T
Tiancheng Nie
Researcher at China University of Mining and Technology
Publications - 10
Citations - 185
Tiancheng Nie is an academic researcher from China University of Mining and Technology. The author has contributed to research in topics: Chemistry & Coal. The author has an hindex of 3, co-authored 7 publications receiving 58 citations.
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Recovery of rare earth elements from coal fly ash by integrated physical separation and acid leaching.
Jinhe Pan,Jinhe Pan,Tiancheng Nie,Behzad Vaziri Hassas,Mohammad Rezaee,Zhiping Wen,Changchun Zhou +6 more
TL;DR: A conceptual process flowsheet was developed for recovery of REY from CFA which maximizes REY resources utilization and enhances sustainability of CFA disposal.
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Recovery of rare-earth elements from coal fly ash via enhanced leaching
TL;DR: In this article, the majority of rare earth elements in coal fly ash were found to be rare earths in fly ash, which is regarded as a potential alternative source of rare-earth elements and has gained much attention over the past few years.
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Froth image feature engineering-based prediction method for concentrate ash content of coal flotation
TL;DR: The feature engineering of coal flotation froth image in this paper can make a good prediction of thecoal flotation concentrate ash content and can be used as the theoretical basis for the intelligent construction of flotation.
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Extraction of rare earth elements from coal fly ash by alkali fusion–acid leaching: Mechanism analysis
Mengcheng Tang,Changchun Zhou,Ningning Zhang,Ningning Zhang,Jinhe Pan,Shanshan Cao,Tingting Hu,Wanshun Ji,Zhiping Wen,Tiancheng Nie +9 more
TL;DR: Coal fly ash (CFA) contains higher levels of rare earth elements (REEs), which can be seen as a potential substitute for rare earth resources as discussed by the authors, and is used as an effective method of hydrometallurg...
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Deep learning-based ash content prediction of coal flotation concentrate using convolutional neural network
TL;DR: Wang et al. as discussed by the authors used convolutional neural networks (CNNs) to classify coal flotation images with different concentrate ash content interval (integer ± 0.5) and achieved an accuracy of 97.1%.