Definitions of
\\n\\n
IntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\\n\\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\\n\\nLaunching 2021
\\n\\nArtificial Intelligence, ISSN 2633-1403
\\n\\nVeterinary Medicine and Science, ISSN 2632-0517
\\n\\nBiochemistry, ISSN 2632-0983
\\n\\nBiomedical Engineering, ISSN 2631-5343
\\n\\nInfectious Diseases, ISSN 2631-6188
\\n\\nPhysiology (Coming Soon)
\\n\\nDentistry (Coming Soon)
\\n\\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\\n\\nNote: Edited in October 2021
\\n"}]',published:!0,mainMedia:{caption:"",originalUrl:"/media/original/132"}},components:[{type:"htmlEditorComponent",content:'With the desire to make book publishing more relevant for the digital age and offer innovative Open Access publishing options, we are thrilled to announce the launch of our new publishing format: IntechOpen Book Series.
\n\nDesigned to cover fast-moving research fields in rapidly expanding areas, our Book Series feature a Topic structure allowing us to present the most relevant sub-disciplines. Book Series are headed by Series Editors, and a team of Topic Editors supported by international Editorial Board members. Topics are always open for submissions, with an Annual Volume published each calendar year.
\n\nAfter a robust peer-review process, accepted works are published quickly, thanks to Online First, ensuring research is made available to the scientific community without delay.
\n\nOur innovative Book Series format brings you:
\n\nIntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\n\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\n\nLaunching 2021
\n\nArtificial Intelligence, ISSN 2633-1403
\n\nVeterinary Medicine and Science, ISSN 2632-0517
\n\nBiochemistry, ISSN 2632-0983
\n\nBiomedical Engineering, ISSN 2631-5343
\n\nInfectious Diseases, ISSN 2631-6188
\n\nPhysiology (Coming Soon)
\n\nDentistry (Coming Soon)
\n\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\n\nNote: Edited in October 2021
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Nonlinearities complicate the mathematical treatment of the seemingly simple action of rotating, and these complications lead to a robust lineage of research. This book is meant for basic scientifically inclined readers, and commences with a chapter on the basics of spaceflight and leverages this remediation to reveal very advanced topics to new spaceflight enthusiasts. The topics learned from reading this text will prepare students and faculties to investigate interesting spaceflight problems in an era where cube satellites have made such investigations attainable by even small universities. 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The origin of the coal was organic matter containing virtually every element in the periodic table, mainly carbon, but also trace elements. The elements with relative higher content in the coal and host rock, such as iron (Fe) and aluminum (Al), which usually take 1–20% of the rock, respectively, and sodium (Na), potassium (K), calcium (Ca), magnesium (Mg), which are usually in the range of 0.01–10% of the rock, respectively. The trace elements refer to the elements at the 10–10,000 ppm levels in coal, rocks, and soil, etc. A variety of chemicals are associated with coal that is either found in the coal or in the rock layers that lie above and beneath the seams of coal [1]. Some of the trace elements are of great health concern. For example, lead (Pb) accounts for most of the cases of pediatric heavy metal poisoning and makes it difficult for children to learn, pay attention, and succeed in school. Mercury (Hg) exposure puts newborns at risk of neurological deficits and increased cardiovascular risk in adults. Arsenic (As) could cause heavy metal poisoning in adults and does not leave the body once it enters.
Coal mining has caused global environmental concern due to mainly two reasons—first, the coal and host rock contains multiple kinds of toxic trace elements, some of which are of great environmental and health issues, most of them (As, Cd, Co, Cr, Cu, Mn, Ni, Pb, Se, Sn V, and Zn) are associated with inorganic matter [2, 3]; second, the trace elements may be released through combustion and water-rock interaction [3, 4, 5, 6, 7, 8, 9].
The coal mine water, containing toxic trace elements, has influenced the water quality of both the groundwater and surface water in China. To control the contamination of trace elements, a lot of efforts have been making in both research and management. According to the Chinese national standard GB/T 19223-2015, the coal mine water is defined as bursting water, infiltrating water from surface water, and working produced water, during coal mining activity. The water is classified into acid (pH < 6.0), neutral (6.0–9.0) and alkaline (pH > 9), low- (<1000 mg/L), medium- (1000–6000 mg/L), and high-mineralized water (>6000 mg/L), and low- (<50 mg/L), medium- (50–500 mg/L), and high-suspended (>500 mg/L) coal mine water, regarding pH value, total dissolved solids, and suspended matter, respectively. Trace elements released from the coal and rock may contaminate surface and groundwater, including selenium (Se), As, Pb, fluorine (F), Hg, etc., leading to some different unique characteristics of the coal mine water. However, the releasing patterns are relatively similar among the coal mine waters. In the coal-bearing seam, the primitive environment is H-rich and reductive, where some reductive minerals are stable, such as pyrite, chalcopyrite, and sphalerite. While the coal and rock seam contact with air, the Eh value of the surrounding environment is elevated, and the minerals are oxidized [10, 11]. Through this process, the pH value may be reduced, accompanying the release of metal elements into the water, and high concentrations of metal trace elements in the water [12, 13, 14]. However, the neutral and alkaline mine water is also common, because of the dissolution of alkaline minerals, such as calcite and dolomite. The net effect of which determines the pH value of coal mine water produces a high mineralization value [12, 15].
Besides the water parameters, the occurrence of trace elements also influences its migration [16, 17, 18, 19]. Main minerals in coal include quartz, clay, sulfur-contained minerals, and a lesser number of feldspars and carbonates [20, 21]. As, Cr, Pb, Hg, Mo, Zn, and Sb were found to be enriched in coal compared with continent crust [22, 23, 24, 25], while compared to coal, host rock and gangue rejected on the land of coal can release up to 10 times toxic elements into water [2, 26, 27, 28].
The migration behavior of trace elements is controlled by two factors, the trace element occurrence and the surrounding environment. However, migration patterns and mechanism of trace elements into a surrounding water body are complex and different depending on the investigating sites. Traditional methods to investigate this process are based on geochemical surveys and testing. The information and pattern behind the data matrix are hard to identify. Along with the development of machine learning, multivariate analytical technology has been applied in some different areas of the geochemical research, the fourth paradigm for the research is becoming a more and more powerful tool to find a solution among the mass data. The multivariate analysis has been used to study the water characteristics [29], source [30, 31], groundwater pathway [32, 33, 34], etc. By using the method of multivariate technology, it is possible to disclose the leaching mechanism from the view of trace element occurrence and leaching behavior.
