| Opis: | Starting points: The purpose of this research was to find the most appropriate cleaning procedure of volatile organic compounds from the flue gas at the production of isolation. To analize the effectiveness of eliminating volatile organic compounds quantitativily and on the basis of engineers' calculated effects of cleaning procedures, get the entrance data. This data would be used as emission information in the models that simulates air pollution from industrial source, there would be a research on how the production influences the environment and predict the concentration of volatile organic compounds in the factory surroundings.
Working procedure: To clean volatile organic compounds from the flue gas at production of isolations, the most appropriate solution would be regenerative thermal oxidation (RTO). On the basis of empirical data from the articles, acquired and measured with real instruments, there have been some calculations. There is a concentration of total organic carbon (TOC) (mg/Nm3) measured out and emission before cleaning is calculated (TOC) (g/h). Both variations examined are measured with three measures and on the basis of those three measured values, there is arithmetical mean counted up for each variation. The cleaning procedure takes effect of 95 to 99 %. We were interested in expected concentration (TOC) and emission (TOC) after the cleaning.
For simulation of air pollution from industrial source , two models have been used. Among the simplified models the Gaussian dispersion model has been used ISC - ISCST3 (EPA, 1995, 1995, Lakes Environmental 2006). In order to compare and as addition to results of Gaussian model for detailed simulation of air pollution, we have chosen, among complex models, Lagrangian puff model CALPUFF, which was developed by a group of scientists on the field of atmosphere (ASG - The Atmospheric Studies Group), and the model was accepted by the United States Environmental Protection Agency (U. S. EPA). Meteorological data for both dispersion models have been acquired at Slovenian Environment Agency (ARSO).
Results: Within the limits of 6 by 6 kilometers, with real relief topography, the dispersing of flue gas has been moulded. The modeling of dispersion has been carried out for three different iterations. The first iteration is the existent state before renewal, the second iteration goes with 95 % effect of flue gas cleaning, the third iteration has 99 % effect of flue gas cleaning. TOC emissions for the first iteration, regarding the separate emissions, move from 0,00939 g/s up to 0,3877 g/s, TOC emissions for the second iteration move from 0,00046 g/s to 0,095 g/s, TOC emissions for the third iteration move from 0,00008 g/s to 0,095. The pollution source is industry (production of isolations), it is so called point source. In the AERMOD model we used three years of prompt meteorological data. Exclusively for the purpose of comparison and as an addition to those results, we chose four month meteorological data for AERMOD and CALPUFF. From the wind rose the dominant wind directions are evident. The results of both models show, that air pollution is dispersing toward the dominant wind direction.
Conclusion: The simulation of air pollution gives the answers about the pollution causes and mechanisms, and above all, the answer for space and time pollution arrangement. Nowdays the most often dispersion models are Gaussian models, because of their simplicity and price accessibility. In the background of these models there are a lot of simplifications and presumptions in form and conduct of a plume. In spite of all, these models give applicable and physical legitimate results. Good addition to the Gaussian model results are so called complex models, for the purpose of detailed view for air pollution simulation (for example Lagrangian dispersion model). These models are good to be used, when the meteorological parameters have changed in the simulated area and when the source and the places concerned, are placed in a very agitated ground. And also when there is a longer period of |
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