KR20130104661A - Mmultidimensional system for modeling water quality - Google Patents
Mmultidimensional system for modeling water quality Download PDFInfo
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Abstract
The present invention relates to a water pollution load estimation decision support system using GIS and its operation method, and characterized in that the pollution load is calculated through a mathematical algorithm based on the finite element method.
The present invention enables the analysis of the characteristics of local pollutants by establishing a variety of pollutant databases in the contaminated area, and provides the information to grasp the location of the pollutant and the accumulation process into the region and the stream where the pollutants are severely discharged. Efficient management of pathways, water quality modeling, and water quality modeling make it easy to use as a basis for water use plans for farmland and cultivated areas, and accurate decision making in establishing basic policies for rural environment management. The purpose is to provide a decision support system for water pollution load estimation decision support using GIS and its operation method.
Description
The present invention relates to a multi-dimensional water quality modeling system, which is a water pollution load estimation decision support system using GIS and its operation method, in particular, the pollutant load is calculated through a mathematical algorithm based on the finite element method. By establishing a database of pollutants, it is possible to analyze the characteristics of local pollutants and to provide efficient information on the location of pollutants, the areas of severe pollutants, and the accumulation process into streams, enabling efficient management of pollutant discharge routes. The GIS, which enables water quality prediction through water quality modeling, can be easily used as basic data for water use plans for farmland and cultivated areas, and supports accurate decision making when establishing basic policies for environmental management in rural areas. Water Pollution Load Estimation Decision Support System And to its method of operation.
The use of computers to solve all engineering problems has emerged as an essential methodology with the explosion of information-related businesses. Therefore, it is important to use computational models as a key analysis tool for water quality prediction and environmental design.
However, since environmental information required for water quality management must simultaneously manage a large amount of related information consisting of attributes, locations, and times of various pollutants, there was a problem that it was almost impossible to manage by the conventional report.
Therefore, there is a need to provide data and manage it through a system that efficiently manages and processes the database by using a GIS that uses spatial and graphic information rather than environmental information management through reports. With the basic information as the basic information, the development and operation of the system is urgently needed to present a system for supporting various scenarios and to propose a plan for reducing water quality through the prediction of pollutant loads by region.
The present invention has been made in view of the above-described state of the art, and it is possible to analyze the characteristics of local pollutant sources by constructing a variety of pollutant source databases in a pollutable area, and to accumulate the source of pollutant sources and the places where the pollutants are severely polluted and streams. By providing information that can be grasped, it enables efficient management of pollutant discharge paths, and enables water quality prediction through water quality modeling so that it can be easily used as basic data in water use plans for farmland and cultivated areas. The purpose is to provide a decision support system for water pollution load determination decision support system using GIS that supports accurate decision making when establishing basic policies for local environmental management and its operation method.
Other objects and specific advantages of the present invention will be further embodied by the following detailed description.
The present invention for achieving the above object is a water pollution load calculation decision support system based on a geographic information system, geographic diagram DB (171) is provided with geographic information of the geographic information system; As the attribute information of the geographic map DB, the status of pollutants according to the population, animal husbandry, aquaculture, industry, and land, environmental foundation facilities such as manure treatment facilities and sewage end treatment facilities, water quality measurement points, water quality simulations and loads An attribute DB 172 having an attribute related to calculation and an association that links all of these attributes; The pollutant load is calculated through a mathematical algorithm based on the finite element method installed on the basis of the pollution source status, environmental foundation, water quality measurement point, and water quality simulation data established in the geomorphic DB (171) and the attribute DB (172). A
Here, the
In addition, the mathematical algorithm based on the finite element method may include one or more of the following
1 input and output module
2. Modules related to spatial domain (including modules for basis and weighting functions and modules for elemental evaluation of node-specific coefficients)
3. Element Matrix Calculation Module
4. Element Matrix Assembly Module
5. Nonlinear System Analysis Module
6. Modules for the formula of coefficients and generating terms
7. Module for boundary inflow concentrations and outflow rates.
In addition, the mathematical algorithm based on the finite element method,
≪ Formula 1 >
(The main variable is the concentration of contaminants and the parameters are flow rate, diffusion coefficient, biochemical reaction coefficient, source of production, etc.)
