Abstract
Accurate and timely investigation to concentrate grade in mining industry is a premise of realizing real time and efficient control in a froth flotation process. This study seeks to use image processing and artificial intelligence technologies to predict the elemental composition of minerals in the flotation froth. The online analyzer is a flotation soft sensor solution that predicts the concentrate grade content of the flotation using froth images and physio-chemical parameters. A froth image dataset from the lead flotation circuit was collected and prepossessed. Frame selection and data augmentation was used for this dataset. Feature extraction includes texture and color distribution using image processing algorithms. Then, several state-of-the-art machine learning algorithms (Linear regression, Random forest, Decision tree, GR Boost) are trained to predict the concentrate grades of minerals. A Convolutional neural network architecture is used on the image dataset to predict the Lead Pb concentrate grade which indicates that the deep learning has a good industrial performance. The promising results of this study demonstrate the significant potential of machine vision and deep learning neural networks in froth image analysis, which is of great importance for development of the mining industry.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Peroni, F.R.: Mining haul roads: theory and practice. Chemical Rubber Company 2019
Mărgulescu, F.S., Moagăr-Poladian, S.S.: GLOBAL ECONOMIC OBSERVER (2017). http://www.globeco.ro/
McKinsey, F.: Report on Economic Impact of disruptive technologies. McKinsey (2015)
Qassimi, S., Abdelwahed, E.H.: Disruptive Innovation in Mining Industry 4.0, Distributed Sensing and Intelligent Systems (2022). https://doi.org/10.1007/978-3-030-64258-7_28
McCoy, J.T., Auret, F.L.: Machine learning applications in minerals processing: a review. J. Minerals Eng. 132, 95–109 (2019)
Danish, A., Frimpong, S.F.: Identification of digital technologies and digitalisation trends in the mining industry. Artificial Intelligence Review Springer (2020)
Barnewold, L., Lottermoser, B.G.: Identification of digital technologies and digitalisation trends in the mining. Int. J. Mining Sci. Technol. 30, 747–757 (2020)
Tabaei, M., Esfahani, M.M., Rasekh, P., Esna-ashari, A.: Mineral prospectivity mapping in GIS using fuzzy logic integration in Khondab area, western Markazi province Iran. J. Tethys (2017)
Iphar, M., Cukurluoz, A.K.: Fuzzy risk assessment for mechanized underground coal mines in Turkey. Int. J. Occup. Safety Ergonom. (2020)
Bui, X-N., Nguyen, H., Le, H.-A., Bui, H.-B., Do, N.-H.: Prediction of blast-induced air over-pressure in open-pit mine: assessment of different artificial intelligence techniques. J. Nat. Resour. Res. (2020). https://doi.org/10.1007/s11053-019-09461-0
Tiile, R.N.: Artificial neural network approach to predict blast-induced ground vibration, airblast and rock fragmentation, Thesis at Missouri University of Science and Technology (2016)
Takbiri-Borujeni, A., Fathi, E., Sun, T., Rahmani, R., Khazaeli, F.: Drilling performance monitoring and optimization: a data-driven approach, air blast and rock fragmentation. J. Petroleum Explor. Prod. Technol. (2019)
Gohel, H.A., Upadhyay, H., Lagos, L., Cooper, K., Sanzetenea, A.: Predictive maintenance architecture development for nuclear infrastructure using machine learning. J. Nuclear Eng. Technol. (2020)
Dusan, P., Fleming-Muñoz, D.: Automation and robotics in mining: jobs, income and inequality implications. J. Extract. Ind. Soc. 8, 189–193 (2021)
Zhiping, W., Changkui, Z., Jinhe, P., Tiancheng, N., Changchun, Z., Zhaolin, L.: Deep learning-based ash content prediction of coal flotation concentrate using convolutional neural network. J. Minerals Eng. (2021)
Walker, C.J.: Fourier Transform Infrared Spectroscopy and Machine Learning Techniques for the Sensitive Identification of Organics in Rocks, Thesis in Delaware State University (2020)
Dalm, M., Buxton, M., van Ruitenbeek, F.: Discriminating ore and waste in a porphyry copper deposit using short-wavelength infrared (SWIR) hyperspectral imagery. J. Minerals Eng. (2017)
Jin, Z., Zhaohui, T., Jinping, L., Zhen, T., Pengfei, X.: Recognition of flotation working conditions through froth image statistical modeling for performance monitoring. J. Minerals Eng. (2016)
Zarie, M., Jahedsaravani, A., Massinaei, M.: Flotation froth image classification using convolutional neural networks. J. Minerals Eng. (2016)
Mengcheng, T., Changchun, Z., Ningning, Z., Cheng, L., Jinhe, P., Shanshan, C.: Prediction of the ash content of flotation concentrate based on froth image processing and BP neural network modeling. Int. J. Coal Preparation Utilization (2021)
Jinping, L., et al.: Online monitoring of flotation froth bubble-size distributions via multiscale deblurring and multistage jumping feature-fused full convolutional networks. J. Trans. Instrum. Meas. (2020)
Acknowledgment
This research is conducted within the framework of the “Smart Connected Mine” project, which has been supported by the Moroccan Ministry of Higher Education, Scientific Research and Innovation, the Digital Development Agency (DDA), the National Center for Scientific and Technical Research of Morocco (CNRST) through the Al-Khawarizmi program. This article is part of the work undertaken by different partners composed of MASCIR (Moroccan Foundation for Advanced Science, Innovation and Research), REMINEX R &D and Engineering subsidiary of MANAGEM group, UCA (University Cadi Ayyad), ENSMR (National School of Mines of Rabat), and ENSIAS (National School of Computer Science and Systems Analysis). We would like to thank MANAGEM Group and its subsidiary CMG for allowing to conduct research and data collection on site as an industrial partner in this project.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Bendaouia, A. et al. (2022). Digital Transformation of the Flotation Monitoring Towards an Online Analyzer. In: Hamlich, M., Bellatreche, L., Siadat, A., Ventura, S. (eds) Smart Applications and Data Analysis. SADASC 2022. Communications in Computer and Information Science, vol 1677. Springer, Cham. https://doi.org/10.1007/978-3-031-20490-6_26
Download citation
DOI: https://doi.org/10.1007/978-3-031-20490-6_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20489-0
Online ISBN: 978-3-031-20490-6
eBook Packages: Computer ScienceComputer Science (R0)