Document Detail


Gasification characteristics of MSW and an ANN prediction model.
MedLine Citation:
PMID:  18420400     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Gasification characteristics make up the important parts of municipal solid waste (MSW) gasification and melting technology. These characteristics are closely related to the composition of MSW, which alters with climates and seasons. It is important to find a practical way to predict gasification characteristics. In this paper, five typical kinds of organic components (wood, paper, kitchen garbage, plastic, and textile) and three representative types of simulated MSW are gasified in a fluidized-bed at 400-800 degrees C with the equivalence ratio (ER) in the range of 0.2-0.6. The lower heating value (LHV) of gas, gasification products, and gas yield are reported. The results indicate that gasification characteristics are different from sample to sample. Based on the experimental data, an artificial neural networks (ANN) model is developed to predict gasification characteristics. The training and validating relative errors are within +/-15% and +/-20%, respectively, and predicting relative errors of an industrial sample are below +/-25%. This indicates that it is acceptable to predict gasification characteristics via ANN model.
Authors:
Gang Xiao; Ming-jiang Ni; Yong Chi; Bao-sheng Jin; Rui Xiao; Zhao-ping Zhong; Ya-ji Huang
Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2008-04-16
Journal Detail:
Title:  Waste management (New York, N.Y.)     Volume:  29     ISSN:  0956-053X     ISO Abbreviation:  Waste Manag     Publication Date:  2009 Jan 
Date Detail:
Created Date:  2008-11-17     Completed Date:  2009-02-12     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9884362     Medline TA:  Waste Manag     Country:  United States    
Other Details:
Languages:  eng     Pagination:  240-4     Citation Subset:  IM    
Affiliation:
Key Laboratory of Clean Coal Power Generation and Combustion Technology of MOD, Thermo-Energy Engineering Research Institute, Southeast University, Nanjing 210096, China. xiaogangtianmen@seu.edu.cn
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MeSH Terms
Descriptor/Qualifier:
Bioelectric Energy Sources
Gases / chemistry*
Incineration / instrumentation*,  methods*
Models, Theoretical*
Neural Networks (Computer)*
Chemical
Reg. No./Substance:
0/Gases

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