Prediction method for river network pollutant purification capacity of water purification plant

Abstract

The invention provides a method for predicting the river network pollutant purification capacity of a water purification plant. The method comprises the following steps: acquiring pollutant data of a target river network; constructing a hydrodynamic model of the target river network based on the pollutant data, the water passing area data of the river network, the hydraulic radius data, the section flow data, the water level data and the flow distance data; constructing a purification model corresponding to the target water purification plant based on the actually measured water quality concentration and the actual pollutant removal rate of the target water purification plant; predicting pollutant data of the target river network purified by the target water purification plant based on the hydrodynamic model and the purification model; and determining the pollutant purification capacity of the target water purification plant based on the hydrodynamic model, the water environment of the target river network and the purified pollutant data. The method provided by the invention can accurately predict the purification capacity of the water purification plant.

Description

Technical Field

[0001] The present application relates to the field of pollution purification technology, and in particular to a method for predicting the ability of a water purification plant to purify river network pollutants. Background Art

[0002] With the continuous development of science and technology, people's production and life have been greatly improved. With the rapid growth of industrialization and urban population, more and more pollutants are discharged into urban river networks, causing water quality to deteriorate. At the same time, urban development has also transformed the river network structure to varying degrees, resulting in poor structural connectivity, increased river resistance, slower flow rate, and reduced self-purification capacity. In order to improve water quality, most cities need to use water replenishment to purify the river network. For this reason, water treatment plants are put into operation. Water treatment plants generally use bypass treatment to introduce slightly polluted water bodies in the river network into water treatment plants, purify water quality through artificial rapid infiltration, coagulation sedimentation, filter cloth filter tanks and other methods, and then discharge them into the river. However, the prior art does not have a method for accurately predicting the ability of water treatment plants to purify pollutants. Therefore, how to accurately predict the ability of water treatment plants to purify pollutants has become a technical problem that technicians in this field need to solve urgently. Summary of the invention

[0003] Based on the above problems, the present application provides a method for predicting the ability of a water treatment plant to purify river network pollutants so as to accurately predict the ability of a water treatment plant to purify pollutants.

[0004] The present application provides a method for predicting the ability of a water purification plant to purify river network pollutants, the method comprising the following steps:

[0005] Obtain pollutant data of the target river network;

[0006] Constructing a hydrodynamic model of the target river network based on the pollutant data, the water flow area data, hydraulic radius data, cross-sectional flow data, water level data and flow distance data of the river network;

[0007] Constructing a purification model corresponding to the target water purification plant based on the measured water quality concentration and actual pollutant removal rate of the target water purification plant;

[0008] Predicting pollutant data of the target river network after being purified by the target water purification plant based on the hydrodynamic model and the purification model;

[0009] The pollutant purification capacity of the target water purification plant is determined based on the hydrodynamic model, the water environment of the target river network and the purified pollutant data.

[0010] In a possible implementation, constructing the hydrodynamic model of the target river network based on the pollutant data, the water flow area data of the river network, the hydraulic radius data, the cross-sectional flow data, the water level data and the flow distance data includes:

[0011] Constructing the Saint-Venant equations of the target river network based on the pollutant data, the water flow area data, hydraulic radius data, cross-sectional flow data, water level data and flow distance data of the river network, wherein the Saint-Venant equations include a water flow continuity equation, a water flow momentum equation and a one-dimensional convection equation;

[0012] The water flow continuity equation is expressed by the following formula:

[0013]

[0014] Among them, X represents the flow distance, t represents the flow time, A represents the water flow area, and Q represents the cross-sectional flow rate;

[0015] The water flow momentum equation is expressed by the following formula:

[0016]

[0017] Wherein, X represents the flow distance, t represents the flow time, A represents the water flow area, Q represents the cross-sectional flow rate, α represents the momentum correction coefficient, R represents the hydraulic radius, and n represents the roughness coefficient of the target river network;

[0018] The one-dimensional convection equation is expressed by the following formula:

[0019]

[0020] Wherein, A represents the water flow area, C represents the pollutant data in the target river network, t represents the flow time, X represents the flow distance, Q represents the cross-sectional flow, D represents the water flow diffusion coefficient of the target river network, and K represents the attenuation coefficient.

