Method and system for optimizing disinfection process of waterworks

Abstract

The invention discloses a waterworks disinfection process optimization method and system, and the method comprises the steps: carrying out the polynomial fitting of collected unit consumption data of post-chlorination, and obtaining a frequency domain input through Laplace transformation; respectively inputting the input quantity into a pre-constructed library entering residual chlorine attenuation linear time-invariant system (LTI) and a factory leaving water residual chlorine attenuation LTI system to obtain residual chlorine response of the Laplace domain; and outputting a predicted reservoir entering residual chlorine value and a predicted factory leaving water residual chlorine value in a time domain through inverse Laplacian transformation, and dynamically adjusting a post-chlorination dosing strategy according to the predicted reservoir entering residual chlorine value and the predicted factory leaving water residual chlorine value. According to the method, data-driven modeling and frequency-domain system analysis are combined for the first time, the technical bottleneck of residual chlorine attenuation quantitative prediction in the internal process links of a water plant is broken through, precise closed-loop control over disinfectant adding is achieved, the operation cost is remarkably reduced while water quality safety (microbial reproduction and disinfection by-product generation are inhibited) is guaranteed, and the method is suitable for large-scale popularization and application. And core technical support is provided for intelligent water plant construction.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of waterworks disinfection process, and relates to a waterworks disinfection process optimization method and system. BACKGROUND

[0002] Water resources are key elements to ensure sustainable development. Chlorine disinfection has become the main means to ensure drinking water safety due to its safety, efficiency, low price, and easy preparation. In the water purification process, raw water needs to go through multiple links such as filter tank, clear water reservoir and water distribution network after being treated by the waterworks. The dynamic change of water flow rate leads to obvious fluctuation of residual chlorine concentration in finished water. If the residual chlorine concentration is too low, microorganisms are easy to multiply, which threatens human health. On the contrary, excessive addition of disinfectant will generate harmful disinfection by-products (DBPs) such as chloroform, which poses potential harm to health. Therefore, under the premise of ensuring water quality, precise control of disinfectant dosage, reduction of by-product generation and resource conservation have become the core needs of building green and intelligent waterworks.

[0003] However, the existing waterworks disinfectant dosage control faces significant challenges. Since residual chlorine online monitoring equipment cannot be deployed in the whole process, the concentration regulation effect of post-chlorination has serious hysteresis, and its influence cannot be reflected at the finished water outlet for a long time. At present, waterworks mostly rely on manual experience to determine the dosage, which lacks precise quantitative basis and is easy to cause waste of disinfectant, substandard water quality or excessive generation of by-products. Although residual chlorine prediction and decay model research is the focus in the field of water quality, the existing model still has obvious shortcomings: first, there is a lack of quantitative characterization model for the dynamic decay law of residual chlorine in the internal process links of waterworks (especially key nodes such as clear water reservoir and filter tank); second, the model structure is single and has weak adaptability, which makes it difficult to accurately simulate the residual chlorine change behavior under complex conditions.

[0004] In order to break through the above technical bottlenecks, it is urgent to build a prediction model that can accurately simulate the residual chlorine decay process in waterworks and adapt to various conditions, so as to realize fine and intelligent control of disinfectant dosage, reduce operation cost and health risk while ensuring water quality safety, and promote the upgrading of waterworks to safe, reliable and green intelligence. SUMMARY

[0005] The purpose of the present application is to solve the technical problems of the prior art, such as the lack of quantitative characterization model for the dynamic decay law of residual chlorine, the single structure and weak adaptability, and the difficulty in accurately simulating the residual chlorine change behavior under complex conditions, and to provide a waterworks disinfection process optimization method and system.

[0006] To achieve the above purpose, the following technical solutions are adopted in the present application: The first aspect of the present application provides a waterworks disinfection process optimization method, comprising the following steps: Polynomial fitting is performed on the collected post-chlorination unit consumption data; The fitting result is subjected to Laplace transformation to obtain post-chlorination unit consumption under Laplace transformation; The post-chlorination unit consumption under Laplace transformation is input into the reservoir residual chlorine decay linear time-invariant system and the finished water residual chlorine decay linear time-invariant system respectively to obtain the reservoir residual chlorine and the finished water residual chlorine under Laplace domain; The reservoir residual chlorine and the finished water residual chlorine under Laplace domain are subjected to inverse transformation to obtain the predicted reservoir residual chlorine and the predicted finished water residual chlorine under time domain; The post-chlorination data is adjusted according to the predicted reservoir residual chlorine and the predicted finished water residual chlorine.

[0007] Further, the construction method of the reservoir residual chlorine decay linear time-invariant system and the finished water residual chlorine decay linear time-invariant system is as follows: The collected post-chlorination unit consumption data, the reservoir residual chlorine data and the finished water residual chlorine data are subjected to periodic division respectively; The data of L periods of the post-chlorination unit consumption data, the reservoir residual chlorine data and the finished water residual chlorine data after periodic division are averaged respectively; The averages of the post-chlorination unit consumption data, the reservoir residual chlorine data and the finished water residual chlorine data of the L periods are fitted respectively to obtain post-chlorination unit consumption fitting curve, reservoir residual chlorine fitting curve and finished water residual chlorine fitting curve; The post-chlorination unit consumption fitting curve, the reservoir residual chlorine fitting curve and the finished water residual chlorine fitting curve are subjected to Laplace transformation respectively to generate the transfer function of the reservoir residual chlorine decay linear time-invariant system and the finished water residual chlorine decay linear time-invariant system.

[0008] Further, the transfer function of the reservoir residual chlorine decay linear time-invariant system is as follows:

[0009] wherein, is the Laplace transformation of the reservoir residual chlorine fitting curve; is the Laplace transformation of the post-chlorination unit consumption fitting curve.

[0010] Further, the transfer function of the finished water residual chlorine decay linear time-invariant system is as follows:

[0011] wherein, is the Laplace transformation of the finished water residual chlorine fitting curve; is the Laplace transformation of the post-chlorination unit consumption fitting curve.

[0012] Further, the periodic division adopts the method of taking minutes forward and minutes backward from the peak point of each period to form a length of The method for data segments.

[0013] Furthermore, the aforementioned The value of is the minimum of the lengths of all periodic decay segments.

[0014] Furthermore, the polynomial fitting employs the least squares method.

[0015] A second aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the waterworks disinfection process optimization method.

[0016] A third aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the waterworks disinfection process optimization method.

[0017] A fourth aspect of the present invention provides a disinfection process optimization system for waterworks, comprising: The data fitting module performs polynomial fitting on the collected post-chlorination unit consumption data; The Laplace transform module performs a Laplace transform on the fitting results to obtain the post-chlorination unit consumption under the Laplace transform. The prediction module inputs the post-chlorination unit consumption under Laplace transform into the linear time-invariant system of residual chlorine decay in the inlet and the linear time-invariant system of residual chlorine decay in the effluent, respectively, to obtain the residual chlorine in the inlet and the residual chlorine in the effluent in the Laplace domain. The time-domain transformation module performs an inverse transformation on the Laplace domain residual chlorine in the inlet and residual chlorine in the outlet water to obtain the predicted residual chlorine in the inlet and the predicted residual chlorine in the outlet water in the time domain. The post-chlorination optimization module adjusts the post-chlorination data based on the predicted residual chlorine levels in the influent and the predicted residual chlorine levels in the effluent.

[0018] Compared with the prior art, the present invention has the following beneficial effects: The application discloses a disinfection process optimization method for a waterworks, and realizes quantitative description of dynamic decay of residual chlorine in a process chain of the waterworks for the first time through a linear time-invariant system (LTI) model constructed by data driving; the existing residual chlorine decay model is mainly established for a water delivery pipe network, and a quantifiable residual chlorine decay prediction and simulation method has long been lacked for a water treatment link (especially a clear water reservoir storage stage) in the waterworks, and the application fills the processing blank of the link. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some of the embodiments of the application, and therefore should not be regarded as a limitation to the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0020] Figure 1 Figure 1 is a waterworks chlorination process flow diagram; Figure 2 Figure 2 is a linear time-invariant system model; Figure 3 Figure 3 is a linear time-invariant system model construction flow diagram; Figure 4 Figure 4 is a residual chlorine prediction curve; Figure 5 Figure 5 is a real-time residual chlorine prediction curve of the embodiment of the application; Figure 6 Figure 6 is a disinfectant dosing process diagram in a period; Figure 7 Figure 7 is a residual chlorine prediction curve under specific dosing conditions of the embodiment of the application. DETAILED DESCRIPTION

[0021] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some but not all of the embodiments of the present application. The components of the embodiments of the present application described and shown in the drawings can be arranged and designed in various different configurations.

[0022] Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed application, but only represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work are within the scope of protection of the present application.

