Thermal kinetic analysis of mustard biomass with equiatomic iron-nickel catalyst and its predictive modeling

Kinetic mechanism of wheat straw pellets combustion process with a thermogravimetric analyser

In this study, the combustion characteristics of two wheat straw pellets (WSP) (T1: 100% wheat straw and T5: 70% wheat straw; 10% sawdust, 10% biochar; 10% bentonite clay) were performed at a heating rate 20 °C/min under a temperature from 25 to 1200 °C in air atmosphere. A thermogravimetric analyser (TGA) was used to investigate the activation energy (Eα), pre-exponential factor (A), and thermodynamic parameters. The DTG/TG profile of WSP was evaluated by model-free and model-based methods and found the model-based method was suitable for WSP thermal characterisation. The result demonstrates that the thermal decomposition occurred in four stages, comprising four consecutive reaction steps. A→B→C→D→E→F. Further, the model-based techniques were best fitted with kinetic reaction models like Cn (nth-order reaction with auto-catalyst), Fn (reaction of nth order), F2 (second-order phase interfacial reaction) and D3 (diffusion control). The average Eα for Fn, Cn, D3 and F2 models were 164.723, 189.782, 273.88, and 45.0 kJ/mol, respectively, for the T1 pellets. Alternatively, for T5 pellets, the A was 1.17E+2, 1.76E+16, 5.5E+23, and 1.1E+3 (1/s) for F2, D3, Cn and Fn models. Overall, the thermodynamic properties showed that WSP thermokinetic reactions were complex and multi-point equilibrium, indicating a potentiality as a bioenergy feedstock.

Artificial Neural Network Study on the Pyrolysis of Polypropylene with a Sensitivity Analysis

Among machine learning (ML) studies, artificial neural network (ANN) analysis is the most widely used technique in pyrolysis research. In this work, the pyrolysis of polypropylene (PP) polymers was established using a thermogravimetric analyzer (TGA) with five sets of heating rates (5-40 K min^-1). TGA data was used to exploit an ANN network by achieving a feed-forward backpropagation optimization technique in order to predict the weight-left percentage. Two important ANN model input variables were identified as the heating rate (K min^-1) and temperature (K). For the range of TGA values, a 2-10-10-1 network with two hidden layers (Logsig-Tansig) was concluded to be the best structure for predicting the weight-left percentage. The ANN demonstrated a good agreement between the experimental and calculated values, with a high correlation coefficient (R) of greater than 0.9999. The final network was then simulated with the new input data set for effective performance. In addition, a sensitivity analysis was performed to identify the uncertainties associated with the relationship between the output and input parameters. Temperature was found to be a more sensitive input parameter than the heating rate on the weight-left percentage calculation.