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Spatial modeling of lignite energy reserves for exploitation planning and quality control

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  • Pavlides, Andreas
  • Hristopulos, Dionissios T.
  • Roumpos, Christos
  • Agioutantis, Zach

Abstract

Energy resources are distributed in space. Models of spatial variability thus greatly contribute to the optimal exploitation of such resources. This paper concentrates on modeling the spatial distribution of energy content based on geostatistical interpolation and simulation methods. We focus on lignite, a fossil fuel which plays a key role in the energy budget in several parts of the world. Nonetheless, geostatistical tools are also relevant for the analysis of renewable and other fossil-based energy resources. Quantitative understanding of the spatial variability of lignite energy reserves helps to optimize mine exploitation and to reduce fluctuations in the quality of the fuel supplied to power plants. We also introduce the spatial profitability index as an analytical tool for the design and medium-term exploitation of multiseam mines. Based on this index we propose an empirical equation which allows fast and practical estimation of changes in energy reserves due to variations in expected costs or revenues. We illustrate the proposed modeling framework using lignite data from the Mavropigi mine in Northern Greece.

Suggested Citation

  • Pavlides, Andreas & Hristopulos, Dionissios T. & Roumpos, Christos & Agioutantis, Zach, 2015. "Spatial modeling of lignite energy reserves for exploitation planning and quality control," Energy, Elsevier, vol. 93(P2), pages 1906-1917.
    • Handle: RePEc:eee:energy:v:93:y:2015:i:p2:p:1906-1917
    DOI: 10.1016/j.energy.2015.10.049
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    1. Liu, Heping & Shi, Jing & Erdem, Ergin, 2010. "Prediction of wind speed time series using modified Taylor Kriging method," Energy, Elsevier, vol. 35(12), pages 4870-4879.
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