KR102193196B1 - Method for analysis of mixing and reaction of contaminants using a river storage zone model reflecting hydraulic and geometric characteristics of river and apparatus thereof - Google Patents

Abstract

In one embodiment of the present invention, provided is a method for analyzing the mixing and reaction behavior of pollutants using a river storage model reflecting irrigation and topographic characteristics of the river. The method comprises: an equation design step of designing the governing equation for a numerical model in consideration of delay characteristics which delay the movement of pollutants in the river; a model construction step of constructing the numerical model based on the designed governing equation; a comparison step of simulating an analytical solution of the designed governing equation in a virtual water channel and comparing the simulated result with the constructed numerical model; and a determination step of determining the constructed numerical model as a verified numerical model when the comparison result satisfies a preset condition. Disclosed is the method for analyzing the mixing and reaction behavior of pollutants using a river storage model reflecting the irrigation and topographic characteristics of the river.

Description

A method for analysis of mixing and reaction of contaminants using a river storage zone model reflecting hydraulic and geometric characteristics of river and apparatus thereof}

The present invention relates to a method for analyzing the mixing and reaction behavior of pollutants using a river storage model model that reflects river repair and topographic characteristics, and more specifically, analyzes the movement and mixing behavior of chemical substances introduced into the river. It relates to a method for predicting and an apparatus for implementing the method.

As the recent industrial development progresses rapidly, the use of chemical raw materials is also rapidly increasing, and fire, explosion, and transportation accidents occur during the handling and distribution of chemical raw materials, and accidents in which various hazardous chemicals are introduced into the river frequently occur. Is occurring. Therefore, when such an accident occurs, it is required to establish a water quality accident response system for rapid response, and to this end, a tracking model is required to analyze and predict the movement and mixing behavior of chemical substances introduced into the river.

In order to analyze the movement and mixing behavior of chemical substances introduced into the river, a one-dimensional transport-diffusion model adopted by the National Institute of Environmental Sciences is used in Korea. This model is used to transport pollutants in the river by the average flow rate and turbulent flow. It is a basic model that can analyze the dispersive mixing behavior.

However, if you look at the behavior of the material that actually flows into the river, there is a strong phenomenon that some of the pollutants are retarded by shoals and puddles, vegetation, hand structures, and riverbed materials, and as only some of the pollutants are delayed, the pollutant cloud This long tailed form is observed. In addition, if the movement is delayed for only a part of the pollutant, the concentration change curve over time is also distorted to form a tail. If the above distortion occurs, accurate prediction is impossible with the existing one-dimensional transfer-diffusion model. There are limitations.

Republic of Korea Patent Publication No. 10-2009-0111976 (published on October 28, 2009)

1.Simulation of solute transport in a mountain pool-and-riffle stream: A transient storage model. Water Resources Research. 19, 718-724. (1983) 2.One-dimensional Transport with Inflow and Storage (OTIS): a solute transport model for streams and rivers, Water-Resources Investigation Report 98-4018, Reston, VA: US Geological Survey. (1998) 3.Parameter uncertainty estimation of transient storage model using Bayesian inference with formal likelihood based on breakthrough curve segmentation. (2019.10.14) 4. Uncertainty evaluation of the storage model model in preparation for the leakage of hazardous substances in rivers (2019.02.21)

The technical problem to be solved by the present invention is a numerical model capable of comprehensively predicting the behavior of pollutants in consideration of the characteristics of a storage zone and a bed that delay the movement of pollutants when pollutants are introduced into a river. To provide.

A method according to an embodiment of the present invention for solving the above technical problem is a method for analyzing the mixing and reaction behavior of pollutants using a river storage model model that reflects river repair and topographic characteristics, wherein the pollutant movement in the river Equation design step of designing a governing equation for the numerical model in consideration of the delay characteristic that delays the value; A model building step of constructing a numerical model based on the designed governing equation; A comparison step of simulating an analytical solution of the designed governing equation in a virtual channel and comparing the simulated result with the simulation result of the constructed numerical model; And a determining step of determining the constructed numerical model as a verified numerical model when the comparison result satisfies a preset condition.

