• Journal of Institute of Control, Robotics and Systems
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• v.8 no.10
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• pp.827-832
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• 2002

We propose a prediction method of the pollutant and a synchronous classification of the current state of SOx emission in the power plant. We use the auto-regressive with exogeneous (ARX) model as a predictor of SOx emission and use a radial basis function network (RBFN) as a pattem classifier. The ARX modeling scheme is implemented using recursive least squares (RLS) method to update the model parameters adaptively. The capability of SOx emission monitoring is utilized with the application of the RBFN classifier. Experimental results show that the ARX model can predict the SOx emission concentration well and ARX modeling parameters can be a good feature for the state monitoring. in addition, its validity has been verified through the power spectrum analysis. Consequently, the RBFN classifier in combination with ARX model is shown to be quite adequate for monitoring the state of SOx emission.

• Journal of Environmental Health Sciences
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• v.30 no.3
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• pp.293-299
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• 2004

Traffic represents one of the largest sources of primary air pollutants in urban area. As a consequence, numerous abatement strategies are being pursued to decrease the ambient concentration of pollutants. A characteristics of most of the these strategies is a requirement for accurate data on both the quantity and spatial distribution of emissions to air in the form of an atmospheric emission inventory database. In the case of traffic pollution, such an inventory must be compiled using activity statistics and emission factors for vehicle types. The majority of inventories are compiled using passive data from either surveys or transportation models and by their very nature tend to be out-of-date by the time they are compiled. The study of current trends is towards integrating urban traffic control systems and assessments of the environmental effects of motor vehicles. In this study, a model of vehicle emission calculation by using real-time traffic data was studied. Traffic data, which are required on a street-by-street basis, is obtained from induction loops of traffic control system. It is possible that characteristics of hourly air pollutants emission rates is obtained from hourly traffic volume and speed. An emission rates model is allocated with a high resolution space by using geographic information system (GIS). Vehicle emission model was developed with a high resolution spatial, gridded and hourly emission rates.

• Advances in Computational Design
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• v.4 no.4
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• pp.367-379
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• 2019

Multiple Inputs and Multiple Outputs (MIMO) Fuzzy logic model is developed to predict the engine performance and emission characteristics of pongamia pinnata biodiesel with hydrogen injection. Engine performance and emission characteristics such as brake thermal efficiency (BTE), brake specific energy consumption (BSEC), hydrocarbon (HC), carbon monoxide (CO), carbon dioxide ($CO_2$) and nitrous oxides ($NO_X$) were considered. Experimental investigations were carried out by using four stroke single cylinder constant speed compression ignition engine with the rated power of 5.2 kW at variable load conditions. The performance and emission characteristics are measured using an Exhaust gas analyzer, smoke meter, piezoelectric pressure transducer and crank angle encoder for different fuel blends (Diesel, B10, B20 and B30) and engine load conditions. Fuzzy logic model uses triangular and trapezoidal membership function because of its higher predictive accuracy to predict the engine performance and emission characteristics. Computational results clearly demonstrate that, the proposed fuzzy model has produced fewer deviations and has exhibited higher predictive accuracy with acceptable determination correlation coefficients of 0.99136 to 1 with experimental values. The developed fuzzy logic model has produced good correlation between the fuzzy predicted and experimental values. So it is found to be useful for predicting the engine performance and emission characteristics with limited number of available data.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.55.1-55.1
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• 2019

We investigate broadband emission properties of the pulsar wind nebula (PWN) 3C 58 using a spectral energy distribution (SED) model. We attempt to match simultaneously the broadband SED and spatial variations and emission about 3C 58 in X-ray band. We further the model to explain a possible far-IR feature of which a hint is recently suggested in 3C 58: a small bump at ~10^11 GHz in the PLANCK and Herschel band. While external dust emission may easily explain the observed bump, it may be internal emission of PWNe implying an another additional population of particles. Although significance for the bump in 3C 58 is not higher than other PWNe, here we explore possible origins of the IR bump using the emission model and find that a population of electrons with GeV energies can explain the bump. If it is produced in the PWN, it may provide new insights into particle acceleration and flows in PWNe.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.2D
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• pp.103-110
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• 2012

This study presents O/D based emission estimation model and methodology under cold- and hot-start conditions. Contrasting with existing link-based model, new model is able to estimate cold-start emissions with actual traffic characteristics. The results of the case study with new model show similar amount of emission with existing model under hot-start conditions, but five times much more than existing model under cold-start conditions. The annual social benefit estimated by this model is 56.2 hundred million won, which is 48% higher than the result from existing model. It means current green transportation policies are undervalued in terms of air quality improvement. Therefore, New model is expected to improve the objectivity of air quality evaluation results regarding green transportation policies and be applied in various transportation-environment policies.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.4
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• pp.543-550
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• 1998

