Pipe network water body iron ion dissolution amount prediction method and device, equipment, storage medium

Abstract

This application provides a method, apparatus, equipment, and storage medium for predicting the amount of iron ions dissolved in pipeline water, belonging to the field of pipeline corrosion protection technology. The method includes: acquiring pipeline water source quality data and pipeline water residence time, the pipeline water source quality data including chloride ion concentration, alkalinity, temperature, pH value, and sulfate concentration; if the pipeline water source quality data and pipeline water residence time meet a first prediction condition, then based on chloride ion concentration, alkalinity, and temperature, the amount of iron ions dissolved is predicted using a first iron ion dissolution model; the first prediction condition includes alkalinity less than a first alkalinity threshold, pipeline water residence time greater than a duration threshold, chloride ion concentration less than a chloride ion concentration threshold, pH value greater than a pH threshold, and sulfate concentration less than a sulfate concentration threshold; the first iron ion dissolution model is obtained based on orthogonal experimental data covering the parameter range of the first prediction condition. This application can achieve accurate prediction of the amount of iron ions dissolved in pipeline water.

Description

Technical Field

[0001] This application belongs to the field of pipeline corrosion protection technology, and more specifically, it relates to a method, device, equipment, and storage medium for predicting the amount of iron ion leaching from pipeline water. Background Technology

[0002] In the municipal water supply sector, desalinated seawater, due to its significant potential for water replenishment, has become an important source for alleviating water shortages. It needs to be mixed with local natural water bodies with high alkalinity and hardness or switched to other water sources before entering the municipal water supply network. Among these factors, the amount of iron ions dissolved is a key indicator for assessing the stability of the water quality in the network. Excessive iron ion dissolution can lead to "yellow water" and "red water" phenomena, severely affecting the sensory properties of drinking water and water supply safety. The "Standards for Drinking Water Quality" clearly stipulates that the concentration of iron ions in drinking water must be below 0.3 mg / L.

[0003] To ensure the safe operation of pipe networks, numerous water quality stability evaluation standards have been established both domestically and internationally. For example, the World Health Organization's "Guidelines for Drinking-water Quality" proposes evaluation indicators such as alkalinity, hardness, Langelier saturation index, and calcium carbonate precipitation potential, aiming to inhibit pipe network corrosion by controlling the scaling tendency of water bodies. In addition, related technologies also include methods for determining water quality stability by measuring water quality difference indices or ratios of water quality parameters.

[0004] However, existing technologies have significant drawbacks: First, most existing evaluation indicators indirectly determine the scaling or corrosion trend of water bodies. For example, the Langelinie saturation index requires complex calculations based on multiple water quality indicators, which deviates from actual pipeline network conditions and cannot directly predict iron ion dissolution, making it difficult to proactively avoid the risk of "yellow water." Second, existing methods either require testing numerous water quality indicators and are cumbersome to operate, or can only identify the causes of water quality changes but cannot pinpoint the core factors affecting iron ion dissolution, making it difficult to provide accurate data support for water quality control in scenarios involving water source mixing and switching. Therefore, there is an urgent need for an iron ion dissolution prediction technology that can adapt to the actual operation requirements of pipeline networks to address the shortcomings of existing technologies. Summary of the Invention

[0005] To address the aforementioned technical problems, this application provides a method, apparatus, equipment, and storage medium for predicting the amount of iron ions dissolved in water in a pipe network, thereby enabling accurate prediction of the amount of iron ions dissolved in the pipe network.

[0006] The embodiments of this application disclose the following technical solutions: Firstly, a method for predicting the amount of iron ion dissolution in pipe network water is provided, including: Obtain water quality data of the pipeline network source water and the residence time of the pipeline network water. The pipeline network source water quality data includes chloride ion concentration, alkalinity, temperature, pH value and sulfate concentration; If the water quality data of the pipeline source and the residence time of the pipeline water meet the first prediction condition, the amount of iron ion dissolution can be predicted by the first iron ion dissolution model based on chloride ion concentration, alkalinity and temperature. The first iron ion dissolution model is:

[0007] in, This represents the amount of iron ions dissolved. Chloride ion concentration, For temperature, Alkalinity; The first prediction conditions include alkalinity less than the first alkalinity threshold, water retention time in the pipe network greater than the duration threshold, chloride ion concentration less than the chloride ion concentration threshold, pH value greater than the pH threshold, and sulfate concentration less than the sulfate concentration threshold. The first iron ion dissolution model is obtained based on orthogonal experimental data covering the range of first prediction condition parameters.

[0008] Secondly, a device for predicting the amount of iron ion dissolution in pipe network water is provided, comprising: The data acquisition module is used to acquire water quality data of the pipeline network source water and the residence time of the pipeline network water. The water quality data of the pipeline network source water includes chloride ion concentration, alkalinity, temperature, pH value and sulfate concentration. The first iron ion prediction module is used to predict the amount of iron ion dissolution based on chloride ion concentration, alkalinity and temperature, if the water quality data of the pipeline source and the residence time of the pipeline water meet the first prediction conditions. The first iron ion dissolution model is:

[0009] in, This represents the amount of iron ions dissolved. Chloride ion concentration, For temperature, Alkalinity; The first prediction conditions include alkalinity less than the first alkalinity threshold, water retention time in the pipe network greater than the duration threshold, chloride ion concentration less than the chloride ion concentration threshold, pH value greater than the pH threshold, and sulfate concentration less than the sulfate concentration threshold. The first iron ion dissolution model is obtained based on orthogonal experimental data covering the range of first prediction condition parameters.

[0010] Thirdly, embodiments of this application also provide an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method for predicting the amount of iron ions dissolved in pipeline water provided by any possible implementation of the first aspect.

[0011] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method for predicting the amount of iron ions dissolved in pipeline water provided by any possible implementation of the first aspect.

[0012] The beneficial effects of the technical solution provided in this application are as follows: The method, apparatus, equipment, and storage medium for predicting iron ion dissolution in pipe network water provided in this application embodiment are compared with related technologies as follows: To address the issue that existing evaluation indicators can only indirectly determine corrosion trends and cannot directly predict the amount of iron ion leaching, this application's embodiments identify chloride ion concentration, alkalinity, and temperature as core indicators affecting iron ion leaching and construct a dedicated first iron ion leaching model. The amount of iron ion leaching can be directly calculated based on these core indicators without the need for complex indirect index calculations. This allows for direct and accurate prediction of "yellow water" and "red water" risks, enabling early avoidance of water quality problems in the pipeline network.

