Multivariate analysis of trace elements leaching from coal and host rock

Shan, Yao; Wang, Wenfeng; Qin, Yong; Gao, Linsheng

doi:10.1016/j.gsd.2019.01.001

Cited by 10 publications

(7 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For some environmental and geological problems, the target variables cannot be labelled, then the unsupervised ML algorithm, such as principal components analysis (PCA) are applied. For example, Shan et al [11] applied the PCA method to analyse the occurrence and leaching mechanism in coal and host rock, Pumure et al [12] found out successfully of the occurrence of As and Se in coal host rock. Self-Organising Maps (SOM) is a kind of unsupervised artificial neural network (ANN) used in a large data amount scenario [13][14].…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Sources Identification of Water Inrush in Coal Mines Using Technique of Multiple Non-Linear Machine Learning Modelling

Shan¹

2021

Groundwater Management and Resources

Self Cite

View full text Add to dashboard Cite

Water inrush is a major threat to the working safety for coal mines in the Northern China coal district. The inrush pattern, threaten level, and also the geochemical characteristics varies according to the different of water sources. Therefore, identifying the water source correctly is an important task to predict and control the water inrush accidents. In this chapter, the algorithms and attempts to identify the water inrush sources, especially in the Northern China coal mine district, are reviewed. The geochemical and machine learning algorithms are two main methods to identify the water inrush sources. Four main steps need to apply, namely data processing, feature selection, model training, and evaluation, in the process of machine learning (ML) modelling. According to a calculation instance, most of the major ions, and some trace elements, such as Ti, Sr, and Zn, were identified to be important in light of geochemical analysis and machine learning modelling. The ML algorithms, such as random forest (RF), support vector machine (SVM), Logistica regression (LR) perform well in the source identification of coal mine water inrush.

show abstract

Section: Machine Learning Methodsmentioning

confidence: 99%

Sources Identification of Water Inrush in Coal Mines Using Technique of Multiple Non-Linear Machine Learning Modelling

Shan¹

2021

Groundwater Management and Resources

Self Cite

View full text Add to dashboard Cite

show abstract

“…The data of ground water and surface water samples are shown in related article [1] . The data of solid samples and the water samples collected from the leaching experiments are shown in the DiB article, as supporting material for the related article.…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

“… Related research article Y. Shan, W. Wang, Y. Qin, L. Gao. Multivariate analysis of trace elements leaching from coal and host rock, Groundwater for Sustainable Development., 8, 2019 , 402–412 [1] . …”

mentioning

confidence: 99%

Data on trace element concentrations in coal and host rock and leaching product in different pH values and open/closed environments

2019

Self Cite

View full text Add to dashboard Cite

The data presented in this article are related to the research article entitled “Multivariate analysis of trace elements leaching from coal and host rock” [1]. During coal mining, coal and host rock undergo water-rock interaction, leading to release of trace elements to surface and ground water. The batch experiments were designed and implemented to investigate the leaching behavior and mechanisms during the process of water-rock interaction. In different experimental sets, various types of leaching water, open/closed environments, temperature, and initial pH values were used to evaluate their impact on leaching of trace elements. These data could be used to analyze leaching mechanisms of trace elements from coal and host rock, and understand, predict, control trace elements’ contamination to surrounding waters.

show abstract

“…In an acid environment, metal element, Pb, Cd, Cu, Ni, have high mobility, while some metalloid element, As, Se, tend to migrate in an alkaline environment. Damaging effects of AMD are reported in Asia [34,[90][91][92][93], North America [72,[94][95][96], Europe [83,97], South America [47,98,99]. When AMD enters surface water bodies, the effects include biotic impacts on stream and lake organisms through direct toxicity, habitat alteration by metal precipitates, visual changes from orange or yellow staining of stream sediments, nutrient cycle disruptions, or other mechanisms, and the water often becomes unsuitable for domestic, agricultural, and industrial uses.…”

Section: Contamination By Coal Mine Watermentioning

confidence: 99%

“…The PCA method has also used to investigate trace element occurrence in rock/ coal, and reaction pathway, which may be the source of TEs that have contaminating potential on surrounding water bodies. Shan et al [34] found that in coal host rock seam, Se/Cd/Hg/ As occurred in sulfide minerals, Be and V occurred in carbonate minerals, Cr and Pb occurred in clay minerals, respectively; while in coal seam, Se/Cr/ Pb occurred in clay minerals, As and Hg occurred in sulfide minerals. Se, As and Hg immigrated through dissolution of sulfide minerals, Cr immigrated through transformation of clay minerals in coal host rock.…”

Section: Te Occurrence and Reaction Pathwaymentioning

confidence: 99%

Data Mining for Source Apportionment of Trace Elements in Water and Solid Matrix

Shan¹,

Shi²

2021

Trace Metals in the Environment - New Approaches and Recent Advances

Self Cite

View full text Add to dashboard Cite

Trace elements migrate among different environment bodies with the natural geochemical reactions, and impacted by human industrial, agricultural, and civil activities. High load of trace elements in water, river and lake sediment, soil and air particle lead to potential to health of human being and ecological system. To control the impact on environment, source apportionment is a meaningful, and also a challenging task. Traditional methods to make source apportionment are usually based on geochemical techniques, or univariate analysis techniques. In recently years, the methods of multivariate analysis, and the related concepts data mining, machine learning, big data, are developing fast, which provide a novel route that combing the geochemical and data mining techniques together. These methods have been proved successful to deal with the source apportionment issue. In this chapter, the data mining methods used on this topic and implementations in recent years are reviewed. The basic method includes principal component analysis, factor analysis, clustering analysis, positive matrix fractionation, decision tree, Bayesian network, artificial neural network, etc. Source apportionment of trace elements in surface water, ground water, river and lake sediment, soil, air particles, dust are discussed.

show abstract

Multivariate analysis of trace elements leaching from coal and host rock

Cited by 10 publications

References 61 publications

Sources Identification of Water Inrush in Coal Mines Using Technique of Multiple Non-Linear Machine Learning Modelling

Sources Identification of Water Inrush in Coal Mines Using Technique of Multiple Non-Linear Machine Learning Modelling

Data on trace element concentrations in coal and host rock and leaching product in different pH values and open/closed environments

Data Mining for Source Apportionment of Trace Elements in Water and Solid Matrix

Contact Info

Product

Resources

About