Investigation on the pyrolysis process, products characteristics and BP neural network modelling of pine sawdust, cattle dung, kidney bean stalk and bamboo

Abstract

To realize resource utilization of waste and alleviate associated environmental pollution, the pyrolysis behaviour of pine sawdust (PS), cattle dung (CD), kidney bean stalk (KS) and bamboo (BA) was investigated. The mass loss, gaseous product evolution and kinetic parameters of these four materials during pyrolysis were analysed via TG-MS. According to the TG-MS results, a back propagation (BP) neural network model was developed for the mass loss prediction of different biomass pyrolysis. More importantly, FTIR and SEM-EDX were used to analyse the characteristics of bio-oil and biochar to facilitate further utilization of these pyrolysis products. The results indicated that PS exhibited the highest mass loss (87.25%) during pyrolysis at 800 °C, and the higher D value (1.23E-05) indicated that PS was more easily decomposed than other materials. In terms of gaseous products, PS produced more H2, C2H6, C3H8 and CO2 than did the other materials during pyrolysis, while BA produced more CH4 and H2O. In addition, the content of phenols or aromatic compounds in PS bio-oil was the highest, and the surface pores in the obtained PS biochar were uniform and regular, which verified that PS achieved a higher utilization value than that of the other materials considered. Finally, the established BP neural network models realized a satisfactory mass loss prediction performance with increasing temperature.