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Mineral matter in coals has been found to host valuable elements including germanium, lithium and rare-earth elements (REE), but such content of Australian coals remains under-explored. The aim of this paper is to provide a proof-of-concept study that determines elemental concentrations in the mineral matter of the high-ash, Fort Cooper Coal Measures (FCCM), Bowen Basin, Queensland. Coal in the FCCM has good coking properties but has not been extensively explored owing to its high ash content. This study performs a preliminary assessment of the elements from the coal portion that would typically become waste after washing or combustion. Given that the needs for industrial extraction methods will change over time, cataloguing and documenting the elemental composition of coal may assist future development of multiple products for the benefit of both the coal and mineral industries. This preliminary study has revealed similar elevations of REE and higher-than-average crustal concentrations of lithium, bismuth and scandium in some of the samples. Further analyses will be required on a larger dataset to better understand elemental associations and explore spatial patterns of elemental content that may indicate localised enrichments. Understanding the potential of valuable elements in the FCCM may stimulate interest in multiple-product-coal (or elemental-coal) mining in this under-exploited resource and further encourage the reduction of waste from other Australian coals at the wash-plant stage. [ABSTRACT FROM AUTHOR]

Inaccuracies in spatial modelling of coal properties can impact coal resource and reserve estimates. Geostatistical methods which account for compositional data have previously been recommended as a preferred method for modelling of coal properties. These methods can quantify uncertainty, can use auxiliary information to improve estimates, and ensure that compositional data (such as coal proximate analysis) is modelled in a mathematically consistent way. However, geostatistical methods have drawbacks in that have rigid statistical assumptions about the distribution and stationarity of the target variable and require variogram interpretation which can be onerous when then number of domains and target variables are numerous. Due to these drawback, inverse distance weighting (a simple deterministic method) remains the most popular method for modelling coal properties in Australian resource estimates. To address the drawbacks of geostatistical methods, a machine learning approach based on the quantile regression forest algorithm is proposed as an alternative method to spatially model coal properties. This newly proposed method, accounts for spatial arrangement of data, requires minimal pre-processing steps, can quantify uncertainty and can easily incorporate auxiliary information of various types. To evaluate the performance of this method, the accuracy of predictions of coal proximate analysis properties, and coal density are compared to the predictions of inverse distance weighting and regression kriging. All methods incorporate isometric log ratio transform and back-transform of data in order to account for the compositional nature of coal proximate analysis. Data from an active coal mine in the Bowen Basin, Queensland Australia was used as basis of the results. A unique feature of the mine site in this basin is the presence of intrusion which impacts coal quality properties to various degrees. Using evaluation metrics from leave-one-out cross-validation, this paper demonstrates that quantile regression forest has higher accuracy, lower bias and higher precision than inverses distance weighting across all coal properties. The paper also shows that results of the new method are very similar or better than regression kriging. It is also demonstrated that the prediction maps produced by quantile regression forest could be used to more accurately map out coal impacted by intrusion compared to inverse distance weighting, and that inverse distance weighting overestimates the impact of intrusion and intrusion extent. A drawback of the method compared to inverse distance weighting, is that it is more computationally demanding, less intuitive and is currently not available in existing geological packages. [ABSTRACT FROM AUTHOR]

A large, incomplete fragment of arthropod cuticle with distinctive tuberculate ornamentation from the late Permian Kaloola Member, Baralaba Coal Measures of the Bowen Basin in central Queensland, is assigned to the mycteropoid clade of sea scorpions. It is Changhsingian (254–252 Ma) in age and occurs in a freshwater, possibly fluvial, environment. The cuticle ornamentation consists of round to oval tubercles in two size classes and differs, in detail, from all previously described mycteropoids. We therefore propose a new species, ?Woodwardopterus freemanorum sp. nov., to accomodate this new specimen. It is at least 11 Ma younger than any previously known eurypterid fossil. This significant discovery demonstrates that large sweep-feeding eurypterids persisted until the end-Permian extinction in the southern high latitudes of Gondwana. Ecological factors that may have contributed to the survival of these freshwater eurypterids into the late Palaeozoic are also briefly discussed. [ABSTRACT FROM AUTHOR]

Coal resource tonnage estimates are reliant on spatial modeling of critical data including in-situ density. To model coal in-situ density, numerous methods are available including deterministic, geostatistical, and machine learning-based methods. In most studies the focus is on comparing differing spatial model method accuracy, however, in this study, the focus is on determining if different in-situ density spatial model methods materially impact resource tonnage estimates. This study used data from an active open-cut coal mine located in the Bowen Basin, Queensland Australia. The coal mine has extensive areas that are impacted by lamprophyre and dolerite intrusion. This study compared tonnage estimates derived from in-situ density modeling from four spatial model methods including inverse distance weighting, ordinary kriging, geographic quantile regression forest, and random forest regression kriging. This study found that, at local scale and in areas highly impacted by intrusion, variation in tonnage estimates between in-situ density model methods was up to 4.40%. This variation was considered material as it was higher than the error of each spatial model. It is, therefore recommended that, especially in coal deposits which are impacted by intrusion, spatial model methods are carefully selected and evaluated. [ABSTRACT FROM AUTHOR]

Abstract: There is a broad consensus that the corporate sector has an active role in facilitating community development through corporate-community investment. However, there remains uncertainty as to how much the sector should invest without taking on responsibilities and influencing decisions which are traditionally within the realm of the state. This paper explores the contemporary role of mining companies in regional governance through a case study of housing and residential land use planning and supply within the Bowen Basin coal mining and coal seam gas extraction region in Queensland, Australia. Mining companies were found to have a broad role in planning and development with far-reaching implications for land use and the wider regional community. These arrangements have emerged in response to corporate sensibilities, some recent policy requirements and most importantly, in pragmatic response to the institutional void apparent in many regional communities. In the absence of an effective regional framework with a clear and defined role for governments and corporations, powerful corporate interests risk effectively ‘capturing’ the regional development agenda within a framework that works on short-term paternalism rather than long-term regional partnerships. This also has implications for the efficiency of governance. Stronger institutional arrangements need to be developed to strengthen the capacity of the state to oversee these relationships. Regional governance and planning theory also needs to address this deficit. [Copyright &y& Elsevier]

Abstract: Late Permian age samples of black coal from the coeval Bandanna Formation and Baralaba Coal Measures of Queensland''s Bowen Basin, as well as Carboniferous coal from Germany''s Ruhr Basin, were investigated. This paper presents scanning electron microscope (SEM) analysis of the mode of occurrence of minerals within these coals, and characterisation of the natural fractures present within the samples. The coal samples located proximal to orogenic fronts in both basins are structurally complex, with multiple generations of mineralisation evident. Stable isotope analyses of carbonates from selected samples indicate that methanogenesis-related CO2 reduction has occurred at two of the Bowen Basin sampling locations. Ankerite precipitated from a magmatic fluid or basinal brine is associated with significant sulphide mineralisation within brecciated Ruhr coal proximal to major fault intersections with anticlines, and has been meteorically altered to ferroan calcite and sulphates in coal from immediately below the Carboniferous/Cretaceous unconformity. Similar alteration has occurred within a Bowen Basin coal sample close to the present day land surface. Variation of mineralisation within both the cleats and the coal matrix occurs on a fine scale, with crystals of multiple mineral species often being present within micrometres of each other. [Copyright &y& Elsevier]