Steam Cracking : Kinetics and Feed Characterisation
Steam Cracking : Kinetics and Feed Characterisation
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蒸汽裂解：动力学和进料表征
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
J. Moreira
中科院分区：
文献类型：
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作者：
J. Moreira
本文提出了蒸汽裂解炉的数学模型，并根据工业乙烷、丙烷和石脑油原料加工炉的数据，对文献中的分子和自由基动力学方案进行了实施和验证。结果表明，对于气体原料，所实现的动力学能够准确地预测产物收率，自由基方案取代分子方案。然而，对于石脑油裂解，从文献中实现的自由基动力学似乎无法预测植物数据。对替代稀释剂相对于蒸汽的稳态研究也进行了，得出的结论是，如果不愿意进一步提高线圈出口温度，那么稀释剂实际上可能没有区别，尽管在没有温度约束的情况下，氦气是最佳替代。最后，由于动力学方案的实施需要原料的分子组成，并且由于液体原料通常由其他指标而不是详细的碳氢化合物分析来表征，因此开发了一个原料表征模型。该模型的目的是根据通常表征这种石油馏分的商业指数，确定石脑油原料的分子组成。然而，结果表明，该模型不能准确地确定这些成分，已经得出结论，必须包括先验知识，以提高其预测。
In the present work a mathematical steam cracking furnace model is presented and several kinetic schemes from literature, both molecular and radical, were implemented and validated against data from industrial ethane, propane and naphtha feedstocks processing furnaces. The results showed that, for gaseous feedstocks, the implemented kinetics were able to accurately predict product yields, with the radical scheme superseding the molecular one. Regarding naphtha cracking, however, the implemented radical kinetics from literature seemed to fail at predicting plant data. A steady-state study on alternative diluents relatively to steam was also carried out and it was concluded that there may actually be no difference between diluents if one is not willing to further increase the coil outlet temperature, although helium posed the best alternative if no constraints on temperature exist. At last, since the implementation of kinetic schemes require the molecular composition of the feed and because liquid feedstocks are usually characterised by other indices rather than a detailed hydrocarbon analysis, a feed characterisation model was developed. This model had the objective to determine the molecular composition of naphtha feedstocks given the commercial indices that usually characterise such petroleum fractions. The results, however, showed that the model is not able to accurately determine such compositions, having been concluded that a priori knowledge had to be included to improve its predictions.