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The potential of commercial deployment of CO 2 sequestration in unmineable coal seams, as a measure to mitigate the greenhouse gas effect, has been tested through a series of small- to medium-scale CO 2 injection projects in the Appalachian Basin, USA. Essential tools for these projects are reservoir simulations in which the involved processes, prior to injection field tests are modeled to enhance understanding and to be used as a decision tool. Due to the large number of modeling input parameters and the high uncertainty in determining their values, an initial model sensitivity analysis of the key input parameters is required for the development of representative models. In this paper, preliminary single-well reservoir parametric simulations were conducted to identify how the variation of selected reservoir parameters and properties affect model results. More specifically, this sensitivity analysis focused on three areas: a) the effect of selected initial conditions (adsorption isotherm, fracture and matrix porosity, reservoir pressure, desorption time, water saturation and permeability), b) the influence of the production mechanism (permeability, face to butt cleat ratio, relative permeability, Langmuir volume, matrix and fracture porosities, desorption time, water saturation, initial reservoir pressure, and pressure gradient), and c) the contribution of the well characteristics (skin factor, explicitly defined hydraulic fractures and high permeability zones). This modeling work forms the basis for calibrating reservoir models used in simulations of CO 2 injection. [ABSTRACT FROM AUTHOR]

Abstract: Although there are several formulas available for predicting Hardgrove grindability of coal, most of them are linear and do not simultaneously take into consideration most of the relevant factors. The artificial neural network is an information processing tool that is capable of establishing an input–output relationship by extracting controlling features from a database presented to the network. In this paper, a neural network approach was proposed to deal with the grindability behavior of coal. 195 sets of experimental data were evaluated with artificial neural network to predict the HGI of Kentucky coals. Two different kinds of the trained artificial neural network were undertaken using the database created in this study. It is shown from the examples that the artificial neural network adequately recognized the characteristics of the coal experimental data sets, retaining a generality for further prediction. It is believed that an artificial neural network based prediction procedure shown in this paper can be further employed for Hardgrove grindability index prediction. The influence of liptinite, vitrinite, ash, and sulfur content on HGI was studied by a parametric study. [Copyright &y& Elsevier]

This paper examines the relationship between renewable energy production and greenhouse gas emissions (GHG) using U.S. state-level data for 2010. After controlling for other sources of emissions, U.S. states that produce a larger share of renewable energy are found to have lower GHG emissions. It is estimated that a 10% increase in the share of renewable energy could decrease CH 4 emissions by about 0.26%. Since the use of renewable energy sources does not release GHG emissions, this effect can be interpreted as stabilizing if renewable energy is added to coal use or as corrective if it replaces coal. After accounting for the role of coal as a baseload power source, an increase in the share of renewable energy is estimated to mitigate N 2 O emissions at the U.S. state level only if states individually decrease their share of coal use to levels below 41.47%. These findings have significant policy implications for the provision of guidance to policymakers in identifying optimal energy mixes and in pursuing realistic goals to enhance renewable energy penetration and to contribute to the current efforts of tackling climate change. [ABSTRACT FROM AUTHOR]

Abstract: In spite of its large endowment of coal resources, recent studies have indicated that United States coal production is destined to reach a maximum and begin an irreversible decline sometime during the middle of the current century. However, studies and assessments illustrating coal reserve data essential for making accurate forecasts of United States coal production have not been compiled on a national basis. As a result, there is a great deal of uncertainty in the accuracy of the production forecasts. A very large percentage of the coal mined in the United States comes from a few large-scale mines (mega-mines) in the Powder River Basin of Wyoming and Montana. Reported reserves at these mines do not account for future potential reserves or for future development of technology that may make coal classified currently as resources into reserves in the future. In order to maintain United States coal production at or near current levels for an extended period of time, existing mines will eventually have to increase their recoverable reserves and/or new large-scale mines will have to be opened elsewhere. Accordingly, in order to facilitate energy planning for the United States, this paper suggests that probabilistic assessments of the remaining coal reserves in the country would improve long range forecasts of coal production. As it is in United States coal assessment projects currently being conducted, a major priority of probabilistic assessments would be to identify the numbers and sizes of remaining large blocks of coal capable of supporting large-scale mining operations for extended periods of time and to conduct economic evaluations of those resources. [Copyright &y& Elsevier]

Abstract: The paper investigates the causality relationships among industrial production index, coal consumption and employment in industrial sector for the period of 1973:1–2011:10 in USA. After noticing that there are breaks in the regression model, the Hatemi-J test for cointegration is employed to the cases that take into account two possible regime shifts. It is concluded that there is a long run relationship between industrial production and industrial coal consumption with the breaks at 1983:4 and 1998:4. We found a negative relationship between coal consumption and industrial production for the period of 1973:1–1983:4 and positive relationship for 1983:5–1998:4 period. For the last period that covers 1983:5–2011:10, the cointegration relationship turned to negative. In addition, the results show that causal relationship between coal consumption and industrial production changes over time. [Copyright &y& Elsevier]

Abstract: This paper estimates an unobserved components model to explore coal consumption in the USA. We ask whether coal consumption exhibits hysteresis, defined as a dynamic structure in which the cyclical component of coal consumption has permanent effects on the natural component. In contrast to previous analysis, we provide evidence in favor of hysteresis in coal consumption, by using the nonlinear framework proposed recently by Pérez-Alonso and Di Sanzo , in which threshold type nonlinearities are introduced by allowing past cyclical consumption to have a different impact on the natural component depending on the regime. The article discusses implications of the findings for energy conservation policies. [Copyright &y& Elsevier]

This paper presents data on the properties of coal and fly ash from two coal mines and two power plants that burn single-source coal from two mines in Indiana. One mine is in the low-sulfur (<1%) Danville Coal Member of the Dugger Formation (Pennsylvanian) and the other mines the high-sulfur (>5%) Springfield Coal Member of the Petersburg Formation (Pennsylvanian). Both seams have comparable ash contents (∼11%). Coals sampled at the mines (both raw and washed fractions) were analyzed for proximate/ultimate/sulfur forms/heating value, major oxides, trace elements and petrographic composition. The properties of fly ash from these coals reflect the properties of the feed coal, as well as local combustion and post-combustion conditions. Sulfur and spinel content, and As, Pb and Zn concentrations of the fly ash are the parameters that most closely reflect the properties of the source coal. [Copyright &y& Elsevier]