[0013] To address the issues of existing methods having numerous indicators, cumbersome operations, or failing to provide precise support for water quality control, the embodiments of this application only require obtaining some key water quality data and water retention time, and set clear first prediction conditions to define the applicable boundaries of the model. The data acquisition and operation process is simple and easy to implement. At the same time, the model is constructed based on orthogonal experimental data that fits the actual working conditions, and the prediction results fit the actual operation of the pipeline network. It can provide direct and accurate data support for water quality control of seawater desalination water mixing and water source switching, making it easier for water supply units to carry out targeted control and ensure that the iron ion concentration in the effluent meets the national standards. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 A schematic flowchart illustrating the method for predicting the amount of iron ions dissolved in pipeline water provided in this application embodiment; Figure 2 A schematic diagram of a pipeline water transmission and distribution simulation device provided in the embodiments of this application; Figure 3 This is a schematic diagram of the electrochemical corrosion test results provided in the embodiments of this application; Figure 4 The effect curves provided for the embodiments of this application; Figure 5pH comparison diagram before and after orthogonal experiment provided in the embodiments of this application; Figure 6(a) is a graph showing the change of iron ion dissolution over time in the orthogonal experiment of the 30 alkalinity group provided in the embodiment of this application; Figure 6(b) is a graph showing the change of iron ion dissolution over time in the orthogonal experiment of the 60 alkalinity group provided in the embodiments of this application; Figure 6(c) is a graph showing the change of iron ion dissolution over time in the orthogonal experiment of the 90 alkalinity group provided in the embodiments of this application; Figure 6(d) is a graph showing the change of iron ion dissolution over time in the orthogonal experiment of the 120 alkalinity group provided in the embodiments of this application; Figure 6(e) is a graph showing the change of iron ion dissolution over time in the orthogonal experiment of the 150 alkalinity group provided in the embodiments of this application; Figure 7 Structural block diagram of the device for predicting the amount of iron ions dissolved in pipeline water provided in the embodiments of this application; Figure 8 A schematic block diagram of a computer device provided in an embodiment of this application; Figure 9 Another schematic block diagram of the computer device provided in the embodiments of this application. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0017] It should be noted that the terms "first," "second," and similar terms used in this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Unless the context clearly indicates otherwise, the singular forms "a," "one," or "the," etc., do not indicate a quantity limitation, but rather indicate the presence of at least one. The quantities of "multiple" or "multiple copies" mentioned in the embodiments of this application all refer to a quantity of "at least two," for example, "multiple" means "at least two," and "multiple copies" means "at least two copies." The terms "comprising" and "having," and any variations thereof, as used in this application, are intended to cover non-exclusive inclusion. The term "and / or" as used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0018] The method in this application is consistent with the actual situation to the greatest extent possible, including water quality conditions, temperature, hydraulic conditions, pipe network materials, water volume and pipe network contact area ratio, etc., which are consistent with the actual situation. This ensures that the experimental simulation is consistent with the actual operation of the pipe network and that the corrosion-deposition process in the experimental simulation is consistent with the actual situation. It solves the problem that the iron ion concentration in the actual pipe network cannot be predicted when the water source is switched and the water quality is regulated.

[0019] The method of this application obtains iron ion dissolution data through simulation experiments, which are correlated with changes in water quality parameters. Combined with the mathematical model obtained by range analysis and nonlinear regression statistical analysis, the factors affecting iron ion dissolution can be qualitatively and quantitatively identified.

[0020] The method described in this application can predict the amount of iron ion dissolution during pipeline water quality switching, water quality fluctuations, and water quality control under laboratory conditions. It is convenient, simple, quick, and easy to promote.

[0021] like Figure 1 As shown in the embodiments of this application, the method for predicting the leaching of iron ions in pipeline water can be executed by computer equipment in each step. Computer equipment refers to electronic equipment with data calculation, processing, and storage capabilities. The method may include: S101: Obtain water quality data of the pipeline water source and the residence time of the pipeline water body. The water quality data of the pipeline water source includes chloride ion concentration, alkalinity, temperature, pH value and sulfate concentration.

[0022] In this embodiment, acquiring water quality data from the pipeline water source includes: Determine the mixing ratio of water sources for different types of pipe network water sources, including desalinated seawater and natural water bodies; Obtain first raw water quality data of desalinated seawater and second raw water quality data of natural water bodies; the first raw water quality data includes first chloride ion concentration, first temperature, first pH value, first alkalinity and first sulfate concentration; the second raw water quality data includes second chloride ion concentration, second temperature, second pH value, second alkalinity and second sulfate concentration; The water quality data of the pipeline network after water mixing is obtained based on the water mixing ratio, the first original water quality data, and the second original water quality data.

[0023] In this embodiment, the water quality data of the pipeline network after water mixing is obtained based on the water mixing ratio, the first original water quality data, and the second original water quality data, including: Use the first temperature or the second temperature as the temperature in the water quality data of the pipeline water source; The chloride ion concentration in the pipeline water source quality data is obtained by weighting the first chloride ion concentration and the second chloride ion concentration based on the water source mixing ratio. The alkalinity of the first and second alkalinity is calculated by weighting the mixing ratio of the water source to obtain the alkalinity in the water quality data of the pipeline network. The sulfate concentration in the pipeline water quality data is obtained by weighting the first sulfate concentration and the second sulfate concentration based on the water mixing ratio. The pH value in the pipeline water quality data is obtained by weighting the first pH value and the second pH value based on the mixing ratio of the water source.

[0024] In this embodiment, the pipeline water source quality data refers to the set of core parameters characterizing the pipeline water source quality, used to support the prediction of iron ion dissolution, including chloride ion concentration, alkalinity, temperature, pH value, and sulfate concentration; pipeline water retention time refers to the length of time the water remains in the pipeline; pipeline water source type refers to the category of water bodies constituting the pipeline water source, such as desalinated seawater and natural water bodies; water source mixing ratio refers to the mixing ratio of desalinated seawater and natural water bodies, such as 2:1; desalinated seawater refers to water sources obtained by treating seawater through desalination processes, such as reverse osmosis desalination water produced in a coastal area; natural water bodies refer to naturally formed water sources, such as high-alkalinity and high-hardness surface water in a certain watershed; the first original water quality data refers to the original water quality parameters of desalinated seawater, and the second original water quality data refers to the original water quality parameters of natural water bodies.

[0025] The core objective of this embodiment is to ensure that the water quality data of the mixed water sources closely matches the actual pipeline network conditions. The temperatures of the desalinated seawater and natural water before and after mixing are close to ambient temperatures, with negligible temperature fluctuations; therefore, either temperature is acceptable. Parameters such as chloride ion concentration are directly affected by the mixing ratio, and weighted calculations can reflect the actual proportions. This embodiment not only matches the water quality characteristics of the two types of water sources but also provides accurate input for the iron ion dissolution prediction model, ensuring the reliability of the prediction results.

