Given the high costs and general shortage of coking coals on the domestic and international markets, and because the nature and qualities of many of the coking coals available on the markets are themselves mixed products, conventional mechanisms and tried and trusted formulae for manufacturing coke products based on single coals of known qualities can no longer apply. There is therefore an urgent need to develop more effective techniques for evaluating and assessing the properties of individual coals rapidly and reliably and in a manner that could provide useful data for use in modelling the effect of new coal components in a coke blend. Towards this end, the current research has sought to find more accurate coal characterisation techniques at laboratory scale than currently exists in industry at present. Seventeen coking or blend coking coals from widely different sources were selected and cokes were produced from them in as close to full scale conventional conditions as possible. Both coals and cokes were analysed using conventional chemical, physical, petrographic and rheological coking methods. The results indicated that, whilst all coals had acceptable chemical, physical and petrographic properties as evaluated on individual parameters thereby indicating their potential values as prime coking coals, in fact the resultant cokes of some of the coals had properties that disproved this assessment. These anomalies were investigated by integrating all characteristics and statistically evaluating them. The result [outcome] indicated that the series of coals under review fall naturally into three distinct categories according to rank, as determined by the reflectance of vitrinite, and that the coking coals in each rank category were further characterised by parameters specific to that level of rank. In this way more accurate predictions of coke quality were obtained than has been the case to date when using single set evaluations or previously devised formulae. On this basis it was concluded that, when selecting coals for coke making, it is essential to first establish the rank of the coal by vitrinite reflectance and then to apply coke evaluating parameters specific to that level of rank. The formulae developed for this purpose held good for all coals tested, however, it remains to be seen whether this applies universally to an even wider source of coals.

The Bowen Basin is one of the most intensely explored sedimentary basins in Australia and hosts one of the world’s largest coking coal deposits. This study focuses on the Lenton deposit in the north-central part of the Bowen Basin and targets the Rangal Coal Measures, which are the youngest (245 Ma), most areally extensive and least structurally deformed coal measures in the study area. Six lithofacies were identified from detailed bed-by-bed logging of two cores and stratigraphically-upward comprise peatmire deposits of the Permian Blackwater Group overlain unconformably by braided fluvial strata of the Triassic Rewan Group. Coal-bearing strata of the Blackwater Group form a large-scale drying up sequence showing a change from permanent to seasonal waterlogged conditions related to the onset of regional uplift. Sedimentation was then terminated and a regional erosion surface formed by uplift related to the Hunter Bowen Orogeny. This, then, was overlain by braided fluvial strata of the Triassic Rewan Group.

The Bowen Basin is one of the most intensely explored sedimentary basins in Australia and hosts one of the world’s largest coking coal deposits. This study focuses on the Lenton deposit in the north-central part of the Bowen Basin and targets the Rangal Coal Measures, which are the youngest (245 Ma), most areally extensive and least structurally deformed coal measures in the study area. Six lithofacies were identified from detailed bed-by-bed logging of two cores and stratigraphically-upward comprise peatmire deposits of the Permian Blackwater Group overlain unconformably by braided fluvial strata of the Triassic Rewan Group. Coal-bearing strata of the Blackwater Group form a large-scale drying up sequence showing a change from permanent to seasonal waterlogged conditions related to the onset of regional uplift. Sedimentation was then terminated and a regional erosion surface formed by uplift related to the Hunter Bowen Orogeny. This, then, was overlain by braided fluvial strata of the Triassic Rewan Group.

The Bowen Basin is one of the most intensely explored sedimentary basins in Australia and hosts one of the world’s largest coking coal deposits. This study focuses on the Lenton deposit in the north-central part of the Bowen Basin and targets the Rangal Coal Measures, which are the youngest (245 Ma), most areally extensive and least structurally deformed coal measures in the study area. Six lithofacies were identified from detailed bed-by-bed logging of two cores and stratigraphically-upward comprise peatmire deposits of the Permian Blackwater Group overlain unconformably by braided fluvial strata of the Triassic Rewan Group. Coal-bearing strata of the Blackwater Group form a large-scale drying up sequence showing a change from permanent to seasonal waterlogged conditions related to the onset of regional uplift. Sedimentation was then terminated and a regional erosion surface formed by uplift related to the Hunter Bowen Orogeny. This, then, was overlain by braided fluvial strata of the Triassic Rewan Group.

[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.