Sustainable Energy Conversion from Biomass Waste Combustion - Experimental and Multiscale Modelling Studies
Doctoral thesis, 2022
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.
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.
Stefan flow
particulate matter
biomass
combustion
soot
furnace
orthogonal collocation
CFD simulation
Online
Opponent: Prof. Jacobo Porteiro, Department of Mechanical Engineering, University of Vigo, Spain.
Author
Maulana Nugraha
Chalmers, Chemistry and Chemical Engineering, Chemical Technology
Particle modelling in biomass combustion using orthogonal collocation
Applied Energy,;Vol. 255(2019)
Journal article
On the Sherwood number correction due to Stefan flow
Chemical Engineering Science,;Vol. 249(2022)
Journal article
Analysis of Particulate Matter Emission in Biomass Combustion
Biomass for energy is the most important renewable energy source in the EU, with the heating and cooling sectors being the major end consumer. However, many of today's energy plant technologies are incapable of meeting the EU's target for lower emissions e.g., maximum 30 mg/m3 for PM emission. PM is a harmful substance which estimated causing around 4.2 million premature mortalities in 2016. Therefore, this study aims to develop better knowledge of how PM is formed and reduced in the biomass combustion system by combining experimental and simulation techniques.
The experimental furnace is successfully developed and equipped with on-line sensors to accurately monitor biomass combustion including PM emissions. Different combustion conditions, i.e., gas residence time, air temperature level and air/fuel ratio, are explored to examine the influence of these conditions on the emission level. CFD simulations are developed to predict the combustion behavior inside the biomass furnace. The validated CFD simulation using experimental results, provide better understanding of how PM is formed and reduced in the furnace. The availability of PM precursors (e.g., acetylene) and high temperature in the furnace promote high formation of PM, while oxygen level and residence time dictates the PM reduction. The important findings from the current work might inspire further works to develop better furnace design and/or operating conditions in order to reduce emissions from biomass combustion while maintaining high energy conversion.
The experimental furnace is successfully developed and equipped with on-line sensors to accurately monitor biomass combustion including PM emissions. Different combustion conditions, i.e., gas residence time, air temperature level and air/fuel ratio, are explored to examine the influence of these conditions on the emission level. CFD simulations are developed to predict the combustion behavior inside the biomass furnace. The validated CFD simulation using experimental results, provide better understanding of how PM is formed and reduced in the furnace. The availability of PM precursors (e.g., acetylene) and high temperature in the furnace promote high formation of PM, while oxygen level and residence time dictates the PM reduction. The important findings from the current work might inspire further works to develop better furnace design and/or operating conditions in order to reduce emissions from biomass combustion while maintaining high energy conversion.
Subject Categories
Environmental Engineering
Chemical Engineering
Areas of Advance
Energy
ISBN
978-91-7905-723-7
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5189
Publisher
Chalmers
Online
Opponent: Prof. Jacobo Porteiro, Department of Mechanical Engineering, University of Vigo, Spain.