Kinetic research of red mud waste oxidative pyrolysis and comparison under different atmospheres.

Red mud, as a solid waste with high alkalinity, had a detrimental impact on the environment and required urgent attention. Currently, the mass processing and consumption of red mud were typically conducted under thermal conditions, so it was essential to gain a comprehensive understanding of the oxidative pyrolysis process. The thermogravimetric experiments were conducted at multiple heating rates in air and exhibited three obvious stages. The activation energy and reaction mechanism of three oxidative pyrolysis stages were explored using model-free and model-fitting methods, revealing the activation energies of 162.2, 265.8, 214.1 kJ/mol and the most suitable reaction mechanisms of g(α)=[-ln(1-α)]³, g(α)=1-(1-α)1/4, g(α)=[-ln(1-α)]1/2 for each stage, respectively. Furthermore, the estimated kinetic parameters and reaction mechanisms were applied to extra heating rate to verify the accuracy. More important, the effect of air on the pyrolysis process of red mud was examined by comparing the results with those obtained from pure nitrogen pyrolysis. The obtained oxidative pyrolysis characteristics of red mud could provide valuable insights of its co-pyrolysis or combustion for resources recycling.

Thermogravimetric experiments based prediction of biomass pyrolysis behavior: A comparison of typical machine learning regression models in Scikit-learn.

A variety of machine learning (ML) models have been extensively utilized in predicting biomass pyrolysis owing to their prowess in deciphering complex non-linear relationships between inputs and outputs, but there is still a lack of consensus on the optimal methods. This study elaborates on the development, optimization, and evaluation of three ML methodologies, namely, artificial neural networks, random forest (RF), and support vector machines, aimed to determine the optimal model for accurate prediction of biomass pyrolysis behavior using thermogravimetric data. This work assesses the utility of thermal data derived from these models in the computation of kinetic and thermodynamic parameters, alongside an analysis of their statistical performance. Eventually, the RF model exhibits superior physical interpretability and the least discrepancy in predicting kinetic and thermodynamic parameters. Furthermore, a feature importance analysis conducted within the RF model framework quantitatively reveals that temperature and heating rate account for 98.5 % and 1.5 %, respectively.