&Quot; (2) "
(A governing equation related to temperature change, ambient water is temperature, parameters are air velocity, diffusion coefficient, air temperature, temperature exchange coefficient, density, specific heat of water, temperature load source, etc. to analyze temperature exchange with air)
<
(The mass transfer equation and the temperature transfer equation are very similar. Therefore, if the reaction coefficient and load source are symmetrical as follows, the subprograms for the reaction coefficient and load source are not modified. Only by modifying can we analyze the temperature change. To apply the multidimensional finite element algorithm to the governing equations,
On the other hand, the present invention, using the above-described decision support system, i) analyzing the association of each data from the attribute DB and geographic map DB and classify / build the data through basic geographic information inquiry; Ii) calculating and inquiring generation load, discharge load and delivery load of the target site based on the classified information; Iii) identifying the pollutant behavior using data from water quality simulations of major streams and establishing the pollutant behavior data; Iii) Water quality using GIS, comprising the steps of supporting the decision-making process by performing the water quality model according to the scenario desired by the user using the load and pollutant behavior data of the corresponding site, and obtaining the result. It is also possible to operate a pollution load determination decision support system.
The details of other embodiments are included in the detailed description and drawings.
As described above, according to the present invention, it is possible to analyze the characteristics of the local pollutant by establishing the basic map and thematic map of the polluted area, and the database of various pollutant sources in the polluted area, and the location and source of the pollutant are seriously affected by the river. It is possible to efficiently manage the emission route of pollutants by understanding the accumulation process of pollutants.
In addition, it is possible to use the water quality modeling through water quality modeling as a basic data for water use plans for farmland and cultivated areas, and to function as a support system when establishing a basic policy for environmental management in rural areas. There is an advantage to that.
1 is a block diagram showing the configuration of a decision support system according to an embodiment of the present invention.
2 is a block diagram showing the configuration of the system drive unit of FIG.
3 illustrates a mapping of multidimensional element from global to local coordinate according to an embodiment of the present invention.
4 illustrates linear basis and weighting functions according to an embodiment of the present invention.
5 is for explaining multi-dimensional modeling for verifying the model according to an embodiment of the present invention.
6 is a 1, 2, 3D modeling result for verifying a model according to an embodiment of the present invention.
7 is a result of examining the stability of the model according to an embodiment of the present invention.
BRIEF DESCRIPTION OF THE DRAWINGS The present invention is capable of various modifications and various embodiments, and specific embodiments are illustrated in the drawings and will be described in detail in the detailed description. It is to be understood, however, that the invention is not to be limited to the specific embodiments, but includes all modifications, equivalents, and alternatives falling within the spirit and scope of the invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, the present invention will be described in detail with reference to the accompanying drawings.
The terminology used herein is for the purpose of describing particular example embodiments only and is not intended to be limiting of the present invention. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this application, the terms "comprise" or "have" are intended to indicate that there is a feature, number, step, operation, component, part, or combination thereof described in the specification, and one or more other features. It is to be understood that the present invention does not exclude the possibility of the presence or the addition of numbers, steps, operations, components, components, or a combination thereof.
The terms first, second, etc. may be used to describe various components, but the components should not be limited by the terms. The terms are used only for the purpose of distinguishing one component from another.
1 is a block diagram of a decision support system according to an embodiment of the present invention, Figure 2 is a block diagram of a system driving unit of FIG.
1 and 2, a decision support system according to an embodiment of the present invention is referred to as a memory (hereinafter referred to as “RAM”) having a central control unit (hereinafter referred to as “CPU”) 110 as a main axis. 140, the graphical user interface (hereinafter referred to as "GUI") 130 is electrically connected, the
A key input unit (not shown) is electrically connected to the
At this time, the
On the other hand, the
In this case, the
The pollutant source
The pollution load
The water quality
The calculation /
On the other hand, the
Looking at this in more detail, the geographic map DB 171 according to the present invention is provided with geographic figure information such as administrative facilities, roads, water purification plant, manure treatment facilities, such as infrastructure facilities and the like in Table 1 below. same.
In addition, the attribution DB (172) is provided with information on the status of pollutants such as population status, livestock status, land use status, wastewater discharge facility status, environmental basic facilities status, and water quality status information such as water quality measurement points and quality observation points. There is a characteristic, as shown in Table 2 below.
As shown in Table 2 above, the attribute DB 172 according to the present invention is classified into a major classification, a middle classification, and a small category, respectively, and the major classification, the middle classification, and the small classification are classified according to the attribute type; There is a distinction between the basic items for load estimation.
In the decision support system according to an embodiment of the present invention, the result of calculating the occurrence load, the discharge load, and the delivery load based on the standard watershed can be inquired. Although it is consistent with the district and district boundaries, it is necessary to divide administrative boundaries in standard watersheds, and it is necessary to accurately estimate the share of the provinces and villages included in the watershed.
Data on pollutant sources by local governments are to be investigated and submitted to the relevant watersheds by the city and county governing the area according to the guidelines outlined in the Water Quality Guidelines for each pollutant such as population, livestock, industry, land use, and farm.
Therefore, in the construction of the attribute DB 172 included in the decision support system according to an embodiment of the present invention, it is possible to construct a database that is easier to construct using the pollutant status data submitted to and registered with the watershed.