The geochemical issues involve a sample-parameter matrix, which includes a co-existence pattern among the parameters and samples. It is cumbersome and hard to identify the patterns using traditional geochemical technology. Thanks to the technological development of artificial intelligence, and the technique of machine learning, the multivariate parameter problem could be solved or mined to discover knowledge or criteria. In the field of geochemistry, the problems are feasible to be solved by using the multivariate analysis method. The multivariate analysis method can be classified to be supervised, unsupervised, and semi-supervised, depending on whether the target parameters are labeled. The unsupervised algorithms refer to principal component analysis (PCA), factor analysis (FA), clustering analysis (CA), positive matrix fractionation (PMF), etc., while the supervised algorithms refer to linear regression, logistic regression, support vector machine (SVM), decision tree (DT), random forest (RF), artificial neural network (ANN), and discriminant analysis (DA).
While the target parameter can be labeled, a supervised machine learning algorithm should be used in priority as accurate and stable models are expected. In the USA, the research tried to identify the source of salt ions (Mg, CL, and Na). As the samples were collected from known sites or environments, including (oceans, atmospheric deposition, weathering of common rocks, minerals and soils, and salt deposits and brines landfills, wastewater and water treatment, agriculture), the samples can be labeled. Therefore, discriminant analysis and clustering analysis were applied [35]. In Belgium, a Bayesian isotope mixing model was used to estimate proportional contributions of multiple nitrate sources in surface water [36]. In a coal mine, water inrush constantly threatens the production and human health and causes financial losses. The source apportionment technology is used in coal mines to determine the source of water inrush [37]. The water inrushes could be categized into four sources: quaternary sand-gravel pore aquifer, Dyas sandstone aquifer, limestone aquifer from Ordovician and Carboniferous, and abandoned coal mine districts, respectively. Different sources show various features and need suitable treating strategies. To set up the discriminant model, geochemical and data mining analytical protocol should be established. As the samples were collected from identified aquifers, a supervised machine learning method could be used. Huang et al. [37] proposed a technology system, the Piper-PCA-Bayes-LOOCV discrimination model to determine water inrush types in coal mines. The piper diagram is a geochemical technique to show the water characteristics, and abnormal samples/points were screened in this research. PCA was used to lower the dimension of the sample matrix, to make less variates standing for all the original variates. Then, the supervised ML model, Bayes DA, is used to train and implement a model for water source discriminant. LOOCV means leave-one-out cross-validation, to validate and improve the quality of the model. Wang et al. used discriminant analysis to determine water bursting sources in coal mines [38].
Comparing the supervised ML method, the unsupervised ML algorithms are used more frequently, for the samples are not always labeled. Pumure et al. [39] investigated the occurrence of selenium and arsenic in coal by the method of two-step PCA, founding that ultrasound leachable selenium concentrations were associated with 14 Å d-spacing phyllosilicate clays (chlorite, montmorillonite, and vermiculite all 2:1 layered clays), while ultrasound leachable arsenic concentrations were closely related to the concentration of illite, another 2:1 phyllosilicate clay. The PCA and PMF methods are often used to identify the source of trace elements. For example, lake sediment was analyzed [40] in southwest China using the PCA method, and it is shown that Cd/Hg/Pb/Zn and As were mainly from nonpoint anthropogenic sources, especially with the atmospheric emission from nonferrous metal smelting and coal consumption [41]. In Costa Rica, by using the method of PMF, eight important sources of PM 2.5 and PM 10 were identified. Vehicle exhaust, residual oil combustion, and fresh sea salt were the first three sources. Crustal, or dust aerosols originated, organic carbon and sulfate, secondary sulfate, secondary nitrate, and heavy fuels are the other potential sources [42]. In Pakistan, factor analysis was used to identify sources of surface soil contamination. It was found that Ni, Cr, Zn, and Cu were originated from industrial activates, and vehicular emission, and anthropogenic activities such as automobiles brought Pb, Cd, and Co; some other important contaminants, including Fe and Mn, were natural source origin [43]. In Turkey, the PCA was used to find latent factors that influence the water quality, mineral pollution, nutrient pollution, and organic pollution were identified to be the major factors.
This study was carried out at the Xuzhou-Datun coal mine district, located at the northwest of Jiangsu province, eastern China (Figure 1). The area of Xuzhou city is in the plain of Huanghuai, South part of northern China. Sediment stratum covering the Archean system are Simian, Cambrian, middle-lower Ordovician, middle-upper Carboniferous, Permian, Jurassic, Cretaceous, Tertiary, and Quaternary system, from bottom to top. The hydrogeology cell selected for this study is isolated by a series of faults. This includes Sanhejian, Yaoqiao, and Longdong coal mines shown in Figure 1. In this area, groundwater flows from northeast to southwest.
Location of the study area.
The coal seams that are being mined are located in the Carboniferous and Permian systems, the former include Benxi and Taiyuan formations, and the latter include Shanxi and Lower-Shihezi formations, listed from the bottom to top in both systems. In Permian strata, there are mostly low sulfur content Gas coal and fat coal. The lower formation in Carboniferous has a higher content of sulfur than the upper layers. Mass percentage of sulfur in Permian Shanxi formation coal seams is around 0.83% in coal seam No.7 and 1.09% in coal seam No.9. In coal seam No.17 and No.19 in the Taiyuan formation, the average sulfur content was tested to be 1.87 and 3.49%, respectively. The two mining coal seams (No.2 and No.7) in the Permian system were included in this study; these are located in the middle Lower-Shihezi formations (No.2) and Shanxi formations (No.7). The two formations give thickness of 187–302.95 m and 81.67–136.13, respectively. White feldspar, quartz granule-sandstone, and silicon-mudstone cementation are the main minerals in the lower Shanxi formation. In addition, siltstone, siderite, carbon-mudstone, and plant-fossil clast can also be found. Gray mudstone, sand-mudstone, and sandstone are the major rocks in the middle Shanxi formation with some silicon-mudstone and siderite also present.
