance and the water collection well;

[0053] During actual operation, the chlorine dioxide concentration prediction model of the clear water tank calculates the corresponding dosage based on the current water temperature and the target concentration of 0.02 mg / L chlorine dioxide concentration at the outlet of the clear water tank.

[0054] The difference between the minimum chlorine dioxide concentration of the factory water output by the intelligent management platform and the chlorine dioxide concentration value at the outlet of the clear water tank after chlorination by the front chlorination equipment is used as the dosage required by the post-chlorination equipment;

[0055] The agent added by the pre-chlorination equipment is to inactivate pathogenic microorganisms including bacteria and viruses in the water to ensure the disinfection of drinking water safety. The amount of agent added by the pre-chlorination equipment meets the consumption of the contact reaction in the clear water tank and the attenuation during the residence time;

[0056] The agent added by the post-chlorination equipment is mainly to maintain the residual disinfectant concentration of drinking water in the water supply network, so that the water can maintain a certain continuous disinfection ability during the distribution process, kill microorganisms in the water, prevent the water quality from being affected by external pollution, and ensure the biological safety of the water quality. The amount of agent added by the post-chlorination equipment meets the consumption required for the drinking water to stay in the pipe network. The post-chlorination equipment in this embodiment is a closed-loop control link of the entire dosing system, which realizes precise control of the dosage.

[0057] The chlorine dioxide intelligent dosing system for a water supply plant in this embodiment is specifically carried out according to the following steps:

[0058] Input the outlet target concentration into the intelligent management platform;

[0059] The intelligent management platform controls the front chlorination equipment to add chemicals at the front end of the clean water tank;

[0060] The chlorine concentration at the outlet of the clean water tank is obtained through online chlorine dioxide monitoring and input into the intelligent management platform;

[0061] The intelligent management platform controls the post-chlorination equipment to add chemicals to the water collection well, and then the secondary pump station mixes and lifts the chemical-containing water in the water collection well to discharge it to the water plant, thereby controlling the chlorine concentration at the water plant outlet;

[0062] The intelligent management platform controls the dosage of the pre-chlorination equipment and the post-chlorination equipment according to the target concentration of the clear water tank outlet, the reference value of the factory water concentration, the chlorine concentration value obtained by the online monitoring of chlorine dioxide at the water plant outlet, and the chlorine concentration and water temperature obtained by the online monitoring of chlorine dioxide and water temperature at the end of the pipe network;

[0063] The intelligent management platform includes a chlorine dioxide concentration prediction model at the outlet of the clear water tank, a clear water tank dosage optimization model, a chlorine dioxide concentration prediction model at the end of the pipe network, a chlorine dioxide concentration optimization model for the outgoing water, and a dosing control module;

[0064] The chlorine dioxide concentration prediction model at the outlet of the clean water tank is used to predict the chlorine dioxide concentration at the outlet of the clean water tank under the condition of known water temperature;

[0065] The clear water tank dosage optimization model is used to solve the minimum dosage that meets a certain target concentration at the outlet of the clear water tank;

[0066] The chlorine dioxide concentration prediction model at the end of the pipe network is used to predict the chlorine dioxide concentration at the end of the pipe network based on the chlorine dioxide concentration of the outgoing water and the water temperature data at the end of the pipe network;

[0067] The factory water chlorine dioxide concentration optimization model is used to solve the minimum factory water chlorine dioxide concentration that can ensure that the chlorine dioxide concentration at the end of the pipeline network meets the standard under the current pipeline network operating conditions;

[0068] The dosing control module is used to control the dosing amount of two points according to the solution of the optimization algorithm;

[0069] The dosing equipment in this embodiment includes a chlorine dioxide generator and a dosing pump;

[0070] The chlorine dioxide generator is used to prepare high-concentration chlorine dioxide as a disinfectant to achieve a disinfection effect; the dosing pump is used to quantitatively add the disinfectant into the water;

[0071] In this embodiment, the chlorine dioxide online monitoring device is used to monitor water quality;

[0072] In this embodiment, the clear water tank dosage optimization model is calculated using the clear water tank dosage optimization algorithm;

[0073] In this embodiment, the optimization model of chlorine dioxide concentration of factory water is calculated using the optimization algorithm of chlorine dioxide concentration of factory water;

