KR100661595B1 - Decision Support System for Air pollution source planning: ApspDSS - Google Patents

Abstract

The present invention relates to a decision support system that can be used in the course of work such as administration, construction, facility operation and research related to air pollution. Such computer systems are particularly useful for use in the field of the environment, particularly in the environmental, health and construction sectors associated with air pollution. In order to improve air diffusion modeling in complex terrain, the present invention predicts airflow in consideration of geographic information and reflects the result of dispersion coefficient correction pretreatment in the modeling process. Can be. To this end, it provides a computer system for the rapid assessment of the environmental impact necessary for the determination of the location of air pollution point sources, the design and operating conditions of the discharge facility and the air pollution control facility. Due to the present invention, decision makers who are involved in air pollution source planning can obtain air quality modeling results for complex real terrain conditions in a short time, thereby promptly determining the location of air pollution source and the design and operation conditions of the source and air pollution control system. And accurate analysis and decisions can be reached.

Air Pollution, Air Pollution Plan Support System, Model Management System, Diffusion Model, Expert System, Knowledge Based System, Model Selection, Diffusion Parameter

Description

Decision Support System for Air pollution source planning: ApspDSS}

1 is a block diagram of a support system for determining an air pollution emission source plan according to the present invention;

FIG. 2 is an example of a window screen in which information of an emission source is displayed when the GIS of FIG. 1 displays the surrounding area of the emission source as a numerical map and designates a specific location on the map.

3 is a block diagram showing the configuration of information gathering, various functions and necessary tools between a GIS and a knowledge base system (KBS) according to the present invention;

4 is a block diagram showing a process of encoding the knowledge obtained from an interview with an expert and literature and storing it in KBS;

5 is a block diagram showing an inference process of selecting a parameter suitable for a scenario of an ApspDSS user through linkage between a model parameter and a KBS;

FIG. 6A is a spatial distribution graph of 24 hour average maximum concentration of SO ₂ in winter 2002 in Boryeong-Rhine region calculated using the system according to the present invention and an ADMS model capable of reflecting geographic data.

6b is a spatial distribution graph of 24 hour average maximum concentrations of NO _x of winter 2002 in Boryeong-Rhine region calculated using the system according to the present invention and an ADMS model that can reflect geographic data.

7 is a graph showing the wind field at sea wind on the three-dimensional topographical map of the Busan area using the system according to the present invention and using the ADMS model that can reflect geographic data;

FIG. 8 is a graph showing the wind winds on the three-dimensional topographical map of the Busan area using the system according to the present invention and using an ADMS model that can reflect geographic data.

The present invention relates to a system for supporting Decision Support System for air pollution source planning (hereinafter abbreviated as ApspDSS). Generally, the Decision Support System (DSS) is used in the economic, industrial, and policy-making fields, but the use of the Environmental Decision Support System (EDSS) is not frequent. It is not.

In particular, since environmental systems are complex and diverse, unlike other fields, they require vast amounts of information about various variables in making decisions about future forecasting, policy making, resource management, and pollution control. It takes a lot of time and money to collect and manage. In addition, since many variables are involved (for example, non-linearly) in the future air quality prediction process that is premised on the above-mentioned decision making, it is necessary to use computer for rapid calculation and the diffusion model of simple structure is needed for such calculation process. Done.

Accordingly, the present invention has been made in view of this need, and the first object of the present invention is to address the atmospheric field of the environmental field, that is, design and operation conditions of an air pollution control system, location and design conditions of an air pollution discharge facility, and the like. It is to provide a decision support system for the air pollution source plan that supports the decision of the government.

In addition, a second object of the present invention is to provide a decision support system for an air pollution emission source plan for prompt evaluation of the impact of the complex terrain air environment necessary for the decision of the location, design and operating conditions of the air pollution discharge facility.

The third object of the present invention is to propose a correction method according to surface relief, surface roughness, and plume non-high altitude for air dispersion coefficients to improve air quality modeling accuracy. It provides a knowledge-based system for determining high equations, wind velocity at plume altitudes, determining atmospheric diffusion rates, and determining dispersion coefficient parameters. Using this knowledge-based system, ApspDSS users can help in selecting parameters of various mathematical models, and can select more accurate atmospheric diffusion models.