There are six aquifers in the sediment stratum of the hydrogeology cell. A grit aquifer in the Quaternary, a conglomerate rock aquifer in the Jurassic, two sandstone aquifers—one in the lower-Shihezi formation, and one above the coal seam in the Shanxi formation; and two limestone aquifers—one is located in the Carboniferous Taiyuan formation (thickness of 180–200 m) and the other in the Ordovician (thickness of 600 m). These last two aquifers are the main water sources of the coal seam.
A total of 16 water samples and 28 rock/coal samples were collected from the study area. Water samples were collected in 1000 mL Nalgene bottles previously acid-cleaned and rinsed twice using the water to be collected. pe and pH of water samples were taken in the field by using a JENCO 6010 pH/ORP meter. Coal and rock samples were collected from the working area at the mine and put into plastic bags that were immediately sealed.
Major ions and physical parameters of water samples were determined according to Chinese standard protocols in Jiangsu Provincial Coal Geology Research Institute. Solid samples were acid digested to determine the concentration of trace elements. The concentration of trace elements in water/coal/rock samples was determined by ICP-MS and the ICP-AES. The ICP-MS analysis was carried out in the China University of Mining and Technology using the X-Series ICP-MS—Thermo Electron Co. An internal standard of Rh was used to determine the limit of detection (0.5 pg/mL) and analytical deviation (less than 2%). The ICP-AES analysis was carried out in the Nanjing University using a JY38S ICP-AES model. The limit of detection and deviation for the analysis carried out by such equipment are 0.01 μg/mL and less than 2%, respectively.
Leaching experiments were conducted using the batch mode to simulate conditions in a coal seam where water movement is slow and dissolution reactions tend to achieve equilibrium, with regard to the previous studies [44, 45]. To simulate a “closed environment” (with low pO2; see Stumm and Morgan [46] for details), bottles were closed with a rubber stopper; samples were taken out using syringes. The pe of the solution during experiments was determined by a JENCO 6010 pH/ORP meter.
Three subsamples were used for each sample: one per 1000 mL aliquot of deionized water at the following pHs: 2, 5.6, 7, and 12. Flasks were sealed and shaken every 2 h for up to 10 days. The temperature was controlled using a water bath at about 40°C. Leachate solutions were collected using syringes at 2, 6, 24, and 48 h. A total of 0.5 mol/L HNO3 was added into all the samples. Leachate aliquots were titrated with HCl or NaOH, depending on the pH conditions, to compare the behavior of leaching elements in acid, neutral, and alkali environments. In addition to leaching experiments, water samples including those collected from the Zhaoyang Lake and Yunlong lakes, shown in Figure 1, were shaken every 2 h for up to 10 days at a constant temperature of 40°C.
While univariate statistical analysis of a large scale of data could be cumbersome and cause misunderstanding and error in the interpretation, multivariate statistical techniques are more robust. Therefore, it becomes a more useful tool for environmental data treatment and identification of anomalous patterns. During the immigration process of the trace elements from coal bedding seam to groundwater and surface water, in the complex matrix system, solid and liquid bodies are involved. In each system, the elements show different or similar coexisting patterns, and immigration behavior, including dissolution, transport, adsorption. Therefore, the multivariate analysis can be used to find out different and similar components, which suggest similar and dissimilar occurrences in solids, and immigration mechanisms during the process of water-rock interaction.
In the area of hydrochemical studies, the PCA method has been widely used to reduce dimensions and analyze the relations among the variates and samples [32, 33, 34, 47, 48, 49, 50, 51]. The PCA is a typical nonsupervised analytical method. To calculate the PCA result, data are first standardized by mean centering each column within the original data matrix and then dividing each of the values within each column by the column standard deviation. With PCA, the large data matrix is reduced to smaller ones that consist of PC loadings and scores. PC loadings are the eigenvectors of the correlation matrix depending on PC scores. Therefore, it contains information on all of the variables combined into a single number, with the loadings indicating the relative contribution that each variable makes to that score. PCs are calculated so that they take into account the correlations present in the original data but are uncorrelated with others. Typically, the data can be reduced to two or three dimensions representing the majority of the variance within the original data. Sometimes, more dimensions may have to be included to present more variance of the original data [33]. Based on the PCA analytical result, the loadings and scores of the data frame were then clustered in the dimensions that PCA has reduced. As the axis of coordinates was rotated to achieve maximum loadings of elements, the rotated axis of coordinates was marked as RCs.
The bi-plot of the PCA result is usually drawn to show patterns of parameters and samples. However, the loading and score of the PCA axis show different aspects of the result. In our study, the loadings of every drawn show coexisting pattern of elements, and scores of every drawn show the coexisting pattern of samples. What we focus on is the coexisting pattern of elements to disclose their migration mechanism. The clustering result of loadings shows similar and different patterns among elements and parameters. Therefore, the coexisting behavior of elements and parameters can be summarized. The clustering result of scores shows similar and different patterns among samples. Therefore, the coexisting behavior of samples, which means types of solid and liquid samples, can be summarized. The clustering method was based on the Gaussian mixture model. The GM model can cluster target reasonably. Comparing with K-means algorithm, the GM model does not divide the different group by stiff border but allows some mixture of different groups. So, the classifying probability for each group can be calculated.
We have applied software R as a tool, the packages psych and mclust were used to calculate PCA and GM model clustering results.
A total of 16 water samples were collected from the study site, including 12 coal mine waters, two surface waters, and two carbonate waters, respectively. Concentrations of major ions are drawn in a piper plot (Figure 2). Figure 2 suggests that the carbonate water and coal mine water belong to medium-mineralized water, and surface water belongs to low-mineralized water, respectively. The surface water is Na-Mg-Ca-Cl−-SO42−-HCO3−-type water, the carbonate water is Na-Mg-Ca-SO42−-type water, and the coal mine water is Na-Ca-SO42−-, Na-SO42−-, or Na-HCO3−-type water, respectively. Coal mine waters showed characteristics of high-soluble minerals. [SO42−] of most coal mine water samples were higher than USEPA and Chinese highest limit, 250 mg/L. Besides [SO42−], [Cl−], TDS, and hardness were also higher than the Chinese-regulated limit. The combination of higher levels of Ca2+, Mg2+, HCO3−, and SO42− concentrations in the groundwater suggests that the coupled reactions involving sulfide oxidation and carbonate dissolution largely control the solute acquisition processes in the study area [52].
Piper plot of the water samples.