[0021] In a possible implementation, the water environment of the target river network can be represented by chemical oxygen demand data, ammonia nitrogen data, dissolved oxygen data, and total phosphorus data;

[0022] The chemical oxygen demand data is expressed by the following formula:

[0023]

[0024] Among them, C COD represents the chemical oxygen demand concentration, t represents time, k COD represents the first-order degradation rate of chemical oxygen demand at a preset temperature, C DOrepresents the concentration of dissolved oxygen, HS_COD represents the half-saturated oxygen concentration corresponding to the chemical oxygen demand, The amount of sediment released to represent the chemical oxygen demand, θ COD represents the Arrhenius temperature coefficient, T represents temperature, and T-20 represents that the preset temperature is 20°C;

[0025] The ammonia nitrogen content data is expressed by the following formula:

[0026]

[0027] in, represents the concentration of ammonia nitrogen, t represents time, UN P Indicates the total amount of ammonia nitrogen taken up by plants in the target river network, UN B represents the total amount of ammonia nitrogen taken up by bacteria in the target river network, Indicates the first-order decay rate of ammonia nitrogen at a preset temperature, represents the temperature coefficient of the decay rate of ammonia conversion to nitrite, HS_NH4 represents the half-saturated oxygen concentration corresponding to ammonia nitrogen, Indicates the amount of ammonia nitrogen released from the sediment, T indicates the temperature, and T-20 indicates that the preset temperature is 20°C;

[0028] The dissolved oxygen data is expressed by the following formula:

[0029]

[0030] Among them, C DO represents the concentration of dissolved oxygen, t represents time, k2 represents the atmospheric reoxygenation rate, C S represents the saturated dissolved oxygen concentration, phtsyn represents the oxygen production of the plants in the target river network through photosynthesis, and respt represents the oxygen consumption of the plants in the target river network through plant respiration. Indicates the first-order decay rate of ammonia nitrogen at a preset temperature, The temperature coefficient representing the decay rate of ammonia conversion to nitrite, Indicates the concentration of ammonia nitrogen, C DO represents the concentration of dissolved oxygen, HS_NH4 represents the half-saturated oxygen concentration corresponding to ammonia nitrogen, k COD represents the first-order degradation rate of chemical oxygen demand at a preset temperature, θ COD represents the Arrhenius temperature coefficient, C COD represents the chemical oxygen demand concentration, HS_COD represents the semi-saturated oxygen concentration corresponding to the chemical oxygen demand, θ SOD represents the temperature coefficient of SOD, HS_SOD represents the half-saturated oxygen concentration corresponding to SOD, T represents temperature, and T-20 represents that the preset temperature is 20°C;

[0031] Total phosphorus data is expressed by the following formula:

[0032]

[0033] Among them, C TP represents the concentration of phosphorus, k TP represents the yield of phosphorus released during the release of chemical oxygen demand, k COD represents the first-order degradation rate of chemical oxygen demand at a preset temperature, θ COD represents the Arrhenius temperature coefficient, C COD represents the chemical oxygen demand concentration, C DO represents the concentration of dissolved oxygen, HS_COD represents the half-saturated oxygen concentration corresponding to the chemical oxygen demand, Indicates the release of total phosphorus from sediment, UP P It represents the total amount of ammonia nitrogen taken up by plants in the target river network, and z is the total phosphorus coefficient.

[0034] The present application also provides a device for predicting the ability of a water purification plant to purify river network pollutants, the device comprising the following modules:

[0035] An acquisition module is used to obtain pollutant data of the target river network;

[0036] A hydrodynamic model module, used for constructing a hydrodynamic model of the target river network based on the pollutant data, the water flow area data of the river network, the hydraulic radius data, the cross-sectional flow data, the water level data and the flow distance data;

[0037] A purification model module, used to construct a purification model corresponding to the target water purification plant based on the measured water quality concentration and actual pollutant removal rate of the target water purification plant;

[0038] A prediction module, used for predicting pollutant data of the target river network after being purified by the target water purification plant based on the hydrodynamic model and the purification model;

[0039] A determination module is used to determine the pollutant purification capacity of the target water treatment plant based on the hydrodynamic model, the water environment of the target river network and the purified pollutant data.

[0040] The present application also provides an electronic device, the electronic device comprising a processor and a memory:

[0041] The memory is used to store a computer program and transmit the computer program to the processor;

[0042] The processor is used to execute the steps of the above-mentioned method for predicting the ability of a water treatment plant to purify river network pollutants according to the instructions in the computer program.

[0043] The present application also provides a computer-readable storage medium, characterized in that the computer-readable storage medium is used to store a computer program, and when the computer program is executed by an electronic device, the steps of the above-mentioned method for predicting the ability of a water treatment plant to purify river network pollutants are implemented.

