Abstract
Optimizing coal blending strategy is important for increasing the running efficiency and lowering down the emissions of utility boilers. A model, considering price, calorific value, ash content, volatile matter content, moisture content and sulfur content of the coal, has been established using quantum-behaved particle swarm optimization algorithm. The calculation result showed that, compared with the particle swarm algorithm, the quantum particle swarm had better global search capability and astringency, the optimal coal blending ratio can be quickly searched at reasonable boiler running cost. The blending mode is in line with the actual requirements, and the algorithm has high stability.
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