The PCA analysis is used to reduce the dimensions of the water matrix. In this study case, dimension means water parameters. Water samples are represented by 10s of conventional inorganic and organic parameters, some of which are an indicator of the environment and reaction pathways, and some others a redundant or collinear. The PCA method could solve problems of not only parameter redundant and collinear, but also shows principal components in the data matrix, and relationships between parameters and among the parameters and samples could also be shown by using the parameters’ loading and samples’ score, respectively.
In this study, the traditional method of PCA calculation was applied, and principal components and variance that the PC explained were calculated. In the original table, 16 parameters were tested, and the PCA calculation used 16 new components to represent the original parameters, which explain the variance of samples, in descending order. The head six components explained 29, 21, 17, 10, 9, and 5% of the variance, respectively. Considering the balance of more variance explained and less components, we chose two principal components to stand for the sample data. The GM method was used to group the ions and trace elements in the water sample, which is shown in Figure 3. The parameters were clustered into four groups. Group 1 includes K+ + Na+ and Cl−; group 2 includes Ca2+, Mg2+, Cl−, SO42−, TDS, and hardness; group 3 includes HCO3−, CO32−, and pH; group 4 includes As, Hg, Se, Cd, Pb, respectively. The samples were collected in or around the coal mine district, so the clustering result is representative, and the groups were separated from others distinctly. From the clustering result, it is suggested that group 2 stands for the dissolution of carbonate, and group 4 stands for the trace element. The trace element contaminant could be identified from this result.
Loadings of the multivariate analysis and clustering result of water samples.
To investigate the leaching mechanism of trace elements from the coal host rock, both the rock sample and water sample were tested. The rock samples were those collected from coal roof, which then was processed in a standard treatment to decide its content. The milled rock samples were mixed with deionized water in the batch experiments to observe and evaluate the leaching behavior and mechanism of the trace elements from rock to water. The major and trace element concentrations in host rock and leachate are listed in the Table 1 in Shan et al. [53]. A hypothesis was that the occurrence and leaching mechanism of the trace elements in the solid samples were related to their concentrations in the water samples. Therefore, the PCA was applied to reduce dimensions of the rock and water samples, and then, the analytical results of solid and liquid samples are discussed parallelly.
For the rock samples, 18 elements were tested, and then, the PCA method was applied. The first two components explained 91% of all variance; therefore, the two PCs were used to stand for information of the data. For the water samples, 16 ions and trace elements were tested. The same analytical process was applied. The first two PCs explained 87% of all variance, which were used to stand for information in the water samples. By using the new PCs, parameters were assigned loadings on every new component. Then, the parameters of rock and water samples can be drawn in a two-dimensional (2D) scatter diagram. Figure 4 shows the elements of rock samples, and Figure 5 shows the ions and elements of water samples in a 2D scatter diagram, respectively.
Loadings of the multivariate analysis and clustering result of rock samples.
Loadings of the multivariate analysis and clustering result of rock leachate.
The PCA-treated data were clustered using the expectation maximization (EM) algorithm. The EM algorithm could make several clustering results. By considering the BIC score and conciseness of every clustering model, the parameters in the rock samples were clustered into three groups. The first group includes Mo, Pb, Cr, V, Ti, and Al, which are marked in solid circles; the second group includes Zn, Ba, Mn, Fe, Mg, As, Hg, Se, and Cd, which are shown in hollow squares; the third group includes Cu, Sr, and Ca, which are shown in solid triangles. As mentioned before, the clustering could help to analyze the elements’ occurrence in solid samples. Cr has a high affinity of clay and ash yield in gangue [3]. Zhou et al. [2] reported a high relationship of Pb and Se and with Fe in gangue, so high-sulfide mineral affinity was observed. Zn and Cd were found to have a high association with pyrite and sphalerite. Xiong et al. [26] found that Cd is mainly in sulfide form in the coal host rock. As and Mo are mainly carbonate- and silicate-related form. Finkelman et al. [3] found that Mo, Pb, Cr, Ti, and Al are mainly in clay minerals, As, Hg, Cd, and Zn mainly occur in sulfide form, and Ca and Sr are mainly carbonate-related. The PCA analysis corroborates the previous studies. As the Figure 5 shows, the first group stands for clay affinity elements, the second group stands for elements with sulfur-mineral affinity, and the third group stands for the carbonate-related elements.
The ions and trace elements in the rock leachate could be clustered into three groups, the first group includes Al, Si, Cr, Mn, Fe, Cd, and Pb; the second group includes Ti, V, As, Se, Mo, and Hg; and the third group includes Zn, Sr, and Ba, respectively. The coexisting pattern of ions and elements in the water are controlled not only the occurrence in rock, but also the water-rock interaction, and adsorption behavior. Therefore, the clustering result of solid and liquid results was not exactly the same. However, two results are comparable to find out certain or probable reaction mechanisms in the water-rock interaction pathway. The three groups clustered for the water samples can be compared with those of the solid samples. Therefore, a primary deduction could be made. The first group of elements in the water samples suggests the reaction pathway of clay reaction with water. When the clay mineral reacts with water, the transformation of illite to kaolinite could happen, and some minerals, such as Cr, could be released. Cd was clustered to the second group in the rock analysis but was clustered to group 1 in water analysis. The result could be explained by two reasons: first, Pb and Cd embedded in both sulfur minerals and clay minerals, and second, Pb and Cd were controlled not only by dissolution, but also by adsorption. When the water has a low pH value, metal elements tend to release, while they could be adsorbed in a higher pH environment. According to our observation, the concentration of Pb and Cd in the surface water in the coal mine district was evidently higher than that in the non-coal mine district. As, Hg, and Se have a similar pattern in the solid and liquid samples. It is apparent that they were controlled by the dissolution of sulfur minerals. The content of the sulfur mineral in the rock was not high in our samples. However, the oxidation and dissolution processes were distinct, leading to the release of toxic trace elements.