[0044] Compared with the prior art, this application has the following beneficial effects:

[0045] The method provided in the present application can construct a hydrodynamic model of the target river network based on pollutant data, water flow area data of the river network, hydraulic radius data, cross-sectional flow data, water level data and flow distance data. The hydrodynamic model can accurately predict the hydraulic data flowing into the bypass of the water treatment plant. A purification model corresponding to the target water treatment plant is constructed based on the measured water quality concentration and actual pollutant removal rate of the target water treatment plant. The purification model can accurately predict the water purification efficiency of the water treatment plant. The pollutant data of the target river network after purification by the target water treatment plant is predicted based on the hydrodynamic model and the purification model. The pollutant purification capacity of the target water treatment plant is determined based on the hydrodynamic model, the water environment of the target river network and the pollutant data after purification, and finally the purification capacity of the water treatment plant is accurately predicted. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0047] Figure 1 A flow chart of a method for predicting the ability of a water purification plant to purify river network pollutants provided in an embodiment of the present application;

[0048] Figure 2 A schematic diagram of a river network structure provided as an example in an embodiment of the present application;

[0049] Figure 3 A schematic diagram of water circulation flow distribution in a river network when a water purification plant is in operation provided by an embodiment of the present application;

[0050] Figure 4 A schematic diagram of an intermediate monitoring section of a river network water quality in an example provided in an embodiment of the present application;

[0051] Figure 5 A schematic diagram of the structure of a device for predicting the ability of a water purification plant to purify river network pollutants provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] As described above, water treatment plants generally use bypass treatment to introduce slightly polluted water from the river network into the water treatment plant, purify the water through artificial rapid infiltration, coagulation sedimentation, filter cloth filter, etc., and then discharge it into the river. However, the existing technology does not have a method to accurately predict the ability of water treatment plants to purify pollutants.

[0053] After research, it was found that the purification capacity of a water treatment plant is to a certain extent related to the water environment of the target river network. It is rather one-sided to only consider the purification capacity of the water treatment plant. The plants that can carry out photosynthesis and the bacteria with purification ability in the water environment, as well as the self-purification factors of the river, will ultimately affect the prediction of the water treatment plant's ability to purify pollutants.

[0054] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0055] It is understandable that the method provided in the present application can be applied to a processing device, which is a processing device that can predict the purification capacity of a water treatment plant, such as a terminal device or a server that can predict the purification capacity of a water treatment plant. The method provided in the present application can be executed independently by a terminal device or a server, or it can be applied to a network scenario in which a terminal device and a server communicate, and is executed by the cooperation of a terminal device and a server. Among them, the terminal device can be a computer, a mobile phone and other devices. The server can be understood as an application server or a Web server. In actual deployment, the server can be a stand-alone server or a cluster server.

[0056] Figure 1 A flow chart of a method for predicting the ability of a water purification plant to purify river network pollutants provided in this application, the method comprising the following steps:

[0057] S101: Obtain pollutant data of the target river network.

[0058] The processing device obtains pollutant data of the target river network, and the pollutant data of the target river network may include primary controlled pollutants of the target river network and corresponding concentrations. The pollutant data of the target river network may also include secondary controlled pollutants and corresponding concentrations.

[0059] S102: Constructing a hydrodynamic model of the target river network based on pollutant data, water flow area data of the river network, hydraulic radius data, cross-sectional flow data, water level data and flow distance data.

[0060] The processing equipment can construct a hydrodynamic model of the target river network based on pollutant data, water flow area data of the river network, hydraulic radius data, cross-sectional flow data, water level data and flow distance data.

[0061] In a possible implementation, since design drawings are required when building a river network, information such as the connectivity layout of the river network and the location and scale of the gate and pump engineering will be recorded. In the target river network, there will be corresponding water flow and pollutant migration and transformation laws, which can be obtained by measuring the water quality. Based on the connectivity layout of the river network, the location and scale of the gate and pump engineering, the measured water quality data and other information, according to the flow and pollutant migration and transformation laws, the processing equipment can obtain pollutant data, river network water flow area data, hydraulic radius data, cross-sectional flow data, water level data and flow distance data under the condition of determining reasonable boundary conditions and initial conditions, and then construct the hydrodynamic model of the target river network based on pollutant data, river network water flow area data, hydraulic radius data, cross-sectional flow data, water level data and flow distance data. The hydrodynamic model can be used to predict the water flowing into the bypass of the water treatment plant.

[0062] Specifically, the processing equipment can extract the geometric information corresponding to the river network and generalize the river network structure, size and position through the geometric information. Then, the river section is reasonably generalized according to the measured terrain and water conservancy project data. Then, according to the design parameters, location and scheduling rules of the pump gate, it is generalized into the hydrodynamic model, including gate width, number of gate openings and closings, opening height, pump station drainage flow and time. Then, the measured water quality concentration and water level flow values ​​at the initial and boundary of the river network are input into the model as initial conditions and boundary conditions. Finally, the pollutants discharged into the river network in the study area are generalized and input into the model in the form of point sources. The required data include drainage and pollutant discharge concentration based on time series. The river roughness, diffusion coefficient and pollutant attenuation coefficient in the hydrodynamic calculation model and water quality calculation model are calibrated and verified.