[0074] The chlorine dioxide concentration prediction model at the outlet of the clean water tank is a mechanism model developed based on the EPANET 2.0 software platform, and the specific construction steps include:

[0075] 1. Obtain the parameters of the water supply system, that is, collect basic information: investigate various parameters of the water supply system, including the structural size parameters of the clear water tank, the diameter and length of the inlet and outlet pipes, the control logic of the drinking water treatment process, and the water consumption of the water supply area every hour within 24 hours;

[0076] 2. Hydraulic model construction: Construct the following hydraulic model in the EPANET platform: Figure 2 The model shown, Figure 2 It is a topological structure diagram of the hydraulic model of the EPANET clear water tank; the hydraulic model includes a first node, a second node, a third node, and a fourth node;

[0077] The first node 1 represents the water treatment process before the clean water tank, and the initial water quality of this node can represent the concentration of the chlorine dioxide disinfectant put into the clean water tank;

[0078] The second node 2 is a water pump, which can simulate the opening and closing of the water treatment process by changing its switch state, thereby controlling the water inflow of the clean water tank and inputting the drinking water treatment process control logic in the basic data into EPANET;

[0079] The third node 3 is a clear water tank. The size parameters of the node are assigned according to the collected basic data. The mixing model of the clear water tank is set to a FIFO plunger flow mixing model, and the reaction order is one.

[0080] The fourth node 4 is the outlet of the clean water tank. The water consumption of the water supply area every hour within 24 hours is input into this node. The water demand is the size of the water flow rate of the clean water tank.

[0081] 3. Determine the chlorine dioxide attenuation coefficient through the beaker experiment:

[0082] Take 3 portions of unsterilized treatment process effluent and put them into 500ml brown glass bottles, add 1.0mg / L chlorine dioxide disinfectant and stir evenly, place them in a constant temperature box, set the temperature of the constant temperature box to 25℃, 30℃, and 35℃ respectively; and measure the residual chlorine dioxide concentration value C at 0h, 2h, 4h, 6h, 8h, 10h, 12h, 24h, 36h, 48h, 60h, and 72h, and each measurement is performed 3 times; the concentration time series of each water temperature is made into ln(C t / C0)~t curve, the slope of the curve is K d It is the attenuation coefficient of chlorine dioxide at this water temperature, that is, the attenuation rate constant; according to the Arrhenius model, the reaction rate constant K b The following relationship exists between the Kelvin temperature T:

[0083]

[0084] Where A is the pre-exponential factor, also known as the Arrhenius constant; R is the molar gas constant; E a represents the experimental activation energy, which can generally be regarded as a constant independent of temperature;

[0085] 4. Construction of water quality prediction model: Based on the mathematical expression of the Arrhenius model obtained in the previous step, the corresponding chlorine dioxide attenuation coefficient can be solved when the water temperature is known. The attenuation coefficient and the amount of chlorine dioxide added at the inlet of the clear water tank are input into the hydraulic model of the clear water tank to obtain the change of chlorine dioxide concentration at the outlet of the clear water tank.

[0086] The optimization model for the dosage of the clean water tank in this embodiment is an algorithm developed based on the prediction model of the chlorine dioxide concentration at the outlet of the clean water tank, which can solve the minimum dosage that meets a certain target concentration at the outlet of the clean water tank. The solution process is:

[0087] First, the chlorine dioxide attenuation coefficient corresponding to the current water temperature is input into EPANET;

[0088] Then, the chlorine dioxide concentration was input into the EPANET model in ascending order within the range of 0 to 1 mg / L to simulate water quality for a certain period of time;

[0089] And determine whether the chlorine dioxide concentration at the water outlet is all higher than the target concentration;

[0090] Finally, the minimum dosage that meets the requirements is output;

[0091] In this embodiment, the chlorine dioxide concentration prediction model at the end of the pipeline network is an LSTM neural network model built based on the Self Attention mechanism, which predicts the chlorine dioxide concentration at the end of the pipeline network according to the chlorine dioxide concentration of the outgoing water and the water temperature data at the end of the pipeline network.

