The experimental investigation and data–driven modeling for thermal decomposition kinetics of Green River Shale
The experimental investigation and data–driven modeling for thermal decomposition kinetics of Green River Shale
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DOI：
10.1016/j.fuel.2022.123899
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发表时间：
2022
期刊：
影响因子：
7.4
通讯作者：
Jiahui You;K. Lee
中科院分区：
文献类型：
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作者：
Jiahui You;K. Lee
由于干酪根是大量烃类和有机结合无机元素的来源，因此了解干酪根的热分解动力学非常重要。绿色河页岩含有大量未成熟干酪根（I型），是实验研究的理想样品来源。采用热重-微分热重法（TGA/DTG）对美国犹他州绿色河页岩的热解过程进行了定量分析，并基于Friedman方法和数据驱动建模方法建立了干酪根热解动力学模型。采用热重分析-差示扫描量热-气相色谱（TGA-DSC-GC）联用技术，确定了热解过程中的两步反应机理和产物组成。用傅里叶变换红外光谱（FTIR）分析了热解前后的化学键。根据实验，当加热速率低于30 °C/min时，我们观察到烃演化窗口中的两个阶段反应，而在较高的加热速率下仅观察到一个阶段。在烃类演化过程中，C14烃不断生成，表明绿色河页岩中含有丰富的C14烃，并得到了有机质和无机质混合物以及有机质（干酪根）分解的动力学参数。采用人工神经网络（ANN）方法对TGA/DTG实验获得的动力学参数进行训练。当升温速率小于5 °C/min时，外推结果的预测效果较好。生成的代理模型可与各种物理模型耦合，以高精度模拟干酪根的热分解过程。
Given that kerogen is a source of vast amount of hydrocarbons and organically–bound inorganic elements, it’s important to understand the thermal decomposition kinetics of kerogen. Green River Shale contains a significant amount of immature kerogen (Type I), which can be an ideal source for the sample of experimental study. In this study, Thermogravimetric Analysis and Derivative Thermogravimetry (TGA/DTG) was used to quantify the weight loss during the pyrolysis process of the Green River Shale from Utah and subsequently establish the kinetic model of thermal decomposition of kerogen based on the Friedman method followed by the data–driven modeling approach. A two–step reaction mechanism and components of production during the pyrolysis were determined by implementing Thermogravimetry Analysis–Differential Scanning Calorimetry–Gas Chromatography (TGA–DSC–GC). The chemical bonds were analyzed with Fourier–Transform Infrared spectroscopy (FTIR) before and after the pyrolysis. From the experiments, we observed the two–stage reactions in the hydrocarbon evolution window when the heating rate was lower than 30 °C/min, while only one stage was observed with the higher heating rates. C14 hydrocarbon was generated continuously during the hydrocarbon evolution, which indicated that the Green River Shale contained a plentiful amount of it. The kinetic parameters were obtained for the decomposition of organic and inorganic mixture and the organic matter (kerogen) only. The Artificial Neural Network (ANN) method was implemented to train the kinetic parameters obtained from the TGA/DTG experiment. The prediction of extrapolated cases showed a good performance when the heating rate was smaller than 5 °C/min. The generated proxy model can be coupled with various physical models to simulate the thermal decomposition of kerogen with high accuracy.