Aruna Sudame, Azza Al-Ghamdi, Ubio Obu, Jaiyeyemi Adekunle
carbon, End of life tyres(ELT), tyre waste, machine learning
Waste tyre constitute a large percentage of wastes that affect the ecosystem. With an approximation of 1.5 billion tyres reaching their end of life every year, it is important to critically understand what constitutes waste tyres. End of life tyres commonly called ELT has given researchers lot of concerns with understanding it, how it operates and how to better recycle it. This paper is one of such contribution to help with understanding the compositions of ELT tyres but specifically on different carbons produced from ELT tyres. Obviously carbon is the most dominant and useful remnant of such waste, but In this paper we examined 3 types of end of life tyre carbon, namely IB 550A, IB 330B and IB 660C and their characteristics. We used Machine learning Algorithms to differentiate these carbons base on their chemical composition, to understand all the constituents. This study gives insight on different carbons derived from waste tyres and its composition. It furnishes us with information on the future utility of waste tyres based on the class of carbon it falls under based on our machine learning clustering model and the best utility for each class. With most studies on waste tyres using chemical processes and methodologies to understand waste tyres carbon, a machine learning approach introduces an entirely new approach.
Article Details
Unique Paper ID: 156860

Publication Volume & Issue: Volume 9, Issue 5

Page(s): 507 - 513
Article Preview & Download

Share This Article

Join our RMS

Conference Alert

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024

Last Date: 15th March 2024

Call For Paper

Volume 10 Issue 10

Last Date for paper submitting for March Issue is 25 June 2024

About Us enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on

Social Media

Google Verified Reviews