Abstract
ABSTRACT A gradient-based method has been developed and programmed to optimize the NH injections of an existing biomass-fired bubbling fluidized bed boiler, the targets being to minimize both the NO and the NH emissions. In this context, the reactive flow inside the boiler is modelled using a custom-built OpenFOAM solver, and then the NO and NH species are calculated using a post-processing technique. The multiobjective optimization problem is solved by optimizing several weight combinations of the objectives using the gradient-projection method. The required sensitivities were calculated by differentiating the post-processing solver according to the discrete adjoint method. The adjoint-based sensitivities are validated against finite differences calculations. Moreover, in order to evaluate the optimization results, the optimization problem is solved using evolutionary algorithms software. Finally, the optimization results are physically interpreted and the strengths and weaknesses of the proposed method are discussed.
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