The ongoing reliance on fossil fuels for transportation poses critical challenges, particularly as global greenhouse gas emissions near irreversible thresholds. While electrification and hydrogen-based technologies offer solutions for some transport sectors, they remain impractical for aviation and maritime applications. Drop-in biofuels, derived from lignocellulosic biomass via thermochemical processes such as pyrolysis, present a viable alternative. Pyrolysis, particularly fast pyrolysis, can efficiently convert biomass into bio-oil, which can then be upgraded to biofuels through hydrotreatment. However, significant challenges remain, including scalability, process optimization, and feedstock variability.
This thesis presents a conceptual process model in Aspen Plus to simulate the thermal decomposition of lignocellulosic biomass into bio-oil and its subsequent upgrading into usable biofuels. The model combines pyrolysis kinetics with hydrodeoxygenation reactions to predict bio-oil and biofuel yields for various feedstocks. Simplifications, including species lumping, were necessary to ensure model feasibility but limited its accuracy and broader applicability.
The results show that the model performs best at pyrolysis reactor temperatures of 550–650 °C and vapor residence times of 2–3 seconds. Hydrodeoxygenation predictions align with literature in terms of hydrogen requirements and overall fuel yield, though limitations in distillation modelling result in a lack of detailed analysis for the fuel fractions. A comparative analysis of softwood and hardwood feedstocks reveals that woods with higher cellulose content yield greater bio-oil and biofuel quantities. However, this comparison overlooks the influence of other factors, such as specific lignin composition and structural differences, which also affect pyrolysis behaviour in real-life applications.
While the model provides insights into pyrolysis and upgrading processes, it demonstrates the need for further refinement to account for complex reaction mechanisms and real-world variability.