Environmental Optimization of Building Insulation Thickness for Cold Climates using Neural Network Method
الموضوعات : Journal of Environmental Friendly Materials
E Anbarzadeh
1
(Department of Mechanical Engineering, Faculty of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran)
T Shahmohammadi
2
(Department of Mechanical Engineering, Faculty of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran)
الکلمات المفتاحية:
ملخص المقالة :
In this research the environmental effects (CO2) of rock wool as a mineral insulation and expanded polystyrene as a polymeric insulation for building insulation is studied. First the intended building is simulated by Design Builder software for cold climates like Tabriz city in Iran, and then the thickness effect of these two insulations is studied at the building’s exterior wall by using Energy Plus simulation engine in Design Builder software in order to find the optimized thickness in terms of environmental effect (CO2). Except considering the gas emission amount of building’s heating and cooling systems in a year, the amount of CO2 gas consumption in production process to installation of various thicknesses has been also taken into account. Finally by using Single Layer Perceptron Artificial Neural Network method, the environmental optimized thickness of insulation in terms of gas emission while consuming energy in building and its production during the manufacturing of insulation over ten years span in cold areas of Iran like Tabriz, is 12.5 centimeters for expanded polystyrene and 8.8 centimeters for rock wool. It is concluded that from gas emission perspective in cold climates, the mineral insulation such as rock wool is has lower optimized thickness comparing to polymeric insulation like expanded polystyrene.
[1] R. Shabanpour and H. sayyaadi, Energ. Convers. Manage., 75, (2013), 348.
[2] E. Deniz and S. Cinar, Energ. Convers. Manage., 126, (2016), 12.
[3] B. Comakli and B. Yüksel, Appl. Therm. Eng., 23, (2003), 473.
[4] S. Jaber and S. Ajib, Energ. Build., 43, (2011), 1829.
[5] E. Cuce, P. M. Cuce, C. J. Wood and S. B. Riffat, Energ. Buildings, 77, (2014), 28.
[6] M. Ozel and K. Pihtili, Build. Environ., 42, (2007), 3051.
[7] H. Ozkahraman and A. Bolattürk, Constr. Build. Mater., 20, (2006), 435.
[8] J. Yu, C. Yang, L. Tian and D. Liao, Appl. Energ., 86, (2009), 2520.
[9] N. Daouas, Z. Hassen, H. Ben and Aissia, Appl. Therm. Eng., 30, (2010), 319.
[10] K. Achten, R. De Coninck, G. Verbeeck and J. Van der Veken, Eleventh International IBPSA Conference, Glasgow, Scotland, (2009), 660.
[11] K. Alanne, A. Salo, A. Saari and S. Gustafsson, Energ. Build., 39, (2007), 1218.
[12] D. Anastaselos and S. Oxizidis, Energ. Build., 43, (2011), 686.
[13] R. Anderson, C. Christensen and S. Horowitz, ASHRAE Annual Meeting, 112, (2006), 330.
[14] M. Beccali, M. Cellura, M. Fontana and S. Longoa, Renew. Sust. Energ. Rev., 27, (2013), 283.
[15] J. Grygierek and K. Grygierek, Energies, 10, (2017), 1570.
[16] O. Kaynakli, Renew. Sust. Energ. Rev., 16(1), (2012), 415.
[17] J. Nyers, L. Kajtar, S. Tomić, and A. Nyers, Energ. Build., 86, (2015), 268.
[18] A. Bolattürk, Appl. Therm. Eng., 26, (2006), 1301.
[19] C. Diakaki and E. Grigoroudis, "Performance study of a multi-objective mathematical programming modelling approach for energy decision-making in buildings, (2013).
[20] E. Anbarzadeh and S. M. G, Yazdi, J. Environ. Friend. Mater., (2019), 11.
[21] E. A. Rad and E. Fallahi, Construct. Build Mater., (2019), 205, 196.
