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

fYear

2013

fDate

4-9 Aug. 2013

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

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks (IJCNN), The 2013 International Joint Conference on

Conference_Location

Dallas, TX

ISSN

2161-4393

Print_ISBN

978-1-4673-6128-6

Type

conf

DOI

10.1109/IJCNN.2013.6706881

Filename

6706881