Comprehensive kinetic modeling and product distribution for pyrolysis of pulp and paper mill sludge

Comprehensive investigation of activator influence on pyrolysis kinetics, thermodynamics, and product characteristics in one-step activated carbon preparation from spirulina

The thermodynamics and kinetics effects of activators on the simultaneous activation pyrolysis of microalgae remain unclear. This study addresses this gap by investigating the effects of activators (KOH, KHCO3, CH3COOK, K2CO3) on Spirulina platensis (SP) pyrolysis using thermogravimetric-Fourier transform infrared spectrometer (TG-FTIR) and isoconversion methods. The results showed that all activators reduced the initial decomposition temperature of SP, leading to the earlier release of volatile pyrolysis products. Kinetics analysis further revealed that the addition of activators lowered the apparent activation energy (Eα) in the initial pyrolysis stage of SP. However, as the devolatilization process transitioned to the charring stage, the Eα gradually increased, surpassing that of SP pyrolyzed alone. Thermodynamic analysis indicated that the carbonization process of microalgae in the presence of activators required higher energy absorption. These findings reveal the mechanisms of activators in microalgae pyrolysis and provide insights into biochar production.

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.