Dimensioning a Recovery Boiler Furnace Using Mathematical Optimization, 2014 PEERS Conference

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The capacities of the largest new recovery boilers are steadily rising, and there is every reason to expect this trend to continue. However, the furnace designs for these large boilers have not been optimized and, in general, are based on semi-heuristic rules and on experience with smaller boilers.

In this paper, we present a multi-objective optimization code that is suitable for diverse optimization tasks and use it to dimension a high-capacity (7,000 tds/d) recovery boiler furnace. The objective was to find the furnace dimensions (width, depth, and height) that optimize eight performance criteria while satisfying additional inequality constraints. The optimization procedure was carried out in a fully automatic manner by means of the code, which is based on a genetic algorithm (GA) optimization method and a radial basis function network (RBFN) surrogate model. The code was coupled with a recovery boiler furnace computational fluid dynamics (CFD) model that was used to obtain performance information on the individual furnace designs considered.