In the above method, the flow of the river includes the flow of the main stream zone, the storage zone, and the bottom zone, and the delay characteristic may be a characteristic in which the movement of the pollutant is delayed by the storage zone.

In the above method, the flow of the river includes the flow of the main stream zone, the storage zone, and the bottom zone, and the delay characteristic may be a characteristic in which the movement of the pollutant is delayed by the bottom zone.

In the above method, in the step of designing the equation, the governing equation may be designed in consideration of the degree to which the pollutant is adsorbed or desorbed in the bed, or volatilized or biodegraded in the bed.

In the method, the comparing step may include discretizing the governing equation using a finite difference method and a Crank-Nicolson algorithm.

An apparatus according to another embodiment of the present invention for solving the above technical problem is an apparatus for analyzing the mixing and reaction behavior of pollutants using a river storage table model reflecting river repair and topographic characteristics, wherein the pollutants of the river An equation design unit for designing a governing equation for the numerical model in consideration of the delay characteristics that delay the movement; A model building unit for constructing a numerical model based on the designed governing equation; A comparison unit that simulates an analytical solution of the designed governing equation in a virtual channel and compares the simulated result with the simulation result of the constructed numerical model; And a determination unit configured to determine the constructed numerical model as a verified numerical model when the comparison result satisfies a preset condition.

In the above apparatus, the flow of the river includes the flow of the main stream zone, the storage zone, and the bottom zone, and the delay characteristic may be a characteristic in which the movement of the pollutant is delayed by the storage zone.

In the above apparatus, the flow of the river includes the flow of the main stream zone, the storage zone, and the bottom zone, and the delay characteristic may be a characteristic in which the movement of the pollutant is delayed by the bottom zone.

In the apparatus, the equation designing unit may design the governing equation in consideration of the degree to which the pollutant is adsorbed or desorbed in the bed, or volatilized or biodegraded in the bed.

In the above apparatus, the comparison unit may discretize the governing equation using a finite difference method and a Crank-Nicolson algorithm.

An embodiment of the present invention discloses a computer-readable recording medium storing a program for executing the method.

The present invention is effectively applied to the field of disaster prevention for responding to water pollution accidents, and when pollutants are discharged into a river, it can help to quickly respond.

In particular, according to the present invention, the behavior of the pollutant can be accurately predicted even when there is a delay in movement of the pollutant according to the storage zone and the bed due to the inherent adsorption characteristic of the pollutant.

The numerical model according to the present invention is a numerical model for analyzing the mixing of hazardous chemicals in a river, and mixing of chemicals that are transported and diffused downstream using the inflow location and inflow amount as input data when an accident occurs It is possible to build a river water accident response system that can quickly respond to water quality accidents by simulating the behavior and outputting concentration curves at important points such as water intake stations, providing information on arrival time and residence time to river managers and related engineers. There is an effect.

1 is a block diagram showing an example of an apparatus according to the present invention.
2 is a diagram schematically showing the overall flow diagram of the method according to the present invention.
3 is a view showing a mechanism for implementing a phenomenon that may occur when pollutants introduced into a river flow in a numerical model according to the present invention.
4 is a diagram illustrating a method according to another embodiment of the present invention.
FIG. 5 is a flowchart illustrating a process of evaluating parameters and driving a model when an accident of introducing hazardous chemicals into a river occurs.
6 shows various formulas for determining the reaction coefficient.
7 is a diagram schematically showing the result of comparing the numerical model and the analysis solution according to the present invention.
8 is a diagram showing a section in which a tracer experiment was performed in the present invention.
9 and 10 schematically show the result of comparing the result of the simulation by inputting the four storage table parameter values into the numerical model and the result of the concentration measurement from the tracer experiment.
11 is a diagram schematically showing the entry point of hazardous chemicals and the location of the Nakseong pumping station in the scenario simulation section of Gamcheon, which is 25 km long.
12 is an example of a graph showing a result of predicting a concentration distribution for each pollutant introduced into Gamcheon.
13 is a diagram schematically showing the rate of change of the peak concentration value of the concentration curve according to the reaction coefficient and the hydraulic conditions.

