Machine Learning-based Prediction of Remaining Useful Life of Mobile Devices for Circular Economy E-Waste Management

Authors

Mulisa Thovhale, University of Pretoria, South Africa

Seani Rananga, University of Pretoria, South Africa

Rozeena Ebrahim, CSIR, South Africa

Moshe Masonta, CSIR, South Africa

Published in

IST-Africa 2026 Conference Proceedings

ISSN: 2576-8581

ISBN: 978-1-905824-76-2

DOI: https://doi.org/10.67725/IST-Africa.2026.RWDA9756

Publisher

IST-Africa Institute and IIMC International Information Management Corporation Ltd

Published in Ireland

Abstract

This paper presents a Machine Learning (ML) approach for predicting the Remaining Useful Life (RUL) of mobile phone devices to support circular economy strategies aimed at reducing e-waste. The study addresses the challenge of premature device disposal by developing a prediction model that estimates how long a device can continue functioning before reaching end of life. A synthetic dataset representing 1,000 devices operating over 1,460 days was generated using mathematical degradation equations, and four ML models were evaluated: Random Forest, Gradient Boosting, Support Vector Regression and a Long Short-Term Memory (LSTM) network. The LSTM achieved the best performance, with a Mean Absolute Error (MAE) of 153 days and an R² (coefficient of determination) of 0.47. The results show that RUL prediction can support repair, refurbishment and recycling decisions, enabling more sustainable device lifecycle management.

Keywords

Circular Economy, E-waste Management, LSTM, Machine Learning, Predictive Maintenance, Remaining Useful Life

Cite this paper

M. Thovhale, S. Rananga, R. Ebrahim and M. Masonta (2026) "Machine Learning-based Prediction of Remaining Useful Life of Mobile Devices for Circular Economy E-Waste Management", IST-Africa 2026 Conference Proceedings, Miriam Cunningham and Paul Cunningham (Eds), IST-Africa Institute and IIMC, 2026, ISSN: 2576-8581, ISBN: 978-1-905824-76-2, https://doi.org/10.67725/IST-Africa.2026.RWDA9756

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