[0092] like Figure 3 As shown, Figure 3 This is a schematic diagram of the Self Attention-LSTM neural network structure. The Self Attention mechanism is used to build an optimized LSTM neural network structure. The specific process is as follows:

[0093] Assuming that m indicators are used as features, the data at time i can be represented by vector X i =[x1,x2,x3,...,x m ] means that if the size of the moving window is n, the i+1 data point needs to be predicted;

[0094] Then the data n×m matrix [X i-n+1 ,X i-n+2 ,X i-n+3 ,...,X i ];

[0095] Then it enters the LSTM hidden layer, and the hidden layer outputs the weight vector H=[h1,h2,h3,...,h n ];

[0096] The weight vector H of the hidden layer output is [h1,h2,h3,...,h n ] Enter the Self Attention mechanism layer to redistribute weights;

[0097] The Self Attention mechanism is a technique for capturing the relationship between different parts of an input sequence.

[0098] It determines which parts are more important in a given task by calculating the attention weights between each element and other elements. This mechanism enables the model to focus on the most relevant information in the input sequence.

[0099] The self-attention mechanism helps to better understand the dependencies and patterns of input data in time series data prediction.

[0100] For example, some data points may be more critical to predicting future values, while other data points may be relatively unimportant. The Self Attention mechanism can help the model automatically identify these important data points. In this process, the features with high correlation will be strengthened, and a new weight vector A = [a1, a2, a3, ..., a n ]. Finally, the fully connected layer transforms the dimension of vector H to obtain the predicted value X i+1 .

[0101] like Figure 4 As shown, Figure 4 It is a comparison chart between the predicted results of the actual pipeline network terminal chlorine dioxide concentration prediction model and the actual value.

[0102] This model uses the Sigmod function as the activation function of the LSTM unit and the ReLU function as the activation function of the output layer.

[0103] During the training phase of the model, after each batch of training data is fed into the model, the predicted value is output through forward propagation, and then the loss function calculates the error between the predicted value output by the model and the true value, that is, the loss value.

[0104] Afterwards, the model uses back propagation to update the parameters in the neural network model based on the loss value, thereby reducing the loss between the true value and the predicted value, so that the predicted value output by the model continues to approach the true value. This is the process of model learning.

[0105] The specific construction steps are as follows:

[0106] 1. Equipment installation and data collection: Online monitoring equipment is installed at the water plant outlet and the end of the pipe network to collect chlorine dioxide concentration data and water temperature data at the water plant outlet and the end of the pipe network.

[0107] 2. Data preprocessing: The processing flow includes identification and replacement of outliers, data denoising, and data normalization.

[0108] 3. Dataset division: The dataset is divided into training set, validation set and test set in order according to the ratio of 64%, 16% and 20%. The training set is used to train the neural network model, and then the validation set is used to evaluate the effectiveness of the model, so as to select the model with the best effect.

[0109] 4. Model training: Input the data set into the Self Attention-LSTM neural network model, adjust the model's hyperparameters such as the sliding window size, the number of hidden layer neurons, and the training batch size, compare and select combinations with better performance parameters, and complete the determination of the model's hyperparameters.

[0110] like Figure 5 As shown, Figure 5 The figure is a flow chart of the optimization model of the dosage of the clear water tank, and the optimization model of the dosage of the clear water tank is specifically carried out according to the following steps:

[0111] At the beginning, set the chlorine dioxide dosage x of the clean water tank: 0mg / L, the chlorine dioxide control target C at the outlet of the clean water tank, and the chlorine dioxide decay rate constant K b ;

[0112] Determine whether the amount of chlorine dioxide added to the clean water tank satisfies the following relationship: x<Th 投 , if no, then output the chlorine dioxide dosage; if yes, then go to the next step;

[0113] In this embodiment, Th 投 Set to 1mg / L; (Th 投 The value can be 0.5~1mg / L according to the actual project)

[0114] The prediction results are calculated by using the clear water tank chlorine dioxide concentration prediction model;

[0115] Output the predicted result y of chlorine dioxide concentration at the outlet of the clean water tank;

[0116] Determine whether the prediction result satisfies the following relationship: y ≥ C. If not, increase the chlorine dioxide dosage by a preset value; if yes, output the chlorine dioxide dosage and then end.

[0117] Among them, C is the chlorine dioxide control target at the outlet of the clean water tank;

[0118] The preset value can be set to 0.01 mg / L; (depending on the dosage accuracy of the water plant, it can be set to 0.005-0.1 mg / L)

[0119] like Figure 6 As shown, Figure 6 This is a flowchart of the optimization model for the concentration of chlorine dioxide in factory water. The optimization model for the concentration of chlorine dioxide in factory water is specifically carried out in the following steps:

[0120] At the beginning, the chlorine dioxide concentration x of the factory water is set as the target concentration at the outlet. In this example, the target concentration at the outlet is set to 0.1 mg / L. Other parameters: current monitoring value;

[0121] Determine whether the chlorine dioxide concentration of the factory water satisfies the following relationship: x<Th1. If not, output the chlorine dioxide concentration of the factory water; if yes, proceed to the next step; Th1 represents the preset concentration value of chlorine dioxide of the factory water; Th1 in this embodiment is set to 0.8 mg / L;

[0122] The prediction results are calculated through the network terminal chlorine dioxide concentration prediction model (Attention-LSTM prediction model);

[0123] Output the predicted result y of the terminal chlorine dioxide concentration;

[0124] Determine whether the prediction result satisfies the following relationship: y≥Th2. If not, change the chlorine dioxide concentration of the factory water. In this embodiment, the chlorine dioxide concentration of the factory water is increased by 0.01 mg / L. If yes, output the chlorine dioxide concentration of the factory water, and then end. Th2 is represented by a preset predicted concentration value; Th2 in this embodiment is set to 0.2 mg / L.

[0125] like Figure 7 As shown, Figure 7 This is a graph of real water quality data at the outlet of a water plant and at the end of a pipe network on a certain day in an actual project. The upper blue curve represents the residual chlorine concentration in the water at the outlet of the water plant, and the lower red curve represents the residual chlorine concentration in the water at the end of the pipe network. The water quality data of the pipe network on this day can be divided into two time periods according to the characteristics of the change in chlorine dioxide concentration. The chlorine dioxide dosage in time period 1 is relatively low, and the chlorine dioxide concentration of the factory water and the terminal chlorine dioxide concentration generally fail to meet the standards. After the factory water concentration is increased in time period 2, the terminal chlorine dioxide concentration increases accordingly, with the highest value approaching 0.16 mg / L, which is much higher than the specified value of 0.02 mg / L.

[0126] like Figure 8 As shown, Figure 8 This is the simulation result of the water quality prediction module after model optimization. The water quality prediction module can adjust the chlorine dioxide concentration of the outgoing water in time according to the current working conditions of the pipe network, and stably control the terminal chlorine dioxide concentration at around 0.02 mg / L. The terminal water qualification rate has increased from 22.6% to 88.9%, greatly improving the accuracy of disinfectant addition.

[0127] The factory water chlorine dioxide concentration optimization model is an algorithm developed based on the pipeline network terminal chlorine dioxide concentration prediction model, which can solve the minimum factory water chlorine dioxide concentration that can ensure that the pipeline network terminal chlorine dioxide concentration meets the standard under the current pipeline network conditions. The solution process is:

[0128] In the range of 0.1-0.8 mg / L, the chlorine dioxide concentration of the factory water is input into the SelfAttention-LSTM model from small to large for prediction until the chlorine dioxide concentration at the end of the pipeline network in the output prediction result is completely qualified. At this time, the input chlorine dioxide concentration of the factory water is the minimum value.

[0129] The dosing control module includes a front chlorination module located at the inlet of the clean water tank and a post chlorination module at the outlet of the clean water tank, and controls the dosing amount of the two points according to the solution result of the optimization algorithm.

[0130] This embodiment constructs a chlorine dioxide concentration prediction model for the clear water tank outlet, water plant outlet, and terminal point of the pipe network in the water supply system. It can accurately predict the chlorine dioxide concentrations at the clear water tank outlet and the terminal point of the pipe network based on the current operating conditions of the water supply system, and solve the minimum dosage that can ensure that the concentration parameters of the disinfectant at the terminal of the pipe network meet the standards, provide theoretical guidance and basis for the addition of disinfectants in the water supply plant, and adjust the dosage of the dosing equipment in real time to achieve intelligent and precise addition, reducing the risk of microbial growth and excessive disinfection by-products in the water supply system.