[0026] For example, this embodiment determines the type of water source in the pipeline network and the mixing ratio of the water sources, specifying that the water source consists of desalinated seawater and natural water bodies, and determining the mixing ratio to be 3:1. Before executing this embodiment, the preliminary work includes: Representative water samples were collected from the effluent of a seawater desalination plant and the intake of a natural water body, with a minimum sample volume of 1 L for each type. Collection was conducted during periods of water disturbance to ensure sample homogeneity. Chloride ion concentration in both types of water samples was determined according to GB / T5750.5-2023 standard; alkalinity was determined according to Chapter 9 of GB 8538-2022, expressed as calcium carbonate; water sample temperature was directly measured using a thermometer, recorded hourly, and the average of three measurements was taken; sulfate concentration was determined using ion chromatography, with simultaneous recording of measurement time points to ensure data consistency; pH ​​was measured using a pH meter, repeated three times, and the average was used as the final result.

[0027] After the preliminary work is completed, this embodiment calculates the water quality data of the pipeline source water based on the relevant data obtained. Specifically, the temperature is taken as either the first temperature or the second temperature; the chloride ion concentration is calculated by weighting according to the mixing ratio. For example, the chloride ion concentration of desalinated seawater is 200 mg / L and that of natural water is 50 mg / L. Under a 3:1 ratio, the concentration is (200×3+50×1)÷4=162.5 mg / L; alkalinity, pH value and sulfate concentration are calculated according to the same weighting logic.

[0028] In this embodiment, all calculated and measured water quality data of the pipeline water source are recorded according to the index categories. The units of each parameter are verified to be consistent and the values ​​are within a reasonable range. After ensuring that there are no abnormalities in the data, it is used for subsequent prediction of iron ion dissolution.

[0029] The water quality data acquisition method in this embodiment accurately reflects the actual water quality of the mixed water source, ensuring high data input reliability and providing a data foundation for predicting iron ion dissolution. The operation process closely aligns with actual water plant operation and maintenance, requiring no complex equipment and offering convenience and efficiency. It effectively avoids parameter deviations caused by theoretical calculations, ensuring the accuracy of subsequent prediction models, providing precise data support for pipeline water quality control, and guaranteeing water supply security.

[0030] S102: If the water quality data of the pipeline source and the residence time of the pipeline water meet the first prediction condition, the amount of iron ion dissolution is predicted by the first iron ion dissolution model based on chloride ion concentration, alkalinity and temperature. The first iron ion dissolution model is:

[0031] in, This represents the amount of iron ions dissolved. Chloride ion concentration, For temperature, Alkalinity; The first prediction conditions include alkalinity less than the first alkalinity threshold, water retention time in the pipe network greater than the duration threshold, chloride ion concentration less than the chloride ion concentration threshold, pH value greater than the pH threshold, and sulfate concentration less than the sulfate concentration threshold. The first iron ion dissolution model is obtained based on orthogonal experimental data covering the range of first prediction condition parameters.

[0032] In this embodiment, all parameters in the first iron ion dissolution model are dimensionless. The first iron ion dissolution model is an empirical correlation mathematical model obtained by nonlinear regression fitting based on orthogonal experimental measured data. Its core is to establish a quantitative nonlinear correlation between key influencing factors such as chloride ion concentration, alkalinity, and temperature and iron ion dissolution through mathematical statistical methods, rather than a theoretical derivation model based on chemical reaction kinetics. The first iron ion dissolution model is based on experimental data, and the fitting process incorporates the influence weights of each factor determined by range analysis. The model form and parameter settings closely match the actual working conditions of iron ion dissolution in the pipe network water, accurately characterizing the influence of each factor on iron ion dissolution within a specific parameter range. It represents a mathematical refinement and engineering application of experimental results. The orthogonal experimental data covering the first prediction condition parameter range includes multiple sets of factor level data for chloride ion concentration, alkalinity, and temperature that meet the first prediction condition threshold range, experimental control condition parameters, raw iron ion concentration data corresponding to each set of factor level data, and water quality auxiliary parameter data. The first iron ion dissolution model is based on orthogonal experimental data covering the range of first prediction condition parameters, and is obtained in the following way: The raw data on iron ion concentration and auxiliary water quality parameters were preprocessed to obtain an effective experimental dataset. Range analysis was performed on the valid experimental dataset to obtain data on the influence of chloride ion concentration, alkalinity, and temperature on the amount of iron ion dissolution. Nonlinear regression fitting was performed on the effective experimental dataset and the data on the influence of chloride ion concentration, alkalinity, and temperature on the amount of iron ion dissolution to obtain the first iron ion dissolution model in relation to chloride ion concentration, alkalinity, and temperature.

[0033] In this embodiment, the orthogonal experimental data were determined in the following manner: The upper limit of chloride ion concentration, the lower limit of alkalinity, and the temperature boundary range are determined as test boundary values; based on the test boundary values, multiple sets of factor level data for chloride ion concentration, alkalinity, and temperature that meet the threshold range of the first prediction condition are determined. The following parameters were used as test control conditions: circulating water volume, sampling and testing cycle, pipe network material sample parameters, pipe network water flow rate, sample corrosion-deposition equilibrium treatment time, sulfate concentration and dissolved oxygen concentration, hardness to alkalinity ratio, and the standard specification data of the nominal diameter of the water supply network. The circulating water volume was determined based on the ratio of pipe network water volume to the internal surface area of ​​the pipe network. The sampling and testing cycle was determined based on the open-circuit potential test data of the pipe network metal material sample and the test water sample. The pH value, conductivity, and water quality stability evaluation index data of the test samples corresponding to each of the multiple sets of factor level data are obtained as auxiliary water quality parameter data.

[0034] In this embodiment, range analysis was performed on the effective experimental dataset to obtain data on the influence of chloride ion concentration, alkalinity, and temperature on the amount of iron ion dissolution, including: The valid experimental dataset is homogenized to obtain a homogenized experimental dataset; Based on the homogenized test dataset, the mean values ​​of iron ion dissolution corresponding to chloride ion concentration, alkalinity and temperature were determined. Extreme values ​​were calculated for the mean iron ion dissolution data corresponding to chloride ion concentration, alkalinity, and temperature, respectively, to obtain the range data of iron ion dissolution at chloride ion concentration, alkalinity, and temperature. Data on the influence of chloride ion concentration, alkalinity, and temperature on the amount of iron ion dissolved were determined based on the range of iron ion dissolution data for chloride ion concentration, alkalinity, and temperature.

