Hybrid features extraction for the online mineral grades determination in the flotation froth using Deep Learning

Bendaouia, Ahmed; Abdelwahed, El Hassan; Qassimi, Sara; Boussetta, Abdelmalek; Benzakour, Intissar; Benhayoun, Abderrahmane; Amar, Oumkeltoum; Bourzeix, François; Baïna, Karim; Cherkaoui, Mouhamed; Hasidi, Oussama

doi:10.1016/j.engappai.2023.107680

Cited by 8 publications

(4 citation statements)

References 39 publications

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“…By combining supervised and unsupervised feature extraction methods, including CNN, proposed by Bendaouia et al [38], the Hybrid Features Extraction (HFE) approach achieved a high accuracy in predicting elemental composition (Pb, Fe, Cu, Zn) of minerals in real time. Comparative analysis demonstrates HFE's superiority over other feature extraction methods, particularly regarding average prediction error.…”

Section: Predictions Of Flotation Performance and Feature Importance ...mentioning

confidence: 99%

“…The growing interest in monitoring and controlling the flotation process with machine learning models is evident. The year 2024 has already started with some insightful research studies presented by Bendaouia et al [38,40] in the area of froth image extraction and analysis, and it is expected that more research in this field will be presented.…”

Section: Summaries and Future Workmentioning

confidence: 99%

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Advancements in Machine Learning for Optimal Performance in Flotation Processes: A Review

Szmigiel,

Apel,

Skrzypkowski

et al. 2024

Minerals

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Flotation stands out as a successful and extensively employed method for separating valuable mineral particles from waste rock. The efficiency of this process is subjected to the distinct physicochemical attributes exhibited by various minerals. However, the complex combination of multiple sub-processes within flotation presents challenges in controlling this mechanism and achieving optimal efficiency. Consequently, there is a growing dependence on machine learning methods in mineral processing research. This paper provides a comprehensive overview of machine learning and artificial intelligence techniques, presenting their potential applications in flotation processes. The review demonstrates advancements discussed in scholarly research over the past decade and highlights a growing interest in utilizing machine learning methods for monitoring and optimizing flotation processes, as demonstrated by the increasing number of studies in this field. Recent trends also suggest that the course of flotation process monitoring, and control will increasingly focus on the refinement and deployment of deep learning networks developed specifically for froth image extraction and analysis.

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Section: Predictions Of Flotation Performance and Feature Importance ...mentioning

confidence: 99%

Section: Summaries and Future Workmentioning

confidence: 99%

Advancements in Machine Learning for Optimal Performance in Flotation Processes: A Review

Szmigiel,

Apel,

Skrzypkowski

et al. 2024

Minerals

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“…Mineral grade is a crucial performance indicator for froth flotation [1,2], and accurate real-time prediction of mineral grade assists flotation operators in better recognizing the current flotation conditions, allowing for subsequent measures for extracting crucial minerals. Consequently, investigating grade monitoring during the froth flotation process is imperative.…”

Section: Introductionmentioning

confidence: 99%

“…The static features include bubble size and froth color, which are the most obvious features in froth flotation conditions; the dynamic features include froth stability and load bearing rate, reflecting the overall change trend in the froth; and the statistical features mainly refer to the texture of bubbles, which reflects the fineness of mineral granules attached to the surface of the froth. For instance, Popli et al [6] employed a support vector machine (SVM) to investigate the correlation between multiple visual features extracted from froth images and mineral grades, aiming to predict grade outcomes, whereas Bendaouia et al [1] predicted the grade of a mineral concentrate by extracting features such as bubble size, color, and texture.…”

Section: Introductionmentioning

confidence: 99%

Study on Prediction of Zinc Grade by Transformer Model with De-Stationary Mechanism

Peng,

Luo,

Luo

et al. 2024

Minerals

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At present, in the mineral flotation process, flotation data are easily influenced by various factors, resulting in non-stationary time series data, which lead to overfitting of prediction models, ultimately severely affecting the accuracy of grade prediction. Thus, this study proposes a de-stationary attention mechanism based on the transformer model (DST) to learn non-stationary information in raw mineral data sequences. First, normalization processing is performed on matched flotation data and mineral grade values, to make the data sequences stationary, thereby enhancing model prediction capabilities. Then, the proposed de-stationary attention mechanism is employed to learn the temporal dependencies of mineral flotation data in the transformed vanilla transformer model, i.e., non-stationary information in the mineral data sequences. Lastly, de-normalization processing is conducted to maintain the mineral prediction results within the same scale as the original data. Compared with existing models such as RNN, LSTM, transformer, Enc-Dec (RNN), and STS-D, the DST model reduced the RMSE by 20.8%, 20.8%, 62.8%, 20.5%, and 49.1%, respectively.

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Computer Vision Based Monitoring System for Flotation in Mining Industry 4.0

Bendaouia,

Abdelwahed,

Qassimi

et al. 2024

Communications in Computer and Information Science

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Hybrid features extraction for the online mineral grades determination in the flotation froth using Deep Learning

Cited by 8 publications

References 39 publications

Advancements in Machine Learning for Optimal Performance in Flotation Processes: A Review

Advancements in Machine Learning for Optimal Performance in Flotation Processes: A Review

Study on Prediction of Zinc Grade by Transformer Model with De-Stationary Mechanism

Computer Vision Based Monitoring System for Flotation in Mining Industry 4.0

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