PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Development of a New Method for Measuring the Diameter of Iron Ore Pellets Using Digital Image Processing and Convolutional Neural Networks in Compliance with ISO 4698
Version 1
: Received: 29 September 2023 / Approved: 30 September 2023 / Online: 1 October 2023 (07:12:52 CEST)
How to cite:
Campos, R.M.; De Almeida, G.M. Development of a New Method for Measuring the Diameter of Iron Ore Pellets Using Digital Image Processing and Convolutional Neural Networks in Compliance with ISO 4698. Preprints2023, 2023092179. https://doi.org/10.20944/preprints202309.2179.v1
Campos, R.M.; De Almeida, G.M. Development of a New Method for Measuring the Diameter of Iron Ore Pellets Using Digital Image Processing and Convolutional Neural Networks in Compliance with ISO 4698. Preprints 2023, 2023092179. https://doi.org/10.20944/preprints202309.2179.v1
Campos, R.M.; De Almeida, G.M. Development of a New Method for Measuring the Diameter of Iron Ore Pellets Using Digital Image Processing and Convolutional Neural Networks in Compliance with ISO 4698. Preprints2023, 2023092179. https://doi.org/10.20944/preprints202309.2179.v1
APA Style
Campos, R.M., & De Almeida, G.M. (2023). Development of a New Method for Measuring the Diameter of Iron Ore Pellets Using Digital Image Processing and Convolutional Neural Networks in Compliance with ISO 4698. Preprints. https://doi.org/10.20944/preprints202309.2179.v1
Chicago/Turabian Style
Campos, R.M. and Gustavo Maia De Almeida. 2023 "Development of a New Method for Measuring the Diameter of Iron Ore Pellets Using Digital Image Processing and Convolutional Neural Networks in Compliance with ISO 4698" Preprints. https://doi.org/10.20944/preprints202309.2179.v1
Abstract
Iron ore processing involves critical steps that affect the quality of the final product. Determining the diameter of the pellets is the necessary initial step for volume measurement and, ultimately, for porosity and bulk density measurement, crucial characteristics for optimizing the burning process in the blast furnace. Traditional measurement methods using mercury present issues related to operator safety and environmental preservation, while the practices described in ISO 4698 standard require lengthy preparation and execution time. In light of environmental needs, operator safety, and the time consumed in tests, implementing a new method based on digital image processing and convolutional neural networks for measuring the diameter of burnt iron ore pellets is proposed. The methodology involves capturing images of the pellets using a high-resolution camera and utilizing digital image processing and neural networks capable of performing pixel-by-pixel object segmentation in the images, providing precise information about the pellets to calculate their volume automatically. The results were compared with those obtained using traditional methods, evaluating their conformity with the ISO 4698 standard. In conclusion, this study offers a new approach to measuring the volume of burnt iron ore pellets, providing accurate and reliable results in compliance with safety and environmental preservation standards.
Keywords
Convolutional Neural Networks; Computer Vision; Artificial Intelligence; Iron Ore Pellets; ISO 4698
Subject
Engineering, Metallurgy and Metallurgical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
