Abstract

ABSTRACTThe energy sector in India is highly dependent on coal as a fuel. At present, most of the domestically produced thermal coal is burned raw or after minimal beneficiation. This practice often results in higher operating and maintenance costs, poorer utilization efficiencies, and increased greenhouse gas emissions. To improve the prevailing conditions, the Government of India is taking steps to increase clean coal production by promoting the installation of coal preparation plants across the country. The identification of optimal separation densities and blending protocols for these precombustion cleaning facilities greatly influences the economics of the fuel supply chain and India’s quest for additional low-cost energy. Traditionally, preparation plants have utilized float-sink (washability) data to determine suitable density separation set points for their processing circuits. Very often, these set points are based on the cumulative ash content required to meet contract specifications within India’s bulk commodity coal markets. Unfortunately, the current study indicates that the optimal separation densities are largely independent of coal washability characteristics. This article demonstrates the use of micro-price optimization to identify optimal density set points that maximize coal production for Indian coal feedstocks. This fundamental optimization technique assigns a unit price to each particle passing through the coal supply chain. This approach is in sharp contrast to the prevailing practice that assigns bulk prices to piles/lots of mixed coal and rock. Case studies for 12 different coal samples from India showed that (1) the historic constant cumulative ash method for determining set points is suboptimal, (2) the separation density of all plants/circuits should be held near-constant irrespective of feed washabilities, and (3) misplacement of high-ash rock and low-ash coal must be prevented to avoid large reductions in the inherent worth of India’s coal reserves.

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