The major and trace element concentrations in coal and leachate are listed in the Table 1 in Shan et al. [53]. The same analytical method with rock was applied to the coal and coal leaching analysis. And the PCA and clustering analytical results of coal and coal leaching water are shown in Figures 6 and 7. Two principal components could explain 96 and 91% variance for the coal and leachate, respectively. As Figure 6 shows that elements are clustered into four groups, the group 1 includes Mo, Pb, Cr, V, Cu, Ti, Al, Hg, and Se; group 2 includes Zn and Cd; group 3 includes Ba, Mn, Sr, Mg, and Ca; group 4 includes Fe and As, respectively. The ions and trace elements in coal leachate, as shown in Figure 7, were grouped into three groups. Group 1 includes Al, Se, and Pb; group 2 includes Si, As, Sr, Mo, and Hg; group 3 includes Ti, Cr, Mn, Fe, Zn, Cd, and Ba, respectively. Finkelman et al. [3] investigated the occurrence of most of the trace elements, it is found that 65% of Ti, 90% of Al, and 75% of Cr 25% and 30% of Cu and Mo are in clay minerals, little Pb and Se are in clay form, 75 and 65% of Zn and Cd formed in mono-sulfide form, and 70 and 90% of As and Hg are sulfide form. Pumure et al. [39] argued that As and Se usually occur in clay minerals. Pb was found to be sulfide form as pyrite and galena [54] and organic form [55].
Loadings of the multivariate analysis and clustering result of coal samples.
Loadings of the multivariate analysis and clustering result of coal leachate.
Combining the literature review and PCA-clustering analysis, group 1 for the coal samples stands for clay affinity, groups 2 and 4 are sulfur-mineral elements, and group 3 is related to carbonate minerals. Group 2 has two elements, Zn and Cd. This result is consistent with some previous studies [2, 56]. It is concluded the main occurrence of trace elements: As, Hg, Cd occurred in sulfide minerals, and Pb, Cr, and Se occurred in clay minerals, respectively. Zn and Cd are the primary elements in sphalerite. Compared with the host rock, the sphalerite is more probably to form an independent mineral in coal.
The coal leachate clustering results were relatively different with that of the analytical results of coal. Compared to the rock samples, coal is a more complex matrix and consists of organic and mineral matter, the latter including crystalline minerals, non-crystalline mineraloids, and elements with non-mineral associations [55]. However, some patterns could be concluded. Group 1 includes Al, Se, and Pb, which is similar to group 1 in the coal analysis. Therefore, group 1 stands for the elements that originated from clay minerals. Group 2 stands for the elements related to sulfur-bearing minerals. As and Hg had similar behavior patterns in solid and liquid matrices. So the leaching product in water was mainly from the dissolution of its bearing mineral, the sulfide mineral. Similar to the host rock analysis, low content of sulfur-mineral may lead to trace element concentration.
The trace elements Se, Cr, and Pb have similar behavior patterns in solid and liquid matrices, suggesting a dissolution progress of its bearing minerals. According to the literature research and coexisting analysis, these elements usually occur in continental facies minerals, such as clay minerals.
A data mining workflow, composed of principal component analysis and the Gaussian mixture model, was applied to find the trace elements’ occurrence and leaching mechanism from coal and rock to surface and groundwater bodies. It is found that Se, Cd, Hg, and As were associated with sulfide minerals; Be and V occurred in carbonate minerals; Cr and Pb occurred mainly in clay minerals in the rock samples. While As and Hg were mainly occurred in sulfide minerals, Se, Cr, and Pb were embedded in clay minerals.
When the host rock is leaching with water, As, Hg, and Se were originated from oxidation and dissolution of sulfur-mineral; especially for pyrite, Cr was mainly controlled by the transformation of clay minerals. When the coal is leaching with water, As and Hg showed high affinity of sulfur-minerals, and Se and Cr seemed to be controlled by the water-rock interaction of clay minerals. It suggested that Se exist in sulfide mineral, clay minerals, and also organic matters. Therefore, the leaching mechanism of Se is not unique, and multiple mechanisms may control or influence the leaching behaviors. Cd and Pb showed apparent differences between the solid samples and liquid samples. The mechanism leading to this result was probably explained not only the releasing process, but also the adsorption process. These elements are typical metal elements. They can be easily adsorbed in the alkaline and neutral environment. Therefore, the released metal elements were adsorbed by clay minerals and organic matters. The immigration mechanism and long-term environmental impact need further studies.
The test of samples was carried out in the Jiangsu Provincial Coal Geology Research Institute, the Analysis and Test Center of the China University of Mining and Technology, Imperial College London. We would like to thank all of them for their support.
Gravity data interpretation has been widely used to appraise the different types of subsurface structures and their locations [1, 2, 3, 4, 5, 6, 7, 8]. Gravity methods have been widely applied to ore and mineral exploration [9, 10, 11, 12, 13], hydrocarbon exploration [14, 15, 16], cave detection [17, 18], hydrogeology [19, 20], geothermal and volcanic activity [21, 22, 23], locating of unexploded military ordnance [24], environmental and engineering application [25, 26] and archaeological investigations [27, 28].
The quantitative interpretation of gravity data using simple models (spheres and cylinders) is common in exploratory geophysics and continues to be of interest [29, 30, 31, 32, 33]. In geologic contexts with a single gravity anomaly, it can be quite appropriate [34]. A single isolated causal body can invert this recorded gravity anomaly to establish its distinctive inverted parameters and fit the recorded data.
The simple geometric models can be matched with the subsurface structures encountered during application of several approaches for inversion [35, 36, 37, 38, 39]. These methods include graphical and numerical characteristic points approaches [40, 41, 42], ratio technique [43], Fourier transform method [44], the neural network algorithms [45], Mellin transform technique [36], and Werner deconvolution technique [46]. However, the drawbacks of these methods based on tending to generate high number of invalid solution due to few numbers of points and data used, noise or window size incompatibility. As a result, these approaches are subjective, which can lead to significant inaccuracies in calculating the buried anomalous body’s characteristic inverse parameters [41, 47], which is to be expected. Gupta [48] and Essa [49] developed techniques depending on successive minimization approaches, which utilize the whole measured data to assess the depth parameter and then used some of characteristic points to continue in estimating the rest parameters such as amplitude coefficient. Shaw and Agarwal [37] used the Walsh transform scheme to determine the depth of buried bodies. Mehanee [47] used the regularized conjugate gradient method to construct an effective iterative method based on the use of logarithms of the model parameters for gravity inversion. The method inverts the residual gravity data acquired along profile for evaluating a depth and amplitude coefficient of buried bodies and suitable for subsurface imaging and mineral exploration.