The fourth object of the present invention is to build a database for the input data list related to the operation of the air pollution control system, the intermediate calculation output data list related to the design and operation, and the input data list related to the design and operation of the combustion / incineration facility. You can find it. The established database will help administrators and environmental engineers to properly operate and operate air pollution control systems.

The fifth object of the present invention is to easily understand the characteristics of air pollution emission and to understand the results of policy decisions and implementations, even for those who lack expertise in the air pollution field. It is to provide a decision support system for the air pollution source plan to promote public participation in the target projects.

  The sixth object of the present invention is to determine the future air pollution source plan by software system, so that it is possible to make an objective conclusion about the impact on the air environment during air pollutant discharge or emission control. It is to provide a support system.

An object of the present invention as described above comprises: geographic information means for storing numerical data relating to a shape of a predetermined area as map data;

Database means for storing and retrieving data related to the operation of the air pollution control system, data related to the design / operation of the air pollution control system and data related to the design / operation of the combustion / incineration plant;                         

Model management means for performing a simulation by applying to an embedded environmental model based on data stored in said geographic information means and database means; And

Rules and knowledge-based means for delivering the inference result to the model management means by embedding the information and the inference engine input from the outside; And

It can be achieved by the decision support system of the air pollution source plan, characterized in that it comprises a user interface means.

And, the data related to the operation of the air pollution control system of the database means includes load, air temperature, relative humidity, reference temperature of waste input, quantity of waste, quantity of waste calories, composition of waste, quantity of polluted water, Partial weight of unburned waste, air ratio in air, air temperature, burner temperature, cooling air temperature in combustion chamber, cooling air volume in combustion chamber, boiler outlet temperature, boiler feed water temperature, boiler operating pressure, continuous blowdown amount , Steam temperature, steam pressure at boiler outlet, dust concentration, HCl concentration, SOx concentration, NOx concentration, HF concentration, dioxin concentration, dust concentration at final outlet of incinerator, HCl concentration, SOx concentration, NOx concentration, HF concentration, At least one of data relating to dioxin concentration, steam pressure and temperature of the heat exchanger, coolant pressure of the combustion chamber, heat absorption rate in the combustion chamber, and heat transfer area within the combustion chamber It includes a.

In addition, data relating to the design / operation of air pollution control systems can be found in the heat and mass balance of the boiler, heat and mass balance in the combustion chamber, balance in the ESP, heat and material balance in wet scrubbers, dew point in water vapor, heat and material balance in SCR, At least one of data on heat and material balance in the overall system, flow characteristics of the material, efficiency for dust removal and efficiency for gas removal.

In addition, the data related to the design / operation of the combustion / incineration plant includes the size of the incinerator / combustion furnace, the combustion / incineration system, the height of the stack and the type of boiler, the top diameter of the stack, the top gas velocity of the stack, and the top of the stack. Gas flow rate, stack top gas temperature, operating temperature of incinerator / combustion furnace, air / fuel mixture ratio, fuel type, calorie value, coal component, natural gas, etc., Ca / S ratio, Cl / S ratio, required efficiency, At least one of the data relating to the allowable pressure drop.

The model management means of the present invention comprises: a model structure in which a mathematical model defined in a predetermined format with respect to air pollution is input and stored;

An instance generator interworking with any one model stored in the model structure and corresponding to a tool compiler, the case generator being at least one of a self-analysis generator, a syntax analysis generator, and a semantic analysis generator;

A preprocessor converting the data stored in the geographic information means and the database means into a predetermined text format and outputting the data;

A model solver that receives text data and data generated from the case generator from a preprocessor and executes a simulation according to a predetermined command embedded therein; And

It can be configured as a post-processor that receives the simulation results of the model solver and displays them graphically.

In addition, the model embedded in the model management means is at least one of the emission model, diffusion parameter evaluation model, weather model, diffusion model, ADMS, AERMOD.                         

In addition, the rules and knowledge-based means preferably further comprise means for determining the elevation of the plum, the wind speed at the non-excessive altitude of the plum, and the rate of atmospheric diffusion.

Other objects, advantages and features of the present invention will become more apparent from the following detailed description and the preferred embodiments associated with the accompanying drawings.

With reference to the accompanying drawings, the air pollution emission source plan determination support system according to the present invention will be described in detail.

1 is a block diagram of a system for determining and supporting an air pollution source plan according to the present invention. As shown in FIG. 1, GIS is a system of geographic information (main features, topographical shape, roads, villages, major point source locations (urban, rural, coastal areas, etc.), surface roughness, etc.) for air pollution sources. . The GIS provides a digital map of the area around the source for which the ApspDSS user wishes to conduct an environmental impact assessment.

In addition, the decision support system (DSS) is connected to the table of data to be described later, and connected in parallel with the fact manager by the fact builder and the rule manager by the rule builder. Other non-DSS configurations include users, external programs, hypertext documents, and question builders / question managers.

The inside of the decision support system (DSS) is largely composed of a model management system (MMS), a rule / knowledge-based system (KBS), a domain control module, and a user interface system.

Model Management System (MMS) is composed of pre-processor, processor, and post-processor inside, and the processor is composed of model solver, instance generator, and model structure. Consists of. Each of these will be described in detail as follows.

The preprocessor is where you enter the values of the variables needed for the simulation in the solver, for the settings up to the stage just before the solver calculates them (for example, drawing and modifying them). In particular, it has a configuration that converts the truncation results of the source model, meteorological model, and parametric model together with other input data into a text format for use by the solver in the calculation.

The post-processor is the part that displays the results of the solver in a chart or two- or three-dimensional graph on the screen. For this purpose, a list of display output data, graphical display results, sensitivity analysis, completeness measurement module (static measurement) or error evaluation of model values, graphic programs, and the like can be constructed.

The model solver performs the analysis by applying the data set and parameters in text format input from the preprocessor to the model. The interpreted result is generated in text format or file form and sent to the postprocessor.

The case generator may include a parser generator, a syntax generator, a semantic generator, and the like, corresponding to a component of a tool compiler that supports writing a compiler.

The model structure is a program set structure for various models such as emission model, diffusion parameter evaluation model, weather model, and diffusion model. Gaussian models are especially important and can be composed of puff models, ISC, ADMS, AERMOD, and Valley models for salespeople and crew.

Inside the user interface system, a help / description display unit, a graphic presentation unit, and a scenario manager are built in to allow data transmission with the aforementioned user, external program, hypertext document, question builder / question manager, respectively. The user interface system (UIS) 's query / help function is a description of the model selected for ApspDSS users (theories and functions of the selected diffusion model, the assumptions used in the model, the conditions of use of the model, etc.). To provide. In addition, UIS graphically displays data stored in the database, pretreatment results, and air quality assessment modeling results, which enables ApspDSS users to easily analyze the results within ApspDSS.

FIG. 2 is an example of a window screen in which information of an emission source is displayed when the GIS of FIG. 1 displays a peripheral area of the emission source as a numerical map and designates a specific location on the map. As shown in FIG. 2, when a specific location is designated with a mouse (not shown) on the displayed map, information of the source (for example, the elevation of the source) is displayed on the screen.

3 is a block diagram showing the configuration of information gathering, various functions and necessary tools between a GIS and a knowledge base system (KBS) according to the present invention. As illustrated in FIG. 3, a database table into which non-spatial data is input is defined inside the GIS, and the spatial data is configured to be digitized.

In addition, within the knowledge-based system (KBS), the report generator extracts knowledge from information sources such as experts, literature, reports, etc., and generates a report and outputs it, which can be viewed through the result view.

In the databases shown in FIGS. 1 and 3, extensive information such as emission sources and meteorological data related to air pollution are input. In particular, in the present invention, the input data list related to the operation of the air pollution control system, the intermediate calculation output data list related to the design and operation, and the input data list related to the design and operation of the combustion / incineration facility are stored in a database, thereby allowing the environmental engineers to wait. It is usefully searched and used when determining operating conditions of pollution discharge facilities (incinerators, power plants, etc.) and air pollution control systems.

Each field of the database of data input related to the operation of the air pollution control system includes basic data (load, air temperature, relative humidity), input waste data (reference temperature, amount of waste, amount of waste calories, waste composition, Amount of contaminated water ejected, partial weight of unburned waste, etc.), atmospheric data (air ratio, first air temperature, second air temperature, burner temperature, cooling air temperature in the combustion chamber, cooling air volume in the combustion chamber, etc.), Boiler outlet temperature data, boiler operating condition data (boiler feed water temperature, boiler operating pressure, continuous blowdown amount, etc.), steam condition data (steam temperature, etc.), pollutant concentration data at the boiler outlet (steam pressure, dust concentration, etc.) , HCl concentration, SOx concentration, NOx concentration, HF concentration, dioxin concentration, etc., pollutant concentration data (dust concentration, HCl concentration, SOx concentration, NOx concentration, HF concentration, Dioxin concentration, etc.), heat exchanger condition data (steam pressure, steam temperature, etc.), combustion chamber condition data (coolant pressure, heat absorption rate in the combustion chamber, heat transfer area in the combustion chamber, etc.) and the like are defined.

In addition, each field of the data output database related to the design / operation of the air pollution control system includes the heat and mass balance of the boiler, the heat and mass balance in the combustion chamber, the balance in the ESP, the heat and material balance in the wet scrubber, the dew point of water vapor, The heat and material balance in the SCR, the heat and material balance in the overall system, the flow characteristics of the material, the efficiency required for dust removal, and the efficiency required for gas removal are defined.

In addition, each field of the data input database related to the design / operation of the combustion / incineration plant includes the size of the incinerator / combustion furnace, the combustion / incineration system, the height of the stack and the type of boiler, the top diameter of the stack, Top gas velocity, top gas flow rate of stack, top gas temperature of stack, operating temperature of incinerator / combustion furnace, air / fuel mixture ratio, fuel type, calorie value, coal component, natural gas, Ca / S ratio, Cl / The S ratio, required efficiency, and allowable pressure drop are defined.

Figure 4 is a block diagram showing the process of encoding the knowledge obtained from the interview with the expert and the literature stored in the KBS. As shown in FIG. 4, knowledge obtained through interviews and documents from experts are coded and stored in a computer knowledge based system (KBS). It is also connected to the expert system shell by the inference engine and presented to the user through the results and recommendations obtained through execution. At this time, the computer knowledge base system (KBS) is connected in parallel with the economic evaluation module, the value evaluation module and the fuzzy module, which is one of artificial intelligence inference methods.

According to a specific embodiment, the inventors of the horizontal and vertical dispersion coefficient correction formula according to the plume ratio and the height of the plume constructed by the present inventors in 1998 Boryeong region meteorological data [Equation 1], [Equation 2], [Equation 2] 3] and [Table 1] are stored in the knowledge-based system to increase the accuracy of the diffusion modeling results.

Σ _y = x σ _θ f _y , σ _z = x σ _φ f _z , σ _θ and σ _φ are horizontal and vertical wind direction standard deviations (in radians), respectively, and f _y , f _z are It is a dimensionless function that includes material movement time in the horizontal direction.

Atmospheric stability Horizontal dispersion Vertical dispersion stability σ _θ = 11.0-0.02Z ^-1.03 (Z / h <1.09) σ _θ = 0.09Z ^0.64 (Z / h> 1.09) σ _φ = 33.5 Z ^-0.45 (Z / h <1.09) σ _φ = 0.05 Z ^0.58 (Z / h> 1.09) neutrality σ _θ = 41.29Z ^-0.41 (Z / h <0.41) σ _θ = 0.19Z ^0.57 (Z / h> 0.41) σ _φ = 24.76 Z ^-0.42 (Z / h <0.41) σ _φ = 0.14 Z ^0.54 (Z / h> 0.41) Instability σ _θ = 5.28-0.0002Z ^1.62 (Z / h <0.21) σ _θ = 2.19Z ^0.09 (Z / h> 0.21) σ _φ = 7.80 Z ^-0.22 (Z / h <0.21) σ _φ = 2.24 Z ^0.03 (Z / h> 0.21)

Where s = 0.346-7.49p, p is a power factor (for example, 0.01 in unstable condition, 0.14 in neutral condition, 0.3 to 0.4 in stable condition), and Equation 3 is horizontal This is the correction formula for the directional dispersion coefficient.

In other words, [Equation 1], [Equation 2], [Equation 3], [Table 1] is a high-dependence relational expression of the standard deviation (average time: 15 minutes) data obtained in a certain region according to the stability conditions The standard deviation of the wind direction at each height can be obtained using this equation, and it can be corrected by the dispersion coefficient at the effective height of the plum considering the smoke elevation using [Equation 1] and [Equation 2]. have.

5 is a block diagram illustrating an inference process of selecting a parameter suitable for a scenario of an ApspDSS user through linkage between a model parameter and a KBS. As shown in Fig. 5, the knowledge stored in the knowledge base system can be used by ApspDSS users to model parameters (terrain (urban, rural), pollution concentration, averaging time, emission characteristics, plum height, etc.) and models (ISC, ADMS, AERMOD). Etc.) to help select appropriately. Therefore, the user can select the atmospheric diffusion parameters according to the relevant base, selection base, and question base included in the KBS.

In addition, in this embodiment, by applying the dispersion coefficient corrected according to the ups and downs, the surface roughness, and the altitude of the source between the source damage points to the air diffusion modeling system, the reliability of the air quality modeling results in the complex terrain is improved. This will be described in more detail as follows. Dispersion coefficients (σ _y , σ _z ) for each of the Pasquill-Gifford stability grades below are measured by releasing plumes near the surface of flat rural terrain.

10m high wind speed (m / s) day night Daylight (i) (cal / cm ² hr) Low altitude Steel (i> 50) (25 <i <50) About (i <25) Cloud quantity≥4 / 8 Cloud quantity≤3 / 8 <2 A A ~ B B - - 2 ~ 3 A ~ B B C E E 3 ~ 5 B B-C C D E 5 ~ 6 C C ~ D D D D > 6 C D D D D

Table 3 below is for determining σ _y .

Stable grade I J K A 5.357 0.8828 -0.0076 B 5.058 0.9024 -0.0096 C 4.651 0.9181 -0.0076 D 4.230 0.9222 -0.0087 E 3.922 0.9222 -0.0064 F 3.533 0.9181 -0.0070

Table 4 below is for determining σ _z .

Stable grade I J K A 6.035 2.1097 0.2770 B 4.694 1.0629 0.0136 C 4.110 0.9201 -0.0020 D 3.414 0.7371 -0.0316 E 3.057 0.6764 -0.0450 F 2.621 0.6564 -0.0540

σ _(flat) = exp [I + J (ln x) + K (ln x)] ²

However, since most of the major sources are often emitted from high chimneys and spread along complex terrain, it is not possible to use the Paquill-Grfford dispersion coefficient according to [Table 2] to [Table 4] and [Equation 4] as it is. Follows. Therefore, using the following equations and data shown in [Equation 5], [Equation 6] and [Equation 1] to [Equation 3] and [Table 1] in a high chimney in a complex terrain with surface relief Correct the vertical and horizontal dispersion coefficients for the emission plume Equations 5 and 6 represent Okamoto's equations for obtaining the modified dispersion coefficients of complex terrain with surface ups and downs from the dispersion coefficients of flat terrains using the ups and downs of complex terrains.

σ _{y (complex)} = a σ _{y (flat)} + b

σ _{z (complex)} = c σ _{z (flat)} + d

Where a = 1.07697 + 0.00539X ₁ + 0.00636X ₂ + 0.00508X ₃ + 0.00397X ₄ ,

b = 10.5654 + 0.61884X ₁ + 0.48958X ₂ + 0.08962X ₃ + 0.05825X ₄ ,

c = 1.62982 + 0.00055X ₁ + 0.00078X ₂ + 0.00566X ₃ + 0.00630X ₄ ,

d = 10.72578 + 0.00476X ₁ + 0.01179X ₂ + 0.01936X ₃ + 0.03862X ₄

y is the horizontal direction, z is the vertical direction,

Where X ₁ = 3 Huc (500) + Hur (500) + Huc (500),

X ₂ = 3 Huc (500) + Huc (1000) + Huc (2000),

X ₃ = 2 Huc (500) + Hur (500) + Huc (500),

X ₄ = 2 Huc (500) + Hur (1000) + Huc (2000),

Here, Huc (500) means the reverse wind surface from the stack at the relative height 500m, along the central wind direction, Hur (500) means a reverse wind surface from the stack along the left side 22.5 ° from the center at a relative height 500m.

Hereinafter will be described in detail with reference to the accompanying drawings with respect to the result of the determination support system of the air pollution emission source plan having the configuration as described above according to the present invention.

First, FIG. 6A is a spatial distribution graph of 24 hour average maximum concentration of SO ₂ in winter 2002 in Boryeong-Rhine region calculated using the system according to the present invention and an ADMS model capable of reflecting geographic data. As shown in Figure 6a, it can be seen that the ADMS model graphically represents a highly reliable calculation result. The results of these calculations can be very helpful in making decisions about planning air pollution sources.

FIG. 6B is a spatial distribution graph of 24 hour average maximum concentration of NO _x of winter 2002 in Boryeong-Rhine region calculated using the system according to the present invention and using an ADMS model capable of reflecting geographic data. Environmentally, not only SO ₂ but also NOx are important environmental parameters.

In addition, Figure 7 is a graph showing the wind field during the sea breeze on the three-dimensional topographical map of Busan area using the system according to the present invention and using the ADMS model that can reflect geographic data, Figure 8 is a system according to the present invention It is a graph showing the land winds on the three-dimensional topographic map of Busan area using the ADMS model that can be used and reflect the geographic data.

7 and 8, the result data of the pollutant movement path can be greatly assisted in the decision making to plan the air pollution source.

[Table 5] and [Table 6] below are for evaluating the accuracy of seasonal 24 hour average SO ₂ and NO _X concentration calculation results in Boryeong-Sea area using ADMS and AERMOD models that can reflect geographic information. Statistical measure results are shown. Here, the deviation is the average of the difference between the predicted concentration and the measured concentration, and the total error is the average of the absolute values of the difference between the predicted concentration and the measured concentration. The variance of the difference is the average of the squares of the difference between the predicted and measured concentrations FB is the standardized bias fraction, NMSE is the standardized error square mean, MG is the geometric mean, and VG is the geometric variance.

Deviation (㎍ / ㎥) Total error (㎍ / ㎥) Correlation coefficient (n = 90) Variance of Differences ((㎍ / ㎥)) ² ADMS SO ₂ spring 0.112 1.44 - 3.53 summer -3.6 × 10 ^-8 1.15 - 1.98 autumn -1.11 1.60 0.72 5.70 winter -0.78 5.57 -0.65 46.80 NOx spring 0.10 1.65 - 4.00 summer 2.3 × 10 ^-7 0.84 - 1.20 autumn 0.15 2.88 0.23 12.38 winter -0.10 1.73 -0.11 6.12 AERMOD SO ₂ spring 2.37 5.01 0.23 28.15 summer 1.72 5.28 -0.07 44.31 autumn -6.40 11.01 -0.01 182.79 NOx spring -1.10 9.25 0.29 148.51 summer -8.22 13.49 -0.20 314.33 autumn -4.10 13.09 0.02 256.60

FB NMSE MG VG Within 2 elements ADMS SO ₂ spring -0.03 0.24 0.88 1.23 0.93 summer 8.93 × 10 ^-9 0.12 0.91 0.19 0.86 autumn 0.32 0.50 1.16 1.38 0.81 winter -1.40 0.61 0.95 3.37 0.43 NOx spring 0.02 0.19 0.93 1.23 0.87 summer 7.97 × 10 ^-8 0.14 0.93 1.18 0.94 autumn 0.03 0.51 0.50 19.75 0.68 winter -0.07 2.97 0.34 27.39 0.30 AERMOD SO ₂ spring -0.14 0.10 0.83 1.14 One summer -0.22 0.16 0.82 1.28 0.91 autumn 0.35 0.66 1.07 2.49 0.68 NOx spring 0.04 0.19 0.94 1.26 0.75 summer 0.27 0.40 1.18 1.28 0.91 autumn 0.16 0.41 0.98 1.49 0.77

The statistical measures shown in Tables 5 and 6 indicate that accurate air quality predictions can be made for structures that can reflect geographic information. The present invention stores the knowledge of statistical measures in the KBS to examine the accuracy of atmospheric diffusion modeling results.