[0023] It should be noted that: similar reference numbers and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0024] The present application will be further described in detail below with reference to the drawings: The present application provides a disinfection process optimization method for a waterworks, which is realized by a residual chlorine decay model with simple principle and easy implementation. The waterworks chlorination process is usually two-point chlorination of pre-chlorination and post-chlorination. After raw water enters the waterworks, the first chlorination (referred to as pre-chlorination or pre-chlorination) is performed to reduce algae and bacteria in the raw water. After coagulation, sedimentation and filtration, the raw water is stored in a clear water reservoir. In order to make the residual chlorine content of the finished water meet the requirements, disinfectant is added before entering the clear water reservoir (referred to as post-chlorination). After post-chlorination of raw water, the water enters the clear water reservoir through the pipeline for storage. The residual chlorine value entering the reservoir is measured before entering the clear water reservoir. After post-chlorination of raw water, the water enters the clear water reservoir through the pipeline for storage. After a period of time, the waterworks is opened to release water, and the drinking water enters the urban water supply pipeline network from the waterworks outlet. The residual chlorine value of the finished water is measured at the waterworks outlet.

[0025] The inventive concept of the present application is to establish two single-input single-output linear time-invariant system (LTI) models according to the characteristics and laws of post-chlorination and residual chlorine decay in water plants. Model A, a “residual chlorine value in reservoir prediction model”, takes the post-chlorination unit consumption of raw water as the system input and takes the residual chlorine value in the reservoir as the system output, which is a post-chlorination unit consumption-residual chlorine value LTI model. Model B, a “residual chlorine value in finished water prediction model”, takes the post-chlorination unit consumption of raw water as the system input and takes the residual chlorine value in finished water as the system output, which is a post-chlorination unit consumption-residual chlorine value in finished water LTI model. The residual chlorine value in reservoir prediction model realizes the prediction of the residual chlorine value in the reservoir, which helps to understand the intermediate link of the chlorination process in time and improves the prediction accuracy of the residual chlorine value in finished water. The residual chlorine value in finished water prediction model realizes the prediction and simulation of the residual chlorine value in finished water. The above two LTI models, both of which are functions of time, use the least square method to fit the time domain data collected from water plants to obtain the time domain functions of the linear system input and output, and then establish the mapping between the time domain and the Laplace domain by Laplace transformation. The transfer function of LTI is obtained by dividing the Laplace transform result of the system output by the Laplace transform result of the system input, the relationship between the input function space and the output function space is established, and the prediction and simulation of the residual chlorine value in finished water are realized.

[0026] The present application precisely controls the residual chlorine concentration in finished water, adjusts the disinfectant dosage under the premise of meeting the water quality standards, and realizes reasonable and green disinfectant dosage. The residual chlorine decay model proposed in the present application is a data-driven model, and the data obtained from the water plant includes post-chlorination unit consumption, residual chlorine value in the reservoir and residual chlorine value in finished water. The residual chlorine value in reservoir prediction and residual chlorine value in finished water prediction mathematical models are constructed according to the existing post-chlorination unit consumption, residual chlorine value in the reservoir and residual chlorine value in finished water data. Real-time prediction of the residual chlorine value in the reservoir and the residual chlorine value in finished water is realized, and the residual chlorine value in the reservoir and the residual chlorine value in finished water under different dosages are simulated to achieve the purpose of guiding and optimizing disinfectant dosage.

[0027] Specifically, the intelligent disinfection process optimization method for waterworks based on Laplace transformation proposed in the present application includes three stages. The first stage is the establishment of a linear time-invariant system model, the second stage is the prediction of residual chlorine using the established model, and the third stage is the realization of the residual chlorine simulation dosage function.

[0028] 1. First stage (model establishment stage) Step 1-1, determine the residual chlorine change period according to the post-chlorination unit consumption data, residual chlorine data in the reservoir and residual chlorine data in finished water collected from the water plant. After collecting the data, draw the time-post-chlorination unit consumption function C a ( t ), time-residual chlorine value in the reservoir function C i (t ), time- residual chlorine value of finished water function Co ( t ) curve, by observing the periodic peak of residual chlorine in the change of residual chlorine with time, the period is divided according to the peak, and the change curve of residual chlorine value is inferred C a ( t ), C i ( t ) and Co ( t ) periodic peak, according to the peak, the period is divided, and the change curve of residual chlorine value is inferred T (For example, in the case of only one peak per day, the residual chlorine change period T =24h=1440min).

