首页|期刊导航|热科学学报（英文版）|SO2 Emission Characteristics and BP Neural Networks Prediction in MSW/Coal Co-Fired Fluidized Beds
热科学学报（英文版）2006，Vol.15Issue(3)：281-288,8.
SO2 Emission Characteristics and BP Neural Networks Prediction in MSW/Coal Co-Fired Fluidized Beds
SO2 Emission Characteristics and BP Neural Networks Prediction in MSW/Coal Co-Fired Fluidized Beds
摘要
Abstract
The SO2 emission characteristics of typical MSW components and their mixtures have been investigated in a Φ 150mm fluidized bed. Some influencing factors of SO2 emission in MSW fluidized bed incinerator were found out in this study. The SO2 emission is increasing with the growth of the bed temperature, and it is rising with the increasing oxygen concentration at furnace exit. When the weight percentage of auxiliary coal is being raised, the conversion rate of S to SO2 is largely going up. The SO2 emission decreases if the desulfurizing agent (CaCO3) is added during the incineration process, but the desulfurizing efficiency is weakened with the enhancement of the bed temperature. The fuel moisture content has a slight effect on the SO2 emission. Based on these experimental results, a 12 × 6× 1 three-layer BP neural networks prediction model of SO2 emission in MSW/coal co-fired fluidized bed incinerator was built. The prediction results of this model give 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 SO2 emission of MSW/coal co-fired fluidized bed incinerator.关键词
municipal solid waste (MSW), SO2 emission, fluidized bed, BP neural networks, prediction modelKey words
municipal solid waste (MSW), SO2 emission, fluidized bed, BP neural networks, prediction model分类
资源环境引用本文复制引用
Junming WEN ,Jianhua YAN,Dongping ZHANG ,Yong CHI,Mingjiang NI,Kefa CEN..SO2 Emission Characteristics and BP Neural Networks Prediction in MSW/Coal Co-Fired Fluidized Beds[J].热科学学报（英文版）,2006,15(3):281-288,8.基金项目
The financial support of National Natural Science Foundation of China (under project No.59836210) is acknowledged. （under project No.59836210）
