No description available.

Microbes, including fungi and bacteria, and insects are responsible for the consumption and subsequent degradation of plant materials into humus. These microbes directly and indirectly affect the physical and chemical characteristics of coal macerals. Efforts to understand and determine the origins of inertinite macerals are largely misrepresented in the literature, conforming to a single origin of fire. This study focuses on the variability of physical and inferred chemical differences observed petrographically between the different inertinite macerals and discusses the multiple pathways plant material may take to form and or degrade these macerals. Petrographic results show that fungal activity plays a fundamental role in the formation of inertinite macerals, specifically macrinite and non-fire derived semifusinite. Fungal activity chemically removes the structural framework of woody plant tissues, forming less structured to unstructured macerals. Insect activity within a mire also greatly influences the inertinite maceral composition. Wood-consuming insects directly degrade wood tissue leading to the formation of less structured inertinites, as well as producing large conglomerates of inert fecal pellets chemically similar to the original plant tissue that may be represented in the inertinite maceral composition.

The Early Permian coal samples for the study were obtained from the Vasse Shelf, southern Perth Basin, located approximately 200 km south- west of Perth. The selected coal samples for the study were also obtained from the Premier Sub-basin of the Collie Basin and the Irwin Sub-basin of the Perth Basin. The Early Permian coal measures are described as the Sue Coal Measures from the Vasse Shelf, the Ewington Coal Measures from the Premier Sub-basin and the coal measures from the Irwin sub-basin are described as the Irwin River Coal Measures.The Vasse Shelf coal is finely banded and the dominant lithotypes are dull and dull banded types, followed by bright banded and banded types, with minor bright types. The variation of dull and bright lithotypes represents fluctuating conditions of water table level during the growth of peat in the swamp. The maceral composition of the coal is predominantly composed of inertinite, followed by vitrinite and minor exinite and mineral matter. The coal is characterized by very low to medium semifusinite ratio and medium to high vitrinite content, supporting the deposition in anaerobic wet conditions with some degree of oxidation. The coal is classified as sub- bituminous to high volatile bituminous of the Australian classification. In terms of microlithotype group, the predominance of inertite over vitrite suggests the coal was formed under drier conditions with high degree of oxidation during its deposition. On the basis of the interpretations of lithotypes, macerals, microlithotypes and trace elements, the depositional environment of the coal is braided and meandering deltaic-river system without any brackish or marine influence.The maceral composition of the Collie coal predominantly consists of inertinite and vitrinite, with low exinite and mineral matter. The very low to low semifusinite ratio and low to medium vitrinite content of ++ / the coal indicate that the coal was formed under aerobic dry to wet conditions with some degree of oxidation. The coal is categorized as sub-bituminous according to the Australian classification. The domination of inertite and durite over vitrite and clarite contents in the coal reflects the deposition under drier conditions with fluctuations in the water table. On the basis of the interpretations of macerals, microlithotypes and trace elements distribution, the depositional environment of the coal is lacustrine, braided to meandering fluvial system, without the influence of any marine influx.The maceral composition of the Irwin River coal consists predominantly of vitrinite and inertinite, and minor exinite and mineral matter. The coal has very low semifusinite ratio and medium to high vitrinite content, suggesting the coal was deposited in anaerobic wet conditions with some degree of oxidation. The coal is classified as sub-bituminous of the Australian classification. The predominance of vitrite and clarite over inertite and durite contents in the coal indicates that the coal was formed in wetter conditions and in high water covers with a low degree of oxidation. Based on macerals and microlithotypes contents, the depositional environment of the coal is braided fluvial to deltaic, which is in accordance with the interpreted non- marine and mixed marine environment of deposition in the sub-basin.The petrological comparisons of Vasse Shelf, Collie and Irwin River coals show that the average vitrinite content of the Irwin River coal is highest (49.1%) and of the Collie coal is lowest (37.3%) of the three. The inertinite content is highest in Collie coal (49.1%), followed by Vasse Shelf (46.4%) and Irwin River (39.2%) coals. The exinite content is low in Irwin River coal (6.3%) as compared with Vasse Shelf (9.0°/,) and Collie (8.3%) coals. The mineral matter content ++ / is relatively low for all the three coals. The rank of the Vasse Shelf coal is high as compared with the Collie and Irwin River coals, either due to tectonic uplift after the deposition in post-Permian in the southern Perth Basin, or due to the average depth of burial over Vasse Shelf which is much greater than that of Collie and Irwin River coals.The comparisons of the coal from Western Australia with the selected Gondwana coals show that the predominance of inertinite over vitrinite occurs in the Western Australian coals (Vasse Shelf and Collie Basin). On the other hand, the Brazilian, eastern Australian, Indian and Western Australian (Irwin Sub-basin) coals are dominated by vitrinite over inertinite. The exinite content is highest in the Indian coals and lowest in the eastern Australian coals. The mineral matter content is highest in the Brazilian and Indian coals, and lowest in Western Australian (Vasse Shelf) and eastern Australian (Sydney Basin) coals. The rank of the coals ranges from sub- bituminous to medium volatile bituminous according to the Australian classification.

