Predicting Pyrolysis of a Wide Variety of Petroleum Coke Using an Independent Parallel Reaction Model and a Backpropagation Neural Network

Abstract

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 CH x (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.