According to the technical guidelines, a list of standard watershed-administrative zones should be prepared so that pollutants surveyed by administrative unit can be used to calculate loads by standard watershed, where an area is included in two or more standard watersheds. It is mandated to be included in the standard watershed by proportion.
Therefore, in the decision support system according to an exemplary embodiment of the present invention, after dividing the standard basin-dong-ri boundary, the area ratio is calculated for the pre-divided area, and the share of copper and limbs for each standard basin is calculated. In this case, when the divided area is less than 7% of the area before the split, the
The generation load is first calculated through the
The mathematical algorithm based on the finite element method according to the present invention and the characteristics of the present invention using the same will be described in detail.
1. Adoption of finite element method
The multidimensional water quality modeling system according to the present invention is a system combining a multidimensional finite element model using WASP Kinetics (MFEMWASP), which is a water quality analysis model, and ArcGIS, a geographic information system, as a post-processing system for modeling. The present inventors modeled the water quality and stratification phenomenon of the lake to analyze the influence of the Donggang Dam as an example. A multidimensional FEMWASP model was developed by modifying the FEMWASP model. This model has added the function to analyze the stratification phenomenon and was developed as a multi-dimensional model that can be selected and analyzed in 123 dimensions as needed.
In the pretreatment system, input data required for modeling are constructed from ArcGIS drawings, and the main input data are modeling control data, grid data, initial concentration, boundary conditions, physical parameters (diffusion coefficient and flow rate), and biochemical parameters (biochemical reactions). Velocity constant). The MFEMWASP model is a computational model developed using FORTRAN. The MFEMWASP model is a model developed to analyze the water phenomena in one, two, and three dimensional water systems using the multidimensional finite element method. The aftertreatment system allows ArcGIS to show or analyze the concentration, input data, and necessary parameters of each water quality item, which is the result of modeling for all finite elements and nodes. Such analytical functions include three-dimensional analysis using TIN, isoconcentration curves, and the like, and functions for estimating real and erroneous field survey results during inputting of input data and parameters.
The model according to the present invention was applied to one, two, three-dimensional modeling projects in several water systems such as Paldang Lake, Soyang Lake, Yeongwol Dam, and the coast near Donghae City, and verified the feasibility of multidimensional modeling. This model has been applied to projects such as Paldang Lake and Soyang Lake Water Quality Preservation Measure, Yeongwol Dam Environmental Impact Assessment and Validity Verification, and Model for On-the-Water Drainage of Donghae Thermal Power Plant. Since the model was developed to selectively model one, two, and three dimensions, the user automatically configures the input data according to the needs, and the program automatically performs the appropriate modeling. The development of a computational model capable of performing such multidimensional analysis was possible because the multidimensional finite element method was used as the numerical method. The characteristics of the multidimensional finite element method are as follows.
-The mathematical analysis of multidimensional element matrix and multidimensional boundary condition analysis for multidimensional finite element method is established.
Solve space independently from partial differential equations.
-One, two, three-dimensional space analysis can optionally be performed.
Spatial and temporal derivatives can be separated from the overall equation.
-You can separate parameters.
-By combining these separate modules, different partial differential equations can be solved.
-By analyzing the boundary conditions by finite element method, the boundary conditions of three-dimensional problems can be easily analyzed.
In the finite element method, the calculation of the basis function is performed, and the computation speed is dramatically reduced by utilizing the vector computation and parallel processing functions of the supercomputer.
The MFEMWASP model, developed for multi-dimensional analysis, was developed to be able to operate on various types of computers such as Mainframe, Workstation, and PC. In order to increase the utility and transportability of the computational model, the program is structured object-oriented. In order to have such a structure, the finite element method is appropriate rather than the finite difference method. The model was developed as a multidimensional finite element model, and the structure of the program was highly modularized to increase portability between systems.
In program construction, if the algorithm becomes too complicated, the computation time will be very long, and it will be difficult to apply the program to various problems with different situations. Therefore, the logic of simple and effective numerical analysis is essential. In this respect, the finite element method can be used to integrate the spatial differential terms of the governing equations, to maintain the mass balance of each term well, and to separate the spatial representation of the lattice points from the partial partial differential equations. Even if the equation depends on the problem situation, it can be applied to many problems without having to make major program changes. Recently, the finite volume method is used to develop such a general model. Therefore, the program can be highly generalized so that it can be applied by adjusting only parameters. In view of this, the finite element method was adopted and the following modules were developed.
