Title :
Achieving CO2 emission targets for energy consumption at Canadian manufacturing and beyond; using hybrid optimization model
Author :
Marzi, Arash ; Marzi, Elham ; Marzi, Hosein
Author_Institution :
Dept. of Software Eng., Univ. of Ottawa, Ottawa, ON, 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 :
air pollution control; carbon compounds; energy consumption; environmental legislation; environmental science computing; government policies; industrial pollution; manufacturing industries; neural nets; optimisation; production engineering computing; CO2; Canadian manufacturing sector; Copenhagen targets; artificial neural network; bees algorithm; carbon dioxide emission targets; coal emission minimization; energy consumption by-products; global warming; hybrid optimization model; international protocols; natural gas emission minimization; oil emission minimization; sporadic climate change; Artificial neural networks; Barium; Coal; Manufacturing industries; Neurons; Optimization; Artificial Neural Networks; Bees Algorithm; Emission reduction; Optimization; Sensitivity analysis;
Conference_Titel :
Neural Networks (IJCNN), The 2013 International Joint Conference on
Conference_Location :
Dallas, TX
Print_ISBN :
978-1-4673-6128-6
DOI :
10.1109/IJCNN.2013.6706881
