Using Deep Convolutional Neural Networks and Infrared Thermography to Identify Coal Quality and Gangue

Owing to the enormous demand for and growing large-scale use of coal in the China, India, and USA, speculation has arisen about possible hazards to environmental quality and human health. The contents of fly ash and volatile matter in low-quality coal are extremely harmful to the environment. As a r...

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Autores principales:	Refat Mohammed Abdullah Eshaq, Eryi Hu, Hamzah A. A. M. Qaid, Yao Zhang, Tonggang Liu
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Thermal visual sensing infrared machine vision infrared camera (IC) deep learning convolutional neural network (CNN) coal preparation plant (CPP) Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/728f48959abc460386348c60da92ef24
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spelling	oai:doaj.org-article:728f48959abc460386348c60da92ef242021-11-18T00:09:46ZUsing Deep Convolutional Neural Networks and Infrared Thermography to Identify Coal Quality and Gangue2169-353610.1109/ACCESS.2021.3121270https://doaj.org/article/728f48959abc460386348c60da92ef242021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9579439/https://doaj.org/toc/2169-3536Owing to the enormous demand for and growing large-scale use of coal in the China, India, and USA, speculation has arisen about possible hazards to environmental quality and human health. The contents of fly ash and volatile matter in low-quality coal are extremely harmful to the environment. As a result, there is still much to be explored regarding known hazards and harms to the natural environment of the Earth. For the detection of high-quality coal, we propose a new method of distinguishing coal quality in terms of different “coal types” (i.e., anthracite, bituminous coal, subbituminous coal and lignite) and efficiently separating gangue and rock from the production lines of coal preparation plants (CPPs) by exploiting infrared machine vision and convolutional neural networks (CNNs) for deep learning, which can make coal use less harmful to humans and nature and/or more useful for general welfare. In this article, we conducted two experiments. First experiment is to study the reactions of coal types, gangue and rock with infrared radiation at temperatures of 50°C, 70°C, 90°C, 110°C, and 150°C. Second experiment, several common CNN models (i.e., AlexNet, DarkNet-53, GoogLeNet, NasNet_Mobileb, ResNet-18, MobileNet-v2, Inception-v3 and DenseNet-201) are trained and tested to classify coal types and distinguish gangue and rock. We achieve a remarkable classification accuracy of 100% in these training and testing processes when employing the ResNet-18 and DenseNet-201 models.Refat Mohammed Abdullah EshaqEryi HuHamzah A. A. M. QaidYao ZhangTonggang LiuIEEEarticleThermal visual sensinginfrared machine visioninfrared camera (IC)deep learningconvolutional neural network (CNN)coal preparation plant (CPP)Electrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 147315-147327 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Thermal visual sensing infrared machine vision infrared camera (IC) deep learning convolutional neural network (CNN) coal preparation plant (CPP) Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Thermal visual sensing infrared machine vision infrared camera (IC) deep learning convolutional neural network (CNN) coal preparation plant (CPP) Electrical engineering. Electronics. Nuclear engineering TK1-9971 Refat Mohammed Abdullah Eshaq Eryi Hu Hamzah A. A. M. Qaid Yao Zhang Tonggang Liu Using Deep Convolutional Neural Networks and Infrared Thermography to Identify Coal Quality and Gangue
description	Owing to the enormous demand for and growing large-scale use of coal in the China, India, and USA, speculation has arisen about possible hazards to environmental quality and human health. The contents of fly ash and volatile matter in low-quality coal are extremely harmful to the environment. As a result, there is still much to be explored regarding known hazards and harms to the natural environment of the Earth. For the detection of high-quality coal, we propose a new method of distinguishing coal quality in terms of different “coal types” (i.e., anthracite, bituminous coal, subbituminous coal and lignite) and efficiently separating gangue and rock from the production lines of coal preparation plants (CPPs) by exploiting infrared machine vision and convolutional neural networks (CNNs) for deep learning, which can make coal use less harmful to humans and nature and/or more useful for general welfare. In this article, we conducted two experiments. First experiment is to study the reactions of coal types, gangue and rock with infrared radiation at temperatures of 50°C, 70°C, 90°C, 110°C, and 150°C. Second experiment, several common CNN models (i.e., AlexNet, DarkNet-53, GoogLeNet, NasNet_Mobileb, ResNet-18, MobileNet-v2, Inception-v3 and DenseNet-201) are trained and tested to classify coal types and distinguish gangue and rock. We achieve a remarkable classification accuracy of 100% in these training and testing processes when employing the ResNet-18 and DenseNet-201 models.
format	article
author	Refat Mohammed Abdullah Eshaq Eryi Hu Hamzah A. A. M. Qaid Yao Zhang Tonggang Liu
author_facet	Refat Mohammed Abdullah Eshaq Eryi Hu Hamzah A. A. M. Qaid Yao Zhang Tonggang Liu
author_sort	Refat Mohammed Abdullah Eshaq
title	Using Deep Convolutional Neural Networks and Infrared Thermography to Identify Coal Quality and Gangue
title_short	Using Deep Convolutional Neural Networks and Infrared Thermography to Identify Coal Quality and Gangue
title_full	Using Deep Convolutional Neural Networks and Infrared Thermography to Identify Coal Quality and Gangue
title_fullStr	Using Deep Convolutional Neural Networks and Infrared Thermography to Identify Coal Quality and Gangue
title_full_unstemmed	Using Deep Convolutional Neural Networks and Infrared Thermography to Identify Coal Quality and Gangue
title_sort	using deep convolutional neural networks and infrared thermography to identify coal quality and gangue
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/728f48959abc460386348c60da92ef24
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_version_	1718425230386397184

Using Deep Convolutional Neural Networks and Infrared Thermography to Identify Coal Quality and Gangue

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