Achieving CO2 emission targets for energy consumption at Canadian manufacturing and beyond; using Hybrid Optimization Model

Author

Marzi, Arash ; Marzi, Hosein ; Marzi, Elham

Author_Institution

Dr. John Hugh Gillis Regional High Sch., Antigonish, NS, Canada

fYear

2010

fDate

25-27 Aug. 2010

Firstpage

1

Lastpage

5

Abstract

Due to sporadic climate change and global warming, world have signed international protocols promising to reduce their nation´s emissions. This study focuses on the application of the bees algorithm, embedded with an artificial neural network, to determine practical yearly reductions for minimizing oil, natural gas, and coal emissions as by-products of energy consumption in Canada´s manufacturing sector based on the Copenhagen Targets for Canada for 2020.

Keywords

environmental science computing; global warming; manufacturing industries; neural nets; optimisation; CO₂; Canadian manufacturing; artificial neural network; bees algorithm; climate change; coal emissions; energy consumption; global warming; hybrid optimization model; international protocols; natural gas; Artificial neural networks; Barium; Manufacturing industries; Neurons; Optimization; Petroleum; Artificial Neural Networks; Bees Algorithm; Emission reduction; Optimization; Sensitivity analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Electric Power and Energy Conference (EPEC), 2010 IEEE

Conference_Location

Halifax, NS

Print_ISBN

978-1-4244-8186-6

Type

conf

DOI

10.1109/EPEC.2010.5697238

Filename

5697238