面对我国贫油的现状，开发利用油页岩引起了广泛关注。然而，油页岩有机结构的复杂性制约了其清洁高效转化技术的发展。为从微观层面认识油页岩结构与其反应性的关系（简称构效关系），研究了8种油页岩的共价键特征，并利用人工神经网络（Artificial Neural Network, ANN）以R2 > 0.99的高精度量化了油页岩共价键分布、反应温度、反应时间与自由基加氢量的关系。敏感度分析结果表明油页岩中的Cal—Cal、Cal—Car、Car—Car、Cal—O、Car—O和Cal=O会促进自由基的产生及加氢；Cal—H和Car—H发生断键产生的Cal·和Car·会发生缩聚反应，而产生的H·会给其他自由基加氢，间接抑制了溶剂加氢行为；O—H在反应中断键的可能性较低。该研究对油页岩、煤、生物质的温和转化技术具有借鉴意义。

The utilization of oil shales earns popularity due to the shortage of petroleum of China. However, the clean and efficient conversion of oil shales is limited due to the complexity of the organic matters in oil shales. To understand the structure-reactivity relationship of oil shales in a micro-level, this work studied the distribution characteristics of the covalent bonds of 8 oil shales, and used Artificial Neural Network (ANN) to precisely quantify the relationship between bond distributions, reaction temperature, reaction time and the abundance of free radicals that were generated in pyrolysis and then captured by H from hydrogen-donor solvent. From the sensitivity analysis, the Cal—Cal, Cal—Car, Car—Car, Cal—O, Car—O and Cal=O in oil shales could facilitate the generation and hydrogenation of free radicals in hydropyrolysis. While the Cal· and Car· generated from the breakage of Cal—H and Car—H in oil shales tend to take part in the coupling and condensation reactions. Meanwhile, H· from the breakage of Cal—H and Car-H can capture free radicals, decreasing the hydrogenation effect of hydrogen-donor solvent. And the breakage of O—H of oil shales in the reaction is of low-possibility. The above results to some degrees highlight the mild thermal-conversion technologies of oil shales, coals and biomass.

0 引言

1 试验

   1.1 数据

   1.2 人工神经网络

   1.3 参数预处理

   1.4 数据集分割

   1.5 敏感度分析

2 结果与讨论

   2.1 油页岩中共价键的分布特征

   2.2 ANN参数的确定

   2.3 ANN的预测性能

   2.4 敏感度分析

3 结论