1 input and output module
2. Modules related to spatial domain
Modules for Basis and Weight Functions
-Module for elemental evaluation of coefficients per node
3. Element Matrix Calculation Module
4. Element Matrix Assembly Module
5. Nonlinear System Analysis Module
6. Modules for the formula of coefficients and generating terms
7. Module for boundary inflow concentration and outflow rate
The transport and transformation equations for the eight major water quality items related to eutrophication are:
≪
(The main variable is the concentration of contaminants and the parameters are flow rate, diffusion coefficient, biochemical reaction coefficient, source of production, etc.)
&Quot; (2) "
(The governing equation for the hot-water drainage modeling analysis, where the ambient water is the temperature, and the parameters are the flow velocity, diffusion coefficient, air temperature, temperature exchange coefficient, density, specific heat of water, temperature load source, etc. )
<
(The mass transfer equation and the temperature transfer equation are very similar. Therefore, if the reaction coefficient and load source are symmetrical as follows, the subprograms for the reaction coefficient and load source are not modified. Only by modifying can we analyze the temperature change. To apply the multidimensional finite element algorithm to the governing equations,
2. Development of Multidimensional Finite Element Model
2.1, Weighted residual apply
Finite element method is a method to solve the solution to minimize the error of the numerical solution. That is, in the numerical solution, the left side and the right side are different, so the difference between the left side and the right side is defined as the residual. The differential operating functions L (C) and L (T) of the residuals are
In order to minimize the total space to be interpreted above, we add the weighted residuals and construct the expression so that the sum of the residuals becomes zero. The differential over time applies the general difference method, so the residuals are interpreted only for space. The weighted residual equation to find the minimum solution of the governing weighted residual is as follows.
Here, W i is the weight function of the node i. Therefore, the following equation can be derived.
The above method is called Weighted Residual Method and is the fundamental principle of the finite element method. In other words, the algorithm is set to multiply and sum the numerical solutions of the grid points so that the error of the numerical solution is minimized at each of the grid points calculated for the space and time domain. Weighted residual expression using the partial branch Green theorem, is converted into a second derivative term, wherein a first derivative, is combined with the outflow boundary condition.
2.2. Weighted residual Dioxide
In general, in contrast to theoretical rigors in the solution of differential or partial differential equations using computers, computers cannot calculate spatial or temporal problems in succession, so they only compute changes in discrete points (nodes within elements). This is possible. Therefore, whatever numerical techniques (finite difference method, finite element method, etc.) are used, all peripheral numbers, parameters, independent variables, and data related to the governing equations should be discretized. In this study, the representative variables and parameters are discretized using Basis Function as follows.
Since the basis function is related only to the lattice of the spatial domain, it does not depend on the process of the developer itself, and is determined only by the shape of each element. Therefore, the basis function is evaluated once in the program given only the coordinate system of the grid point, so that the computation time can be greatly reduced. In other words, it is evaluated as the coordinate system of the grid network given as input data before evaluating another main mechanism. Therefore, the basis functions are obtained at all Gaussian points and then combined when the integration of the element matrices is performed. The coordinates of the nodes for each element are evaluated using the basis function as follows.
The basis function can be expressed as a global coordinate system (x, y, z) or a calculated coordinate system (ξ, η, ζ). Using the concept of mapping, an irregularly shaped element can be interpreted as a cube element. Therefore, the basis function is calculated in the calculation coordinate system. The linear basis in the coordinate system can be generalized as follows using the linear basis in each direction (Kim, 1989).
Where N i and ig are basis functions at the Gaussian point of the i th node.
3 illustrates a mapping of multidimensional element from global to local coordinate according to an embodiment of the present invention.
The calculated coordinate system (ξ i , η i , ζ i ) of the node at each element is
ξ i = -1, 1, 1, -1, -1, 1, 1, -1 η i = -1, -1, 1, 1, -1, -1, 1, 1
ζ i = 1, 1, 1, 1, -1, -1, -1, -1
The coordinates of the Gaussian point at each element are
ξ ig = 0.577, 0.577, 0.577, -0.577, -0.577, 0.577, 0.577, -0.577
η ig = -0.577, -0.577, 0.577, 0.577, -0.577, -0.577, 0.577, 0.577
ζ ig = 0.577, 0.577, 0.577, 0.577, -0.577, -0.577, -0.577, -0.577
Derivative of Basis Function
Using the Jacobian matrix, [J], which correlates the derivatives between the coordinate system and the global coordinate system, we obtain
The calculation of the Jacobian matrix is
The derivative values in the coordinate system are as follows.
Using the equations of Huyakorn and Nikuha (1979), the asymmetric weighting function is derived by adding the asymmetric weighting term to the base function.
4 illustrates linear basis and weighting functions according to an embodiment of the present invention.
In the coordinate system, the linear weighting function can be generalized as follows.
Here, W i, it is the i-th node ig ig The basis function at the Gaussian point.
The weighting coefficient is estimated from the flow velocity of each node as follows.