As for terms used in the embodiments, general terms that are currently widely used as possible are selected while considering functions in the present invention, but this may vary according to the intention or precedent of a technician working in the field, the emergence of new technologies, and the like. In addition, in certain cases, there are terms arbitrarily selected by the applicant, and in this case, the meaning of the terms will be described in detail in the description of the corresponding invention. Therefore, the terms used in the present invention should be defined based on the meaning of the term and the overall contents of the present invention, not a simple name of the term.

When a part of the specification is said to "include" a certain component, it means that other components may be further included rather than excluding other components unless otherwise stated. In addition, terms such as "... unit" and "... module" described in the specification mean a unit that processes at least one function or operation, which may be implemented as hardware or software or a combination of hardware and software.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those of ordinary skill in the art may easily implement the present invention. However, the present invention may be implemented in various forms and is not limited to the embodiments described herein.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the drawings.

1 is a block diagram showing an example of an apparatus according to the present invention.

Referring to FIG. 1, it can be seen that the apparatus 10 according to the present invention includes a database 110, a data processing unit 130, and a data output unit 150. Depending on the embodiment, the data processing unit 130 may further include a model building unit 131 and a model verification unit 133, and as will be described later, the data processing unit 130 includes a model building unit 131 and a model In addition to the verification unit 133, other modules may be further included. The device 10 according to the present invention may not only be implemented as a physical device, but also may be implemented as a logical device such as a program composed of a script, and well-known smart devices (smartphone, tablet PC, notebook computer) ), it can be operated to provide the most appropriate information to the user.

Hereinafter, after comprehensively describing each component (block) constituting the device 10 according to the present invention, a function of each component will be described in detail with reference to FIGS. 2 to 13.

The database 110 stores and manages information for operating the device 10 according to the present invention. The database 110 may perform a function of receiving a command from the data processing unit 130, searching for information stored in the database 110, and transmitting the searched information to the data processing unit 130. The database 110 may receive, store and manage not only previously stored data, but also newly generated data as a result of processing by the data processing unit 130.

The data processing unit 130 receives various types of data from the database 110 and processes the data. When implemented as a physical device, the data processing unit 130 may include a processor having a specification of an appropriate level to perform various processing functions. The data processing unit 130 may calculate new data by processing the data received from the database 110, and the calculated data may be transmitted to the database 110 to be stored and managed. In addition, if there is data that needs to be output through the display unit of the user's smart device among the calculated data, the data processing unit 130 transmits the data to the data output unit 150 to visually display the necessary information through the display unit of the smart device. Check with and allow specific input.

The data output unit 150 processes visual information so that the result of the data processing unit 130 is displayed on the display unit of the smart device by interlocking with the smart device (PC, tablet PC, smartphone) used by the user. .

In the present invention, the numerical model is a model that predicts the behavior of pollutants in a stream based on the input stream information and pollutant information, and is regarded as a model constructed by a governing equation. In addition, the data processing unit 130 is a module that performs a function of processing and processing various types of data, and may include a model building unit 131 and a model verification unit 133 as shown in FIG. 1, and will be described later according to embodiments. It may include an equation design section, a model building section, a comparison section, and a decision section. Functions performed by the equation design unit, the model building unit, the comparison unit, and the determination unit will be described later with reference to FIGS. 3 to 13.

2 is a diagram schematically showing the overall flow diagram of the method according to the present invention.

Since the method according to FIG. 2 can be implemented by the apparatus of FIG. 1, it will be described below with reference to FIG. 1.

More specifically, FIG. 2 shows the method according to the present invention most comprehensively, and the subject performing each step may vary according to embodiments.

The data processing unit 130 designs a governing equation for the behavior of pollutants in a stream in consideration of delay characteristics in the storage zone and the river bed (S210). The flow of the river consists of the flow of the main stream zone, storage zone, and river bed, and the pollutants in the main stream have the characteristics of moving according to the average flow velocity of the river, but the movement of pollutants in the storage zone and the river bed is It has different properties from the transport of pollutants.

3 is a diagram showing a mechanism for implementing a phenomenon that may occur while a pollutant introduced into a river flows in a numerical model according to the present invention.

