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A rapid and nondestructive machine vision sensor was developed for predicting the vibrated bulk density (VBD) of petroleum coke particles based on image texture analysis. It could be used for making corrective adjustments to a paste plant operation to reduce green anode variability (e.g., changes in binder demand). Wavelet texture analysis (WTA) and gray level co-occurrence matrix (GLCM) algorithms were used jointly for extracting the surface textural features of coke aggregates from images. These were correlated with the VBD using partial least-squares (PLS) regression. Coke samples of several sizes and from different sources were used to test the sensor. Variations in the coke surface texture introduced by coke size and source allowed for making good predictions of the VBD of individual coke samples and mixtures of them (blends involving two sources and different sizes). Promising results were also obtained for coke blends collected from an industrial-baked carbon anode manufacturer.
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Acknowledgements
The authors would like to acknowledge the financial support of the Natural Sciences and Engineering Research Council of Canada (NSERC), Fonds de Recherche du Québec – Nature et Technologies (FRQNT), Alcoa and the Aluminum Research Centre – REGAL. The assistance of Julien Lauzon-Gauthier for image texture analysis is gratefully appreciated. The support of Alcoa Deschambault anode plant technical staff is also greatly acknowledged.
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Azari, K., Bogoya-Forero, W., Duchesne, C. et al. Measurement of Vibrated Bulk Density of Coke Particle Blends Using Image Texture Analysis. JOM 69, 1613–1623 (2017). https://doi.org/10.1007/s11837-017-2320-z
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DOI: https://doi.org/10.1007/s11837-017-2320-z