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Review on modelling approaches based on computational fluid dynamics for biomass combustion systems

Focus on fixed bed and moving grate systems

  • Review Article
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Biomass Conversion and Biorefinery Aims and scope Submit manuscript

Abstract

Biomass combustion is an important pathway of energy generation from renewable resources. Even though biomass combustion is an established conversion process, there is a high potential for further optimization of the used technologies. In particular, the advantage of numerical methods for the improvement of biomass combustion systems is not used to its full extent, because the simulation of these complex systems requires various sub-models for the thermo-chemical conversion of the biomass and sufficient computational resources for the combustion simulation. However, simulations are a valuable tool to enhance the design and operating conditions of biomass combustion systems with regard to high efficiency, low emissions and high flexibility. In addition, comparison of experimental and numerical results leads to a better understanding of the processes involved in biomass combustion. The present study gives a comprehensive overview of simulations of biomass combustion systems based on computational fluid dynamics that are available in the literature. It focusses on systems with fixed bed and covers various technologies (moving bed, pellet boilers, wood log stoves) as well as a wide range of sizes from laboratory reactors to industry scale. Besides woody biomass, also alternative fuels such as straw or municipal solid waste are considered. All relevant sub-models for the thermo-chemical conversion of the fuel on the one hand and for the gas-phase combustion on the other hand are discussed in detail. The recent advances in the concerned research fields are described.

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Abbreviations

BTX:

benzene, toluene, xylene

CFD:

computational fluid dynamics

CPU:

central processing unit

DEM:

discrete element method

DNS:

direct numerical simulation

DOM:

discrete ordinate model

DPM:

discrete particle model

DTRM:

discrete transfer radiation model

EBU:

eddy break-up model

EDC:

eddy dissipation concept

EDCM:

eddy dissipation combustion model

EDM:

eddy dissipation model

FR-ED:

finite rate/eddy dissipation model

FTIR:

Fourier transform infrared spectroscopy

GC-TCD:

gas chromatography–thermal conductivity detector

GRI:

gas research institute

ISAT:

in situ adaptive tabulation

JL:

Jones and Lindstedt

LES:

large eddy simulation

MSW:

municipal solid waste

PAC:

polyaromatic compounds

PAH:

polycyclic aromatic hydrocarbons

PDF:

probability density function

PFR:

plug flow reactor

RANS:

Reynolds-averaged Navier-Stokes equations

RNA:

reactor network array

RNG:

re-normalization group (k-ε model)

RSM:

differential Reynolds stress model

RTE:

radiative ransfer equation

SFM:

steady flamelet model

SST:

shear stress transport (k-ω model)

TGA:

thermogravimetric analysis

UDF:

user-defined function

UFM:

unsteady flamelet model

WD:

Westbrook and Dryer

WSSGM:

weighted-sum-of-gray-gases model

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Funding

This research work is kindly supported by the German Federal Ministry of Food and Agriculture (BMEL).

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Authors and Affiliations

  1. DBFZ Deutsches Biomasseforschungszentrum gemeinnützige GmbH, Torgauer Straße 116, 04347, Leipzig, Germany

    Andrea Dernbecher & Fouzi Tabet

  2. Institute of Energy Engineering, Chair for Energy Process Engineering and Conversion Technologies for Renewable Energies, Technische Universität Berlin, Fasanenstraße 89, 10623, Berlin, Germany

    Alba Dieguez-Alonso

  3. Hochschule Merseburg, Eberhard-Leibnitz-Str. 2, 06217, Merseburg, Germany

    Andreas Ortwein

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  1. Andrea Dernbecher
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  2. Alba Dieguez-Alonso
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  3. Andreas Ortwein
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  4. Fouzi Tabet
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Correspondence to Andrea Dernbecher.

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Dernbecher, A., Dieguez-Alonso, A., Ortwein, A. et al. Review on modelling approaches based on computational fluid dynamics for biomass combustion systems. Biomass Conv. Bioref. 9, 129–182 (2019). https://doi.org/10.1007/s13399-019-00370-z

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