Referring to FIG. 3, it can be seen that the pollutants introduced into the river are transported and dispersed by the flow in the main stream, storage, and upper streams constituting the stream. Specifically, contaminants that reach the reservoir, such as shoals or puddles, are delayed in movement due to the storage effect according to the shape of the reservoir, and the pollutants that reach the bed are adsorbed to the bed surface due to the bending characteristics of the bed. Movement is delayed in the process of becoming or detached.

In addition to the above-described process, when the flow rate of the main stream increases by more than a certain level or the water temperature in the main stream increases above a certain temperature, pollutants introduced into the river are volatilized or biochemically decomposed, and volatilization or biochemical decomposition of pollutants is Contaminants introduced from the upstream are partially lost in the downstream of the river.

Subsequently, the data processing unit 130 constructs a numerical model based on the governing equation (S230). The numerical model constructed in step S230 is based on the governing equation designed in step S210, and in the present invention, in designing the governing equation, it reflects the delay characteristics of the storage zone and the bed that were not previously considered. The numerical model constructed accordingly can more accurately describe the behavior of pollutants compared to the conventional numerical model.

The data processing unit 130 obtains an analytic solution of the governing equation, compares the simulation result according to the analysis solution with the simulation result of the numerical model (S250), and finally determines the numerical model based on the comparison result ( S270). For convenience of explanation, steps S250 to S270 will be schematically described with reference to FIGS. 4 to 13.

4 is a diagram illustrating a method according to another embodiment of the present invention.

FIG. 4 is a diagram illustrating the method described in FIG. 2 in more detail. Referring to FIG. 4, the method according to the present invention largely models (builds) a numerical model and verifies the modeled numerical model from multiple angles. It can be seen that it is divided into.

First, the step of modeling a numerical model (S410) includes a step of developing a governing equation (S411), a step of numerical modeling (S413), a step of deriving an analysis solution (S415), and a step of performing a tracer experiment (S417). can do.

In the one-dimensional stream storage model for the mixing analysis of harmful chemical substances introduced into the river, not only the transport and dispersion of substances in the water body, but also the storage effect, adsorption and desorption to the river bed, volatilization, Biochemical degradation mechanisms are further included. The governing equations of the numerical model for including all of these complex mixing mechanisms are as shown in Equations 1 to 3.

Equations 1 to 3 represent the governing equations required to construct a numerical model that predicts the behavior of pollutants in a river. Specifically, Equation 1, Equation 2, and Equation 3 mean equations for the main stream zone, the storage zone, and the river bed zone, respectively. In Equations 1 to 3, C _F , C _s , C _b denote the concentrations of contaminants in the mainstream zone, storage zone, and bed zone, respectively, and K _F , A _F , A _s , α are dispersion coefficients, The area of the mainstream zone, the area of the storage zone, and the mass exchange coefficient between the mainstream zone and the storage zone are respectively, and k _s , k _v , and λ are reaction coefficients, the degree to which contaminants are adsorbed and desorbed from the bed, the degree of volatilization in the main stream, It refers to the degree of biodegradation in the mainstream, storage, and bottom, respectively.

In particular, in the storage zone, a linear mass exchange occurs at the rate at which the material exchange coefficient α is applied to the difference in concentration from the mainstream zone, and the high concentration of contaminants in the mainstream zone enters the storage zone and stagnates for a certain period of time, then gradually becomes the storage zone. Equation 2 is additionally considered in order to reflect the phenomena that escape from the equation, and since Equation 2 is further considered in the governing equation, the apparatus according to the present invention can simulate the distortion of the concentration curve generated in the mainstream. do.

As described above, after the governing equation is designed in consideration of the storage effect of the reservoir and the adsorption and desorption effect of the bottom, the data processing unit 130 converts the governing equation to the finite difference method and the Crank-Nicolson algorithm. , Discretization (S413). In addition, the data processing unit 130 may derive an analytic solution of the governing equation, simulate it in a virtual watercourse, and compare the simulated result with the constructed numerical model (S415).