[0035] In this embodiment, after acquiring the water quality data of the pipeline water source and the residence time of the pipeline water, the method for predicting the iron ion dissolution amount in the pipeline water further includes: If the water quality data of the pipeline source and the residence time of the pipeline water meet the second prediction conditions, the amount of iron ion dissolution can be predicted by the second iron ion dissolution model based on chloride ion concentration, alkalinity, temperature and residence time of the pipeline water. The second iron ion dissolution model is as follows:

[0036] in, The residence time of water in the pipeline network; The second prediction conditions include alkalinity less than the second alkalinity threshold, water retention time in the pipe network greater than the duration threshold, chloride ion concentration less than the chloride ion concentration threshold, pH value greater than the pH threshold, and sulfate concentration less than the sulfate concentration threshold; the second alkalinity threshold is less than the first alkalinity threshold. The second iron ion dissolution model is derived from orthogonal experimental subset data covering the range of the second prediction condition parameters.

[0037] In this embodiment, all parameters in the second iron ion dissolution model are dimensionless. The first alkalinity threshold refers to the maximum permissible alkalinity value defined in the first prediction conditions, used to define the alkalinity range to which the model is applicable, for example, 200 mg / L (calculated as calcium carbonate); the duration threshold refers to the minimum permissible residence time of the water in the pipe network defined in the first prediction conditions, for example, 4 hours; the chloride ion concentration threshold refers to the maximum permissible chloride ion concentration defined in the first prediction conditions, for example, 250 mg / L; the pH threshold refers to the minimum permissible pH value defined in the first prediction conditions, for example, 7; and the sulfate concentration threshold refers to the maximum permissible sulfate concentration defined in the first prediction conditions, for example, 60 mg / L. Factor level data refers to different combinations of chloride ion concentration, alkalinity, and temperature in the orthogonal experiment. For example, alkalinity of 30 mg / L, chloride ion concentration of 160 mg / L, and temperature of 30℃ constitute a set of factor level data. Experimental control condition parameters refer to a set of fixed parameters that ensure the orthogonal experiment conforms to actual working conditions. For example, the parameters of the pipe network material test piece are Q235 material and corrosion area of ​​2544 mm². Auxiliary water quality parameter data refers to auxiliary water quality parameters collected synchronously during the experiment, such as pH value and conductivity before and after the experiment. Valid experimental dataset refers to the set of experimental data after preprocessing to remove outliers. The impact level data refers to data characterizing the strength of each factor's effect on iron ion dissolution; nonlinear regression fitting refers to the process of establishing a nonlinear relationship between variables through mathematical statistical methods; experimental boundary values ​​refer to the range boundaries of each factor in the orthogonal experiment, such as the upper limit of chloride ion concentration test value of 250 mg / L; circulating water volume refers to the circulating water volume in the orthogonal experiment, such as 3L; sampling and detection cycle refers to the time interval for collecting water samples in the experiment, such as 4 hours; pipe network material test piece parameters refer to the specifications of the test piece simulating the pipe network material used in the experiment, such as the size 12.7×146×4.73mm; pipe network water flow velocity refers to the simulated water flow velocity in the pipe network in the experiment, such as 0.85m / s; test piece corrosion... The corrosion-deposition equilibrium treatment time refers to the pretreatment time required to bring the test specimen to the actual corrosion state of the pipeline network before the test, for example, 15 days; the hardness to alkalinity ratio refers to the equivalent concentration ratio of hardness to alkalinity in the test water, for example, 1.5; the nominal diameter standard specification data of the water supply network refers to the standard inner diameter specification of the water supply network, for example, DN50; the open circuit potential test data refers to the potential data between the pipeline network metal material and the water sample determined by electrochemical test, used to determine the sampling and testing cycle; the average value of horizontal iron ion dissolution refers to the average value of iron ion dissolution under the same factor at the same level; the iron ion dissolution range data refers to the difference between the maximum and minimum values ​​of iron ion dissolution under different levels of the same factor.

[0038] The core objective of this embodiment is to construct an accurate and reliable prediction model for iron ion dissolution by simulating actual pipeline network conditions. This embodiment selects chloride ion concentration, alkalinity, and temperature as the core influencing factors. This is based on the characteristics of two types of water sources and previous research conclusions, excluding minor factors with small fluctuations and focusing on key variables. This embodiment sets a first prediction condition to define the applicable boundary of the model, avoiding prediction deviations caused by parameters exceeding reasonable ranges. In the orthogonal experimental design, high chloride ion values ​​and low alkalinity values ​​are used to explore extreme operating conditions. The experimental control parameters all closely match the actual pipeline network operating state, ensuring the authenticity of the experimental data. Through preprocessing and range analysis, this embodiment can purify effective data and clarify the influence weight of each factor, providing a reliable basis for nonlinear regression fitting, ultimately achieving accurate prediction of iron ion dissolution by the model, overcoming the shortcomings of existing technologies that cannot directly predict iron ion dissolution.

[0039] In this embodiment, based on desalinated seawater and high-alkalinity, high-hardness water, the main influencing factors affecting iron ion concentration during the blending process or water source switching process are studied. This embodiment uses a combination of orthogonal analysis and mathematical statistics to establish a mathematical model of water quality indicators for predicting iron ion concentration (i.e., iron ion dissolution model) and predict the change of iron ion concentration during the change of the main influencing factors (chloride ion concentration, alkalinity, and temperature).

[0040] For example, (1) the main influencing factors of iron ion dissolution. Specifically, the influencing factors of the characteristics of desalinated product water and its mineralized product water are alkalinity (alkalinity and hardness of product water are usually in a fixed ratio according to different mineralization processes) and chloride ion concentration. The influencing factors of the characteristics of natural water bodies with high alkalinity and high hardness are alkalinity, chloride ion concentration and temperature. Other water quality indicators fluctuate relatively little and can be ignored. Therefore, alkalinity, chloride ion concentration and temperature are taken as the influencing factors in orthogonal analysis.

[0041] (2) Determine the levels of orthogonal analysis factors. Specifically, first, determine the boundary values ​​of each influencing factor, set several factor levels based on the differences in boundary values, and design an orthogonal analysis experiment. The selection principle for the factor level range of the main technical parameters "alkalinity, chloride ions, and temperature" is as follows: alkalinity is beneficial to improving buffering capacity, and the lowest value should be taken as much as possible to explore the extreme value that the pipe network can withstand; chloride ions easily cause corrosion of the pipe network by the water body, so the highest value should be taken as much as possible to find the extreme value; temperature can be set according to the actual situation. Based on the boundary conditions determined by the orthogonal experiment, calculate water quality stability indicators such as the Langerier Saturation Index (LSI), Larson Ratio (LR), and Calcium Carbonate Precipitation Potential (CCPP) to preliminarily verify the rationality of the orthogonal factor levels.

