Adjoint-based optimization in the development of low-emission industrial boilers

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Adjoint-based optimization in the development of low-emission industrial boilers. / Kanellis, Georgios; Oksanen, Antti; Konttinen, Jukka.

julkaisussa: Engineering Optimization, 2020.

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Bibtex - Lataa

@article{d38c5f97dda9473885727b8788685bd2,

title = "Adjoint-based optimization in the development of low-emission industrial boilers",

abstract = "A gradient-based method has been developed and programmed to optimize the NH (Formula presented.) injections of an existing biomass-fired bubbling fluidized bed boiler, the targets being to minimize both the NO and the NH (Formula presented.) emissions. In this context, the reactive flow inside the boiler is modelled using a custom-built OpenFOAM (Formula presented.) solver, and then the NO and NH (Formula presented.) 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.",

keywords = "adjoint, boiler, CFD, emissions, optimization",

author = "Georgios Kanellis and Antti Oksanen and Jukka Konttinen",

year = "2020",

doi = "10.1080/0305215X.2020.1781842",

language = "English",

journal = "Engineering Optimization",

issn = "0305-215X",

publisher = "Taylor & Francis",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Adjoint-based optimization in the development of low-emission industrial boilers

AU - Kanellis, Georgios

AU - Oksanen, Antti

AU - Konttinen, Jukka

PY - 2020

Y1 - 2020

N2 - A gradient-based method has been developed and programmed to optimize the NH (Formula presented.) injections of an existing biomass-fired bubbling fluidized bed boiler, the targets being to minimize both the NO and the NH (Formula presented.) emissions. In this context, the reactive flow inside the boiler is modelled using a custom-built OpenFOAM (Formula presented.) solver, and then the NO and NH (Formula presented.) 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.

AB - A gradient-based method has been developed and programmed to optimize the NH (Formula presented.) injections of an existing biomass-fired bubbling fluidized bed boiler, the targets being to minimize both the NO and the NH (Formula presented.) emissions. In this context, the reactive flow inside the boiler is modelled using a custom-built OpenFOAM (Formula presented.) solver, and then the NO and NH (Formula presented.) 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.

KW - adjoint

KW - boiler

KW - CFD

KW - emissions

KW - optimization

U2 - 10.1080/0305215X.2020.1781842

DO - 10.1080/0305215X.2020.1781842

M3 - Article

JO - Engineering Optimization

JF - Engineering Optimization

SN - 0305-215X

ER -

TUTCRIS