Multiscale modeling and experimental studies on Bio-oil upgradation

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Changing global climate and depletion of fossil resources are the major issues before the current generation. Consequently, the development of alternative renewable energy resources has been intensely promoted. Compared to other renewable energy sources, biomass has the potential to replace a large fraction of fossil fuels as feedstocks and thus capable to cater the energy, chemicals and materials requirement of mankind. The manufacturing facilities that produce biofuels and bio-chemicals from various biomass feedstock are called biorefineries and conceptually, this is analogous to current petroleum refinery that produces fuels and chemicals from crude oil. In order to convert biomass into valuable products within a biorefinery approach, several processes must be applied. Among them fast pyrolysis is a thermo-chemical process for the production of liquid fuel and chemicals from biomass. Bio-oil is main product of fast pyrolysis and depending on the biomass source, oxygen content remains in the range of 35-40 wt% due to several oxygen-containing components. A higher oxygen content is responsible for a lower heating value of bio-oil. But at the same time, high concentration of oxygen containing compounds make bio-oil a good raw material for the isolation of various chemicals which are attractive in the commercial sense. The first step in the recovery of chemicals from bio-oil is primary fractionation by water where bio-oil is separated into less complex fractions and then using Liquid-liquid extraction targeted chemicals can be isolated from the phases derived from bio-oil. This thesis mainly focusses on the extraction of bio-chemicals such as acetic acid, acetol and furfural due to its large composition in bio-oil. Accurate experimental data and reliable thermodynamic models are basic requirements for such optimized extraction process design. These have been achieved through multicomponent Liquid-liquid Equilibria measurements and subsequent validation through multiscale strategies such as Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) and Monte Carlo Simulation.
Supervisor: Tamal Banerjee