Affiliations: Clean Energy and Environment Engineering Key
Laboratory of MOE, Institute for Thermal Power Engineering of Zhejiang
University, Hangzhou 310027, China | Suyuan Environment Protection Engineering Ltd. Co.,
Nanjing 210024, China
Abstract: The HCl emission characteristics of typical municipal solid
waste (MSW) components and their mixtures have been investigated in a
ϕ150 mm fluidized bed. Some influencing factors of HCl emission in
MSW fluidized bed incinerator was found in this study. The Hclemission 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 × 6
× 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 cofired fluidized bed incinerator.
Keywords: municipal solid waste (MSW), HCl emission, fluidized bed, BP neural networks, prediction model