[0131] The water supply plant in this embodiment is mainly used for small water plants, and the small water plant refers to a water plant that meets the requirements of the "Technical Specifications for Standardization and Improvement of Small Rural Water Supply Projects" (SL / T 825-2024) issued by the Ministry of Water Resources.

[0132] Example 2

[0133] The method for intelligently adding chlorine dioxide disinfectant to a water supply plant provided in this embodiment includes the following steps:

[0134] Obtain the chlorine dioxide concentration at the outlet of the clean water tank and the water temperature at the end of the pipe network, the outlet of the clean water tank includes the outlet of the clean water tank, the outlet of the water plant, and the end of the pipe network, and use the collected data to build a chlorine dioxide concentration prediction model and control the chlorine addition amount in real time;

[0135] Construct a chlorine dioxide concentration prediction model, calculate the dosage required by the pre-chlorination equipment and the post-chlorination equipment under the current working conditions through the chlorine dioxide concentration prediction model, and control the dosing equipment to automatically add;

[0136] The dosing equipment includes a front chlorination equipment arranged at the front end of the clean water tank and a rear chlorination equipment arranged at the rear end of the clean water tank. The front end of the clean water tank is the clean water tank entrance, and the rear end of the clean water tank is the water collection well, that is, the front chlorination and the rear chlorination are respectively performed at the clean water tank entrance and the water collection well;

[0137] The dosage required for the post-chlorination equipment is calculated in the following manner: the minimum chlorine dioxide concentration of the factory water is obtained, and the difference between the minimum chlorine dioxide concentration of the factory water and the chlorine dioxide concentration value at the outlet of the clear water tank after chlorination by the pre-chlorination equipment is calculated, and the difference is the dosage required for the post-chlorination equipment.

[0138] The above-described embodiments are only preferred embodiments for fully illustrating the present invention, and the protection scope of the present invention is not limited thereto. Equivalent substitutions or changes made by those skilled in the art based on the present invention are within the protection scope of the present invention. The protection scope of the present invention shall be subject to the claims.

Claims

1. Intelligent dosing system of chlorine dioxide disinfectant in water supply plant, characterized by: Including chlorine dioxide online monitoring equipment, intelligent management platform, and dosing equipment; The chlorine dioxide online monitoring device is arranged at the water outlet, which includes the outlet of the clean water tank, the outlet of the water plant, and the end of the pipe network, and is used to obtain the chlorine dioxide concentration at the water outlet and the water temperature at the end of the pipe network, and the collected data is used to construct a chlorine dioxide concentration prediction model and to control the chlorine addition amount in real time; The intelligent management platform calculates the dosage required for the pre-chlorination device and the post-chlorination device respectively through the chlorine dioxide concentration prediction model, and controls the dosing device to add the dosage; The dosing equipment includes a front chlorination equipment arranged at the front end of the clear water tank and a rear chlorination equipment arranged at the rear end of the clear water tank. The front end of the clear water tank is the clear water tank entrance, and the rear end of the clear water tank is a water collection well, and the front chlorination and the rear chlorination are performed respectively.

2. The intelligent chlorine dioxide disinfectant dosing system for a water supply plant as claimed in claim 1, characterized in that: The intelligent management platform uses a chlorine dioxide concentration prediction model to calculate the dosage required for the previous chlorination equipment based on the current water temperature and the chlorine dioxide concentration at the outlet of the clear water tank as the target concentration.

3. The intelligent chlorine dioxide disinfectant dosing system for a water supply plant according to claim 1, characterized in that: The difference between the minimum chlorine dioxide concentration of the factory water output by the intelligent management platform and the chlorine dioxide concentration value at the outlet of the clear water tank after chlorination by the pre-chlorination equipment is used as the dosage required for the post-chlorination equipment.

4. The intelligent chlorine dioxide disinfectant dosing system for a water supply plant according to claim 1, characterized in that: The intelligent management platform includes a chlorine dioxide concentration prediction model at the outlet of the clear water tank, a clear water tank dosage optimization model, a chlorine dioxide concentration prediction model at the end of the pipe network, a chlorine dioxide concentration optimization model for factory water, and a dosage control module.