Here, the study proposed an application of the robust R-parameter imaging method to interpret residual gravity data along a profile over idealized geometric bodies such as semi-infinite vertical cylinder, infinitely long horizontal cylinder, and sphere models. The goal is to establish the underlying approximative model by determining the body parameters, which include its origin, depth, amplitude coefficient, and shape. The R-parameter imaging method depends on the correlation coefficient amongst the analytic signal of the collected and calculated gravity data. The optimum solution occurs at the maximum R-parameter value.
The benefit behind the use of this method is fall in estimating the depth and body location with an acceptable value compared to the true ones and used the whole gravity data points of the profile, instead of just a few characteristic points. In addition to the method does not require priori information of the subsurface and directly interpret the anomaly from the given observed data. This chapter begins with a layout of the forward modeling, which contains a theoretical gravity formula, an R-parameter imaging approach description, numerical models test without and including noise, and a field data for slat dome investigation.
A closed-form solution for the gravity anomaly caused by simple geometric structures at a measured point (
Geometry and parameters of sphere (top), semi-infinite vertical cylinder (middle and infinitely long horizontal cylinder (bottom) (re-drawn from [
where
Case | |||
---|---|---|---|
Sphere | 1 | 3/2 | |
Horizontal cylinder | 2 | 1 | 1 |
Vertical cylinder | 0 | 1/2 |
Definitions of
The gravitational anomaly’s analytic signal is written as follows [52, 53]:
where
The amplitude of the analytic signal
By an adapting the horizontal and vertical derivatives to Eq. (1), and putting the obtained outcomes into Eq. (3), we get the following:
where
The analytic signal
To verify constancy in performance of the proposed method, numerical example without noise (noise-free) and with a 20% random noise (noisy) is tested. Another numerical example to evaluate the accuracy and stability in assessing the model parameters in case of interference/neighboring influence.
The R-parameter imaging method is applied to noise-free numerical gravity anomaly due to simple model consisting of a a horizontal cylinder model (
Model 1: Noise-free data. (a) Horizontal cylinder gravity anomaly, (b) Horizontal and vertical gradients of (a), (c) Analytic signal anomaly using the data of (b), and (d) 2-D mosaic of the R-parameter and the R-max value.
Table 2 shows a different shape values that employed in the interpretation process. The results (Figure 3 and Table 2) reveal that at
Shape factor | Maximum R-parameter |
---|---|
(q) | (R-max ) |
0.5 | 0.6696 |
0.6 | 0.7768 |
0.7 | 0.8897 |
0.8 | 0.9620 |
0.9 | 0.9930 |
1.1 | 0.9962 |
1.2 | 0.9883 |
1.3 | 0.9791 |
1.4 | 0.9700 |
1.5 | 0.9611 |
Model 1: Noise-free data. The R-parameter computed for the different shape factors.
Model 1: Noise-free data. The R-parameter, depth and shape factor relationship.
We applied the same procedures (by utilizing Eq. (4) as the forward modelling formula in this case) to the analytic signal data presented in Figure 2c to explore the recital of the current scheme when used to the analytic signal data themselves instead of the residual gravity data. Figure 4 shows the outcomes, which are match with those derived from the above-mentioned elucidation of gravity data (Table 3).
Model 1: Noise-free data. (a) Analytic signal anomaly (
Estimated model parameters | Analytic signal data | Gravity anomaly data |
---|---|---|
100 | 100 | |
5 | 5 | |
1 | 1 | |
51 | 51 |
Model 1: Noise-free data. Comparison between the model parameters estimated from the interpretation of using residual anomaly and analytic signal anomaly.
Given the lack of totally noise-free gravity field data, a 20% random noise (Figure 5a) has been introduced to the data in Figure 2a. The horizontal and vertical derivatives, besides the magnitude of the analytic signal of the measured gravity anomaly, are depicted in Figure 5b and c. The R-parameter values were evaluated utilizing Eq. (5) and created a 2-D mosaic surface (Figure 5d). The maximum R-parameter value is 0.94. The imaging-derived model parameters (
Model 1: noisy data. (a) Noisy gravity anomaly of
The amplitude coefficient (
Model 1: Noisy data. (a) Analytic signal anomaly (
Estimated model parameters | Analytic signal data | Gravity anomaly data |
---|---|---|
212.90 | 100 | |
7 | 5 | |
1 | 1 | |
51 | 51 |
Model 1: Noisy data. Comparison between the model parameters estimated from the interpretation of using residual anomaly and analytic signal anomaly.
The performance of the proposed inversion method with complicated field anomalies and the effect of interfering subsurface structures was investigated. To achieve this, we once again generate a synthetic model data from multiple source bodies as a horizontal cylinder model with
Model 2: Interference/neighboring effect. (a) Gravity anomaly generated by two different adjacent bodies, (b) Horizontal and vertical gradients of (a), (c) Analytic signal anomaly using the data of (b), and (d) 2-D mosaic of the R-parameter and the R-max values.
Figure 7b and c illustrate the horizontal and vertical gradients of the composite gravity anomaly, as well as the amplitude of the analytic signal. The R-parameter values were determined using Eq. (5) for each source location and a 2-D mosaic surface S of 101 × 11 m in the X- and Z-directions constructed and discretized into 1-m intervals in both directions. The 2-D mosaic (Figure 7d) indicates that the R-max value for each source is 0.8 and 0.62 at
To better understand the procedure, we tainted the composite anomaly (Figure 7a) with a 20% noise level (Figure 8a). The horizontal and vertical derivatives, as well as the corresponding amplitude of the analytic signal, are shown in Figure 8b and c. The retrieved R-parameter image is shown in Figure 8d, with R-max values of 0.79 and 0.61 for a horizontal cylinder and a sphere, respectively. The drop in maximum parameter values compared to (Figure 7d) is attributable to the noise introduced into the data as well as the effect of the nearby objects. The model parameters for the first and second bodies revealed by imaging are:
Model 2: Interference/neighboring effect with noise. (a) Noisy gravity anomaly of
Figure 9 depicts the results of the study of the noisy analytic signal data seen in Figure 8c. The amplitude coefficients and burial depths recovered from the elucidation are exaggerated (Figure 9a–d), as shown in Table 5, which coincides with and confirms the aforementioned results.