[0029] Step 1-2, according to the results of step 1-1, the post-chlorination unit consumption data, the residual chlorine data of the warehouse, and the residual chlorine data of the finished water are divided by period. First, select the peak point of each period of C a ( t ), C i ( t ) and Co ( t ) function, and the residual chlorine sampling data between the peak point of one period and the peak point of the next period is regarded as the residual chlorine decay section of the water body. The length of the decay section is affected by the selection of the peak, in order to ensure the consistency of the data length of each period, the shortest decay section length m (min) is taken as the standard, and m minute sampling data is taken from the peak point of each period. Secondly, in order to ensure that the model can accurately predict the change of residual chlorine in a period, the sampling data of n minutes before the peak point of each period is taken as a supplement, so that the length of each data m + n reaches a period T .

[0030] Step 1-3, obtain the average value of the post-chlorination unit consumption data, the residual chlorine data of the warehouse, and the residual chlorine data of the finished water in a period, and obtain the average function by using the least square method. According to step 2, take out the complete L period, and take the average value of C a ( t ), the residual chlorine value C i ( t ), and the residual chlorine value Co ( t ) of L periods, l indicates thel The average value of the chlorine residual in the water tank is calculated as follows:

[0031] To construct a linear time-invariant system, the transfer function expression of the system is obtained, and the time function expression of the average value is obtained using polynomial fitting. Since the variation trends of the chlorine residual in the water tank and the chlorine residual in the water distribution network are similar, a suitable order polynomial (e.g., 7) is selected to fit the chlorine residual in the water tank, and a suitable order polynomial (e.g., 6) is selected to fit the chlorine residual in the water distribution network. The fitting curve of the chlorine residual in the water tank with a period of 24 hours is as follows: The fitting curve of the chlorine residual in the water distribution network with a period of 24 hours is as follows: p p The fitting curve of the chlorine residual in the water tank with a period of 24 hours is as follows: q q T

[0032] The fitting results are measured by the root mean square error (RMSE) and the R-square. Let the actual value of the i-th original data obtained by sampling be denoted as xi, the average value of the actual value be denoted as x, and the i-th data obtained by fitting be denoted as yi, then the RMSE of the fitting result is expressed as follows: t t

[0033] The R-square is expressed as follows:

[0034] The RMSE is a value greater than zero, which is used to measure the average difference between the fitting value and the actual observed value, and the smaller the RMSE, the smaller the fitting error. R 2 The value of the R-square is in the range of [0, 1], which is used to measure the fitting degree of the regression model to the data, and the closer to 1, the better the fitting degree of the model to the data. To make the fitting effect best, the RMSE should be minimized, and the R-square should be closest to 1. Considering the actual calculation speed and feasibility, the order of the polynomial fitting is limited to within 10. p q

[0035] ​​​​​​​​​​​​​​​​​​Step 1-4, Establishing the linear time-invariant system model of the residual chlorine decay process. Establishing the LTI model of the residual chlorine in the reservoir and the LTI model of the residual chlorine in the finished water. Fitting the average value of the input and output of the LTI 、 、 Performing Laplace transform. For the polynomial t p , the result of its Laplace transform is an expression of the form , where p ! denotes the factorial of p , so the Laplace transform of is:

[0036] The Laplace transform of

[0037] The Laplace transform of

[0038] According to the characteristics of linear time-invariant systems, the transfer function of the post-chlorination dosage-residual chlorine in the reservoir LTI H into-reservoir ( s ) and the transfer function of the post-chlorination dosage-residual chlorine in the finished water LTI H out-plant ( s ) can be expressed as:

[0039] 2. Second stage (residual chlorine prediction stage) Step 2-1, post-chlorination data fitting. Use a p degree polynomial to fit the post-chlorination dosage data of a period to obtain its function about time :

[0040] Step 2-2, Laplace transform of post-chlorination data. Perform Laplace transform on to obtain:

[0041] Step 2-3, find the Laplace domain output of the two linear systems obtained in the first stage. Let the predicted value of the residual chlorine in the reservoir be , its Laplace transform is , and use the linear system transfer function obtained in step 1-4 H into-reservoir ( s), the relationship between the input and output of the linear system is obtained as follows:

[0042] The predicted value of the residual chlorine in the outflow water is set as , and the Laplace transform is , and the linear system transfer function obtained in steps 1-4 is used H out-plant ( s ), the relationship between the input and output of the linear system is obtained as follows:

[0043] Step 2-4, the time-domain outputs of the two linear systems obtained in the first stage are obtained. The inverse Laplace transform is performed on , obtained in step 2-2, and the predicted value of the residual chlorine in the incoming water and the predicted value of the residual chlorine in the outflow water are obtained:

[0044] The above two stages realize the prediction of the residual chlorine value in the incoming water and the residual chlorine value in the outflow water. In the model training stage, the training set is used to establish a linear time-invariant system model of residual chlorine decay in the first stage (steps 1-1 to 1-4), and the test set is used to evaluate the model training results in the second stage (steps 2-1 to 2-4), so as to verify the correctness and feasibility of the model.

