Moisture Determination for Fine-Sized Copper Ore by Computer Vision and Thermovision Methods-Reference-Cited by-同舟云学术

Moisture Determination for Fine-Sized Copper Ore by Computer Vision and Thermovision Methods

Published:2023-01-20 Issue:3 Volume:23 Page:1220
ISSN:1424-8220
Container-title:Sensors
language:en
Short-container-title:Sensors

Author:

Buchczik Dariusz¹^ORCID,Budzan Sebastian¹^ORCID,Krauze Oliwia¹^ORCID,Wyzgolik Roman¹^ORCID

Affiliation:

1. Department of Measurements and Control Systems, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, 44-100 Gliwice, Poland

Abstract

The moisture of bulk material has a significant impact on the energetic efficiency of dry grinding, resultant particle size distribution and particle shape, and conditions of powder transport. This research aims to develop computer vision and thermovision techniques for the on-site estimation of moisture content in copper ore, for use, e.g., in dry grinding installations. The influence of particle size on the results of moisture estimation is also studied. The tested granular material was copper ore of particle size 0–2 mm and relative moisture content of 0.5–11%. Both vision and thermovision images were taken at standard and macro scales. The results suggest that median-intensity vision images monotonically reflect copper ore moisture in the range of about 0.5–5%. Suitable models were identified and cross-validated here. In contrary, thermograms should not be analyzed simply for their mean temperature but treated with computer vision processing algorithms.

Funder

Polish Ministry of Education and Science

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Link

https://www.mdpi.com/1424-8220/23/3/1220/pdf

Reference47 articles.

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