Model 2: Interference/neighboring effect with noise. (a) Analytic signal data (
Estimated model | Noisy contaminated interference/neighboring effect | |||
---|---|---|---|---|
Analytic signal data | Gravity anomaly data | |||
Parameters | First anomaly | Second anomaly | First anomaly | Second anomaly |
A (mGal m2q−η) | 174.2 mGal m | 1554.3 mGal m2 | 126.9 mGal m | 556.3 mGal m2 |
zo (m) | 4.2 | 7.6 | 3.8 | 4.7 |
q | 1 | 1.5 | 1 | 1.5 |
xo (m) | 30 | 80 | 30 | 80 |
Model 2: Interference/neighboring effect with noise. Comparison between the model parameters estimated from the interpretation of using residual anomaly and analytic signal anomaly.
On the basis of the theoretical models presented above, it can be inferred that the technique described here is stable and robust.
A published field example over a salt dome anomaly is examined in order to thoroughly test the applicability of the established methodology. For a variety of reasons, this case was chosen. First, the residual gravity profile was created by a simple body that may be truthfully inferred. Second, the drilling information helps in estimating the density contrast of the underlying body. Knowing the density contrast, the radius can be calculated and the depth to the top also can be inferred by using the definition of the amplitude coefficient (Table 1). Moreover, the depth of the vertical cylinder model is measured to the top but the depth of a horizontal cylinder and sphere model is measured to the center of the body (Figure 1). Third, the gravity data was taken from an area with recognized drilling information, allowing the results obtained from the technique proposed here to be cross-validated against those received via drilling.
Gravity map was acquired over the Humble Dome, Houston, Texas. At the earth’s surface, the measurements of this salt dome structure reveal a negative circular contoured Bouguer anomaly ([41], Figures 8–16). A Bouguer gravity profile is taken across the center of the Humble salt dome gravity map in Houston ([41], Figures 8–16). The Bouguer gravity profile was subject to a suitable separation method to remove the regional anomaly and obtain the residual gravity anomaly. The residual gravity anomaly profile of about 26 km long was digitized at an interval of 0.26 km (Figure 10a).
The Humble dome anomaly, USA. (a) Gravity anomaly profile (red dotted lines) and the optimum-fitting model (solid black line), (b) Horizontal and vertical gradients of (a), (c) Analytic signal anomaly using the data of (b), and (d) 2-D mosaic of the R-parameter and the R-max value.
The R-parameter method procedures were applied to the residual gravity anomaly profile for the available shape parameters (Table 6). Figure 10b–d express the horizontal and vertical derivative anomalies, the amplitude analytic signal, and the R-parameter 2-D mosaic. It is found that the R-max value is 0.99 corresponds to a spherical shape (
R-max | |
---|---|
0.5 | 0.999462 |
0.6 | 0.997341 |
0.7 | 0.996274 |
0.8 | 0.992509 |
0.9 | 0.986895 |
1 | 0.984110 |
1.1 | 0.987549 |
1.2 | 0.992912 |
1.3 | 0.996863 |
1.4 | 0.998792 |
The Humble dome gravity anomaly, USA. The R-parameter calculated from different shape factors.
The humble dome anomaly has been interpreted by several authors assuming a spherical source to decide the depth of the salt body. The obtained results agree well with those depths to the center that obtained by the published literatures of [36, 41, 47, 54, 55, 56] (Table 7).
Model parameters | Approaches and techniques of | Present study | ||||
---|---|---|---|---|---|---|
[28] | [33] | [39] | [46] | [47] | ||
— | — | −292.54 | — | −279.81 | − | |
4.96 | 4.97 | 4.62 | 4.81 | 4.58 | ||
1.5 | 1.5 | 1.5 | 1.5 | 1.5 | ||
— | — | — | — | — |
The Humble dome gravity anomaly, USA. The Estimated parameters.
By using a density contrast of −0.13 gm/cm3 of [41], then the depth to the top of the spherical body of the humble dome obtained from the proposed technique is 315 m, which in excellent covenant with the true depth (305 m) confirmed by drilling and seismic information [41]. Table 8 shows that several other researchers utilizing the same density contrast found some differences in the depths to the top of this spherical source. The use of simple geometrically bodies in the constrained class of spheres, horizontal cylinders, and vertical cylinders is thus suggested as a way to accurately apply the current methodology to extract depth information. As a result, if exact density contrasts are used, the related radii can be correctly computed as well.
A residual gravity map was acquired over a salt dome off the coast of Louisiana, USA ([41], Figures 8–20). The residual gravity anomaly profile [57] is redrawn across the center of the map, normal to the causal anomaly’s striking. The residual gravity anomaly profile of about 13,000 m long was digitized at sampling interval of 200 m (Figure 11a).
The Louisiana dome anomaly, USA. (a) Gravity anomaly profile (red dotted lines) and the optimum-fitting model (solid black line), (b) Horizontal and vertical gradients of (a), (c) Analytic signal anomaly using the data of (b), and (d) 2-D mosaic of the R-parameter and the R-max value.
By applying the R-parameter method procedures mentioned before to the residual gravity anomaly profile of Louisiana we get the available shape parameters corresponding to the maximum R-parameter (R-max) as shown in Table 9. Figure 11b–d shows the horizontal and vertical derivative anomalies, the amplitude analytic signal, and the R-parameter 2-D mosaic of the Louisiana anomaly. It is found that the R-max value is 0.96 corresponds to
R-max | |
---|---|
0.5 | 0.943033 |
0.6 | 0.934839 |
0.7 | 0.926284 |
0.8 | 0.957334 |
1 | 0.957410 |
1.1 | 0.942955 |
1.2 | 0.926284 |
1.3 | 0.909983 |
1.4 | 0.894917 |
0.881254 |
The Louisiana dome gravity anomaly, USA. The R-parameter calculated from different shape factors.
The Louisiana dome anomaly has been interpreted by different authors assuming a horizontal source to determine the depth to the center of the salt body. The obtained results have a good agreement with those depths to the center that obtained by the published literatures of [5, 41] (Table 10). In addition, the proposed method has the lowest misfit compared to the other method (Table 10).
The proposed method of R-parameter imaging technique was deployed to visualizes the salt dome anomalies from the gravity data measured across a 2D profile. The method fitting the anomaly of the measured gravity profile by a single geometric shape body (sphere & cylinder). Such as the spherical source (
The obtained results by the R-parameter method of the Humble dome anomaly was compared with other results in the published literature (Table 7) and confirmed with drilling to insure the depth to the top of the buried anomaly (Table 8). For the Louisiana dome anomaly, the obtained results by R-parameter approach was compared with the pervious published literature and weighted by the misfit error between the observed and calculated anomaly for the different techniques used (Table 10) to increase the efficiency of the proposed method.
