Abstract
Biomass consists of biodegradable and non-fossilized organic matter, including a wide range of materials such as forestry, agricultural, municipal, and industrial solid residues. Due to its renewable nature, high carbon content, and availability in several countries, biomass is a promising alternative to fossil fuels. In this context, thermochemical conversion processes such as fast pyrolysis can be an interesting option to produce heat, power, fuels, and chemicals of lower greenhouse gas emissions from biomass sources. Fast pyrolysis uses high temperatures (~500 °C), inert atmospheres (e.g., nitrogen), and short residence times (1 – 5 s) to convert biomasses mostly into bio-oil (liquid stream), also producing char (solid) and gas fractions. Biomass composition highly affects the bio-oil properties and, although much work has been done to understand such an effect focusing on heat and power production, little has been done aiming to design pyrolysis for high-added value chemicals. This work performed the kinetic simulation and multivariate analysis of a fast pyrolysis (FP) plant fed by multiple feedstocks to assess the effect of biomass composition on the bio-oil functional groups and provide guidelines to produce high-added value bio-oil components. The simulation was built in Aspen PlusTM v.10, validated against experimental data, and used to obtain a dataset correlating biomass compositions from 60 sources and FP outputs. The dataset was analyzed via hierarchical cluster analysis (HCA) and principal component analysis (PCA), showing the correlations between biomass and bio-oil properties and highlighting key biomass features to obtain specific high-value functional groups. Among the results, biomasses of higher cellulose and hemicellulose contents such as agricultural feedstocks may produce higher amounts of anhydro-sugars, ketones, and aldehydes. In contrast, feedstocks of higher lignin contents such as wood may generate increased concentrations of phenols and aromatics. This work shows how simulation and multivariate analysis tools can be used in the design of fast pyrolysis aiming at multiple bio-oil applications.
