Mineralization of REE-Y-Nb-Ta-Zr-Hf in Wuchiapingian coals from the Liupanshui Coalfield, Guizhou, southwestern China: Geochemical evidence for terrigenous input

Mineralogy and geochemistry of the Late Triassic coal from the Caotang mine, northeastern Sichuan Basin, China, with emphasis on the enrichment of the critical element lithium

The paper Geochemical Characteristics of Rare-Metal, Rare-Scattered, and Rare-Earth Elements and Minerals in the Late Permian Coals from the Moxinpo Mine, Chongqing, China by Qin et al. in Energy & Fuels provides potentially useful data on the proximate analysis, geochemical, and mineralogical properties of the Late Permian (No. K2) coal from the Moxinpo Mine, Chongqing, China. However, after a careful examination of the whole paper, we regrettably found that the paper by Qin et al. is affected by issues of coal rank determination, mineralogical identification, geochemical composition, and geological interpretation, some of which at least may need careful reconsideration.

https://pubs.acs.org/doi/pdf/10.1021/acs.energyfuels.8b00976

Comments on Geochemical Characteristics of Rare-Metal, Rare-Scattered, and Rare-Earth Elements and Minerals in the Late Permian Coals from the Moxinpo Mine, Chongqing, China

Enrichment of Bi-Be-Mo-Cd-Pb-Nb-Ga, REEs and Y in the Permian coals of the Huainan Coalfield, Anhui, China: Discussion

The accurate measurement of vitrinite reflectance (especially for mean maximum vitrinite reflectance, MMVR) is an important issue in the fields of coal mining and processing. However, the application of MMVR has been somewhat hampered by the subjective and the time-consuming characteristic of manual measurements. Semi-automated methods that are oversimplified might affect the accuracy in measuring MMVR values. To address these concerns, we propose a novel MMVR measurement strategy based on machine learning (MMVRML). Considering the complex nature of coal, adaptive K-means clustering is firstly employed to automatically detect the number of clusters (i.e., maceral groups) in photomicrographs. Furthermore, comprehensive features along with a support vector machine are utilized to intelligently identify the regions with vitrinite. The largest region with vitrinite in each photomicrograph is gridded for further regression analysis. Evaluations on 78 photomicrographs show that the model based on random forest and 15 simplified grayscale features achieves the state-of-the-art root mean square error of 0.0424. In addition, to facilitate the usage of petrologists without strong expertise in the machine learning domain, we released the first non-commercial standalone software for estimating MMVR.

https://www.mdpi.com/1996-1073/12/20/3855/pdf

Intelligent Estimation of Vitrinite Reflectance of Coal from Photomicrographs Based on Machine Learning

The mineralogical and geochemical compositions of the Lopingian coals from an exploratory drill core (ZK4-1) in the Zhongliangshan Coalfield, southwestern China are reported in this paper. The Zhongliangshan coals are medium volatile bituminous in rank (random vitrinite reflectance, average 1.38%), characterized by a medium-ash yield (26.84%) and high sulfur content (3.38%). Minerals in the Zhongliangshan coals are mainly composed of clay assemblages (kaolinite, the illite/smectite mixed layer (I/S) and chamosite), pyrite, quartz, carbonate minerals (calcite, marcasite, ankerite, and dolomite), and anatase, followed by rutile, jarosite, natrojarosite, bassanite, gypsum and K-feldspar, with traces of apatite, rhabdophane and barite. Compared with the average concentrations of the world hard coals, some trace elements including Li, V, Co, Cu, Se, Y, Zr, Nb, rare earth elements (REE), Cd, Ta, Hf and Hg, are enriched in the Zhongliangshan coals. The modes of occurrence of chamosite, barite, rhabdophane, quartz and calcite in the Zhongliangshan coals indicate that the coals have probably been affected by the injection of low-temperature hydrothermal fluids. Based on the concentrations of Sc, V, Cr, Co, Ni, Cu and Zn, the ratios of Al2O3/TiO2 and the upper continental crust-normalized rare earth element and yttrium (REY) distribution patterns of the Zhongliangshan coals, the dominant sediment source regions are the Leshan–Longnvsi Uplift, Hannan Upland, and Dabashan Uplift, with a small proportion of terrigenous materials from the Kangdian Upland. The K7 and the upper portion of K1 coals have the potential as raw materials for the recovery of REY.

/pdf/mineralogical-and-geochemical-compositions-of-the-lopingian-2cfcddw49k.pdf

Feng Han

Papers

Mineralogical and Geochemical Compositions of the Lopingian Coals in the Zhongliangshan Coalfield, Southwestern China