[0045] Based on the above principle, by constructing the relationship between the post-chlorination unit consumption and the residual chlorine value in the incoming water, and the relationship between the post-chlorination unit consumption and the residual chlorine value in the outflow water, the real-time prediction function of the residual chlorine value can be realized for the demand of intelligent water management. After collecting the historical post-chlorination unit consumption, residual chlorine value in the incoming water, and residual chlorine value in the outflow water data of the water plant, the linear time-invariant system model of residual chlorine decay of the water plant is constructed according to the first stage. According to the second stage, the newly collected post-chlorination unit consumption value of one period is used as the input of the model, and the corresponding residual chlorine value in the incoming water and the residual chlorine value in the outflow water in one period can be predicted.

[0046] 3. Third stage (residual chlorine simulation stage) Step 3-1, Set up the disinfectant dosage simulation. To achieve the simulation dosage function, first analyze the chlorination process of the water plant. The dosage of disinfectant is stored in the dosage tank, enters the water pipe through the filling pipe, and the valve size of the filling pipe is controlled by the electromagnetic flow valve, so as to adjust the disinfectant flow. The water treatment process usually maintains a benchmark disinfectant dosage throughout the day. When the water demand increases, it is necessary to maintain an increased disinfectant dosage within a certain time to ensure that the treated water distributed from the water plant meets the water quality (especially residual chlorine) standards. Since the dosage of disinfectant will change randomly with the flow of treated water, a series of rectangular pulses with adjustable starting time, width and amplitude can be used to describe the random dosage process of disinfectant in a period. The simulated disinfectant dosage is shown in Figure 7 .

[0047] Let u 0 be the baseline dosage value, u k ( k = 1, 2,..., K ) be the incremental disinfectant dosage starting at time t k and lasting for ∆k time. The disinfectant dosage in an operation cycle (0 ≤ t ≤ T ) can be expressed as

[0048] where rect ∆k(t) represents a rectangular function starting at time 0 with unit amplitude and width ∆k .

[0049] Step 3-2, Laplace transform of the disinfectant dosage simulation function. The Laplace transform of the time-shifted version of the rect ∆k(t) function is:

[0050] where is the unit step function. Therefore, the Laplace transform of is

[0051] Step 3-3, obtain the Laplace domain output of the linear system in the first stage. After obtaining the Laplace transform of the simulation dosage function , similarly, use the method in step 2-3 in the second stage to obtain the Laplace domain output of the in-plant residual chlorine prediction LTI model and the Laplace domain output of the plant effluent residual chlorine prediction LTI model when the input is .

[0052] Step 3-4, obtain the time-domain output of the linear system obtained in the first stage. Similar to the method of Step 2-4 in the second stage, the Laplace inverse transform is used to obtain the time-domain output of the residual chlorine prediction LTI model of the incoming water and the residual chlorine prediction LTI model of the finished water when the input is , respectively, to obtain the residual chlorine value of the incoming water and the residual chlorine value of the finished water, thereby realizing the simulation of the residual chlorine addition function. Through the above scheme, the chlorine addition amount can be adjusted according to the current residual chlorine value of the finished water or the residual chlorine value of the incoming water, so as to achieve the purpose of guiding the chlorine addition.

[0053] In order to illustrate the superiority of the present application, an embodiment of the present application is realized based on a certain water plant, Figure 1 which is a schematic diagram of the chlorine addition process of the water plant. The raw water enters the water plant inlet, is subjected to pre-chlorination, and then is subjected to a series of water treatment processes such as filtration, is subjected to post-chlorination before entering the clear water reservoir for storage, and finally enters the urban pipe network through the water plant outlet. The water plant is provided with a residual chlorine meter A after the post-chlorination addition point, which is used to measure the residual chlorine value of the incoming water; and is provided with a residual chlorine meter B at the water plant outlet, which is used to measure the residual chlorine value of the finished water.