Derivative of Basis Function
Using the Jacobian matrix, [J], a kind of conversion matrix that correlates their derivatives between the characteristic and global coordinate systems, we obtain
The derivative of the weighting function in the calculated coordinate system is
3. Definition of element matrix and
Finite element formula
3.1. Mass transfer equation Finite element formula
The discretized weighted residual equation is obtained by substituting the basis function for the weighted residual equation.
In order to modularize the programming, the element integral matrix for each element of the above equation is defined as follows.
The numerical integration method shown in the last term of the above equation can be explained as follows.
Integral included in the finite element method can be directly calculated by hand and can be formulated as a computer program. By constructing and executing a computer program using numerical integration, it is possible to reduce mistakes caused by the direct calculation process. A general-purpose program can be constructed.
In the finite element method, the Newton-Cotes integration method and Gauss integration method are mainly used. In this study, the Gauss integration method is used. The Gauss integration method should be determined so that the function value can be well represented when determining the position of the selection point. In this study, since the basis function is linear, it is most suitable when the position of the selection point is 2 in the calculation time and convergence of solution. . Gauss integration can be represented by the following equation.
The differential element matrix defined above is applied to the discretized weighted residual equation as follows.
The algorithm using the concrete element matrix including the spatial differential term and the parameter for the above residual equation can be summarized as follows.
The element matrices in each direction are summed and multiplied by the neighbors as follows .
Dioxide material removable by the matrix element is as follows.
In general, the final system equation is constructed by discretizing the time domain.
The left matrix of the final determinant of this algorithm is a value evaluated by known variables and basis functions at each node, and is calculated for each element matrix and combined into the entire determinant in the form of a band matrix. In addition, the right load vector is also evaluated by each element and then combined into a load vector matrix. Therefore, the right matrix and the left matrix are linear simultaneous equations, and when the determinants are solved, the concentration for each node is obtained. The combining procedure of the element matrix is performed in a direction in which the entire matrix form can use an asymmetric determinant and reduces the storage capacity of the variable.
3.2. Of the temperature transfer equation Finite element formula
As with the derivation of the finite element equation of the mass transfer equation, the finite element news of the temperature transfer equation can be summarized as follows.
The final system equation is composed by discretizing the time domain.
The left matrix of the final matrix of this algorithm is a value evaluated by known variables and basis functions at each node, and is calculated for each element matrix and combined into the entire determinant in the form of a band matrix. In addition, the right load vector is also evaluated by each element and then combined into a load vector matrix. Therefore, the right matrix and the left matrix are linear simultaneous equations, and when the determinants are solved, the concentration for each node is obtained. The combining procedure of the element matrix is performed in a direction in which the entire matrix form can use an asymmetric determinant and reduces the storage capacity of the variable.
In the finite element equation of the governing equation, the term relating to the boundary condition represents the total inflow at the boundary. The finite element equation is applied to each element for the whole area, but since the internal boundary values cancel each other, only the overall boundary boundary is needed. There are three boundary conditions: values given as main variables, flux values, and complex forms. By adding large margins to the right and left sides of the final equation, known boundary values are maintained throughout the model experiment. Inflow integration of boundary conditions is performed using the basis and weighting functions. Complex boundary conditions are cases where the concentration or phase flux is proportional to the difference in concentration or pressure outside the boundary. As with the flux-like boundary conditions, the integration is done using the basis and weighting functions. The boundary conditions can be evaluated by dividing them into the boundary conditions of the first two.
-Best boundary condition
In the case of the first boundary condition, it is time when known density resides in boundary area,
If the penalty value is very large,
Jay boundary condition
In the case of the second boundary condition, the known runoff rate resides in the boundary area, and is evaluated as follows. In the case of a one-dimensional problem,
In the case of a two-dimensional problem:
In the case of a three-dimensional problem:
Third boundary condition
In the case of the third boundary condition, the runoff rate is determined as follows according to the concentration of the boundary area.
The generalization of the time domain is as follows.
The finite difference method is applied as follows.
Since each element matrix is computed by the integration of the basis function at the Gaussian point for all elemental areas, all parameters must be evaluated for the entire area using the Gaussian point. This evaluation method can be expressed as follows.
For nonlinear problems, the iteration calculation is repeated until the error condition is met. It is necessary to predict the nonlinear term in the new time step to avoid nonlinearity, and it is common to use the value from the previous step. In order to reduce the error, it is necessary to condition the iteration and the error. Unpredictable variables or severe nonlinearities require long computation time. Appropriate temporal and spatial parameter segmentation and initial condition determination are essential to reduce the problem of nonlinear instability. In the case of diffusion-dominated dominance, the numerical stability problem is less serious than that in which the dominant equation moves in a hyperbolic form. In the latter case, it is necessary to make the simulation time interval small in addition to using the upper weighting method. This is a big problem when the degree of nonlinearity is severe. To overcome this problem, it is necessary to change the size of time and space sections. The variable time interval may be determined from truncation errors. Variable space interval determination requires a method of finding the moving front of the material. If concentration gradients can be calculated, a drastic change in gradient indicates the front of the shift. This location requires a small space size. In this study, mass bundles, upper weighting functions, and an improved iterative calculation method were used to overcome the nonlinear difficulties.
