Influence of biomass on coal slime combustion characteristics based on TG-FTIR, principal component analysis, and artificial neural network

The development and utilization of solid waste is an effective way to solve the severe environmental and energy crisis. In this study, Thermogravimetry Fourier transform infrared spectrometry (TG-FTIR) was used to carry out the co combustion experiment of coal slime and rice husk under different mixing ratios. With the increase of the mass percentage of rice husk in the sample, the initial ignition temperature and burnout of the sample decreased, and the comprehensive combustion performance improved gradually. The dominant reaction in the co-combustion of coal slime and rice husk was determined by statistical method. When the mass percentage of rice husk in the mixture is between 30 and 90 %, it can inhibit the release of NOx and SO2. Taking Kissinger-Akahira-Sunose method as an example, the calculated average activation energies of coal slime and rice husk combustion are 105.66 and 148.93 kJ/mol respectively. With the increase of the mixing ratio of rice husk in the blend, the combustion mechanism of the sample changed. Finally, the mean absolute error, root mean square error and determination coefficient of the artificial neural network model are 0.52697, 0.67866 and 0.99941 respectively.

Automated summary

This study investigated the co-combustion experiment of coal slime and rice husk using TG-FTIR technology, and found that the combustion performance of the sample improved and the release of NOx and SO2 was inhibited with the increase of rice husk content. The average activation energies of coal slime and rice husk combustion were calculated, and it was observed that the combustion mechanism of the sample changed with the increase of mixing ratio. The artificial neural network model established had high predictive accuracy.