In addition, the data processing unit 130 may perform a tracer experiment in the step of modeling the numerical model (S410) to obtain concentration measurement data (S417), and the concentration measurement data of the pollutant obtained in this process verifies the numerical model. It is utilized in the step (S430). In the present invention, the tracer experiment was performed in Gamcheon and Cheongmicheon, which are the locations of the Republic of Korea, but it is obvious to those of ordinary skill in the art that the results of performing the tracer experiment in other rivers can be applied to the present invention according to the embodiment. something to do.

In addition, each step in FIG. 4 may be performed by a lower module included in the data processing unit 130 instead of the data processing unit 130. For example, the step of designing the governing equation (S411) may be performed by the model building unit or the equation design unit described in FIG. 1.

FIG. 5 is a flowchart illustrating a process of evaluating parameters and driving a model when an accident of introducing hazardous chemicals into a river occurs.

5 shows the results of extending the process performed in the existing one-dimensional transport-diffusion numerical model to the present invention, and elements not considered in designing the governing equation in the existing one-dimensional transport-diffusion numerical model As a result, factors for adsorption and desorption, volatilization, and biochemical degradation of pollutants on the bedside are further added.

Figure 5 is a calculation of the distribution-type storage model parameters for each location by inputting hydraulic and topographic factors calculated through HEC-RAS, a one-dimensional dynamic hydrodynamic model, into the storage model parameter estimation equation for the flow scenario in the target river. It specifically shows the process of doing. 5, after determining the storage zone parameters and reaction coefficients as input data, setting a river inflow scenario of any hazardous chemicals, and applying the river storage zone numerical model for a long section of Gamcheon 25 km or more in Korea. By evaluating the field applicability, analyzing the sensitivity of the simulated result value of the storage model according to the reaction coefficient, the contents of suggesting the significance scale of adsorption and desorption, volatilization, and biodegradation of contaminants are sequentially shown.

6 shows various formulas for determining the reaction coefficient.

As a mechanism similar to the storage effect of the storage bed, the mechanism of adsorption and desorption with the bed is also included in the governing equation. Unlike the storage effect, the adsorption and desorption rate is relatively slow compared to the adsorption rate. It can be seen that the adsorption and desorption rate is determined based on the difference between the concentration of the mainstream zone and the bottom zone concentration in a balanced state. The equilibrium concentration C _b,eq is determined from the distribution coefficient K _p between the water body in the main stream and the stream in the bottom area, and the distribution coefficient K _p of the similar in the bottom area is derived from the unique octanol-water distribution coefficient K _ow for each substance. Is determined.

The adsorption and desorption coefficient k _s can be determined from the linear regression equation with the partition coefficient. The volatilization coefficient k _v can be determined from the reaeration coefficient k _r and the diffusion coefficient D _o2 representing the mass exchange of coral at the interface between liquid and gas based on the film theory of two phases, water and air. At this time, since the number of re-detonation machines k _r is determined from the flow velocity and depth in the open channel, the volatilization coefficient k _v also depends on the flow velocity and depth in the open channel.

In FIG. 6, the biodegradation coefficient λ is determined from the half-life, which is the period required to reduce the concentration of a substance to 50%, and the unit of the biodegradation coefficient is the reciprocal of time. As described in FIG. 5, in the present invention, in the process of verifying and evaluating the numerical model, sensitivity analysis on the reaction coefficient is performed, and the rate of change of the simulation result according to the reaction coefficient is evaluated, so that the reaction factor is significant at the coefficient above the threshold. Evaluate Hanji.

7 is a diagram schematically showing the result of comparing the numerical model and the analysis solution according to the present invention.

7 is a result of obtaining an analysis solution by applying the Laplace transform to the governing equation designed by the data processing unit 130 for the main stream zone, storage zone, and river bed zone, and comparing the numerical model with the analysis solution.

When the governing equations for the storage zone concentration and the bottom zone concentration are converted to the Laplace variable for the time variable, the conversion result has a linear relationship with the mainstream concentration. Based on the above fact, if Equations 1 to 3 are summarized as one equation for the concentration in the mainstream, it is the same as Equation 4 to be described later, and the mathematical definition of the unknowns used in Equation 4 is Equation 5 to be described later. To Equation 7.

The solution of the second-order linear second-order ordinary differential equation for the concentration in the main stream, such as Equation 4, is in the form of an exponential function, and when Equation 4 is solved for the concentration in the main stream, the same solution as in Equation 8 can be obtained.