[0054] According to the above chlorine addition process, in order to realize the prediction and simulation method of the residual chlorine of the water plant, two linear time-invariant system models can be constructed, as shown in Figure 2 . The post-chlorination unit consumption C a ( t ) is taken as the system input, the residual chlorine prediction LTI model of the incoming water takes the residual chlorine value C i ( t ) as the system output; and the residual chlorine prediction LTI model of the finished water takes the residual chlorine value of the finished water C o ( t ) as the system output. The residual chlorine prediction LTI model of the incoming water realizes the prediction of the residual chlorine value of the incoming water, and the residual chlorine prediction LTI model of the finished water realizes the prediction and simulation of the residual chlorine of the finished water. According to the reaction rule of the residual chlorine in the water body, the residual chlorine value of the finished water should be less than the residual chlorine value of the incoming water, that is, the output of the residual chlorine prediction LTI model of the finished water should be less than the output of the residual chlorine prediction LTI model of the incoming water. Therefore, by comparing the output values of the two models, the accuracy of the prediction and simulation results of the linear system model can be directly judged. The specific model building, residual chlorine prediction, and residual chlorine simulation function realization process are shown in Figure 3 .

[0055] To verify the effectiveness of the method, a month of residual chlorine data collection was carried out in a water plant, the sampling interval was 5 minutes, the total sampling time was 43200 minutes, the length of the post-chlorination unit consumption, the residual chlorine in the reservoir and the residual chlorine in the finished water data was 8640 points, each data could be divided into 30 periods, which was used to establish the post-chlorination unit consumption-residual chlorine in the reservoir value and the post-chlorination unit consumption-residual chlorine in the finished water value LTI model of the water plant. The specific method realized by the application is described as follows: 1. Model establishment First, observe the collected data, the residual chlorine of the water plant appears a peak value once a day, because the daily peak addition starts at about 5 to 6 am, the change period of the residual chlorine of the water plant is T =24 hours. According to the peak value, 30 periods of post-chlorination unit consumption C a ( t ), residual chlorine value C i ( t ), residual chlorine value C o ( t ) data are obtained, and the length of each data is 288. Randomly select L =25 periods of C a ( t ), C i ( t ), C o ( t ) data as the training set, and the remaining 5 periods as the test set. The average value of the 25 periods of the training set C a ( t ), C i ( t ), C o ( t ) is obtained:

[0056] To construct a linear time-invariant system, a 7th order polynomial is selected to fit , , a 6th order polynomial is selected to fit , and the post-chlorination unit consumption fitting curve , the residual chlorine value fitting curve and the residual chlorine value fitting curve of the finished water of the water plant with a period of 24 hours are obtained:

[0057] Perform a Laplace transform on the input and output of the LTI:

[0058] Calculate the transfer functions of the two LTIs respectively. H into-reservoir ( s )and H out-plant ( s Two linear system models for residual chlorine were established: one for post-chlorination unit consumption minus influent residual chlorine, and the other for post-chlorination unit consumption minus effluent residual chlorine.

[0059] 2. Residual chlorine prediction The post-chlorination consumption values ​​for one cycle in the test set were fitted using a 7th-order polynomial to obtain the post-chlorination data to be predicted. Perform a Laplace transform on it, and then... Multiply by respectively H into-reservoir ( s )and H out-plant ( s Then, by performing the inverse Laplace transform on each of them, we can obtain the results. Predicted residual chlorine values ​​for the same length entering the warehouse Or the predicted value of residual chlorine in the treated water The prediction results are as follows: Figure 4 As shown, Figure 4 (a) using predict , Figure 4 (b) uses predict Based on this, the implemented real-time prediction function is as follows: Figure 5 As shown. Figure 5 The leftmost figure shows the chlorination consumption for three cycles, with the latest cycle used as the system input. Figure 5 The rightmost image shows 289 data points for that period, plotted as black scatter dots. The input was fitted, and the fitting result is shown below. Figure 5 As shown by the green curve in the rightmost column, Figure 5 The second and third images from the left show the influent and effluent residual chlorine values ​​for three different cycles, respectively. It can be seen that there is a significant delay between post-chlorination and the influent and effluent residual chlorine values. Water plant dispatchers can see the influent residual chlorine value for the day. Figure 5 The purple curves in the second right figure and the predicted residual chlorine value of the treated water ( Figure 5 The orange curve in the second image from the right serves as an early warning.

