2021
DOI: 10.1007/s10489-021-02328-z
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A new froth image classification method based on the MRMR-SSGMM hybrid model for recognition of reagent dosage condition in the coal flotation process

Abstract: Intelligent separation is a core technology in the transformation, upgradation, and high-quality development of coal. Realising the intelligent recognition and accurate classification of coal flotation froth is a key technology of intelligent separation. At present, the coal flotation process relies on artificial recognition of froth features for adjusting the reagent dosage. However, owing to the low accuracy and subjectivity of artificial recognition, some problems arise, such as reagent wastage and unqualif… Show more

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Cited by 17 publications
(3 citation statements)
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“…Deep learning has demonstrated its superiority in various image processing problems (i.e., image enhancement [33,34], image super-resolution [35,36], and image classification [37,38]). In recent years, deep learning-based CS methods also have been shown to significantly outperform traditional model-based methods (e.g., discrete wavelet transform (DWT), total variation augmented Lagrangian alternating-direction algorithm (TVAL3), and D-AMP) in image compressed sensing [11,[16][17][18][23][24][25][26][27][28][39][40][41][42][43][44].…”
Section: Related Workmentioning
confidence: 99%
“…Deep learning has demonstrated its superiority in various image processing problems (i.e., image enhancement [33,34], image super-resolution [35,36], and image classification [37,38]). In recent years, deep learning-based CS methods also have been shown to significantly outperform traditional model-based methods (e.g., discrete wavelet transform (DWT), total variation augmented Lagrangian alternating-direction algorithm (TVAL3), and D-AMP) in image compressed sensing [11,[16][17][18][23][24][25][26][27][28][39][40][41][42][43][44].…”
Section: Related Workmentioning
confidence: 99%
“…The flotation staff often make subjective judgments and perform manual operations on production variables, such as inflation volume and drug dosage, after obtaining information on the surface characteristics of the foam. However, frequent fluctuations in flotation production indices, high chemical consumption rates, and low resource recovery rates may occur depending on factors such as site lighting and personnel experience (Zarie et al, 2020;Wen et al, 2021;Aldrich et al, 2022;Pawlik et al, 2022;Cao et al, 2022). Therefore, using deep learning-based machinevision technology to monitor and identify the foam surface characteristic information during the flotation production process in real-time can guide the adjustment of relevant production elements and improve the mineral resource utilisation and economic efficiency of coal washing plants.…”
Section: Introductionmentioning
confidence: 99%
“…During the flotation process, the onsite staff artificially adjust the factors affecting flotation production. These factors include the addition of flotation reagents and the amount of aeration by observing the characteristic information of the foam surface. However, owing to subjective bias, production environment changes, and other factors, the findings of manual judgment are frequently inaccurate, causing frequent fluctuations in flotation production indicators and low utilization of mineral resources . Consequently, using machine vision to capture the characteristic information of flotation foam, to monitor and identify the foam in the flotation process in real time, and to guide the adjustment of production factors, such as reagent dosage and liquid level, affecting flotation production increases the yield and economic benefits of the flotation concentrate in a coal washing plant. …”
Section: Introductionmentioning
confidence: 99%