In Equation 4, the concentration injection condition at the upstream end is set as the boundary condition.If the set result is set as a heavyside calculation function to inject a certain concentration for a certain period of time, and the set result is converted to Laplace and loaded into the analysis solution, the same as Equation 9 The result is derived.

In order to compare the simulated results using the analysis solution as shown in Equation 9 and the simulated results through the numerical model under the same conditions, an arbitrary virtual channel was assumed as shown in FIG. 7 and the simulation results under two boundary conditions. As a result of comparing with, an accuracy of 0.99 or more was observed for all of the determination coefficients, and these results are schematically shown in the graphs of Case 1 and Case 2 of FIG.

As described above, as a result of comparing the numerical model and the analysis solution of the governing equation, the data processing unit 130 verifies the storage model through a tracer experiment if a predetermined condition such as a result exceeding a certain coefficient of determination is satisfied.

8 is a diagram showing a section in which a tracer experiment was performed in the present invention.

In Figure 8, the tracer experiment was conducted in Cheongmicheon, Korea in 2019, and Gamcheon, Korea in 2019 and 2020, and as a tracer, a 20% solution of rhodamine WT, a fluorescent substance, was used. In addition, a YSI rhodamine sensor (YSI-6130) was used as the concentration measuring sensor.

In (a) and (b) of Fig. 8, injection points and lateral lines within the experimental section are indicated, and 3 to 5 sub-sections are divided based on the lateral line. Table 1 shows the storage parameters optimized by performing inverse modeling for each sub-section included in the experimental section.

9 and 10 schematically show the result of comparing the result of the simulation by inputting the four storage parameter values into the numerical model and the result of the concentration measurement from the tracer experiment.

According to FIGS. 9 and 10, in all three cases, an accuracy of 0.9 or more of the determination coefficient (in order of Cheongmicheon in 2019, Gamcheon in order of 0.91, 0.94, Gamcheon in 2020 is not shown) is calculated, and the storage zone model derived from the governing equation It can be seen that it accurately reproduces the pollutant mixing behavior in the actual river.

11 is a diagram schematically showing the entry point of hazardous chemicals and the location of the Nakseong pumping station in the scenario simulation section of Gamcheon, which is 25 km long.

In FIG. 11, a result of setting a scenario for introducing hazardous chemical substances into a water system and applying the verified storage model to the long section of Gamcheon (about 25 km long) is shown. In FIG. 11, the location where hazardous chemicals were introduced is the junction of Gamcheon and Daegwangcheon adjacent to the Gimcheon Industrial Complex, and the introduced hazardous chemicals are toluene, bromine, and methyl ethyl ketone, and substances actually handled in the Gimcheon Industrial Complex It was selected as

Fig. 11 shows the target watershed and main structures for scenario simulation, and Gamcheon is divided into 40 sub-sections for the storage model, and only some of them are shown in Fig. 11. In the scenario simulation, parameters were determined for each sub-section by using the storage-large model parameter estimation equation, and the equation for obtaining the parameter value is as follows.

Equations 10 to 13 are equations used to calculate the dispersion coefficient K _F of Gamcheon, the main stream area A _F , the storage area A _s, and the mass exchange coefficient α between the main stream and storage areas, respectively, The parameters calculated through Equations 10 to 13 are summarized in Table 2.

In addition, the reaction coefficients that determine the reactivity of the three types of hazardous chemicals to be simulated are shown in Table 3.

Using the parameters and reaction coefficients in Tables 2 and 3 as input conditions, the stream storage model was simulated, and the result of outputting the concentration curve at the Nakseong pumping station located about 20 km downstream from the inflow point is shown in FIG. .

12 is an example of a graph showing a result of predicting a concentration distribution for each pollutant introduced into Gamcheon.

Referring to FIG. 12, it can be seen that the change in the concentration of the pollutant is different according to the type of the chemical. More specifically, referring to FIG. 12, the time to reach the peak concentration of toluene, which has the largest octanol-water partition coefficient value representing the adsorption and desorption mechanism, is the latest, and the mass attenuation of toluene has the least effect of volatilization. It can be seen that the mass attenuation of bromine is the least, and the diffusion coefficient and biodegradation coefficient are the largest.

