Development of a numerical method for the rapid prediction of ignition performance of biomass particles

Mularski, Jakub and Lue, Leo and Li, Jun (2023) Development of a numerical method for the rapid prediction of ignition performance of biomass particles. Fuel, 348. 128520. ISSN 0016-2361 (https://doi.org/10.1016/j.fuel.2023.128520)

[thumbnail of Mularski-etal-Fuel-2023-numerical-method-for-the-rapid-prediction-of-ignition-performance-of-biomass-particles]
Preview
 Text. Filename: Mularski_etal_Fuel_2023_numerical_method_for_the_rapid_prediction_of_ignition_performance_of_biomass_particles.pdf
Accepted Author Manuscript
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo
Download (2MB)| Preview

Abstract

Ignition is a critical step of the combustion process of biomass, due to its substantial impact on flame characteristics, process efficiency, and pollutant formation. This paper aims to develop a robust zero-dimensional ignition model as an effective tool to quickly estimate the ignition behaviour of biomass fuels. The numerical approach integrates an established devolatilization model with a well-tested mechanism for gas-phase reaction kinetics. The ignition delay time is determined from the sum of the devolatilization time and ignition time of the evolved gas species, as specified by the maximum in the OH radical concentration. The central premise of the modelling routine is that kinetics govern the ignition delay during biomass initial heating and thermochemical conversion which makes the elemental fuel composition the most important fuel property and heating rate the most important reactor condition. The predictions of the model are tested and verified against published experimentally measured ignition data for seven pulverized biomass fuels and are found to be in good agreement. The model for the first time can reasonably distinguish ignition behaviours of different biomass fuels, enabling its use in wider industrial applications.

ORCID iDs

Mularski, Jakub, Lue, Leo ORCID logoORCID: https://orcid.org/0000-0002-4826-5337 and Li, Jun ORCID logoORCID: https://orcid.org/0000-0002-7685-8543;
CORE (COnnecting REpositories)