Abstract

Several challenges still exist in successful and reliable operation of entrained flow slagging gasifiers (EFG) using coal as a fuel. The formation of flyash and its carryover with the gas stream causes fouling and plugging of syngas coolers, leading to reduction in heat rate and overwhelming of the solids handling systems. In order to predict flyash formation as well as carbon loss in an EFG, a thorough understanding of the effect of feedstock properties on fuel conversion rates during gasification is required. Current state of the art in gasification modeling treats coal as a homogenous material although it well known that coal is heterogeneous. Different classes of particles, when fed into an EFG, behave differently in terms of carbon conversion and mineral transformations. Therefore, it is critical to develop modeling tools that can incorporate physical and chemical characteristics of the feedstock and the effect of the operating conditions on particle transformations, in order to predict fate of the char/ash particles generated in the gasifier. During the grinding of coal, macerals and minerals separate due to mineral liberation. When subjected to gravity fractionation, the liberated minerals tend to accumulate in heavier density particles. Various physical and chemical characterizations are performed on size separated particle classes of the four gravity fractions with an aim to bring out the heterogeneity in the coal and its impact on conversion. Characterization indicated that almost all of the gasifiable matter is present in the two lighter gravity fractions and bulks of the minerals are present in the two heavier gravity fractions. The mineral matter was characterized and quantified using ash yield measurements, X-ray fluorescence, X-ray diffraction, Computer Controlled Scanning Electron Microscopy (CCSEM) and the iron minerals were measured using Mossbauer spectroscopy. Characterization results show the heterogeneity across the coal in multiple chemical components and prompt the need to account for heterogeneity in models.

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