Abstract

During underground coal gasification (UCG), whereby coal is converted to syngas in situ, a cavity is formed in the coal seam. The cavity growth rate (CGR) or the moving rate of the gasification face is affected by controllable (operation pressure, gasification time, geometry of UCG panel) and uncontrollable (coal seam properties) factors. The CGR is usually predicted by mathematical models and laboratory experiments, which are time consuming, cumbersome and expensive. In this paper, a new simple model for CGR is developed using non-linear regression analysis, based on data from 11 UCG field trials. The empirical model compares satisfactorily with Perkins model and can reliably predict CGR.

Highlights

  • Coal is the largest fossil fuel resource in the world, with proven reserves that are adequate to meet the expected demand, without much increase in production costs (Couch 2009)

  • During underground coal gasification (UCG), whereby coal is converted to syngas in situ, a cavity is formed in the coal seam

  • The cavity growth rate (CGR) or the moving rate of the gasification face is affected by controllable and uncontrollable factors

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Summary

Introduction

Coal is the largest fossil fuel resource in the world, with proven reserves that are adequate to meet the expected demand, without much increase in production costs (Couch 2009). With the depletion in the oil and gas reserves, coal is expected to play a major role in the global energy sector in the near future (BP 2010). Underground coal gasification (UCG) offers the potential for using the energy stored in coal in an. The procedure for in situ gasification of coal is as follows. (1) Injection and production wells are drilled from the surface to the coal seam. (3) Air or oxygen is sent to the coal seam through the injection well. In the early stages of UCG, Prediction of cavity growth rate during underground coal gasification using multiple. 2005; Daggupati et al 2010, 2011; Prabu and Jayanti 2011)

Background
Simple regression and input data selection
Non-linear multiple regression analysis
Validation of developed model
Findings
Conclusions
Full Text
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