- author = "Miha Kovacic and Sandra Sencic and Uros Zuperl",
- title = "Genetic programming and artificial neural network modeling of {PM10} emission close to a steel plant",
- title2 = "Modeliranje emisij PM10 ob zelezarni z genetskim programiranjem in nevronskimi mrezami",
- journal = "RMZ -- Materials and Geoenvironment",
- year = "2013",
- volume = "60",
- number = "1",
- pages = "9--16",
- month = jul,
- keywords = "genetic algorithms, genetic programming, steel plant, PM10 concentrations, modelling, artificial neural network",
- ISSN = "1408-7073",
-
URL = "
http://www.rmz-mg.com/contents.htm#Volume%2060,%20No.%201%20%28July%202013%29",
-
URL = "
http://www.rmz-mg.com/letniki/rmz60/RMZ60_0009-0016.pdf",
- size = "8 pages",
- abstract = "To implement sound air quality policies, regulatory agencies require tools to evaluate the outcomes and costs associated with various emission reduction strategies. The applicability of such tools can also remain uncertain. It is furthermore known that source-receptor models cannot be implemented through deterministic modelling. The article presents an attempt of PM10 emission modeling carried close to a steel production area with the genetic programming and artificial neural network method. The daily PM10 concentrations, daily rolling mill and steel plant production, meteorological data (wind speed and direction - hourly average, air temperature - hourly average and rainfall - daily average), weekday and month number were used for modelling during a monitoring campaign of almost half a year (23. 6. 2010 to 12. 12. 2010). The genetic programming modelling results show superior agreement with measured daily PM10 concentrations.",
-
notes = "In english.
1Store Steel, d. o. o., Zelezarska cesta 3, SI-3220 Store, Slovenia 2Kova, d. o. o., Teharska cesta 4, SI-3000 Celje, Slovenia 3University of Maribor, Faculty of Mechanical Engineering, Smetanova ulica 17, SI-2000 Maribor, Slovenia
",
