Z-number-based selection of suitable underground coal gasification site considering information reliability

Abstract

ABSTRACT Enjoying the twelfth largest coal reserve in the world, only 1% of Iran’s energy consumption basket is supplied by coal. In the recent decade, new technologies that produce low-cost and clean energy from coal have received much attention. One of these methods is underground coal gasification (UCG). The UCG has a potential for converting the world’s coal resources into energy, liquid fuels, and chemicals. For extraction of this valuable energy, firstly, the best site for UCG should be selected to minimize the implementation complications and to distribute this energy source properly in the country. UCG site selection is affected by many uncertainties including environmental, economic, and operational parameters. Therefore, it is important to use methods capable of taking into account the uncertainty of various parameters in the site selection. In this study, Z-numbers which are a new generation of fuzzy numbers are used to overcome this issue in suitable site selection for the UCG method. Technical considerations in UCG site selection consist of coal properties, surrounding rock properties, groundwater, and surface infrastructures of the region. In this paper, first, 15 criteria influencing UCG method and four available alternatives (four coal mines in Iran) are considered. Then, by using the Z-AHP method, which is a combination of Z-numbers and conventional analytical hierarchy process (AHP), the suitable mine site for UCG is determined. Based on the results, the alternative mine named Mazino is selected as the best place for UCG site with a final weight of 0.3713. The comparison of final weights of criteria also illustrates that the two criteria of “existence of fault” (C11) and “permeability of coal seam and overburden” (C9) are the most important parameters in UCG site selection. The Z-AHP method has the capability to make an appropriate decision in the existence of experts’ disagreement or lack of experts’ confidence.