Liquid–Liquid Equilibrium Prediction in Fast Pyrolysis Bio-Oil Systems: A Framework for Incorporating Bio-Oil Complexity

• Published in: Energy & fuels Vol. 38; no. 19; pp. 18769 - 18780
• Main Authors: Salas, Myriam Rojas, Fonseca, Frederico Gomes, de Araujo, Ana Cristina Corrêa, García-Perez, Manuel, Funke, Axel
• Format: Journal Article
• Language: English
• Published: American Chemical Society 03-10-2024
• Subjects: Bioenergy, Biofuels, and Biorefinery
• ISSN: 0887-0624; 1520-5029
• DOI: 10.1021/acs.energyfuels.4c03387

Liquid–liquid extraction, a cornerstone technique for bio-oil refining, suffers from a critical data gap: the lack of reliable partition coefficients (K OW) for key fast pyrolysis bio-oil (FPBO) components. This absence hinders the application of established modeling strategies for the optimization of these processes. To address this challenge, this study employs simulations to calculate K OW values for crucial FPBO components based on a bio-oil surrogate mixture and activity coefficients calculated with UNIFAC Dortmund. This study focuses on the unresolved structure of pyrolytic lignin (‘pyrolignin’), whose chemical composition is largely unknown. Consequently, there is a large variety of representatives proposed for pyrolignin in the literature. To account for the uncertainty of how to best represent pyrolignin, 20 possible structures (phenolic dimers to tetramers) are tested to determine which describes the experimental data best. Also, two different approaches to quantifying the pyrolignin fraction in surrogate FPBO were investigated. Interestingly, the best K OW predictions for levoglucosan are obtained without including any modeled pyrolignin and modeling it with large molecules or molecule mixture. Several specific pyrolignin structures (dimer D2, trimer F1, and tetramers I1 and I3) demonstrate promising agreement with experimental data. This work establishes a framework for incorporating bio-oil complexity into process modeling, particularly the influence of diverse pyrolignin structures. This paves the way for more accurate simulations and ultimately the design of efficient and effective bio-oil refining processes.