Репозиторий КазНИТУ

Predicting Industrial Copper Hydrometallurgy Output with Deep Learning Approach Using Data Augmentation

Авторы:

Kenzhaliyev, B. (Соавтор)

Amangeldy, B. (Соавтор)

Magomedov, D. (Соавтор)

Koizhanova, A. (Соавтор)

Azatbekuly, N. (Соавтор)

Aibagarov, S. (Соавтор)

Издательство:

Multidisciplinary Digital Publishing Institute (MDPI)

Дата издания

30.06.2025

Область исследования

Инжиниринг и технологии

Тип исследования

Фундаментальное

Аннотация (краткое описание)

Sustainable copper extraction presents significant challenges due to waste generation and environmental impacts, requiring advanced predictive methodologies to optimize production processes. This study addresses a gap in applying deep learning to forecast hydrometallurgical copper production by comparing six recurrent neural network architectures: Vanilla LSTM, Stacked LSTM, Bidirectional LSTM, GRU, CNN-LSTM, and Attention LSTM. Using time-series data from a full-scale industrial operation, we implemented a data augmentation approach to overcome data scarcity limitations. The models were evaluated through rigorous metrics and multi-step forecasting tests. The results demonstrated remarkable performance from five architectures, with Bidirectional LSTM and Attention LSTM achieving the highest accuracy (RMSE < 0.004, R2 > 0.999, MAPE < 1%). These models successfully captured and reproduced complex cyclical patterns in copper mass production for up to 500 time steps ahead. The findings validate our data augmentation strategy for enabling models to learn complex known cyclical patterns from limited initial data and establish a promising foundation for implementing AI-driven predictive systems that can enhance process control, reduce waste, and advance sustainability in hydrometallurgical operations. However, these performance metrics reflect the models’ ability to reproduce patterns inherent in the augmented dataset derived from a single operational cycle; validation on entirely independent operational data is crucial for assessing true generalization and is a critical next step.

Соответствие целям устойчивого развития (ЦУР)

Индустриализация, инновации и инфраструктура

URI

https://www.scopus.com/record/display.url?eid=2-s2.0-105011661364&origin=resultslist

DOI

10.3390/min15070702

Тип публикаций

Статья

Сведения о финансировании

This research was funded by the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. BR21882140).

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