Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/30619
Title: Machine Learning and Optimisation to Improve Energy Utilisation
Authors: Ramagiri, S
Zonuzi, A
Lowe, S
El Masri, E
Teyeb, A
Gan, T-H
Keywords: machine-learning;optimisation;heat-treatment;energy-efficiency
Issue Date: 13-Mar-2023
Publisher: IARIA
Citation: Ramagiri, S. et al. (2023) 'Machine Learning and Optimisation to Improve Energy Utilisation', ENERGY 2023, Barcelona, Spain, 13-17 March, pp. 23 - 28. ISBN: .
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.
Description: Slides presented at the conference are available online at: https://www.iaria.org/conferences2023/filesENERGY23/30043_ENERGY.pdf .
URI: https://bura.brunel.ac.uk/handle/2438/30619
ISBN: 978-1-68558-054-4
Other Identifiers: ORCiD: Srinath Ramagiri https://orcid.org/0000-0001-8156-6353
ORCiD: Evelyne El Masri https://orcid.org/0000-0003-3241-5844
ORCiD: Ahmed Teyeb https://orcid.org/0000-0003-0300-1845
ORCiD: Tat-Hean Gan https://orcid.org/0000-0002-5598-8453
Appears in Collections:Brunel Innovation Centre

Files in This Item:
File Description SizeFormat 
FullText.pdfCopyright (c) IARIA, 2023. ISBN: 978-1-68558-054-4. Copyright Information: For your reference, this is the text governing the copyright release for material published by IARIA. The copyright release is a transfer of publication rights, which allows IARIA and its partners to drive the dissemination of the published material. This allows IARIA to give articles increased visibility via distribution, inclusion in libraries, and arrangements for submission to indexes. I, the undersigned, declare that the article is original, and that I represent the authors of this article in the copyright release matters. If this work has been done as work-for-hire, I have obtained all necessary clearances to execute a copyright release. I hereby irrevocably transfer exclusive copyright for this material to IARIA. I give IARIA permission or reproduce the work in any media format such as, but not limited to, print, digital, or electronic. I give IARIA permission to distribute the materials without restriction to any institutions or individuals. I give IARIA permission to submit the work for inclusion in article repositories as IARIA sees fit. I, the undersigned, declare that to the best of my knowledge, the article is does not contain libelous or otherwise unlawful contents or invading the right of privacy or infringing on a proprietary right. Following the copyright release, any circulated version of the article must bear the copyright notice and any header and footer information that IARIA applies to the published article. IARIA grants royalty-free permission to the authors to disseminate the work, under the above provisions, for any academic, commercial, or industrial use. IARIA grants royalty-free permission to any individuals or institutions to make the article available electronically, online, or in print. IARIA acknowledges that rights to any algorithm, process, procedure, apparatus, or articles of manufacture remain with the authors and their employers. I, the undersigned, understand that IARIA will not be liable, in contract, tort (including, without limitation, negligence), pre-contract or other representations (other than fraudulent misrepresentations) or otherwise in connection with the publication of my work. Exception to the above is made for work-for-hire performed while employed by the government. In that case, copyright to the material remains with the said government. The rightful owners (authors and government entity) grant unlimited and unrestricted permission to IARIA, IARIA's contractors, and IARIA's partners to further distribute the work.166.33 kBAdobe PDFView/Open


Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.