Physicochemical properties and pyrolysis behavior of petcoke with artificial neural network modeling

热重分析 热解 化学工程 材料科学 热解炭 焦炭 石油焦 化学 热力学 有机化学 冶金 工程类 物理
作者
Byoung-Hwa Lee,Viet Thieu Trinh,Hyeong-Bin Moon,Ji Hwan Lee,Hyeong-Tae Kim,Jin‐Wook Lee,Chung‐Hwan Jeon
出处
期刊:Fuel [Elsevier BV]
卷期号:331: 125735-125735 被引量:10
标识
DOI:10.1016/j.fuel.2022.125735
摘要

• Petcoke had a high fuel ratio (8.7), high sulfur (7 wt%), and low ash content (0.86 wt%). • Petcoke showed slower pyrolysis reaction, higher ignition temperature, and lower devolatilization performance. • Pyrolysis activation energy and frequency factors for petcoke were in the range of 221.6–235.9 kJ/mol and 10 11 –10 12 s −1 . • Thermal degradation behavior of petcoke was successfully modelled using NN-2–12-12–2 model with Tansig-Tansig transfer functions. Petcoke is a byproduct of heavy crude oil refining at an enormous scale, and insights into the physicochemical properties, thermal degradation behavior, decomposition kinetics, and thermodynamic analysis of petcoke pyrolysis are crucial for the efficient design of pyrolysis reactor systems. In this study, proximate and ultimate analyses were performed, and petcoke was found to have a high fuel ratio with high carbon, high sulfur, and considerably low ash content, implying that it is a less reactive fuel. Advanced analytical techniques such as SEM, BET, FTIR, and petrography indicated that petcoke has a considerably low pore volume and predominantly inorganic graphitic carbon. Furthermore, thermogravimetric analysis was examined at four different heating rates of 10, 20, 30, and 40 K/min and petcoke exhibited a low pyrolysis performance, which was confirmed by the devolatilization index and pyrolytic parameters. The activation energies and frequency factors estimated by three model-free methods (DAEM, FWO, Friedman) were in the range of 221.6–235.9 kJ/mol and 10 11 –10 12 s −1 , respectively. In addition, thermodynamic analyses were examined and the thermal degradation behavior of petcoke pyrolysis was modeled using an artificial neural network; the NN-2-12-12-2 model with Tansig-Tansig transfer functions was found to be the best fit. This study enabled the identification of the fundamental characteristics of petcoke fuel, and these results provide useful information regarding the design, utilization, optimization, and limitations of petcoke pyrolysis systems.
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