[0063] In a possible implementation, the processing device can construct the Saint-Venant equations of the target river network based on pollutant data, water flow area data of the river network, hydraulic radius data, cross-sectional flow data, water level data and flow distance data, and the Saint-Venant equations include a water flow continuity equation, a water flow momentum equation and a one-dimensional convection equation;

[0064] The water flow continuity equation can be expressed by the following formula:

[0065]

[0066] Among them, X represents the flow distance, t represents the flow time, A represents the water flow area, and Q represents the cross-sectional flow rate;

[0067] The water flow momentum equation can be expressed as follows:

[0068]

[0069] Wherein, X represents the flow distance, t represents the flow time, A represents the water flow area, Q represents the cross-sectional flow rate, α represents the momentum correction coefficient, R represents the hydraulic radius, and n represents the roughness coefficient of the target river network;

[0070] The one-dimensional convection equation can be expressed as follows:

[0071]

[0072] Wherein, A represents the water flow area, C represents the pollutant data in the target river network, t represents the flow time, X represents the flow distance, Q represents the cross-sectional flow, D represents the water flow diffusion coefficient of the target river network, and K represents the attenuation coefficient.

[0073] S103: Construct a purification model corresponding to the target water purification plant based on the measured water quality concentration and actual pollutant removal rate of the target water purification plant.

[0074] The treatment equipment can construct a purification model corresponding to the target water treatment plant based on the measured water quality concentration and actual pollutant removal rate of the target water treatment plant.

[0075] In a possible implementation, since the water quality data of the upstream and downstream of the water treatment plant bypass can be easily obtained, the water quality data of the upstream and downstream of the water treatment plant bypass can be called the measured water quality concentration, and the actual pollutant removal rate can be obtained according to the measured water quality concentration. The actual pollutant removal rate can be used to set corresponding purification parameters for the purification model.

[0076] The function of a water treatment plant is to pump water with higher pollutant concentrations in the river network to the water treatment plant bypass, and after purification treatment measures such as filtration and sedimentation, the purified water is discharged back into the river network to participate in the river network water circulation.

[0077] Therefore, the treatment equipment can generalize the water treatment plant into a purification model. In the hydrodynamic model, a specific cross-sectional size is set for the river section to ensure that the pollutants in this section are reasonably simulated in the pollutant purification process of the water treatment plant; the model pollutant attenuation coefficient and sedimentation rate are determined to achieve the simulation of the actual pollutant removal effect of the water treatment plant; according to the measured pollutant concentrations at the inlet and outlet of the water treatment plant, the attenuation coefficient and sedimentation rate of the purified pollutants are adjusted to make them consistent with the measured inlet and outlet water concentrations of the water treatment plant, simulating the actual purification operation process of the water treatment plant.

[0078] S104: Predicting pollutant data of the target river network after being purified by the target water purification plant based on the hydrodynamic model and the purification model.

[0079] The processing equipment predicts pollutant data of the target river network after being purified by the target water purification plant based on the hydrodynamic model and the purification model. The pollutant data may include the concentration of the main controlled pollutants, and the pollutant data may also include the data of the secondary controlled pollutants.

[0080] S105: Determine the pollutant purification capacity of the target water treatment plant based on the hydrodynamic model, the water environment of the target river network and the pollutant data after purification.

[0081] The treatment equipment can determine the pollutant purification capacity of the target water treatment plant based on the hydrodynamic model, the water environment of the target river network and the purified pollutant data.

[0082] In one possible implementation, the treatment equipment can start from two aspects: water body dynamic conditions and water environment changes. The water body dynamic conditions can be determined by a hydrodynamic model. The river flow velocity, water exchange time, and pollutant compliance rate can be determined by the hydrodynamic model. The river flow velocity, water exchange time, and pollutant compliance rate are used as indicators to predict the purification capacity of the water treatment plant and determine the purification range and impact of the water treatment plant on the river network.