In addition, when comparing the simulation results of bromine and methyl ethyl ketone, it was observed that the concentration of methyl ethyl ketone was higher in the whole region, so that the effect of volatilization was greater than that of the biochemical reaction. From FIG. 12, it can be evaluated that the difference in the simulation result according to the response coefficient shows a reasonable meaning.

13 is a diagram schematically showing the rate of change of the peak concentration value of the concentration curve according to the reaction coefficient and the hydraulic conditions.

13 is a view for presenting a measure of significance of a reaction coefficient specific to a chemical substance as a result of a sensitivity analysis of the simulation result of a storage zone model according to the reaction coefficient.

If the result of the simulation that decreases due to the effect of the response coefficient is less than 10%, the simulation result is determined to be insensitive to the corresponding response coefficient, and the evaluation as shown in FIG. 13 was performed. As a result, (a As shown in) and (b), materials with a biochemical half-life longer than 18.98 hours and an octanol-water partition coefficient of 10 ² or less were found to be insensitive to biodegradation, adsorption and desorption reactions. In addition, referring to FIG. 13(c), it is observed that there is little change in peak concentration even if the diffusion coefficient of the water system is changed in the river hydraulic condition at a flow rate of 0.1 m/s or less based on a depth of 1 m. However, at flow rates below 0.1 m/s at a depth of 1 m, the volatility of chemicals was also found to be negligible.

The present invention is effectively applied to the field of disaster prevention for responding to water pollution accidents, and when pollutants are discharged into a river, it can help to quickly respond.

Particularly, according to the present invention, even when the floor of the river is not uniform and there is a delay in the movement of the pollutant according to the storage zone and the river bed, the behavior of the pollutant can be accurately predicted.

The numerical model according to the present invention is a numerical model for analyzing the mixing of hazardous chemicals in a river, and mixing of chemicals that are transported and diffused downstream using the inflow location and inflow amount as input data when an accident occurs It is possible to build a river water accident response system that can quickly respond to water quality accidents by simulating the behavior and outputting concentration curves at important points such as water intake stations, providing information on arrival time and residence time to river managers and related engineers. There is an effect.

The embodiment according to the present invention described above may be implemented in the form of a computer program that can be executed through various components on a computer, and such a computer program may be recorded in a computer-readable medium. In this case, the medium is a magnetic medium such as a hard disk, a floppy disk, and a magnetic tape, an optical recording medium such as a CD-ROM and a DVD, a magneto-optical medium such as a floptical disk, and a ROM. A hardware device specially configured to store and execute program instructions, such as, RAM, flash memory, and the like.

Meanwhile, the computer program may be specially designed and configured for the present invention, or may be known and usable to those skilled in the computer software field. Examples of the computer program may include not only machine language codes produced by a compiler but also high-level language codes that can be executed by a computer using an interpreter or the like.

The specific implementations described in the present invention are examples and do not limit the scope of the present invention in any way. For brevity of the specification, descriptions of conventional electronic configurations, control systems, software, and other functional aspects of the systems may be omitted. In addition, the connection or connection members of the lines between the components shown in the drawings exemplarily represent functional connections and/or physical or circuit connections, and in an actual device, various functional connections that can be replaced or additionally It may be referred to as a connection, or circuit connections. In addition, if there is no specific mention such as “essential” or “importantly”, it may not be an essential component for the application of the present invention.

In the specification of the present invention (especially in the claims), the use of the term “above” and a similar reference term may correspond to both the singular and the plural. In addition, when a range is described in the present invention, the invention to which an individual value falling within the range is applied (unless otherwise stated), and each individual value constituting the range is described in the detailed description of the invention. Same as Finally, unless explicitly stated or contradictory to the steps constituting the method according to the present invention, the steps may be performed in an appropriate order. The present invention is not necessarily limited according to the order of description of the steps. The use of all examples or illustrative terms (for example, etc.) in the present invention is merely for describing the present invention in detail, and the scope of the present invention is limited by the above examples or illustrative terms unless limited by the claims. It does not become. In addition, those skilled in the art can recognize that various modifications, combinations, and changes may be configured according to design conditions and factors within the scope of the appended claims or their equivalents.