In over all the obtained results using the R-parameter method to investigate the salt dome anomalies is good and acceptable in the two given field examples.
In this study, we have introduced and investigated the applicability and the performance of the R-parameter imaging method in elucidating distinctive physical parameters (
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It is an oldest, most efficient, and easiest method to apply any surface without modifying the intrinsic properties of materials. Moreover, the initial phase of fire always occurs on the surface by ignition, and hence, it is important to concentrate on the surface protection of a material. Being an organic nature of conventional surface coating will burn easily and generate smoke and toxic fumes, which may not be suitable for application where fire protection or fire prevention is required. Reaction-to-fire and/or resistance-to-fire are to be considered for assessing both flammable and non-flammable material by using fire retardant and fire resistant or fire protective coatings. The degree of fire retardation mainly depends on the coating thickness, substrates, and efficiency of formulations. This chapter explains briefly the fire retardation of wood by using fire retardant coatings.",book:{id:"5827",slug:"new-technologies-in-protective-coatings",title:"New Technologies in Protective Coatings",fullTitle:"New Technologies in Protective Coatings"},signatures:"Thirumal Mariappan",authors:[{id:"198114",title:"Dr.",name:"Thirumal",middleName:null,surname:"Mariappan",slug:"thirumal-mariappan",fullName:"Thirumal Mariappan"}]},{id:"75967",title:"Recent Advances in Ceramic Materials for Dentistry",slug:"recent-advances-in-ceramic-materials-for-dentistry",totalDownloads:774,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Dental ceramics constitute a heterogeneous group of materials with desirable optical and mechanical proprieties combined with chemical stability. They are inorganic non-metallic materials used in several applications. These materials are biocompatible to tissue, highly esthetic, with satisfying resistance to tensile and shear stress. Over the past years, several developments in new ceramic materials in dental restoration were achieved, including processing techniques and high mechanical properties. Thus, concepts on the structure and strengthening mechanisms of dental ceramic materials are also discussed. The dental practitioner requires best knowledge concerning indications, limitations, and correct use of started materials. The purpose of this book chapter is to overview advances in new ceramic materials and processes, which are used in dentistry. The properties of these materials are also discussed.",book:{id:"9894",slug:"advanced-ceramic-materials",title:"Advanced Ceramic Materials",fullTitle:"Advanced Ceramic Materials"},signatures:"Mohsen Mhadhbi, Faïçal Khlissa and Chaker Bouzidi",authors:[{id:"228366",title:"Dr.",name:"Mohsen",middleName:null,surname:"Mhadhbi",slug:"mohsen-mhadhbi",fullName:"Mohsen Mhadhbi"},{id:"324375",title:"Dr.",name:"Faïçal",middleName:null,surname:"Khlissa",slug:"faical-khlissa",fullName:"Faïçal Khlissa"},{id:"324535",title:"Dr.",name:"Chaker",middleName:null,surname:"Bouzidi",slug:"chaker-bouzidi",fullName:"Chaker Bouzidi"}]},{id:"66615",title:"Survey of Bauxite Resources, Alumina Industry and the Prospects of the Production of Geopolymer Composites from the Resulting by-product",slug:"survey-of-bauxite-resources-alumina-industry-and-the-prospects-of-the-production-of-geopolymer-compo",totalDownloads:1199,totalCrossrefCites:2,totalDimensionsCites:2,abstract:"Guinea is endowed with huge mineral resources. Several geological surveys have identified bauxite, iron, gold, diamond, and several metal ores. Because of the diversity and the magnitude of its resources, the country is referred to as a geological scandal. Nowadays the aluminum industry is still at the quarrying stage of bauxite, the main raw material that is converted into alumina and further to aluminum. Approximately 35–40% of the processed bauxite ore goes into the waste as alkaline red mud RM slurry which consists of 15–40% solids. RM and other industrial wastes material such as fly ash FA, rice husk ash RHA, that poses environmental hazards can be mixed to make them apt for usage in engineering applications. Geopolymers GP represent a new class of materials consisting of Al2O3▬SiO2-based material suitable for several engineering application. The present chapter presents the bauxitic potential of Guinea, the subsequent developing alumina industry. It reviews the application of RM for the production of geopolymer materials in the perspective of the valorization of the huge bauxite potential of Guinea.",book:{id:"8612",slug:"geopolymers-and-other-geosynthetics",title:"Geopolymers and Other Geosynthetics",fullTitle:"Geopolymers and Other Geosynthetics"},signatures:"Sékou Traoré, A. Diarra, O. Kourouma and D.L. Traoré",authors:[{id:"266484",title:"Prof.",name:"Sekou",middleName:null,surname:"Traore",slug:"sekou-traore",fullName:"Sekou Traore"},{id:"272379",title:"Dr.",name:"Doussou L.",middleName:null,surname:"Traoré",slug:"doussou-l.-traore",fullName:"Doussou L. Traoré"}]},{id:"59550",title:"Introductory Chapter: A Brief Introduction to Porous Ceramic",slug:"introductory-chapter-a-brief-introduction-to-porous-ceramic",totalDownloads:1842,totalCrossrefCites:6,totalDimensionsCites:15,abstract:null,book:{id:"6084",slug:"recent-advances-in-porous-ceramics",title:"Recent Advances in Porous Ceramics",fullTitle:"Recent Advances in Porous Ceramics"},signatures:"Uday M. Basheer Al-Naib",authors:[{id:"182041",title:null,name:"Uday",middleName:"M.",surname:"Basheer",slug:"uday-basheer",fullName:"Uday Basheer"}]}],onlineFirstChaptersFilter:{topicId:"155",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:318,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:106,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:15,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403",scope:"Artificial Intelligence (AI) is a rapidly developing multidisciplinary research area that aims to solve increasingly complex problems. In today's highly integrated world, AI promises to become a robust and powerful means for obtaining solutions to previously unsolvable problems. 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Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where he was appointed as South Africa Research Chair in Artifical Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018). 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He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. He is a Senior Member of the IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Societies, and the Association of Computing Machinery (ACM). Finally, his main research interests include data science, computational intelligence, and their applications.",institutionString:null,institution:{name:"University of Córdoba",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"26",title:"Machine Learning and Data Mining",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",isOpenForSubmission:!0,editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. 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