-Selection of appropriate time intervals for nonlinear iterations
Cooley's iterative algorithm is used as a non-linear solution, with a minimum and maximum limit on the number of iterations until convergence is obtained, and if the number of iterations is greater than the maximum, the time interval is automatically reduced and less than the minimum. If you increase the time interval. The algorithm is as follows:
The error in each repetition step is expressed by the following equation.
Where dh represents the difference of the surrounding numbers in each repetition step, k represents the repetition frequency, dh max is the maximum value of dh , and W is the accumulation coefficient of the interval of repetition solutions.
Cooley's algorithm is as follows.
4. Composition of Biochemical Reaction Terms
4.1. Numerical Analysis of Mass Transfer
The basic equation of mass transfer to be applied to the Finite Element Model based on Water Quality Analysis Simulation Program (FEMWASP) developed in the present invention has been described above. The finite element method is applied to the space domain for the mass transport equation, and the final system equation is constructed by general discretization in the time domain.
C in the above equation represents the concentration of each substance to be calculated in this model.
4.2 optional modelling technique
In the above basic equation of mass transport, we can reduce or enlarge the calculation items of the model by selectively constructing the conversion term by biochemical reaction. To calculate BOD and DO as the simplest water quality model, the well-known Streeter-Phelps model can be applied, and the model can be expanded by inserting nitrification process. Therefore, in this study, four cases can be calculated selectively for the water quality management model (FEMWASP) to be developed.
Tree-Phelps model
Streeter-Phelps model
Modified Streeter-Phelps Model
Modified Streeter-Phelps model
Fully linear DO equilibrium model
Completely linear DO balance model
ㅇ Simplified eutrophication model
Simplified eutrophication model
end. Streeter-Phelps Model
The feasibility of modeling non-conservatives was examined by considering the BOD transformation and subsequent DO changes, which are covered in the Streeter-Phelps model. The reaction equation for the BOD item is:
The above equation is shown in the previous section and shows the BOD decomposition process (organic oxidation process) and solid BOD precipitation process among the various processes related to BOD change.
2) Dissolved Oxygen
The scheme for DO is shown like BOD. In the conversion process of DO shown in the figure, the conversion process of DO applied to this model is reaeration process, DO consumption by oxidation of BOD, and the reaction equation is as follows.
I. Modified Streeter-Phelps Model
In this model, based on the Streeter-Phelps model, BOD and DO changes due to nitrification are added to the BOD and DO conversion process. In other words, the reaction rate equation of BOD according to the decomposition of nitrogenous BOD and precipitation of solid nitrogenous BOD material was added. The equations applied to this model are:
ㅇ Carbon-based biochemical oxygen demand (CBOD):
ㅇ Ammonia nitrogen:
ㅇ Dissolved oxygen (DO):
All. Fully Linear DO Equilibrium Model
This model takes into account the effects of phytoplankton photosynthesis and respiration on the equilibrium equation for dissolved oxygen (DO), and applies the nitrogen cycle more complementarily than in the modified Streeter-Phelps model in the previous section. In other words, the process of nitrogen ionization, nitrification, and precipitation of organic solids were added.
ㅇ Carbon-based biochemical oxygen demand (CBOD):
ㅇ Organic Nitrogen:
ㅇ Ammonia nitrogen:
ㅇ Nitric acid nitrogen:
ㅇ Dissolved oxygen (DO):
la. Simple Eutrophic Reaction Model
In this model, complex eutrophication is simplified and applied. In particular, this model applies the nitrogen cycle process in more detail than the previous section and includes the phosphate cycle, phytoplankton growth, and death process. Phytoplankton growth may be limited by inorganic nitrogen, inorganic phosphorus and light, and is shown in the interrelationship between major water quality variables.
In order to include the above conversion process in the program, each water quality item is divided into a response coefficient and a load term as follows. The water quality items applied to the model are as follows.
1) Ammonia Nitrogen
2) Nitrate Nitrogen
3) Inorganic Phosphorus
4) Phytoplankton Carbon
5) Carbonaceous Biochemical Oxygen Demand (CBOD)
6) Dissolved Oxygen
7) Organic Nitrogen
8) Organic Phosphorus
5. Multidimensional
Modeling
through
MFEMWASP
Model Verification
The verification of the computational model can be performed by the following method.
