RESUMEN
The pyrolysis characteristics of six representative organic components of municipal solid waste (MSW) and their mixtures were studied in a specially designed thermogravimetric analysis apparatus with a maximum recorded heating rate of 864.8 degrees Cmin(-1). The pyrolysis behavior of individual components was described by the Avrami-Erofeev equation. The influence of final temperature on individual components was studied, and it was concluded that final temperature was a factor in reaction speed and intensity, but that it played only a limited role in determining the reaction mechanism. The interactions between different components were evaluated, and it was concluded that the interaction between homogeneous materials was minimal, whereas the interaction between polyethylene and biomass was significant.
Asunto(s)
Calor , Compuestos Orgánicos , Eliminación de Residuos , Biomasa , Ciudades , Compuestos Orgánicos/análisis , Compuestos Orgánicos/química , Polietileno/análisis , Polietileno/química , Termogravimetría , Factores de TiempoRESUMEN
The HCl emission characteristics of typical municipal solid waste (MSW) components and their mixtures have been investigated in a phi 150 mm fluidized bed. Some influencing factors of HCl emission in MSW fluidized bed incinerator was found in this study. The HCl emission is increasing with the growth of bed temperature, while it is decreasing with the increment of oxygen concentration at furnace exit. When the weight percentage of auxiliary coal is increased, the conversion rate of Cl to HCl is increasing. The HCl emission is decreased, if the sorbent (CaO) is added during the incineration process. Based on these experimental results, a 14 x 6 x 1 three-layer BP neural networks prediction model of HCl emission in MSW/coal co-fired fluidized bed incinerator was built. The numbers of input nodes and hidden nodes were fixed on by canonical correlation analysis technique and dynamic construction method respectively. The prediction results of this model gave good agreement with the experimental results, which indicates that the model has relatively high accuracy and good generalization ability. It was found that BP neural network is an effectual method used to predict the HCl emission of MSW/coal co-fired fluidized bed incinerator.