Predicting Pyrolysis of a Wide Variety of Petroleum Coke Using an Independent Parallel Reaction Model and a Backpropagation Neural Network.
Predicting Pyrolysis of a Wide Variety of Petroleum Coke Using an Independent Parallel Reaction Model and a Backpropagation Neural Network.
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DOI：
10.1021/acsomega.2c04866
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发表时间：
2022-11-15
期刊：
影响因子：
4.1
通讯作者：
Li, Jing
中科院分区：
文献类型：
--
作者：
Huang, Jindi;Chen, Zhihang;Zhang, Dou;Li, Jing
采用非等温热重分析（TGA）和热重质谱分析（TG-MS）对石油焦的热解行为和气态产物进行了研究。然后，采用独立平行反应（IPR）模型对抚顺（FS）、扶余（FY）、武汉（WH）、镇海（ZH）、齐鲁（QL）和石家庄（SJZ） 6种石油焦的热解动力学进行了测定，得到了动力学参数（活化能和指前因子）。此外，建立了一种有效的反向传播神经网络（BPNN），对6种石油焦的热数据进行了预测。利用bpnn预测的热数据计算了基于IPR模型的动力学参数，并与实验数据计算结果进行了比较。结果表明：6种石油焦的热解过程分为3个阶段，其中第II阶段（250 ~ 900℃）失重显著，对应于石油焦的脱挥发；质谱碎片离子强度分析表明，热解产物主要为甲烷CHx （m/z = 13、14、15和16）、脂肪烃C3H5、H2、CO、CO2和H2O。IPR、BPNN和BPNN-IPR （BPNN与IPR结合）模型预测的热数据与实验数据吻合较好。最重要的是，bpnn预测数据可以进一步应用于IPR动力学模型计算动力学参数。
In this work, the pyrolysis behavior and gaseous products of petroleum coke were investigated by nonisothermal thermogravimetric analysis (TGA) and thermogravimetry–mass spectrometry (TG–MS). Then, the pyrolysis kinetics of six kinds of petroleum coke (Fushun (FS), Fuyu (FY), Wuhan (WH), Zhenhai (ZH), Qilu (QL), and Shijiazhuang (SJZ)) were determined by an independent parallel reaction (IPR) model, and the kinetic parameters (activation energy and preexponential factor) were obtained. In addition, an efficient backpropagation neural network (BPNN) was developed to predict the thermal data of six kinds of petroleum coke. The BPNN-predicted thermal data were used to calculate the kinetic parameters based on the IPR model, and the results were compared with the ones calculated using experimental data. The results showed that the pyrolysis process of six kinds of petroleum coke was divided into three stages, of which stage II (250–900 °C) had the significant mass loss, corresponding to the devolatilization of petroleum coke. MS fragmented ion intensity analysis indicated that the main pyrolysis products were methane CHx (m/z = 13, 14, 15, and 16), aliphatic hydrocarbon C3H5, H2, CO, CO2, and H2O. The thermal data predicted by the IPR, BPNN, and BPNN-IPR (BPNN combined with IPR) models were in good agreement with the experimental data. Most importantly, it was concluded that the BPNN-predicted data can be further applied to calculate the kinetic parameters using the IPR kinetic model.
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用于热解，热分析和热动力学研究的人工神经网络：现状。
DOI：
10.3390/molecules26123727
发表时间：
2021-06-18
期刊：
Molecules (Basel, Switzerland)
影响因子：
--
作者：
Muravyev NV;Luciano G;Ornaghi HL Jr;Svoboda R;Vyazovkin S
通讯作者：
Vyazovkin S
影响因子：
7.8
作者：
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通讯作者：
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影响因子：
9
作者：
Okeke, Ikenna J.;Adams, Thomas A., II
通讯作者：
Adams, Thomas A., II
影响因子：
7.4
作者：
Manasrah, Abdallah D.;Nassar, Nashaat N.;Ortega, Lante Carbognani
通讯作者：
Ortega, Lante Carbognani
影响因子：
7.4
作者：
Naqvi, Salman Raza;Tariq, Rumaisa;Farooq, Wasif
通讯作者：
Farooq, Wasif