Metallurgical Engineering

Metallurgical Engineering

The Model for Monitoring of Pollutants in Pyrometallurgy Industries Located in Different Regions

Document Type : Research Paper

Authors
Abstract
Ecological sustainability and balance are the elements of sustainable development. In this paper, using fuzzy logic, a model is presented to monitor region wise industrial pollutants in pyrometallurgy industries. The model has been used in a case study that will determine which industry, in which region, producing which and how much pollutants, is compatible with ecology. To assess the ecological compatibility, first, the set of industries, regions, pollutants and ecological compatibility were defined, then to calculate the membership degree of the members of the ecology compatibility set, the membership function of ecological compatibility was defined. By ranking different industries in various regions, creating different pollutants, as continuous figures, the ecological compatibility of these industries was accurately compared. Given the degree of ecological compatibility in a region, the type of pollution, and the related industry, identification of the lowest degree of ecological compatibility was the first priority of this case study. Results of the conducted case study, without considering the region coefficient, show that member C241, in December 2005 with an ecological degree of compatibility equivalent to 0.0559 has the most critical condition in producing the air pollutant, carbon dioxide. However, on considering the region coefficient, member C121, in December 2005 with an ecological degree of compatibility equivalent to 0.0655 has the most critical condition in producing the air pollutant, carbon dioxide.
Keywords

 
1-وحدت س.ا.، توحیدی ن.، 1388، توسعه پایدار تولید آهن و فولاد در ایران از جنبه زیست محیطی با استفاده از منطق شلال، مجله محیط شناسی، شماره51، صفحات111 تا 122
2-Sowlat M.H., Gharibi H., Yunesian M., Tayefeh Mahmoudi M., Lotfi S., 2011. A novel fuzzy-based air quality index (FAQI) for air quality assessment, Atmospheric Environment, Volume 45, Issue 12, pages 2050-2059
3-Mintz R., Young B. R., Svrcek W. Y., 2005. Fuzzy logic modeling of surface ozone concentrations, Computers & Chemical Engineering, Volume 29, Issue 10, pages 2049-2059
4-Pokrovsky O.M., Kwok R. H. F., Ng C. N., 2002, Fuzzy logic approach for description of meteorological impacts on urban air pollution species: a Hong Kong case study, Computers & Geosciences, Volume 28, Issue 1, pages 119-127.
5-Astel A., 2007. Chemometrics based on Fuzzy logic principles in environmental studies, Talanta, Volume 72, Issue 1, pages 1-12
6-Chen P.H., J.-H. Lai and C.-T. Lin., 1998. Application of Fuzzy control to a road tunnel ventilation system, Fuzzy Sets and Systems, Volume 100, Issues 1-3, pages 9-28.
7-Icaga Y., 2007.Fuzzy evaluation of water quality classification, Ecological Indicators, Volume 7, Issue 3, pages 710-718
8-Vahdat S.E., Nakhaee F.M., 2011, Air Pollution Monitoring using Fuzzy Logic in Industries, Advanced Air Pollution, chapter2, InTech publication, pages 21-30
10-Lamm K., 2010. The CO2 - Story – A Fairy – Tale – (Or a Nightmare?, Lead Zinc 2010 conference, A john and Sons Inc., MetSoc, COM2010, 953-959.
11-Moradkhani D., Sedaghat B., Rashtchi A., Khodadadi A., 2010. Heavy metal pollution potential of zinc leach residues discarded in IZMDC, Lead Zinc 2010 conference book, A john and Sons Inc., MetSoc, COM2010, 141-149.
12-URL: http://www.lead.org, 2005
13-Geir O. Braathen, 2005. An overview of the 2005. Antarctic Ozone Hole, WMO Global Ozone Research and Monitoring Project, Report No. 49, WMO TD No. 1312, WORLD METEOROLOGICAL ORGANIZATION, pages 76.