Thesis (PhD.(Metallurgy)--University of Pretoria, 2008. / Includes bibliographical references.

[pt] Diferentemente de muitas outras rochas, o carvão é uma rocha sedimentar composta principalmente de matéria orgânica derivada de detritos vegetais, acumulados em turfeiras em diferentes períodos geológicos. O carvão é um recurso econômico essencial em muitos países, tendo sido a principal força motriz por trás da revolução industrial. O carvão é amplamente utilizado industrialmente para diversos fins: carbonização e produção de coque, produção de ferro/aço, carvão térmico para gerar eletricidade, liquefação e gaseificação. A utilização do carvão é ditada pelas suas propriedades que são geralmente classificadas como sua composição, rank e grau. A composição do carvão, em termos dos seus macerais, e a sua classificação são determinadas manualmente por um petrógrafo, devido à sua natureza complexa. Este estudo almejou desenvolver um método automático baseado na aprendizagem de máquina para segmentação automática de macerais a nível de grupo e um módulo para determinação de rank por refletância em imagens petrográficas do carvão que pode melhorar a eficiência deste processo e diminuir a subjetividade do operador. foi desenvolvida uma abordagem de aprendizagem profunda da arquitetura baseada na Mask R-CNN para identificar e segmentar o grupo de maceral vitrinite, o qual é fundamental para a análise do rank, uma vez que a classificação é determinada pela reflectância da collotelinite (maceral desse grupo). Em segundo lugar, foi desenvolvido um método de processamento de imagem para analisar as imagens segmentadas de vitrinite e determinar a classificação do carvão, associando os valores cinzentos à reflectância. Para a segmentação de maceral, foram utilizadas cinco amostras para treinar a rede, 174 imagens foram utilizadas para treino, e 86 foram utilizadas para validação, com os melhores resultados obtidos para os modelos de vitrinite, inertinita, liptinita e colotelinita (89,23%, 68,81%, 37,00% e 84,77% F1-score, respectivamente). Essas amostras foram utilizadas juntamente com outras oito amostras para determinar os resultados de classificação utilizando a reflectância de collotelinite. As amostras variaram entre 0,97% e 1,8% de reflectância. Este método deverá ajudar a poupar tempo e mão-de-obra para análise, se implementado num modelo de produção. O desvio médio quadrático entre o método proposto e os valores de reflectância de referência foi de 0,0978. / [en] Unlike most other rocks, coal is a sedimentary rock composed primarily of organic matter derived from plant debris that accumulated in peat mires during different geological periods. Coal is also an essential economic resource in many countries, having been the main driving force behind the industrial revolution. Coal is still widely used industrially for many different purposes: carbonization and coke production, iron/steel making, thermal coal to generate electricity, liquefaction, and gasification. The utility of the coal is dictated by its properties which are commonly referred to as its rank, type, and grade. Coal composition, in terms of its macerals, and its rank determination are determined manually by a petrographer due to its complex nature. This study aimed to develop an automatic method based on machine learning capable of maceral segmentation at group level followed by a module for rank reflectance determination on petrographic images of coal that can improve the efficiency of this process and decrease operator subjectivity. Firstly, a Mask R-CNN-based architecture deep learning approach was developed to identify and segment the vitrinite maceral group, which is fundamental for rank analysis, as rank is determined by collotelinite reflectance (one of its individual macerals). Secondly, an image processing method was developed to analyze the vitrinite segmented images and determine coal rank by associating the grey values with the reflectance. For the maceral (group) segmentation, five samples were used to train the network, 174 images were used for training, and 86 were used for testing, with the best results obtained for the vitrinite, inertinite, liptinite, and collotelinite models (89.23%, 68.81%, 37.00% and 84.77% F1-score, respectively). Those samples were used alongside another eight samples to determine the rank results utilizing collotelinite reflectance. The samples ranged from 0.97% to 1.8% reflectance. This method should help save time and labor for analysis if implemented into a production model. The root mean square calculated between the proposed method and the reference reflectance values was 0.0978.