Abstract
The paper investigates a comprehensive approach for ash fouling monitoring and an optimized soot-blow mechanism for a thermal power plant reheater. A dynamic nonlinear regression model is designed to monitor the Cleanliness Factor (CF) of reheater and thereby an optimized soot-blow strategy is proposed to determine the critical CF and the duration of soot-blow cycle. The result in this case-study shows that steam consumed per soot-blow cycle is reduced and also the amount of fuel used per day is saved by adopting the proposed soot-blow strategy. The proposed method can be implemented as guidance for soot-blow operation in thermal power plants without any need for additional hardware and with a minimal computation.
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