[0042] (3) Determine other conditions for the orthogonal experiment. Specifically, according to the actual operating conditions of the pipeline network, there is a proportional relationship between the water volume and the internal surface area of ​​the pipeline network, i.e., πr 2 L / 2πrL=r / 2, and the circulating water volume for the orthogonal experiment can be calculated based on this ratio. In this embodiment, the sampling and detection cycle for the orthogonal experiment can be determined based on electrochemical experiments.

[0043] (4) Conduct orthogonal experiments. Specifically, conduct experiments one by one according to the orthogonal experiment and detect the iron ion concentration during the sampling period.

[0044] (5) Establish a mathematical model based on the amount of iron ion dissolution. Specifically, analyze the obtained experimental results, use mathematical statistics to establish a mathematical model of the amount of iron ion dissolution and the main influencing factors, and set the conditions for using the model.

[0045] (6) Predict the amount of iron ion dissolution under different water quality index conditions and different residence times through mathematical models, so as to provide data reference for the stable operation of the pipeline network under water quality regulation or water source switching.

[0046] For example, the experimental and data analysis process using desalinated seawater and a natural water body in northern China is as follows: (1) Determine the main influencing factors of iron ion dissolution. Specifically, the influencing factors of desalinated seawater and high-alkalinity, high-hardness natural water bodies are alkalinity, chloride ion concentration, and temperature. These three factors are used as the influencing factors in an orthogonal experiment. At the same time, the influence level of each factor is determined. Taking a certain desalinated seawater and a certain natural water body in northern China as examples, the alkalinity is taken as the lowest value, the chloride ion concentration is taken as the highest value, and the temperature is set according to the actual monitoring records. Other water quality parameters are sulfate = 60 mg / L, dissolved oxygen (DO) = 7.6 mg / L, and the equivalent concentration of hardness is 1.5 times that of alkalinity. The factor level design results of each main technical parameter are shown in Table 1.

[0047] Table 1. Orthogonal Experimental Factor Level Table for Water Quality Technical Indicators

[0048] To verify the rationality of the selection of upper and lower limits for alkalinity, chloride ions, and temperature, the water quality indicators LSI or Ryznar Stability Index (RSI), CCPP, and LR (pH values ​​in the calculation process are estimates) under extreme conditions of unfavorable factors in the orthogonal experimental setup can be preliminarily estimated. The results show that these indicators basically cover the range of chemical stability characteristics of slightly corrosive water bodies, which is helpful for revealing the limiting boundary conditions of mixed water and preliminarily verifies the rationality of the orthogonal factor levels.

[0049] (2) Establish a set of pipeline water transmission and distribution simulation devices, such as Figure 2 As shown, the pipe network material and area were determined during the test. The specific test conditions and procedures were as follows: the test specimen used was made of Q235 material, with dimensions of 12.7×146×4.73mm and a corrosion area of ​​2544 mm. According to the actual operation of the pipeline network, there is a proportional relationship between the water volume and the internal surface area of ​​the pipeline network, namely πr. 2 L / 2πrL=r / 2, the circulating water volume for the orthogonal experiment can be calculated based on this ratio, and is taken as 3L; the corrosion temperature is controlled by the circulating cooling water in the jacket; the simulated pipeline flow velocity is 0.85m / s, which is controlled by the rotation speed of the test piece and the water circulation speed in the reactor; the corrosion time for each water quality condition under stable corrosion conditions is 24h. Based on the electrochemical experiment, the sampling and detection cycle for the orthogonal experiment is determined to be 4h. The electrochemical experiment results are as follows: Figure 3 As shown in the figure, water samples are taken every 4 hours to detect the amount of iron ion dissolution, which characterizes the degree of corrosion of the pipeline network under different water quality conditions.

[0050] (3) Before the orthogonal experiment began, a natural water body in the north was used to corrode the uncorroded metal material. After 15 days, the corrosion-deposition equilibrium state was close to the actual situation.

[0051] (4) Orthogonal experiments were conducted one by one according to the water quality parameters in Table 1 to detect the iron ion dissolution during the sampling period. For each orthogonal experiment, deionized water was used to add reagents for water preparation: using " + "The method of dissolution regulates alkalinity, and Combined with hardness adjustment, In combination with NaCl, the chloride ion concentration is adjusted. Regulation The concentration, dissolved oxygen, and pH are in their natural state after the water mixture has stabilized, and no adjustments are made.

[0052] (5) Range analysis was performed on the data obtained during the above experiment (the data were normalized), and the results are shown in Table 2 and 2. Figure 4 As shown, alkalinity was initially determined to be the main influencing factor, while chloride ion concentration and temperature were secondary influencing factors.

[0053] Table 2 Orthogonal Table for Range Analysis of Iron Dissolution

[0054] in, Figure 4In the effect curves, using factor levels as the x-axis and the index average as the y-axis, a trend graph of the factor-index relationship is plotted. It can be clearly seen that the effect curves of the three factors show significant differences in their magnitude of change. The alkalinity curve has the steepest slope, with 30 mg / L alkalinity resulting in the most severe corrosion. The temperature curve has the second steepest slope, with 30℃ corresponding to the most severe corrosion. The corrosion effect of chloride ion concentration changes relatively gradually, with 160 mg / L resulting in the most severe corrosion. The magnitude of change in the effect curves reflects the strength of the factor's influence on the index; factors with large fluctuations are the primary influencing factors, while those with small fluctuations are the secondary influencing factors. Therefore... Figure 3 This reflects that alkalinity is the primary influencing factor, while chloride ion concentration and temperature are secondary influencing factors.

[0055] The orthogonal experiment range results showed that RA>RC>RB, indicating that the influence of each factor on pipeline corrosion was as follows: total alkalinity was the most significant influencing factor, while temperature had a slightly greater impact than chloride ion concentration. Specifically, the iron ion dissolution rate in the low alkalinity group was approximately 0.22 mg / L, while that in the high alkalinity group was approximately 0.077 mg / L. Total alkalinity has the most significant impact because this experiment measures the amount of iron leached from the pipeline network after a period of water quality stabilization. Increased alkalinity accelerates the formation of FeO, Fe2O3, and Fe(OH)3. These non-soluble compounds can fill the voids in corrosion products, and the dense passivation layer can reduce the degree of iron leaching. Temperature also has a significant impact on the diffusion rate of ions in water, which can lead to changes in the corrosion rate of pipelines and accelerate the release of iron from the pipelines. Chloride ions have a relatively weak effect because their mechanism involves penetrating the already formed scale layer, causing the water to react with the metal beneath the scale layer. This experiment used water quality data provided by a northern water supply company for water distribution, and the water quality parameters were bicarbonate type water. After the stabilization phase, a relatively dense scale layer formed on the metal surface, which had a significant barrier effect on ions in the water, thus reducing the effect of chloride ions.

