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.

中文翻译：

---

模拟废弃生物质的快速热解：提高预测能力

本文解决了快速热解中对多功能模型的需求，以促进新型原料和工艺配置的探索，最大限度地减少资本和运营费用。我们的模型旨在解决两个关键挑战：定义二次热解网络，使初级热解产物与实验结果保持一致，并解决稳态冷凝回路建模问题，如在各种快速热解实施中观察到的那样，包括 bioliq I工厂，它服务于作为该模型的基础。结果令人鼓舞，揭示了反应器后模型中产物分布 (1.5%) 和冷凝物成分 (6.0%) 与实验值的微小差异。冷凝器建模后，冷凝物成分进一步出现 3.6% 的偏差。值得注意的是，在考虑整个模型时，差异仍然存在，特别是当应用于与校准材料（麦秆）不同的生物质时。这项研究证明了该模型在解决快速热解模拟中的特定挑战方面的功效，强调了其对不同条件的适应性。然而，持续的改进对于提高整体预测准确性至关重要，特别是在具有不同生物质特征的情况下。这些发现为该领域提供了宝贵的见解，为探索快速热解中更强大和适应性更强的模型铺平了道路。