Title :
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
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;
Conference_Titel :
Electric Power and Energy Conference (EPEC), 2010 IEEE
Conference_Location :
Halifax, NS
Print_ISBN :
978-1-4244-8186-6
DOI :
10.1109/EPEC.2010.5697238