10: Mixing and reaction behavior analysis device for pollutants in river
110: database
130: data processing unit
131: model building section
133: model verification department
150: data output unit

Claims (11)

As a method for analyzing the mixing and reaction behavior of pollutants using a river storage model model that reflects river repair and topographic characteristics,
An equation design step of designing a governing equation for the numerical model in consideration of the delay characteristic of delaying the movement of the pollutant in the river by the equation design unit;
A model building step of constructing a numerical model based on the designed governing equation;
A comparison step of simulating an analysis solution of the designed governing equation in a virtual channel and comparing the simulated result with the simulation result of the constructed numerical model; And
A determination step of determining the constructed numerical model as a verified numerical model when the result of the comparison satisfies a preset condition,
The flow of the river includes flows of the main stream zone, the storage zone, and the river bed,
The delay characteristic is a characteristic in which the movement of the pollutant is delayed by the bottom zone,
The river storage zone model is a one-dimensional river storage zone model,
The governing equation includes equations for the main stream zone, storage zone and river bed in consideration of the one-dimensional stream storage zone model,
The mainstream equation is,

ego,
The equation of the storage zone is,

ego,
The lower relative equation is,

Phosphorus (however, C _F , C _s , C _b are the concentrations of pollutants in the mainstream, storage and riverbeds, respectively, and K _F , A _F , A _s , and α are the dispersion coefficients, the area of the mainstream, and the storage area, respectively, The mass exchange coefficients between the mainstream and storage zones, k _s , k _v , and λ are reaction coefficients, and the degree to which contaminants are adsorbed and desorbed in the bed, the degree of volatilization in the main stream, the degree of biodegradation in the main stream, storage and bottom ), stream repair, and analysis method for mixing and reaction behavior of pollutants using a river storage model that reflects topographic characteristics. delete delete delete The method of claim 1,
The comparison step,
Analysis of mixing and reaction behavior of pollutants using a river storage model reflecting river repair and topographic characteristics, including discretizing the governing equation using finite difference method and Crank-Nicolson algorithm Way. A computer-readable recording medium storing a program for executing the method according to any one of claims 1 and 5. As a device for analyzing the mixing and reaction behavior of pollutants using a river storage model model that reflects river repair and topographic characteristics,
An equation design unit for designing a governing equation for a numerical model in consideration of a delay characteristic of delaying the movement of pollutants in the river;
A model building unit for constructing a numerical model based on the designed governing equation;
A comparison unit that simulates an analytical solution of the designed governing equation in a virtual channel and compares the simulated result with the simulation result of the constructed numerical model; And
When the comparison result satisfies a preset condition, comprising a determination unit for determining the constructed numerical model as a verified numerical model,
The flow of the river includes the flow of the main stream zone, the storage zone, and the river bed,
The delay characteristic is a characteristic in which the movement of the pollutant is delayed by the bottom zone,
The river storage zone model is a one-dimensional river storage zone model,
The governing equation includes equations for the main stream zone, storage zone and river bed in consideration of the one-dimensional stream storage zone model,
The mainstream equation is,

ego,
The equation of the storage zone is,

ego,
The lower relative equation is,

Phosphorus (however, C _F , C _s , C _b are the concentrations of pollutants in the mainstream, storage and riverbeds, respectively, and K _F , A _F , A _s , and α are the dispersion coefficients, the area of the mainstream, and the storage area, respectively, The mass exchange coefficients between the mainstream and storage zones, k _s , k _v , and λ are reaction coefficients, and the degree to which contaminants are adsorbed and desorbed in the bed, the degree of volatilization in the mainstream, the degree of biodegradation in the mainstream, storage, and bottom. ), stream repair, and analysis device for mixing and reaction behavior of pollutants using a river storage model that reflects topographic characteristics. delete delete delete The method of claim 7,
The comparison unit,
A device for analyzing mixing and reaction behavior of pollutants using a river storage model that reflects river repair and topographic characteristics, discretizing the governing equation using a finite difference method and a Crank-Nicolson algorithm.

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