Development of context-aware data analysis framework

In a complex environment like mineral extraction, it is difficult to back the cause-effect relationship between a specific process variable and the overall performance (output, cost, GHG emission) with scientific principles. Consequently, process data is used to deduce an empirical correlation between the process variables and their estimated effects. In the present project, process data from a mineral extraction plant will be used to analyse the cause-effect relationship of various plant variables on critical performance metrics like energy consumption, cost, GHG emission etc.. Furthermore, the analysis will factor in the contextual variables like environment, labour, market demands, etc. to make the framework adapt to dynamic changes. The project will advance NTWIST’s ML and AI based data analytics framework to provide superior solutions to their clients. The project will simultaneously help the Canadian mineral industry to enhance their efficiency, and the Canada goal of reducing GHG emissions.

Faculty Supervisor:

Vinay Prasad

Student:

Partner:

NTwist

Discipline:

Engineering

Sector:

Information and cultural industries; Manufacturing; Mining; Professional, scientific and technical services

University:

University of Alberta

Program: