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Counteracting emissions of particulate matter (PM) is an increasingly important goal in sustainable biomass combustion. This work includes a novel approach to investigate the PM emissions, originating from residual biomass combustion, at different combustion conditions in a lab-scale grate-fired furnace and includes in situ PM measurements by using on-line sensors. The interior furnace design allows installation of baffles to suppress the emissions by controlling the residence time. Moreover, the two-thermocouple method is used to measure the true gas temperature, and an on-line spatially resolved PM measurement method is developed to study the evolution of the PM concentration throughout the furnace for different experimental conditions thereby allowing accurate in-situ measurement of the PM reactivity. Experimental results and computational fluid dynamics (CFD) analyses are utilized in the current work to develop a kinetic model for reduction of particulate matter emissions in biomass combustion. The discrete particle model (DPM) is utilized in CFD analysis to improve the understanding of the particle temperature and residence time distribution which are difficult to quantify experimentally. By combining the experimental measurements of real soot formed during biomass combustion and information from the CFD analyses, a predictive kinetic model for PM10 reduction in biomass combustion is successfully developed. [ABSTRACT FROM AUTHOR]

• Physicochemical particle model for drying, devolatilization and combustion. • Highly accurate and stable model providing good agreement with experimental data. • Continuous profiles of temperature and biomass component during conversion. • Computationally efficient model easy to implement in CFD analysis. Development of an accurate and computational efficient biomass particle model to predict particle pyrolysis and combustion is the focus of this paper. Partial differential equations (PDEs) for heat and mass balance are transformed into a system of coupled ordinary differential equations (ODEs) with the use of orthogonal collocation as the particle discretization method. The orthogonal collocation method is incorporated with comprehensive physicochemical mechanisms to predict the behavior of biomass components during particle pyrolysis and combustion. Heat adsorption by evaporated gas and water movement by diffusion inside the biomass matrix are included in the present work, in parallel with the effect of Stefan flow on the heat and mass transfer rates at the particle surface. Abandoning the classical interface-based modelling approach, the present approach allows decoupling between biomass components and spatial resolution, and the prediction of continuous intra-particle profiles. The new particle model is proven to be accurate and stable through its high degree of agreement with simulation results for particle pyrolysis and combustion experiments using different particle moisture contents and geometrical shapes. The intra-particle temperature gradient, as well as particle mass and size evolution, can be predicted accurately, as validated against experimental data. It is shown that six collocation points provide satisfying resolution. The computational efficiency is confirmed by the short simulation time that was found to be approximately three orders of magnitude faster than mesh-based simulations. This implies that the current model can be used for computational fluid dynamic (CFD) analysis through implementation as sub-grid-scale models to design, for example, biomass furnaces. [ABSTRACT FROM AUTHOR]