5. The intelligent chlorine dioxide disinfectant dosing system for a water supply plant as claimed in claim 4, characterized in that: The chlorine dioxide concentration prediction model at the outlet of the clear water tank is constructed according to the following steps: Get the parameters of the water supply system; Constructing a hydraulic model of a clear water tank, the hydraulic model of the clear water tank comprising a first node, a second node, a third node, and a fourth node; the first node is used to set the concentration of a chlorine dioxide disinfectant put into the clear water tank; the second node is used to control the water inlet of the clear water tank; the third node is used to set the size parameters and the mixing model of the clear water tank; the fourth node is used to set the water consumption of the outlet of the clear water tank; Determine the chlorine dioxide attenuation coefficient; A water quality prediction model was constructed, and the chlorine dioxide attenuation coefficient and the amount of chlorine dioxide added at the clear water tank inlet were input into the clear water tank hydraulic model to obtain the predicted value of chlorine dioxide concentration at the clear water tank outlet.

6. The intelligent chlorine dioxide disinfectant dosing system for a water supply plant as claimed in claim 4, characterized in that: The clean water tank dosage optimization model is to calculate the minimum dosage that meets a certain target concentration at the clean water tank outlet through the clean water tank outlet chlorine dioxide concentration prediction model, and the calculation is specifically performed according to the following steps: First, the chlorine dioxide attenuation coefficient corresponding to the current water temperature is input into EPANET; Then, the chlorine dioxide concentration was input into the EPANET model in ascending order within the range of 0 to 1 mg / L to simulate water quality for a certain period of time; Determine whether the chlorine dioxide concentration at the outlet of the clean water tank is higher than the target concentration; Output the minimum dosage that meets the requirements.

7. The intelligent chlorine dioxide disinfectant dosing system for a water supply plant as claimed in claim 4, characterized in that: The chlorine dioxide concentration prediction model at the end of the pipe network is an LSTM neural network model built based on the Self Attention mechanism, which predicts the chlorine dioxide concentration at the end of the pipe network according to the chlorine dioxide concentration of the outgoing water and the water temperature data at the end of the pipe network.

8. The intelligent chlorine dioxide disinfectant dosing system for a water supply plant as claimed in claim 4, characterized in that: The factory water chlorine dioxide concentration optimization model is specifically carried out according to the following steps: At the beginning, the chlorine dioxide concentration x of the factory water is set as the target concentration at the outlet; Determine whether the chlorine dioxide concentration of the factory water satisfies the following relationship: x<Th1. If not, output the chlorine dioxide concentration of the factory water; if yes, proceed to the next step; Th1 represents the preset concentration value of chlorine dioxide of the factory water; The prediction results are calculated by using the chlorine dioxide concentration prediction model at the end of the pipe network; Output the predicted result y of the terminal chlorine dioxide concentration; Determine whether the prediction result satisfies the following relationship: y≥Th2. If not, change the chlorine dioxide concentration of the factory water; if yes, output the chlorine dioxide concentration of the factory water, and then end; Th2 represents the preset predicted concentration value.

9. A method for intelligently adding chlorine dioxide disinfectant to a water supply plant, characterized in that: The following steps are involved: Obtain the chlorine dioxide concentration at the outlet and the water temperature at the end of the pipe network, and use the collected data to build a chlorine dioxide concentration prediction model and control the chlorine addition amount in real time; Construct a chlorine dioxide concentration prediction model, calculate the dosage required by the pre-chlorination equipment and the post-chlorination equipment under the current working conditions through the chlorine dioxide concentration prediction model, and control the dosing equipment to automatically add; The dosing equipment includes a pre-chlorination device arranged at the front end of the clear water tank and a post-chlorination device arranged at the rear end of the clear water tank. The front end of the clear water tank is the clear water tank entrance, and the rear end of the clear water tank is a water collection well, that is, pre-chlorination and post-chlorination are performed at the clear water tank entrance and the water collection well respectively.

10. The method according to claim 9, wherein: The dosage required for the post-chlorination equipment is calculated in the following manner: the minimum chlorine dioxide concentration of the factory water is obtained, and the difference between the minimum chlorine dioxide concentration of the factory water and the chlorine dioxide concentration value at the outlet of the clear water tank after chlorination by the pre-chlorination equipment is calculated, and the difference is the dosage required for the post-chlorination equipment.

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