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Machine Learning and Optimisation to Improve Energy Utilisation Efficiency

Authors:
Srinath Ramagiri
Evelyne El Masri
Arman Zonuzi
Ahmed Teyeb
Shehan Lowe
Tat-Hean Gan

Keywords: machine-learning; Optimisation; heat-treatment; energy-efficiency

Abstract:
The world is moving towards a conservative approach to fulfilling its energy needs due to inevitable uncertainty and disruptions in the supply chain. In addition, climate change, the availability of materials, and making them sustainable through recycling are other topics of high interest. Energy is a common item among all the industries, and demand for it keeps increasing due to developmental activities. In this work, we aim to improve the efficiency of utilising the available energy in the material processing industries. Mining the ore, extracting the material of interest, melting the material, and manufacturing the required components are typical processes in these industries. The manufacturing of the components also includes a heat treatment process. For example, the heat treatment process demands 20% of the total energy in a non-ferrous foundry. Pre-heating and heat treatment operations consume a significant amount of energy in the ferrous-based industry. We intend to investigate the processes in these industries and create a machine-learning model of the processes involved. Later, we use the machine learning models to build an optimization framework that provides the optimal process operating parameters to achieve the best output while using the least amount of energy.

Pages: 23 to 28

Copyright: Copyright (c) IARIA, 2023

Publication date: March 13, 2023

Published in: conference

ISSN: 2308-412X

ISBN: 978-1-68558-054-4

Location: Barcelona, Spain

Dates: from March 13, 2023 to March 17, 2023