From the power plant in a steel plant, environment pollutants such as $SO_x$, $NO_x$, CO and $CO_2$ are emitted by combustion reactions of the fuels which are by-product gases, oil and liquefied natural gas(LNG). To reduce the amounts of the pollutants, it is important to build a predictive model for the emission of the pollutants. In this paper, model that predict the amounts of generated pollutants for the used fuel is developed by using Gibbs free energy minimization method[1] with the temperature correction technique. For some data set, the calculation results from this model are compared with the real emission amounts of $SO_x$, $NO_x$, and the result of the calculation by both ASPEN PLUS which is a commercial simulation software. This model shows good results and can be applied to other power plants.

• Geomechanics and Engineering
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• v.10 no.3
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• pp.297-314
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• 2016

With the mining depth continuously increasing, gas emission behaviors become more and more complex. Gas emission is an important basis for choosing the method of gas drainage, gas controlling. Thus, the accurate prediction of gas emission is of great significance for coal mine. In this work, based on the sources of gas emission from the heading faces and the fluid-solid coupling process, we established a gas continuous dynamic emission model, numerically simulated and applied it to the engineering. The result was roughly consistent with the actual situation and shows the model is correct. We proposed the measures of reducing the excavation distance and borehole gas drainage based on the model. The measures were applied and the result shows the overproof problem of gas emission disappears. The model considered the influence factors of gas emission wholly, and has a wide applicability, promotional value. The research is of great significance for the controlling of gas disaster, gas drainage and pre-warning coal and gas outbursts based on gas emission anomaly at the heading face.

• Journal of Environmental Impact Assessment
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• v.21 no.3
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• pp.409-415
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• 2012

Post-kyoto regime has been discussing with the GHG reduction commitment. GHG energy target management system also has been applied for the domestic measures in the country. Universities are major emission sources for GHG. It is very important for campus to built the GHG inventory system and estimate the potential GHG emission reduction. In general, GHG inventory on the campus was taken by the IPCC guidance with the classification of scope 1, 2, and 3. Electricity was the highest portion of GHG emission on the campus as 5,053.90 $tonsCO_2eq/yr$ in 2009. Manufacturing sector was the second high emission and meant GHG in laboratory. Potential GHG reduction was planned by several assumptions such as installation of occupancy sensor, exchanging LED lamp and photovoltaic power generation. These reduction scenarios was simulated by LEAP model. In 2020, outlook of GHG emission was estimated by 17,435.98 tons of $CO_2$ without any plans of reduction. If the reduction scenarios was applied in 2020, GHG emission would be 16,507.60 tons of $CO_2$ as 5.3% potential reduction.

• Asian Journal of Atmospheric Environment
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• v.5 no.1
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• pp.1-7
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• 2011

This study assesses the dispersion and emission rates of odor form industrial area source. CALPUFF and AERMOD Gaussian models were used for predicting downwind odor concentration and calculating odor emission rates. The studied region was Seobu industrial complex in Korea. Odor samples were collected five days over a year period in 2006. In-site meteorological data (wind direction and wind speed) were used to predict concentration. The BOOT statistical examination software was used to analyze the data. Comparison between the predicted and field sampled downwind concentration using BOOT analysis indicates that the CALPUFF model prediction is a little better than AERMOD prediction for average downwind odor concentrations. Predicted concentrations of AERMOD model have a little larger scatter than that of CALPUFF model. The results also show odor emission rates of Seobu industrial complex area were an order of 10 smaller than that of beef cattle feed lots.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.3
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• pp.114-128
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• 2013

Greenhouse gas emissions have been an important issue in different countries because of their effects on global warming. The government has to organize greenhouse gas reduction measures suitable to regional characteristics by establishing annual implementation plans and comprehensive policies based on the UNFCCC. The transportation sector is one of the major contributors of air pollution; hence increasing need to estimate current and future traffic emissions precisely. Under these circumstances, a number of emission models have been developed recently. However, current methods of estimation cannot carry out effective analyses because it does not reflect vehicle movement characteristics. This study aims to present a new method for calculating road traffic emissions in Goyang city. A travel demand model is utilized to carry out GHG emission estimates according the traffic data (fleet composition, vehicle kilometers travelled, traffic intensity, road type, emission factors and speed). This study evaluates two approaches to estimate the road traffic emissions in Goyang City: Pollution-Emis and the Handbook of Emission Factors for Road Transport (HBEFA v.3.1) which is representative of the "average speed" and the "traffic situation" model types. The evaluation of results shows that the proposed emission estimation method may be a good practice if vigilant implementation of model inputs is observed.