Conv-LSTM for Real Time Monitoring of the Mineral Grades in the Flotation Froth

Author:

Bendaouia Ahmed12,Abdelwahed El1,Qassimi Sara3,Boussetta Abdelmalek4,Benzakour Intissar4,Amar Oumkeltoum2,Bourzeix François2,Soulala Achraf1,Hasidi Oussama12

Affiliation:

1. Computer Systems Engineering Laboratory (LISI), Computer Science Department, Faculty of Sciences Semlalia, Cadi Ayyad University, Marrakech, Morocco, --- Select a Country ---

2. SEIA Departement, Moroccan Foundation for Advanced Science Innovation and Research (MAScIR), Rabat, Morocco, --- Select a Country ---

3. Computer and Systems Engineering Laboratory (L2IS), Computer Science Department, Faculty of Science and Technology, Cadi Ayyad University, Marrakech, Morocco, --- Select a Country ---

4. R&D and Engineering Center, Reminex, Managem Group, Marrakech, Morocco, --- Select a Country ---

Publisher

SCITEPRESS - Science and Technology Publications

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Multi-Method Machine Learning Framework for Simulating Power Dynamics in an Iron Ore Cone Crusher Plant;Mineral Processing and Extractive Metallurgy Review;2024-09-04

2. Advanced Spatio-Temporal Froth Analysis Using Smart Soft Sensors in Mineral Processing;SN Computer Science;2024-03-29

3. Fourier Transform and Machine Learning for Real-Time Monitoring of Froth Flotation in Mining Industry;2023 7th IEEE Congress on Information Science and Technology (CiSt);2023-12-16

4. Smart Soft Sensor for Froth flotation quality monitoring using pre-trained feature extractor and Vision Transformer ViT;2023 17th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS);2023-11-08

5. Generic and scalable multi-layered architecture for Digital Twin implementation in industrial processes: Mineral Processing case study;Proceedings of the 2023 7th International Conference on Advances in Artificial Intelligence;2023-10-13

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