-Comparison of Mathematical Solutions and Computation Results
-Comparison of Experimental Results and Computation Results of Computerized Models
-Comparison of actual measurements and modeling results
-Comparison of modeling results of other models and developed models
-Comparison of 1, 2, and 3D Modeling Results for 1D Problems
The FEMWASP model used in the evaluation of the present invention is a model that has already been compared and verified with mathematical solutions and field data in previous studies. The FEMWASP model was used for the analysis of eutrophication, toxic substances transport and change for Paldang Lake water quality management. In addition, the FEMWASP model was applied to various environmental impact assessment projects such as Yeongwol dam and Donghae thermal power plant. In the present invention, the FEMWASP model has been developed as a three-dimensional model that can add the ability to analyze the stratification phenomenon and can perform multi-dimensional modeling as needed.
In order to verify the availability and accuracy of the developed model, multi-dimensional modeling was performed on the problems of fluid flow and contaminant movement. If the developed model is accurate, the one-, two- and three-dimensional modeling results for one-dimensional problems should be identical. Therefore, in the multi-dimensional modeling, all parameters were input the same regardless of the dimension, and only the grid network was composed of one, two, and three dimensions. As a result of the modeling, one, two, and three-dimensional modeling results were identical to verify the accuracy of the model. Since the model was developed to selectively model one, two, and three dimensions, the user automatically configures the input data according to the needs, and the program automatically performs the appropriate modeling.
The development of a computational model capable of performing this multidimensional analysis was possible because the finite element method was used as the numerical method. The characteristics of the finite element method are as follows.
Solve space independently from partial differential equations.
-Analyze one-, two- and three-dimensional spaces in stages.
Spatial and temporal derivatives can be separated from the overall equation.
-You can separate parameters.
-By combining these separate modules, different partial differential equations can be solved.
-By analyzing the boundary conditions by finite element method, the boundary conditions of three-dimensional problems can be easily analyzed.
FIG. 5 illustrates multidimensional modeling for verifying a model according to an embodiment of the present invention, and FIG. 6 illustrates 1, 2, and 3D modeling results for verifying a model according to an embodiment of the present invention.
6.
MFEMWASP
Model stability analysis
If the movement by flow rate and diffusion is dominant rather than by the response coefficient, it can significantly affect the stability of the water quality model. Therefore, an analysis was conducted to evaluate the stability and sensitivity of the MFEMWASP model.
The ability of the model to interpret the ratio of diffusion and flow velocity of the MFEMWASP model depends on the weighting factor, and the selection of the appropriate weighting factor is important. Therefore, the stability of the model was examined by changing the weighting coefficient in the case of large displacement due to flow velocity, large displacement caused by diffusion, and intermediate case. The contents of the review are as follows.
* When the flow rate is very large (fw11, fw14)
-Flow rate V = 0.369m / day, diffusion coefficient D = 0.0001725m 2 / day
* Movement by diffusion is very large (fw12, fw15)
- Flow rate V = 0.369m / day, the diffusion coefficient D = 0.01725m 2 / day
* Intermediate (fw13, fw16)
- Flow rate V = 0.369m / day, the diffusion coefficient D = 0.001725m 2 / day
The weighting factors used to examine the stability of the model are shown in <Table 3: Weighting factors used to examine the stability of the MFEMWASP model>.
As a result of stability review of the model, as shown in FIG. However, the numerical solution oscillates when the flow velocity is large (fw11), which shows that a stable solution can be obtained using the upper weighting function (fw14). In addition, the modeling results were compared with the mathematical analysis solution, which showed a very similar agreement.
7.
MFEMWASP
Reproduction of Current River Water Quality Using Model
In order to predict the water quality effects before and after the project implementation of the Jeongseon-gun Development Promotion District, the actual water quality was reproduced for the 1996 river. For the calibration of the model, 248 elements and 498 nodes were constructed for the 1996 river case. In order to examine the environmental impact when the lake was formed by dam construction under 1996 conditions, a grid of 551 elements and 873 nodes was constructed for submerged areas. The attribute data of the grid network is composed of the connection diagram, perimeter, area, and x and y coordinates of the grid points for each element.
For the water quality and flow rate data used for calibration, the first water quality survey (Spring) data, which was judged as the water quality of the flat water meter, was used. As a result of reviewing the flow rate data investigated in the back, it was judged that 40CMS, the flow rate at the time of water quality investigation, was valid and used for model calibration. The most basic hydraulic data required for modeling, namely diffusion coefficients and flow rates (both of which were taken into account in the x and y directions), are presented for each case of streams and dams. Through spatial analysis of these input data, it is possible to find out the cause of inaccurate numerical analysis and to construct more reliable input data easily.
