HCl emission characteristics and BP neural networks prediction in MSW/coal co-fired fluidized beds.
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.
Chi
Yong
Y
Clean Energy and Environment Engineering Key Laboratory of MOE, Institute for Thermal Power Engineering of Zhejiang University, Hangzhou 310027, China. chiyong@cmee.zju.edu.cn
Wen
Jun-Ming
JM
Zhang
Dong-Ping
DP
Yan
Jian-Hua
JH
Ni
Ming-Jiang
MJ
Cen
Ke-Fa
KF
D017610:Calcium Compounds / Q000737:chemistry; D003031:Coal; D006851:Hydrochloric Acid / Q000737:chemistry*; D016571:Neural Networks, Computer; D010087:Oxides / Q000737:chemistry; D010100:Oxygen / Q000737:chemistry; D013455:Sulfur / Q000737:chemistry; D013696:Temperature
D017610:Calcium Compounds; D003031:Coal; D010087:Oxides; D013455:Sulfur; C016538:lime; D006851:Hydrochloric Acid; D010100:Oxygen