Sustainable Energy Conversion from Biomass Waste Combustion - Experimental and Multiscale Modelling Studies

Abstract: The development of sustainable energy conversion via residual biomass combustion is one of the scientific and industrial community focus today to fulfilling the global net zero emission commitment in 2050. Despite its potency due to the abundant stock of biomass, hazardous particulate matter (PM) emission from residual biomass combustion remains a big challenge to increase the contribution of biomass combustion as a main renewable energy source. Therefore, this study analyses how particulate matter can be formed and minimized in the system of residual biomass combustion. The study includes multiscale modelling and simulation analysis validated thoroughly using detail and accurate observation in an experimental facility. To facilitate accurate prediction of particle pyrolysis and combustion, a computationally efficient sub-grid model of biomass particle model is developed. The developed particle model that relies on the orthogonal collocation method and a comprehensive physicochemical mechanism is proven to be accurate based on a high degree of agreement with experimental results for particle pyrolysis and combustion experiments. Improved prediction of mass transfer to and from spherical particles during pyrolysis and combustion is also analyzed in the current work by the correction of Sherwood number due to Stefan flow. High resolved computational fluid dynamics (CFD) analysis confirms that the proposed corrected Sherwood number produced better agreement in comparison to the established Spalding and Abrahamson model. Grate-fired biomass furnace is designed and constructed in the current work to allow accurate observation of combustion parameters in different combustion conditions. Online spatially resolved PM measurement system allows accurate in-situ measurement of PM reactivity. The steady CFD model validated thoroughly with experimental observation is proven to predict the global behavior of biomass combustion accurately. In addition, a predictive kinetic model for PM reduction was developed using the Discrete Particle Model (DPM) in CFD analysis. The time-resolved CFD model is also formulated in this study, together with more detailed devolatilization kinetics by the inclusion of different lignocellulosic components. The CFD analysis reveals that 99.3% of the soot is burnt in the combustion chamber. Local concentrations of soot precursors from lignin decomposition i.e., acetylene, and regions with a high temperature in the freeboard promote an increased rate of soot formation. Meanwhile, the residence time and oxygen availability become the most influential factors to minimize the soot emissions.