Pyrolysis kinetic analysis of sequential extract residues from Hefeng sub-bituminous coal based on Coats-Redfern method

Sequential extract residues (Ri, i=1, 2, 3, 4, 5) were obtained from Hefeng acid-washing coal (HFAC) by petroleum ether, carbon disulfide, methanol, acetone and isometric carbon disulfide/acetone mixture, sequentially. Pyrolysis behavior of the samples was carried out using thermogravimetry analysis. Coats-Redfern method with different reaction order was used to analyze the pyrolysis kinetic of each sample, and the kinetic parameters, including correlation coefficient (R2), activation energy (E), pre-exponential factor (A), were calculated. Results showed that the weight loss of extract residues was higher than HFAC, and pyrolysis behavior varies greatly for residues, which may be due to unstable structure after extraction. From conversion-temperature (α-T) curves, pyrolysis process was divided into three stages: low-temperature stage (150-350 oC), medium temperature stage (350-550 oC) and high temperature stage (550-950 oC). And the medium temperature stage made great contribution to the process of pyrolysis, which was dominated by depolymerization and decomposition reaction, and the effect of kinetic fitting to this stage is better, with R2 higher than 0.95. Relationship between kinetic parameters and reaction order showed that swelling effect might be an important reason for the discrepancy of E for each sample in the process of pyrolysis. And Ln(A)-E relationship has a great significance to predict E and the A under higher reaction order.

Application of computational approach in plastic pyrolysis kinetic modelling: a review

During the past decade, pyrolysis routes have been identified as one of the most promising solutions for plastic waste management. However, the industrial adoption of such technologies has been limited and several unresolved blind spots hamper the commercial application of pyrolysis. Despite many years and efforts to explain pyrolysis models based on global kinetic approaches, recent advances in computational modelling such as machine learning and quantum mechanics offer new insights. For example, the kinetic and mechanistic information about plastic pyrolysis reactions necessary for scaling up processes is unravelling. This selective literature review reveals some of the foundational knowledge and accurate views on the reaction pathways, product yields, and other features of pyrolysis created by these new tools. Pyrolysis routes mapped by machine learning and quantum mechanics will gain more relevance in the coming years, especially studies that combine computational models with different time and scale resolutions governed by “first principles.” Existing research suggests that, as machine learning is further coupled to quantum mechanics, scientists and engineers will better predict products, yields, and compositions, as well as more complicated features such as ideal reactor design.