Real-time monitoring of the oil shale pyrolysis process using a bionic electronic nose
Real-time monitoring of the oil shale pyrolysis process using a bionic electronic nose
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DOI：
10.1016/j.fuel.2021.122672
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发表时间：
2021-11
期刊：
影响因子：
7.4
通讯作者：
Rongsheng Zhao;Cheng Kong;Luquan Ren;Youhong Sun;Zhiyong Chang
中科院分区：
文献类型：
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作者：
Rongsheng Zhao;Cheng Kong;Luquan Ren;Youhong Sun;Zhiyong Chang
准确、快速地预测油页岩开采过程中的热成熟度对优化热解工艺、降低生产成本具有重要意义。在此基础上，首次采用仿生电子鼻（BEN）对油页岩热解过程进行实时监测。结果表明：在不同升温速率下，计算得到的Easy%Rochange与实测的镜质组反射率%Ro相比，在不同的分解阶段（脱水、生烃和无机物分解）均较小（±0.004）。这个与时间-温度相关的参数是油页岩热解过程中热成熟过程与BEN信号关联的完美中介。油页岩热解过程的实时监测分为两个步骤：1)定性检查油页岩是否进入生烃阶段，2)当油页岩进入生烃阶段时，定量预测Easy%Roevolution。结合不同的特征提取方法，采用支持向量机（SVM）作为分类器完成第一步，其识别率为91.13%。第二步，采用随机森林（random forest， RF）定量测量Easy%Rovalues， r2达到0.95。在验证阶段，利用所建立的积分模型对加热速率进行识别，在算法模型中训练的加热速率之间，第一步（SVM, 92.96%）的表现明显优于第二步（RF, 0.57），结果在38 ~ 125 s内给出。因此，本方法可以应用于油页岩勘探的实时检测。
Accurate and rapid prediction of thermal maturity during oil shale exploitation is important to optimize pyrolysis processes and decrease production costs. Then, a bionic electronic nose (BEN) was used for the first time for the real-time monitoring of the oil shale pyrolysis process. The results show that the calculated Easy%Rochange, unlike that of the measured vitrinite reflectance (%Ro), was small (±0.004) in decomposition stages (dewatering, hydrocarbon generation stage and inorganic matter decomposition) at the different heating rate. This time–temperature-related parameter made it a perfect intermediary to associate the thermal maturation process with the BEN signal during oil shale pyrolysis. The monitoring of the pyrolysis process of oil shale in real time was divided into two steps: 1) qualitatively checking whether the oil shale had entered the hydrocarbon generation stage, and 2) when entering this stage, quantitatively predicting the Easy%Roevolution. Combined with different feature extraction methods, a support vector machine (SVM) as a classifier was used to complete the first step, which had the best recognition rate of 91.13%. For the second step, random forest (RF) was applied to quantitatively measure the Easy%Rovalues, with an R2reaching 0.95. During the verification stage, the established integration model was used to recognize a heating rate, which, between the trained heating rate in the algorithm model, performed much better in the first step (SVM, 92.96%) than in the second step (RF, 0.57), and those results were given in 38 to 125 s. Thus, the BEN technique can be applied in the real-time detection of oil shale exploration.