As described above, according to the decision support system of the air pollution source plan according to the present invention, by integrating various systems necessary for the analysis of the effect of the air pollution source plan, the policy decision and the design of public facilities, researchers, industrial engineers • It can be a great help for driving engineers to make decisions about the emission of air pollution. In particular, the present invention will be used in the future health, environment, and construction administration by having a system that can quickly evaluate more accurate air quality through atmospheric diffusion modeling in consideration of domestic geographic information that forms a complex terrain.

Although the present invention has been described with reference to the above-mentioned preferred embodiments, it is possible to make various modifications or variations without departing from the spirit and scope of the invention. Accordingly, the appended claims will cover such modifications and variations as fall within the spirit of the invention.

Claims (5)

Geographic information means for storing numerical data on the shape of the predetermined area as map data; Database means for storing and retrieving data related to the operation of the air pollution control system, data related to the design / operation of the air pollution control system and data related to the design / operation of the combustion / incineration plant; Model management means for performing a simulation by applying to an embedded environmental model based on data stored in said geographic information means and database means; And Rules and knowledge-based means for delivering the inference result to the model management means by embedding the information and the inference engine input from the outside; And user interface means; The knowledge-based means connect with expert system shells by inference engines to help select model parameters (terrain, contamination concentration, averaging time, emission characteristics, plume height, etc.) and models (ISC, ADMS, AERMOD, etc.) appropriately. It encodes the knowledge obtained through interviews and literature from experts and stores it in the internal memory, and allows the user to select atmospheric diffusion parameters according to the relevant base, selection base, and question base included in the knowledge base. A decision support system for air pollution emission plan, characterized by displaying the contents. The method of claim 1, wherein the data related to the operation of the air pollution control system of the database means includes: load, air temperature, relative humidity, reference temperature of waste input, amount of waste, calorie quantity of waste, composition of waste, Amount of contaminated water, partial weight of unburned waste, air ratio of air, air temperature, burner temperature, cooling air temperature in combustion chamber, cooling air volume in combustion chamber, boiler outlet temperature, boiler feed water temperature, boiler operating pressure, Amount of continuous blowdown, steam temperature, steam pressure at the boiler outlet, dust concentration, HCl concentration, SOx concentration, NOx concentration, HF concentration, dioxin concentration, dust concentration at the final outlet of the incinerator, HCl concentration, SOx concentration, NOx concentration, HF concentration, dioxin concentration, steam pressure and temperature of the heat exchanger, coolant pressure of the combustion chamber, heat absorption rate in the combustion chamber, heat transfer area in the combustion chamber It includes at least one or more, The data relating to the design / operation of the air pollution control system includes the heat and mass balance of the boiler, the heat and mass balance in the combustion chamber, the balance in the ESP, the heat and material balance in the wet scrubber, the dew point of the water vapor, the heat and material balance in the SCR, and overall. At least one of data relating to heat and material balance in the system, flow characteristics of the material, efficiency for dust removal, efficiency for gas removal, and Data relating to the design / operation of the combustion / incineration plant includes incinerator size / combustion size, combustion / incineration system, stack height and boiler type, stack top diameter, stack top gas velocity, stack top gas flow rate. , Top gas temperature of stack, operating temperature of incinerator / combustion furnace, air / fuel mixture ratio, fuel type, calorie value, coal component, natural gas, Ca / S ratio, Cl / S ratio, required efficiency, A support system for determining an air pollution source plan, the method comprising at least one of data relating to a pressure drop. The method of claim 1, wherein the model management means, A model structure in which a mathematical model defined in a predetermined format with respect to air pollution is input and stored; An instance generator interworking with any one model stored in the model structure and corresponding to a tool compiler, the case generator being at least one of a self-analysis generator, a syntax analysis generator, and a semantic analysis generator; A preprocessor converting the data stored in the geographic information means and the database means into a predetermined text format and outputting the data; A model solver that receives text data and data generated from the case generator from the preprocessor and executes a simulation according to a predetermined command embedded therein; And And a post-processor configured to graphically display the simulation results of the model solver. The system of claim 1, wherein the model embedded in the model management means is at least one of an emission model, a diffusion parameter evaluation model, a weather model, a diffusion model, ADMS, and AERMOD. 2. The method of claim 1, wherein the rule and indication-based means further includes means for determining an elevation of the plum, an air velocity at an unexcessed altitude of the plum, and an air diffusion rate. system.

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