[0060] 3. Residual chlorine simulation Based on the relationship between the constructed post-chlorination unit consumption and the residual chlorine value of the incoming reservoir, the post-chlorination unit consumption and the residual chlorine value of the finished water, a simulation dosing function is designed for the demand of intelligent water affairs. The schematic diagram of the disinfectant dosing process in a cycle is shown in Figure 6 In the embodiment, the parameters that can be set for the simulation of specific dosing are shown in Figure 7 The left side, the water plant dispatchers can select the peak dosing amount, the basic dosing amount, the peak dosing start time and the peak dosing duration on the interactive interface according to the needs to simulate the dosing, Figure 7 The right one is the simulation post-chlorination curve displayed after setting the parameters, Figure 7 The right two and the right three are the prediction of the residual chlorine curve of the incoming reservoir and the residual chlorine curve of the finished water in the day under the set dosing condition, both of which are 24 hours long. The simulation dosing can obtain the residual chlorine curve of the incoming reservoir and the residual chlorine curve of the finished water under the corresponding dosing condition, and predict the dosing process in a cycle. Through the scheme, the post-chlorination dosing amount can be adjusted through the current residual chlorine value of the outlet or the residual chlorine value of the incoming reservoir, so as to achieve the purpose of guiding the chlorination.

[0061] An embodiment of the present application provides a tap water plant disinfection process optimization system, comprising: A data fitting module is configured to perform polynomial fitting on the collected post-chlorination unit consumption data; A Laplace transform module is configured to perform Laplace transform on the fitting result to obtain post-chlorination unit consumption under Laplace transform; A prediction module is configured to input the post-chlorination unit consumption under Laplace transform into an incoming reservoir residual chlorine decay linear time-invariant system and a finished water residual chlorine decay linear time-invariant system respectively to obtain incoming reservoir residual chlorine and finished water residual chlorine in the Laplace domain; A time domain transform module is configured to perform inverse transform on the incoming reservoir residual chlorine and the finished water residual chlorine in the Laplace domain to obtain predicted incoming reservoir residual chlorine and predicted finished water residual chlorine in the time domain; A post-chlorination optimization module is configured to adjust post-chlorination data according to the predicted incoming reservoir residual chlorine and the predicted finished water residual chlorine.

[0062] The application provides an electronic device in one embodiment, the electronic device includes a processor and a memory, the memory is used for storing a computer program, the computer program includes program instructions, and the processor is used for executing the program instructions stored by the computer storage medium. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components and the like, which are the computing core and control core of the terminal, and are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions to implement a corresponding method flow or corresponding function; the processor in the embodiment of the application can be used for the operation of the disinfection process optimization method of the waterworks.

[0063] In another embodiment of the application, the application further provides a storage medium, specifically a computer readable storage medium (Memory), the computer readable storage medium is a memory device in a terminal device, and is used for storing programs and data. It can be understood that the computer readable storage medium herein can include a built-in storage medium in the terminal device, and of course can include an expansion storage medium supported by the terminal device, and can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, device or apparatus. The computer readable storage medium provides a storage space, and the storage space stores an operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and the instructions can be one or more computer programs (including program codes). It should be noted that more specific examples (non-exhaustive list) of the computer readable storage medium include an electrical connection with one or more wires, a portable disc, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0064] Computer readable storage media further includes data signals transported through a carrier wave and a communications medium, in which the tangible storage medium holds program code. Such a delivered data signal can take a variety of forms, including, but not limited to, electro-magnetic, optical or any suitable combination thereof. Program code transmitted using a carrier wave can be downloaded into the operating memory of a digital processing unit from the data signals embodied in such a carrier wave, such as for example when downloaded by using the Internet from an internet website using an Internet browser. The program code can be transmittable by using a wireless or wired connection, using for example a wireless access protocol, such as Bluetooth, Wi-Fi, or WiMax, or using a wired access protocol, such as Ethernet, USB, IEEE 1394 or a serial connection, or using any suitable combination thereof.

[0065] The program code may, through the processing unit, be applied to translate, execute or interpret the instructions and statements of a program to perform the methods described herein and achieve the results stated or to be expected by the technology. The program code can also be written as one or more algorithms, which can be implemented in any suitable computer readable medium, including but not limited to semiconductor, magnetic or optical diskettes, punched cards or paper tape, magnetic cassette, RAM, ROM, FLASH memory, a network connection, or any suitable combination thereof.

