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Developing detailed kinetic models of syngas production from bio-oil gasification using Reaction Mechanism Generator (RMG)

机译:使用反应机理发生器(RMG)开发生物油气化生产合成气的详细动力学模型

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摘要

Detailed kinetic models for the conversion of bio-oil to syngas through gasification were developed automatically using the open source software package Reaction Mechanism Generator (RMG). The influences of process operating conditions and of RMG parameters on the performance of models were investigated. Both temperature and pressure alter the product yields, although including pressure-dependent (chemically activated and fall-off) kinetics have minimal impact on these predictions. The model size is important, although currently constrained by available RAM, motivating development of improved memory-management algorithms in RMG. To validate the RMG-built mechanisms, simulations performed with Cantera were compared with experimental data from the literature. Agreements and disagreements between RMG-built models and literature show that the automated mechanism generation approach is promising, but reveal some families of reactions involving heteroatomic cycles that require improved estimates for bio-mass derived fuels. Research in this area would be greatly helped by more quantitative experimental data, ideally showing intermediate species profiles. These findings motivate extra studies and guide further RMG development. (C) 2015 Elsevier Ltd. All rights reserved.
机译:使用开源软件包反应机理发生器(RMG)自动开发了通过气化将生物油转化为合成气的详细动力学模型。研究了工艺操作条件和RMG参数对模型性能的影响。温度和压力都会改变产品的产量,尽管包括压力依赖性(化学活化和下降)动力学对这些预测影响很小。尽管当前受可用RAM的限制,但是模型的大小很重要,这促使开发RMG中改进的内存管理算法。为了验证RMG内置的机制,将Cantera进行的仿真与文献中的实验数据进行了比较。 RMG建立的模型与文献之间的协议和分歧表明,自动机制生成方法是有前途的,但揭示了涉及杂原子循环的某些反应家族,需要改进对生物质衍生燃料的估计。该领域的研究将得到更多定量实验数据的帮助,理想情况下应显示中间物种的概况。这些发现激发了更多的研究,并指导了RMG的进一步发展。 (C)2015 Elsevier Ltd.保留所有权利。

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