[0083] In a possible implementation, the water environment of the target river network can be represented by chemical oxygen demand data, ammonia nitrogen data, dissolved oxygen data, and total phosphorus data;

[0084] Chemical oxygen demand data can be expressed by the following formula:

[0085]

[0086] Among them, C COD represents the chemical oxygen demand concentration, t represents time, k COD represents the first-order degradation rate of chemical oxygen demand at a preset temperature, C DO represents the concentration of dissolved oxygen, HS_COD represents the half-saturated oxygen concentration corresponding to the chemical oxygen demand, The amount of sediment released to represent the chemical oxygen demand, θ COD represents the Arrhenius temperature coefficient, T represents temperature, and T-20 represents that the preset temperature is 20°C;

[0087] The ammonia nitrogen content data can be expressed by the following formula:

[0088]

[0089] in, represents the concentration of ammonia nitrogen, t represents time, UN P Indicates the total amount of ammonia nitrogen taken up by plants in the target river network, UN B represents the total amount of ammonia nitrogen taken up by bacteria in the target river network, Indicates the first-order decay rate of ammonia nitrogen at a preset temperature, represents the temperature coefficient of the decay rate of ammonia conversion to nitrite, HS_NH4 represents the half-saturated oxygen concentration corresponding to ammonia nitrogen, Indicates the amount of ammonia nitrogen released from the sediment, T indicates the temperature, and T-20 indicates that the preset temperature is 20°C;

[0090] Dissolved oxygen data can be expressed by the following formula:

[0091]

[0092] Among them, C DO represents the concentration of dissolved oxygen, t represents time, k2 represents the atmospheric reoxygenation rate, C S represents the saturated dissolved oxygen concentration, phtsyn represents the oxygen production of the plants in the target river network through photosynthesis, and respt represents the oxygen consumption of the plants in the target river network through plant respiration. Indicates the first-order decay rate of ammonia nitrogen at a preset temperature, The temperature coefficient representing the decay rate of ammonia conversion to nitrite, Indicates the concentration of ammonia nitrogen, C DO represents the concentration of dissolved oxygen, HS_NH4 represents the half-saturated oxygen concentration corresponding to ammonia nitrogen, k COD represents the first-order degradation rate of chemical oxygen demand at a preset temperature, θ COD represents the Arrhenius temperature coefficient, C COD represents the chemical oxygen demand concentration, HS_COD represents the semi-saturated oxygen concentration corresponding to the chemical oxygen demand, θ SOD represents the temperature coefficient of SOD, SOD_SOD represents the half-saturated oxygen concentration corresponding to SOD, T represents the temperature, and T-20 represents that the preset temperature is 20°C;

[0093] Total phosphorus data can be expressed by the following formula:

[0094]

[0095] Among them, C TP represents the concentration of phosphorus, k TP represents the yield of phosphorus released during the release of chemical oxygen demand, k COD represents the first-order degradation rate of chemical oxygen demand at a preset temperature, θ COD represents the Arrhenius temperature coefficient, C COD represents the chemical oxygen demand concentration, C DO represents the concentration of dissolved oxygen, HS_COD represents the half-saturated oxygen concentration corresponding to the chemical oxygen demand, Indicates the release of total phosphorus from sediment, UP PIt represents the total amount of ammonia nitrogen taken up by plants in the target river network, and z is the total phosphorus coefficient.

[0096] In one possible implementation, the treatment equipment can calculate the cross-sectional flow velocity in the river network to determine whether it reaches the preset ecological flow velocity, which can be 0.05 m / s, and count the percentage of the river length that reaches the ecological flow velocity in the total river length. The treatment equipment can then quantify the water exchange time in the river. Dissolved conservative substances are used as tracers, and conversion and degradation are not considered, only the convection and diffusion of substances are considered. The water exchange time is the time consumed when the concentration of the tracer substance in the water body is reduced to 1 / e (37%) of the initial concentration. According to the local river network management requirements, determine the compliance time and cross-sectional compliance rate of pollutants in key sections. All indicators are standardized, and each indicator layer is assigned a certain weight. The weighted average calculation method is used to quantitatively predict the improvement effect of the water treatment plant on the river network water quality.

[0097] The method provided in the present application can construct a hydrodynamic model of the target river network based on pollutant data, water flow area data of the river network, hydraulic radius data, cross-sectional flow data, water level data and flow distance data. The hydrodynamic model can accurately predict the hydraulic data flowing into the bypass of the water treatment plant. A purification model corresponding to the target water treatment plant is constructed based on the measured water quality concentration and actual pollutant removal rate of the target water treatment plant. The purification model can accurately predict the water purification efficiency of the water treatment plant. The pollutant data of the target river network after purification by the target water treatment plant is predicted based on the hydrodynamic model and the purification model. The pollutant purification capacity of the target water treatment plant is determined based on the hydrodynamic model, the water environment of the target river network and the pollutant data after purification, and finally the purification capacity of the water treatment plant is accurately predicted.