[0066] The program code may, through the processing unit, be applied to translate, execute or interpret the instructions and statements of a program to perform the methods described herein and achieve the results stated or to be expected by the technology. The program code can also be written as one or more algorithms, which can be implemented in any suitable computer readable medium, including but not limited to semiconductor, magnetic or optical diskettes, punched cards or paper tape, magnetic cassette, RAM, ROM, FLASH memory, a network connection, or any suitable combination thereof.

[0067] The above merely provides the preferred embodiments of the present application and is not intended to limit the present application. The present application can be variously changed and modified by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall fall within the scope of the present application.

Claims

1. A method for optimizing a disinfection process in a water treatment plant, characterized in that, The method comprises the following steps: polynomial fitting is performed on the collected post-chlorination unit consumption data; Laplace transformation is performed on the fitting result to obtain post-chlorination unit consumption in the Laplace domain; the post-chlorination unit consumption in the Laplace domain is input into a reservoir residual chlorine decay linear time-invariant system and a finished water residual chlorine decay linear time-invariant system respectively to obtain reservoir residual chlorine and finished water residual chlorine in the Laplace domain; inverse transformation is performed on the reservoir residual chlorine and the finished water residual chlorine in the Laplace domain to obtain predicted reservoir residual chlorine and predicted finished water residual chlorine in the time domain; the post-chlorination data are adjusted according to the predicted reservoir residual chlorine and the predicted finished water residual chlorine.

2. The method of optimizing a waterworks disinfection process according to claim 1, wherein, The construction method of the reservoir residual chlorine decay linear time-invariant system and the finished water residual chlorine decay linear time-invariant system comprises the following steps: periodic division is performed on the collected post-chlorination unit consumption data, reservoir residual chlorine data and finished water residual chlorine data respectively; the data of L periods of the post-chlorination unit consumption data, the reservoir residual chlorine data and the finished water residual chlorine data after periodic division are averaged respectively; the averages of the L periods of the post-chlorination unit consumption data, the reservoir residual chlorine data and the finished water residual chlorine data are fitted respectively to obtain post-chlorination unit consumption fitting curves, reservoir residual chlorine fitting curves and finished water residual chlorine fitting curves; Laplace transformation is performed on the post-chlorination unit consumption fitting curves, the reservoir residual chlorine fitting curves and the finished water residual chlorine fitting curves respectively to generate transfer functions of the reservoir residual chlorine decay linear time-invariant system and the finished water residual chlorine decay linear time-invariant system.

3. The method of optimizing a waterworks disinfection process according to claim 2, wherein, The transfer function of the reservoir residual chlorine decay linear time-invariant system is: wherein is the Laplace transform of the fit curve for the residual chlorine in the tank; is the Laplace transform of the fit curve for the chlorine make-up consumption.

4. The method of optimizing a waterworks disinfection process according to claim 2, wherein, The transfer function of the finished water residual chlorine decay linear time-invariant system is: wherein, Laplace transform of the fitted curve for residual chlorine in finished water; Laplace transform of the fitted curve for chlorine dosage.

5. The method of optimizing a waterworks disinfection process according to claim 2, wherein, The periodic division takes the peak point of each period forwardly and backwardly for 1 minute, forming a data segment with a length of .

6. The method of optimizing a waterworks disinfection process according to claim 5, wherein, The is the minimum value of all periodic decay segment lengths.

7. The method of optimizing a waterworks disinfection process according to claim 1 or 2, characterized in that, The polynomial fitting adopts a least square method.

8. An electronic device, comprising: The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the waterworks disinfection process optimization method according to any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the waterworks disinfection process optimization method according to any one of claims 1-7.

10. A disinfection process optimization system for a waterworks, characterized in that, The method comprises: a data fitting module, which performs polynomial fitting on the collected post-chlorination unit consumption data; a Laplace transformation module, which performs Laplace transformation on the fitting result to obtain post-chlorination unit consumption in the Laplace domain; a prediction module, which inputs the post-chlorination unit consumption in the Laplace domain into a reservoir residual chlorine decay linear time-invariant system and a finished water residual chlorine decay linear time-invariant system respectively to obtain reservoir residual chlorine and finished water residual chlorine in the Laplace domain; a time domain transformation module, which performs inverse transformation on the reservoir residual chlorine and the finished water residual chlorine in the Laplace domain to obtain predicted reservoir residual chlorine and predicted finished water residual chlorine in the time domain; a post-chlorination optimization module, which adjusts post-chlorination data according to the predicted reservoir residual chlorine and the predicted finished water residual chlorine.

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