The various parameters and water quality factors used to calibrate the model for current and submerged cases are shown in <Table 4: Paratrophic parameters applied to model calibration>.
Based on the above water quality and flow rate data and parameters, the results of water quality reproduction for the 1996 river case and the water quality of the case where the appeal was generated are compared with BOD, Org.-N, NH 3 -N, NO 3- Eight major eutrophication items such as N, Org.-P, PO 4 -P, Phyto-C, and DO are shown.
For the conditions of 1996, the water quality tends to be slightly higher than that of the rivers when the dams are formed. In particular, water quality tends to increase as the dam reaches R22. This is because the flow rate is constant, but the capacity of the flooded area increases due to dam generation, so the flow rate of the water body decreases closer to the dam point.
In the case of lakes, the BOD increased by about 0.5mg / l at the planned dam, and the Org.-N, NH 3 -N, and NO 3 -N were 0.147, 0.153 and 0.250mg / l, respectively. It shows an increase in water quality. In the case of phosphorus, Org.-P increased 0.011 mg / l and PO 4 -P increased 0.011 mg / l in the dam government. In addition, Phyto-C increased about 0.098 mg / m 3 , and DO decreased about 1 mg / l.
In order to improve the operability of the model and to easily analyze the modeling results, the MFEMWASP model can be linked with ArcView to operate on ArcView, and the modeling results can be visually analyzed.
The discharge load is calculated by considering the reduction efficiency by treatment facility or method in consideration of the treatment route for each pollutant based on the above-described generated load. The treatment efficiency in each treatment facility and discharge route is measured by the discharge of public treatment facilities such as sewage treatment facilities, manure treatment facilities, livestock wastewater treatment facilities, and wastewater treatment facilities. The treatment efficiency of individual treatment facilities such as discharge facilities shall be based on standard treatment rates, effluent water quality standards, and discharge allowance standards.
The above equations represent a mathematical algorithm, and the
While the present invention has been particularly shown and described with reference to preferred embodiments thereof, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, It will be apparent to those skilled in the art that changes may be made.
110: central control unit, 120: drive unit of the decision support system,
121: pollutant status search module, 122: pollutant load calculation query module,
123: figure search module, 124: operation / processing module,
126: water quality prediction modeling module, 127: output module,
130: graphical user interface (GUI),
140: Memory (RAM), 160: Displayer
170: database, 171: geographic shape DB,
172: property DB, 180: plotter,
190: interface unit.
Claims (4)
1 input and output module
2. Modules related to spatial domain (including modules for basis and weighting functions and modules for elemental evaluation of node-specific coefficients)
3. Element Matrix Calculation Module
4. Element Matrix Assembly Module
5. Nonlinear System Analysis Module
6. Modules for the formula of coefficients and generating terms
7. Module for boundary inflow concentrations and outflow rates.
<Equation 1>
(The main variable is the concentration of contaminants and the parameters are flow rate, diffusion coefficient, biochemical reaction coefficient, source of production, etc.)
<Equation 2>
(A governing equation related to temperature change, ambient water is temperature, parameters are air velocity, diffusion coefficient, air temperature, temperature exchange coefficient, density, specific heat of water, temperature load source, etc. to analyze temperature exchange with air)
&Quot; (3) "
(The mass transfer equation and the temperature transfer equation are very similar. Therefore, if the reaction coefficient and load source are symmetrical as follows, the subprograms for the reaction coefficient and load source are not modified. Only by modifying can we analyze the temperature change. To apply the multidimensional finite element algorithm to the governing equations,
Water pollution load estimation decision support system using GIS, characterized in that.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105468926A (en) * | 2015-12-29 | 2016-04-06 | 北京师范大学 | Underground water type drinking water source pollution source analysis method |
| CN116340980A (en) * | 2023-04-04 | 2023-06-27 | 临沂市生态环境局费县分局 | Water environment pollution analysis management system and method based on big data |
| CN117875559A (en) * | 2024-01-16 | 2024-04-12 | 广东博创佳禾科技有限公司 | Heavy metal load capacity analysis method and system based on urban environment medium |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105468926A (en) * | 2015-12-29 | 2016-04-06 | 北京师范大学 | Underground water type drinking water source pollution source analysis method |
| CN116340980A (en) * | 2023-04-04 | 2023-06-27 | 临沂市生态环境局费县分局 | Water environment pollution analysis management system and method based on big data |
| CN116340980B (en) * | 2023-04-04 | 2023-09-05 | 临沂市生态环境局费县分局 | Water environment pollution analysis management system and method based on big data |
| CN117875559A (en) * | 2024-01-16 | 2024-04-12 | 广东博创佳禾科技有限公司 | Heavy metal load capacity analysis method and system based on urban environment medium |
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