[0056] In the orthogonal experiment, changes in parameters such as pH and conductivity before and after the experiment were also detected, and the amount of iron ion dissolution at different time points was measured. All orthogonal experimental results were statistically analyzed, such as... Figure 5 As shown in Figures 6(a)-6(e).

[0057] Based on the results of orthogonal experiments, a mathematical model of the relationship between the amount of iron ions dissolved in the pipeline network and the main influencing factors was obtained using mathematical statistics: The condition for using this mathematical model is: [Cl - <250mg / L, [SO4] 2- ]<60mg / L, Alk<200mg / L, pH>7, HRT>4h; [Cl - [SO4] represents the chloride ion concentration. 2- SO42- Concentration. During the analysis, the factors affecting alkalinity were examined, and a mathematical model was obtained showing how the iron ion concentration changes with the main influencing factors under low alkalinity conditions: The condition for using this mathematical model is: [Cl - <250mg / L, [SO4] 2- [Cl] <60mg / L, Alk <90mg / L, pH >7, HRT >4h. The stability of low-alkalinity water bodies is influenced by more factors. To maintain water stability, it is advisable to minimize temperature, reduce water retention time, and decrease [Cl] concentration. - This provides more favorable water quality conditions for a low-alkalinity buffer. The power in the upper right corner of each term in the mathematical model represents the nonlinear relationship between the parameters, obtained through mathematical fitting. Specifically, the iron ion concentration in the original formula has an exponential relationship with each parameter; this is converted to a linear formula by taking the logarithm of both sides, and then these power values ​​are derived by reverse calculation.

[0058] (7) The amount of iron ion dissolution under different water quality conditions and different residence times was predicted by mathematical model, providing data reference for the stable operation of the pipeline network under water quality control or water source switching. Taking a certain seawater desalination and a certain northern pipeline network as examples, different proportions of mixing and control were carried out. The chloride ion concentration of the desalination water was calculated as 200 mg / L, the chloride ion concentration of the pipeline network operation water was calculated as 50 mg / L, the alkalinity of the pipeline network operation water in summer was calculated as 120 mg / L, and the alkalinity in winter was calculated as 195 mg / L. The amount of iron ion dissolution was predicted by the obtained mathematical model, and the results are shown in Table 3.

[0059] Table 3. Prediction of iron ion leaching after mixing of operating water and desalinated water in the pipeline network.

[0060] In summary, based on the data analysis results and the national standard requirement for iron ion concentration (0.3 mg / L), the boundary conditions and warning lines for this mathematical model are determined as follows: 1. The upper limit of iron ion concentration is 0.20 mg / L; 2. Chloride ion concentration <125 mg / L; 3. pH >7; 4. Alkalinity >80 mg / L.

[0061] The method in this embodiment is consistent with the actual situation to the greatest extent possible, specifically including: (1) water quality conditions, temperature, hydraulic conditions, etc. are consistent with the actual situation; (2) the pipe network material is consistent with the actual situation; (3) the ratio of water volume in the pipe network operation to the pipe network contact area is consistent, so that the iron ion dissolution in this experiment is consistent with the actual situation; (4) the balance test before the water source switching or water quality fluctuation test further ensures that the iron ion dissolution is consistent with the iron ion dissolution in the actual pipe network; (5) the water quality index detection cycle is obtained by the open circuit potential test between the pipe network metal material and the surface water, reducing the test error caused by the sampling cycle. The above measures ensure that the test simulation is consistent with the actual operation of the pipe network and that the corrosion-deposition process simulated in the test is consistent with the actual situation.

[0062] The conclusions drawn from this experiment provide the following water quality control suggestions for mixing surface water with desalinated water for municipal water supply in northern China: (a) The mixed water should first meet the conditions of chloride ion <125mg / L, pH>7, and alkalinity>80mg / L; (b) Water quality stability is relatively high in winter, and water supply companies can appropriately increase the mixing ratio, which can be gradually adjusted from 2:1 to 1:1; (c) Water quality stability is relatively poor in summer, and water supply companies need to increase the frequency of water quality monitoring, which can be gradually adjusted from 3:1 to 2:1, and closely monitor water quality changes under high temperature conditions to avoid the occurrence of "red water".

[0063] The method in this embodiment obtains iron ion dissolution data through simulation experiments, which is correlated with changes in water quality parameters. Range analysis of these data can qualitatively determine the degree of influence of each water quality parameter. The mathematical model obtained by nonlinear regression mathematical statistical analysis of these data can qualitatively and quantitatively determine the degree of influence of each water quality parameter, and predict the iron ion dissolution after changes in water quality parameters.

[0064] The method in this embodiment can predict the amount of iron ion dissolution under simulated conditions such as water quality switching, water quality fluctuation, and water quality regulation in the pipeline network under laboratory conditions. It is convenient, simple, quick, and easy to promote.

[0065] Based on the same principle as the method for predicting the leaching of iron ions in pipe network water provided in the embodiments of this application, the embodiments of this application also provide a device for predicting the leaching of iron ions in pipe network water, such as... Figure 7 As shown, the iron ion dissolution prediction device 20 for the pipeline water body may specifically include: a data acquisition module 21 and a first iron ion prediction module 22. The data acquisition module 21 is used to acquire pipeline water source water quality data and pipeline water body residence time. The pipeline water source water quality data includes chloride ion concentration, alkalinity, temperature, pH value and sulfate concentration. The first iron ion prediction module 22 is used to predict the amount of iron ion dissolution based on the chloride ion concentration, alkalinity and temperature, if the water quality data of the pipeline source and the residence time of the pipeline water meet the first prediction conditions. The first iron ion dissolution model is:

[0066] in, This represents the amount of iron ions dissolved. Chloride ion concentration, For temperature, Alkalinity; The first prediction conditions include alkalinity less than the first alkalinity threshold, water retention time in the pipe network greater than the duration threshold, chloride ion concentration less than the chloride ion concentration threshold, pH value greater than the pH threshold, and sulfate concentration less than the sulfate concentration threshold. The first iron ion dissolution model is obtained based on orthogonal experimental data covering the range of first prediction condition parameters.

[0067] In one embodiment of this application, the orthogonal experimental data covering the range of first prediction condition parameters includes multiple sets of factor level data for chloride ion concentration, alkalinity, and temperature that meet the threshold range of the first prediction condition, experimental control condition parameters, raw data of iron ion concentration corresponding to each set of factor level data, and water quality auxiliary parameter data; the pipe network water iron ion dissolution prediction device 20 further includes: a model determination module, used for: The raw data on iron ion concentration and auxiliary water quality parameters were preprocessed to obtain an effective experimental dataset. Range analysis was performed on the valid experimental dataset to obtain data on the influence of chloride ion concentration, alkalinity, and temperature on the amount of iron ion dissolution. Nonlinear regression fitting was performed on the effective experimental dataset and the data on the influence of chloride ion concentration, alkalinity, and temperature on the amount of iron ion dissolution to obtain the first iron ion dissolution model in relation to chloride ion concentration, alkalinity, and temperature.

