Machine learning model development for blast furnaces

Iron and Steel are the backbone of major industrial sectors, like automobile, building and construction, heavy machine manufacturing etc. and Blast Furnace (BF) Ironmaking is the chief production route of iron throughout the world. The objective of this project is to develop novel models for blast furnaces to enhance their productivity in a sustainable way with no further pollution related risks. Industrial data has been collected from different steel plants. This project concentrates on using Artificial Neural Network and Support Vector Machine techniques to develop a novel model keeping those data as supporting reference. The model will be unique since it will consider the effect of almost all process variables in BF ironmaking and will indicate optimum values of the variables to cost effectively achieve maximum productivity of the BF. The enhanced sustainable productivity will positively affect the afore-mentioned giant industrial sectors and thereby, the outcome of this project can play a pivotal role in Industry 4.0.

Faculty Supervisor:

Kinnor Chattopadhyay

Student:

Partner:

Indian Institute of Engineering Science and Technology, Shibpur

Discipline:

Engineering

Sector:

Advanced Manufacturing; Technology; Sustainability & the Environment

University:

University of Toronto

Program: