Achieving CO2 Emission Targets for Energy Consumption at Canadian Manufacturing and beyond; using Hybrid Optimization Model

被引：0
|
作者：
Marzi, Arash [1 ]
Marzi, Elham [2 ]
Marzi, Hosein [2 ]
机构：
[1] Dr John Hugh Gillis Reg High Sch, Antigonish, NS B2G 2W5, Canada
[2] St Francis Xavier Univ, Dept Informat Syst, Antigonish, NS B2G 2W5, Canada
来源：
2013 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2013年
关键词：
Bees Algorithm; Artificial Neural Networks; Optimization; Emission reduction; Sensitivity analysis;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
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页数：5
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