[0068] In one embodiment of this application, the pipe network water iron ion dissolution prediction device 20 further includes: an orthogonal experimental data acquisition module, used for: The upper limit of chloride ion concentration, the lower limit of alkalinity, and the temperature boundary range are determined as test boundary values; based on the test boundary values, multiple sets of factor level data for chloride ion concentration, alkalinity, and temperature that meet the threshold range of the first prediction condition are determined. The following parameters were used as test control conditions: circulating water volume, sampling and testing cycle, pipe network material sample parameters, pipe network water flow rate, sample corrosion-deposition equilibrium treatment time, sulfate concentration and dissolved oxygen concentration, hardness to alkalinity ratio, and the standard specification data of the nominal diameter of the water supply network. The circulating water volume was determined based on the ratio of pipe network water volume to the internal surface area of ​​the pipe network. The sampling and testing cycle was determined based on the open-circuit potential test data of the pipe network metal material sample and the test water sample. The pH value, conductivity, and water quality stability evaluation index data of the test samples corresponding to each of the multiple sets of factor level data are obtained as auxiliary water quality parameter data.

[0069] In one embodiment of this application, the model determination module is specifically used for: The valid experimental dataset is homogenized to obtain a homogenized experimental dataset; Based on the homogenized test dataset, the mean values ​​of iron ion dissolution corresponding to chloride ion concentration, alkalinity and temperature were determined. Extreme values ​​were calculated for the mean iron ion dissolution data corresponding to chloride ion concentration, alkalinity, and temperature, respectively, to obtain the range data of iron ion dissolution at chloride ion concentration, alkalinity, and temperature. Data on the influence of chloride ion concentration, alkalinity, and temperature on the amount of iron ion dissolved were determined based on the range of iron ion dissolution data for chloride ion concentration, alkalinity, and temperature.

[0070] In one embodiment of this application, the pipe network water iron ion dissolution prediction device 20 further includes: a second iron ion prediction module, used for: If the water quality data of the pipeline source and the residence time of the pipeline water meet the second prediction conditions, the amount of iron ion dissolution can be predicted by the second iron ion dissolution model based on chloride ion concentration, alkalinity, temperature and residence time of the pipeline water. The second iron ion dissolution model is as follows:

[0071] in, The residence time of water in the pipeline network; The second prediction conditions include alkalinity less than the second alkalinity threshold, water retention time in the pipe network greater than the duration threshold, chloride ion concentration less than the chloride ion concentration threshold, pH value greater than the pH threshold, and sulfate concentration less than the sulfate concentration threshold; the second alkalinity threshold is less than the first alkalinity threshold. The second iron ion dissolution model is derived from orthogonal experimental subset data covering the range of the second prediction condition parameters.

[0072] In one embodiment of this application, the data acquisition module 21 is specifically used for: Determine the mixing ratio of water sources for different types of pipe network water sources, including desalinated seawater and natural water bodies; Obtain first raw water quality data of desalinated seawater and second raw water quality data of natural water bodies; the first raw water quality data includes first chloride ion concentration, first temperature, first pH value, first alkalinity and first sulfate concentration; the second raw water quality data includes second chloride ion concentration, second temperature, second pH value, second alkalinity and second sulfate concentration; The water quality data of the pipeline network after water mixing is obtained based on the water mixing ratio, the first original water quality data, and the second original water quality data.

[0073] In one embodiment of this application, the data acquisition module 21 is further configured to: Use the first temperature or the second temperature as the temperature in the water quality data of the pipeline water source; The chloride ion concentration in the pipeline water source quality data is obtained by weighting the first chloride ion concentration and the second chloride ion concentration based on the water source mixing ratio. The alkalinity of the first and second alkalinity is calculated by weighting the mixing ratio of the water source to obtain the alkalinity in the water quality data of the pipeline network. The sulfate concentration in the pipeline water quality data is obtained by weighting the first sulfate concentration and the second sulfate concentration based on the water mixing ratio. The pH value in the pipeline water quality data is obtained by weighting the first pH value and the second pH value based on the mixing ratio of the water source.

[0074] Each module in the aforementioned pipe network water iron ion dissolution prediction device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0075] In some embodiments, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 8As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores data such as water quality data for the pipeline network. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When executed by the processor, the computer program implements a method for predicting the amount of iron ions dissolved in pipeline network water.

[0076] In some embodiments, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 9 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage medium. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a method for predicting the amount of iron ions dissolved in pipe network water. The display unit of the computer device is used to form a visually visible image. It can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0077] Those skilled in the art will understand that Figure 8 , Figure 9The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0078] In some embodiments, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method for predicting the amount of iron ions dissolved in the water of the pipe network.

[0079] In some embodiments, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method for predicting the amount of iron ions dissolved in the water of a pipe network.

[0080] In some embodiments, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the above-described method for predicting the amount of iron ions dissolved in water in a pipe network.

[0081] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0082] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logic devices, etc., and are not limited to these.

[0083] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0084] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for predicting the amount of iron ions dissolved in water in a pipe network, characterized in that, include: Acquire water quality data of the pipeline water source and the residence time of the water in the pipeline. The water quality data of the pipeline water source includes chloride ion concentration, alkalinity, temperature, pH value and sulfate concentration. If the water quality data of the pipeline water source and the residence time of the pipeline water meet the first prediction condition, then the amount of iron ion dissolution is predicted by the first iron ion dissolution model based on the chloride ion concentration, alkalinity and temperature. The first iron ion dissolution model is as follows: in, This represents the amount of iron ions dissolved. Chloride ion concentration, For temperature, Alkalinity; The first prediction conditions include alkalinity less than a first alkalinity threshold, water retention time in the pipe network greater than a duration threshold, chloride ion concentration less than a chloride ion concentration threshold, pH value greater than a pH threshold, and sulfate concentration less than a sulfate concentration threshold. The first iron ion dissolution model is obtained based on orthogonal experimental data covering the range of first prediction condition parameters.

