Predicting acoustic and pollutant emissions from combustion equipment using experiments and machine learning

Several engineering equipment ranging from those used for portable power generation, to medium scale gas turbine aircraft engines, and large scale land-based power generation units burn fuel to produce either electric energy or generate propulsive force. This energy conversion takes place inside a combustion chamber which emits noise and combustion pollutants. The objective of the present study is to first perform experiments and analyze data to understand the relations between noise and pollutant emissions. Then, artificial neural networks will be used to perform data mining, developing models that facilitate prediction of both noise and pollution emission from the combustors.

Faculty Supervisor:

Sina Kheirkhah;Anas Chaaban

Student:

Partner:

Machinery Analytics

Discipline:

Engineering

Sector:

Information and cultural industries; Professional, scientific and technical services

University:

The University of British Columbia - Okanagan

Program: