Journal
ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS
Volume -, Issue -, Pages -Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/15567036.2020.1871448
Keywords
Oil rolling sludge; pyrolysis; kinetic; Malek; mechanism
Funding
- Basic Research Project Funds of the Education Department of Liaoning Province, China [L2017LQN013]
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This study investigated the pyrolysis behavior of oil rolling sludge (ORS) using thermogravimetric analysis and X-ray diffraction, revealing that the process can be divided into four stages. By applying kinetic analysis methods, the study determined the activation energy, pre-exponential factor, and mechanisms for each stage, providing theoretical support for process optimization and product distribution prediction.
Oil rolling sludge (ORS) is a kind of hazardous secondary energy and pyrolysis is an effective technology to recover oil with little secondary pollution. Extant research focused on the pyrolysis of petrochemical sludge (PS) under low-medium temperature (<900 K), few research was about ORS. Therefore, the pyrolysis behavior of ORS was investigated under a wide temperature range (300-1270 K) by thermogravimetric analyzer (TG/DTG) and X-ray diffraction spectrometer (XRD) in this research, which showed that the pyrolysis process could be divided into four stages: desorption of moisture and adsorbed gas, decomposition of light oil, cracking of heavy oil and coke formation, and interaction of mineral and coke. In addition, the model-free Starink and the Malek methods were applied to calculate kinetic triplets and determine the most probable mechanism of stage-2 to stage-4 based on various mechanism formulas. The results showed that the apparent activation energy varied from 44.77 to 196.36 kJ/mol and increased with the increase of conversion rate, while pre-exponential factor changed irregularly with heating rate. The stages 2-4 complied with the 0.7-level chemical order reaction model, random nucleation and nuclear growth reaction model and phase boundary reaction model, respectively. This study provided a theoretical support for the optimization of process conditions, prediction of product distribution and design of industrial equipment.
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