2. The method for predicting the amount of iron ion dissolution in pipe network water as described in claim 1, characterized in that, The orthogonal experimental data covering the range of the first prediction condition parameters includes multiple sets of factor level data of chloride ion concentration, alkalinity and temperature that meet the threshold range of the first prediction condition, experimental control condition parameters, raw data of iron ion concentration corresponding to each set of factor level data and water quality auxiliary parameter data. The first iron ion dissolution model is based on orthogonal experimental data covering the range of first prediction condition parameters, and is obtained in the following way: The raw data of iron ion concentration and the auxiliary water quality parameters are preprocessed to obtain a valid experimental dataset; Range analysis was performed on the effective experimental dataset to obtain data on the influence of chloride ion concentration, alkalinity, and temperature on the amount of iron ion dissolution. Nonlinear regression fitting was performed on the effective experimental dataset and the data on the influence of chloride ion concentration, alkalinity and temperature on the amount of iron ion dissolution to obtain the first iron ion dissolution model in relation to chloride ion concentration, alkalinity and temperature.

3. The method for predicting the amount of iron ion dissolution in pipe network water as described in claim 2, characterized in that, The orthogonal experimental data were determined in the following manner: The upper limit of chloride ion concentration, the lower limit of alkalinity, and the temperature boundary range are determined as test boundary values; based on the test boundary values, multiple sets of factor level data of chloride ion concentration, alkalinity, and temperature that meet the first prediction condition threshold range are determined. The circulating water volume, sampling and testing cycle, pipe network material sample parameters, pipe network water flow rate, sample corrosion-deposition equilibrium treatment time, sulfate concentration and dissolved oxygen concentration, hardness to alkalinity ratio, and the standard specification data of the nominal diameter of the water supply pipe network are obtained as test control condition parameters; the circulating water volume is determined based on the ratio of pipe network water volume to pipe network internal surface area; the sampling and testing cycle is determined based on the open circuit potential test data of the pipe network metal material sample and the test water sample; The pH value, conductivity, and water quality stability evaluation index data of the test samples corresponding to each of the multiple sets of factor level data are obtained as auxiliary water quality parameter data.

4. The method for predicting the amount of iron ion dissolution in pipe network water as described in claim 2, characterized in that, The effective experimental dataset was subjected to range analysis to obtain data on the influence of chloride ion concentration, alkalinity, and temperature on the amount of iron ion dissolution, including: The effective test dataset is homogenized to obtain a homogenized test dataset; Based on the homogenized test dataset, the mean values ​​of iron ion dissolution corresponding to chloride ion concentration, alkalinity, and temperature were determined. Extreme value calculations were performed on the mean iron ion dissolution data corresponding to the chloride ion concentration, alkalinity, and temperature, respectively, to obtain the range data of iron ion dissolution at chloride ion concentration, alkalinity, and temperature. Data on the influence of chloride ion concentration, alkalinity, and temperature on the amount of iron ion dissolved were determined based on the range of iron ion dissolution data for chloride ion concentration, alkalinity, and temperature.

5. The method for predicting the amount of iron ions dissolved in pipe network water as described in claim 1, characterized in that, After acquiring the water quality data of the pipeline water source and the residence time of the pipeline water, the following is also included: If the water quality data of the pipeline water source and the residence time of the pipeline water meet the second prediction conditions, then the amount of iron ion dissolution is predicted by the second iron ion dissolution model based on the chloride ion concentration, alkalinity, temperature and the residence time of the pipeline water. The second iron ion dissolution model is as follows: in, The residence time of water in the pipeline network; The second prediction conditions include alkalinity less than a second alkalinity threshold, water retention time in the pipe network greater than a duration threshold, chloride ion concentration less than a chloride ion concentration threshold, pH value greater than a pH threshold, and sulfate concentration less than a sulfate concentration threshold; the second alkalinity threshold is less than the first alkalinity threshold. The second iron ion dissolution model is obtained based on a subset of orthogonal experimental data covering the range of the second prediction condition parameters.

6. The method for predicting the amount of iron ion dissolution in pipe network water as described in claim 1, characterized in that, Obtain water quality data from the pipeline network, including: Determine the mixing ratio of water sources of different types in the pipeline network, including desalinated seawater and natural water bodies; Obtain first raw water quality data of the desalinated seawater and second raw water quality data of the natural water body; the first raw water quality data includes first chloride ion concentration, first temperature, first pH value, first alkalinity, and first sulfate concentration; the second raw water quality data includes second chloride ion concentration, second temperature, second pH value, second alkalinity, and second sulfate concentration. Based on the water source mixing ratio, the first original water quality data, and the second original water quality data, the water quality data of the pipeline network after water source mixing is obtained.

7. The method for predicting the amount of iron ion dissolution in pipe network water as described in claim 6, characterized in that, The process of obtaining the network water quality data after mixing based on the water source blending ratio, the first original water quality data, and the second original water quality data includes: Use the first temperature or the second temperature as the temperature in the water quality data of the pipeline water source; Based on the water source mixing ratio, the first chloride ion concentration and the second chloride ion concentration are weighted and calculated to obtain the chloride ion concentration in the pipeline water source quality data; The alkalinity of the first alkalinity and the second alkalinity are weighted and calculated based on the water source mixing ratio to obtain the alkalinity in the pipeline water source quality data; Based on the water source mixing ratio, the first sulfate concentration and the second sulfate concentration are weighted and calculated to obtain the sulfate concentration in the pipeline water source quality data. The pH value in the pipeline water quality data is obtained by weighting the first pH value and the second pH value based on the water mixing ratio.

8. A device for predicting the amount of iron ion dissolution in pipe network water, characterized in that, include: The data acquisition module is used to acquire water quality data of the pipeline water source and the residence time of the water in the pipeline. The water quality data of the pipeline water source includes chloride ion concentration, alkalinity, temperature, pH value and sulfate concentration. The first iron ion prediction module is used to predict the iron ion dissolution amount based on the chloride ion concentration, alkalinity and temperature, by means of the first iron ion dissolution amount model if the water quality data of the pipeline water source and the residence time of the pipeline water meet the first prediction conditions. The first iron ion dissolution model is as follows: in, This represents the amount of iron ions dissolved. Chloride ion concentration, For temperature, Alkalinity; The first prediction conditions include alkalinity less than a first alkalinity threshold, water retention time in the pipe network greater than a duration threshold, chloride ion concentration less than a chloride ion concentration threshold, pH value greater than a pH threshold, and sulfate concentration less than a sulfate concentration threshold. The first iron ion dissolution model is obtained based on orthogonal experimental data covering the range of first prediction condition parameters.

9. A computer device, characterized in that, The computer device includes a processor and memory: The memory is used to store computer programs; The processor is used to execute the method for predicting the amount of iron ions dissolved in the water of the pipeline network according to any one of claims 1-7, based on the computer program.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a computer program, which, when executed by a computer device, implements the method for predicting the amount of iron ions dissolved in the water of the pipeline network as described in any one of claims 1-7.

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