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This paper addresses the need for versatile models in fast pyrolysis to facilitate the exploration of novel feedstocks and process configurations, minimizing capital and operational expenses. Our model aims to resolve two key challenges: defining a secondary pyrolysis network to align primary pyrolysis products with experimental results and addressing issues in modeling condensation loops in steady-state, as observed in various fast pyrolysis implementations, including the bioliq I plant, which serves as the basis for this model. The outcomes are promising, revealing minor discrepancies with experimental values in product distribution (1.5%) and condensate composition (6.0%) postreactor modeling. Further deviations of 3.6% in condensate composition emerge after condenser modeling. Notably, when considering the entire model, discrepancies persist, particularly when applied to biomasses diverging from the calibration material (wheat straw). This research demonstrates the model's efficacy in addressing specific challenges in fast pyrolysis simulation, emphasizing its adaptability to diverse conditions. However, ongoing refinement is essential for enhancing overall predictive accuracy, particularly in scenarios with varying biomass characteristics. The findings contribute valuable insights to the field, paving the way for more robust and adaptable models in the exploration of fast pyrolysis.
Fonseca et al. (Wed,) studied this question.