Intelligent Systems Design Research Group

ACHIEVING CO2 EMISSION TARGETS FOR ENERGY CONSUMPTION AT CANADIAN MANUFACTURING AND BEYOND; USING HYBRID OPTIMIZATION MODEL

Forecasting and emission reduction planning are essential skills that must be available to companies to guarantee their survival in the modern corporate era; particularly because there are such high demands for companies, especially manufacturing companies, to meet set waste fuel emission targets. With a hybrid pattern recognition optimization algorithm accurate predictions can be made to generate an economical model for annual emission reduction targets. By setting practical and achievable companies will be taking the next step in becoming greener and eco-friendly and minimizing their carbon footprint on the world.

The Intelligent Systems Design Research Team, at St. Francis Xavier University, proposes a hybrid algorithm to plan and generate an optimal emission (coal, oil and natural gas emissions) and energy consumption reduction model for the Canadian manufacturing sector with the intention of meeting Copenhagen Emission Targets.

Industrial partners can utilize the algorithm by providing past data about the quantities of their emissions, any economical constraints, minimum and maximum reduction constraint and their desired future goal. There is also potential for the algorithm for different applications in planning and forecast modeling.