Manufacturing & Industrial Engineering Faculty Publications

Document Type

Article

Publication Date

3-29-2024

Abstract

In the transformative field of mineral processing, the need for innovative technologies to overcome inherent difficulties and a critical shortage of high-quality data is an acute challenge. This study addresses these pressing issues by leveraging advanced spatio-temporal deep learning techniques, specifically Convolutional Long Short-Term Memory (ConvLSTM). Focused on the Zinc flotation circuit at CMG Managem Group in Morocco, our comprehensive approach encompasses meticulous data collection from a real-world industrial setting, rigorous spatial and temporal analyses, practical and accurate data augmentation, and the development of a ConvLSTM model for precise prediction of mineral grades. By capturing the temporal intricacies of froth behavior through video data, we gain insights into mineral concentration fluctuations, a critical aspect of traditional analysis. By offering a comprehensive solution that combines cutting-edge technology, meticulous data analysis, and practical augmentation methods, we aim to elevate not only mineral processing efficiency but also a broader spectrum of resource utilization and sustainable practices. This study offers a promising solution to elevate mineral processing efficiency and accuracy in the face of data limitations. It underscores the potential for transformative impacts on mineral processing operations, paving the way for more effective resource utilization and sustainable practices.

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Publication Title

SN Computer Science

DOI

10.1007/s42979-024-02706-7

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