[0098] Next, we will take the case river network as an example to explain it in detail. The case river network belongs to a typical plain river network area, with a relatively flat terrain and a small altitude difference, which also leads to a relatively slow flow rate in most rivers, poor water dynamic exchange capacity in the river, and poor self-purification ability of the river.

[0099] The water purification plant project is located outside the outer ring road and uses the existing Dagu drainage river section for water purification. The current construction scale is to purify 200,000 tons of water per day. By constructing a water purification plant-river network hydrodynamic water quality model, the actual effect of the water purification plant on the water circulation and water quality improvement of the case river network is simulated.

[0100] Firstly, nutrients such as nitrogen and phosphorus and oxygen-consuming organic matter are the main causes of black and smelly river water and eutrophication. The present invention takes chemical oxygen demand, ammonia nitrogen and total phosphorus as the main controlled pollutants.

[0101] Then, based on the connectivity layout of the river network, the location and scale of the sluice and pump projects, the measured water quality data and other information, and according to the flow and pollutant migration and transformation laws, the boundary and initial conditions are reasonably determined to construct a river network hydrodynamic and water quality model. The controlling equations consist of the one-dimensional open channel unsteady flow Saint-Venant equations, the one-dimensional convection-diffusion equations and the water quality dynamics equations.

[0102] Figure 2 A schematic diagram of a river network structure for an example provided in the present application embodiment is used to extract and obtain the river network connectivity structure and river channel geometric dimensions from remote sensing images. The river network for the example includes two first-level rivers, namely Ziya River and Haihe River, and 13 second-level rivers, namely Dongchang Diversion Canal, Nanyunhe River, Jinhe River, Weijin River, Fuxing River, Changtai River, Sihua River, Jizhuangzi Drainage River, Nanfengchan River, Jingang Canal, Xianfeng Drainage River, Waihuan River, and Chentaizi Drainage River. According to the design parameters, location, and dispatching rules of the pump gate, it is input into the hydrodynamic model, including gate width, number of gate openings and closings, opening height, pump station diversion and drainage flow, and time data.

[0103] The water treatment plant is generalized as a 5.2km long river channel, and the river channel section is set as a trapezoidal section with a width of 40m at the top. At this time, the flow rate of the water body flowing through the river section of the water treatment plant is 0.02m / s. According to the measured pollutant concentrations at the inlet and outlet of the water treatment plant, the attenuation coefficient and sedimentation rate of the corresponding pollutants are adjusted to simulate the actual operation of the water treatment plant and verify the effect of pollutant removal.

[0104] The measured data of the river water purification plant project are shown in Table 1.

[0105] Table 1

[0106]

[0107] The water quality is monitored using influent, and the simulated water quality data is shown in Table 2.

[0108] Table 2

[0109]

[0110]

[0111] After that, the initial and boundary conditions and model parameters of the river network model can be determined; the measured water quality concentration and water level flow values ​​of the river network are input into the model as initial and boundary conditions. According to the river network engineering data, the hydrodynamic water quality calculation model is input to determine the key model parameters such as river roughness, diffusion coefficient, and pollutant degradation rate.

[0112] After determining the relevant parameters, the hydrodynamic-water quality model is calibrated and verified based on the measured water level and water quality data.

[0113] Figure 3A schematic diagram of water circulation flow distribution in a case river network during operation of a water purification plant provided in an embodiment of the present application.

[0114] The water treatment plant draws water from the Outer Ring River, and then discharges it into the Outer Ring River after the treated water meets the quality standards. By closing the Zhenxi Gate in the Yangliuqing section of the South Grand Canal and opening the corresponding node gates on the circulation route, the effluent from the water treatment plant can flow northward through the Outer Ring River into the South Grand Canal, participating in the water circulation in the central urban area. The designed circulation route is water treatment plant effluent - Outer Ring River - South Grand Canal - Jin River - Wei Jin River, Sihua River, Jizhuangzi River, Fuxing River - Outer Ring River. The water circulation scheme is simulated to predict the changes in water quality of the river network sections, and the purification capacity of the water treatment plant is evaluated based on indicators such as section flow velocity, pollutant removal rate, and section compliance rate. When the water treatment plant is in operation, the river flow distribution of the water circulation in the river network is as follows: Figure 3 The maximum flow velocity, minimum flow velocity and average flow velocity of the river section in the river network are calculated, and the results are shown in Table 3.

[0115] Table 3

[0116]

[0117]

[0118] Determine whether the flow rate of each river reaches the ecological flow rate (0.05m / s), analyze the flow state of the river, and quantify the water exchange time in the river. Use dissolved conservative substances as tracers, without considering transformation and degradation, only considering the convection and diffusion of substances. The water exchange time is the time consumed when the concentration of the tracer substance in the water body is reduced to 1 / e (37%) of the initial concentration.

[0119] Figure 4 A schematic diagram of an intermediate monitoring section of water quality in a river network as provided in an example embodiment of the present application.

[0120] The present application also provides a Figure 5 The schematic diagram of the structure of a device for predicting the ability of a water purification plant to purify river network pollutants is shown. The device 500 for predicting the ability of a water purification plant to purify river network pollutants includes the following modules:

[0121] An acquisition module 501 is used to acquire pollutant data of a target river network;

[0122] A hydrodynamic model module 502 is used to construct a hydrodynamic model of the target river network based on the pollutant data, the water flow area data of the river network, the hydraulic radius data, the cross-sectional flow data, the water level data and the flow distance data;

[0123] A purification model module 503 is used to construct a purification model corresponding to the target water purification plant based on the measured water quality concentration and the actual pollutant removal rate of the target water purification plant;

[0124] A prediction module 504, configured to predict pollutant data of the target river network after being purified by the target water purification plant based on the hydrodynamic model and the purification model;

[0125] The determination module 505 is used to determine the pollutant purification capacity of the target water treatment plant based on the hydrodynamic model, the water environment of the target river network and the purified pollutant data.

[0126] The device provided in the present application can construct a hydrodynamic model of the target river network based on pollutant data, water flow area data of the river network, hydraulic radius data, cross-sectional flow data, water level data and flow distance data. The hydrodynamic model can accurately predict the hydraulic data flowing into the bypass of the water treatment plant. A purification model corresponding to the target water treatment plant is constructed based on the measured water quality concentration and actual pollutant removal rate of the target water treatment plant. The purification model can accurately predict the water purification efficiency of the water treatment plant. The pollutant data of the target river network after purification by the target water treatment plant is predicted based on the hydrodynamic model and the purification model. The pollutant purification capacity of the target water treatment plant is determined based on the hydrodynamic model, the water environment of the target river network and the pollutant data after purification, and finally the purification capacity of the water treatment plant is accurately predicted.

[0127] An embodiment of the present application also provides a device for predicting the ability of a water treatment plant to purify pollutants in a river network, wherein the device includes a memory and a processor, the memory is used to store instructions or codes, and the processor is used to execute the instructions or codes so that the device executes the steps of the method for predicting the ability of a water treatment plant to purify pollutants in a river network as described in any embodiment of the present application.

[0128] In practical applications, the computer-readable storage medium may be any combination of one or more computer-readable media, which may be a computer-readable signal medium or a computer-readable storage medium.

[0129] The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0130] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0131] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0132] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0133] It should be noted that each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device embodiment described above is merely schematic, in which the unit described as a separate component may or may not be physically separated, and the component prompted as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative work.

[0134] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for predicting the ability of a water treatment plant to purify river network pollutants, characterized in that: include: Obtain pollutant data of the target river network; Constructing a hydrodynamic model of the target river network based on the pollutant data, the water flow area data, hydraulic radius data, cross-sectional flow data, water level data and flow distance data of the river network; Constructing a purification model corresponding to the target water purification plant based on the measured water quality concentration and actual pollutant removal rate of the target water purification plant; Predicting pollutant data of the target river network after being purified by the target water purification plant based on the hydrodynamic model and the purification model; The pollutant purification capacity of the target water purification plant is determined based on the hydrodynamic model, the water environment of the target river network and the purified pollutant data.

2. The method according to claim 1, characterized in that The step of constructing the hydrodynamic model of the target river network based on the pollutant data, the water flow area data, hydraulic radius data, cross-sectional flow data, water level data and flow distance data of the river network includes: Constructing the Saint-Venant equations of the target river network based on the pollutant data, the water flow area data, hydraulic radius data, cross-sectional flow data, water level data and flow distance data of the river network, wherein the Saint-Venant equations include a water flow continuity equation, a water flow momentum equation and a one-dimensional convection equation; The water flow continuity equation is expressed by the following formula: Among them, X represents the flow distance, t represents the flow time, A represents the water flow area, and Q represents the cross-sectional flow rate; The water flow momentum equation is expressed by the following formula: Wherein, X represents the flow distance, t represents the flow time, A represents the water flow area, Q represents the cross-sectional flow rate, α represents the momentum correction coefficient, R represents the hydraulic radius, and n represents the roughness coefficient of the target river network; The one-dimensional convection equation is expressed by the following formula: Wherein, A represents the water flow area, C represents the pollutant data in the target river network, t represents the flow time, X represents the flow distance, Q represents the cross-sectional flow, D represents the water flow diffusion coefficient of the target river network, and K represents the attenuation coefficient.

3. The method according to claim 1, characterized in that The water environment of the target river network can be represented by chemical oxygen demand data, ammonia nitrogen data, dissolved oxygen data and total phosphorus data; The chemical oxygen demand data is expressed by the following formula: Among them, C COD represents the chemical oxygen demand concentration, t represents time, k COD represents the first-order degradation rate of chemical oxygen demand at a preset temperature, C DO represents the concentration of dissolved oxygen, HS_COD represents the half-saturated oxygen concentration corresponding to the chemical oxygen demand, The amount of sediment released to represent the chemical oxygen demand, θ COD represents the Arrhenius temperature coefficient, T represents temperature, and T-20 represents that the preset temperature is 20°C; The ammonia nitrogen content data is expressed by the following formula: in, represents the concentration of ammonia nitrogen, t represents time, UN P Indicates the total amount of ammonia nitrogen taken up by plants in the target river network, UN B represents the total amount of ammonia nitrogen taken up by bacteria in the target river network, Indicates the first-order decay rate of ammonia nitrogen at a preset temperature, represents the temperature coefficient of the decay rate of ammonia conversion to nitrite, HS_NH4 represents the half-saturated oxygen concentration corresponding to ammonia nitrogen, Indicates the amount of ammonia nitrogen released from the sediment, T indicates the temperature, and T-20 indicates that the preset temperature is 20°C; The dissolved oxygen data is expressed by the following formula: Among them, C DO represents the concentration of dissolved oxygen, t represents time, k2 represents the atmospheric reoxygenation rate, C S represents the saturated dissolved oxygen concentration, phtsyn represents the oxygen production of the plants in the target river network through photosynthesis, and respt represents the oxygen consumption of the plants in the target river network through plant respiration. Indicates the first-order decay rate of ammonia nitrogen at a preset temperature, The temperature coefficient representing the decay rate of ammonia conversion to nitrite, Indicates the concentration of ammonia nitrogen, C DO represents the concentration of dissolved oxygen, HS_NH4 represents the half-saturated oxygen concentration corresponding to ammonia nitrogen, k COD represents the first-order degradation rate of chemical oxygen demand at a preset temperature, θ COD represents the Arrhenius temperature coefficient, C COD represents the chemical oxygen demand concentration, HS_COD represents the semi-saturated oxygen concentration corresponding to the chemical oxygen demand, θ SOD represents the temperature coefficient of SOD, SOD_SOD represents the half-saturated oxygen concentration corresponding to SOD, T represents the temperature, and T-20 represents that the preset temperature is 20°C; Total phosphorus data is expressed by the following formula: Among them, C TP represents the concentration of phosphorus, k TP represents the yield of phosphorus released during the release of chemical oxygen demand, k COD represents the first-order degradation rate of chemical oxygen demand at a preset temperature, θ COD represents the Arrhenius temperature coefficient, C COD represents the chemical oxygen demand concentration, C DO represents the concentration of dissolved oxygen, HS_COD represents the half-saturated oxygen concentration corresponding to the chemical oxygen demand, Indicates the release of total phosphorus from sediment, UP P It represents the total amount of ammonia nitrogen taken up by plants in the target river network, and z is the total phosphorus coefficient.

4. A device for predicting the ability of a water purification plant to purify river network pollutants, characterized in that: include: An acquisition module is used to obtain pollutant data of the target river network; A hydrodynamic model module, used to construct a hydrodynamic model of the target river network based on the pollutant data, the water flow area data of the river network, the hydraulic radius data, the cross-sectional flow data, the water level data and the flow distance data; A purification model module, used to construct a purification model corresponding to the target water purification plant based on the measured water quality concentration and actual pollutant removal rate of the target water purification plant; A prediction module, used for predicting pollutant data of the target river network after being purified by the target water purification plant based on the hydrodynamic model and the purification model; A determination module is used to determine the pollutant purification capacity of the target water treatment plant based on the hydrodynamic model, the water environment of the target river network and the purified pollutant data.

5. An electronic device, characterized in that: comprising a memory and a processor, wherein: The memory is used to store the computer program; The processor is used to execute the computer program to implement the method for predicting the ability of a water treatment plant to purify river network pollutants as described in any one of claims 1-3.

6. A computer-readable storage medium, characterized in that: Used to store a computer program, wherein when the computer program is executed by a processor, the method for predicting the ability of a water treatment plant to purify river network pollutants as described in any one of claims 1 to 3 is implemented.

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