Electric utility coal quality analysis using artificial neural network techniques

Electric power generators are facing more stringent standards for smog-forming nitrogen oxides, tiny fly ash particles, and air toxic emissions. US Department of Energy recently funded several projects to investigate advanced methods for reducing air pollutants from conventional coal-fired power pla...

Full description

Saved in:
Bibliographic Details
Published in:Neurocomputing (Amsterdam) 1998-12, Vol.23 (1), p.195-206
Main Authors: Salehfar, H, Benson, S.A
Format: Article
Language:English
Subjects:
Air pollution control
Artificial intelligence
Ash forming species
Coal
Coal quality
Computer control
Electric utilities
Mathematical models
Neural networks
Power plants
Scanning electron microscopy
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Electric power generators are facing more stringent standards for smog-forming nitrogen oxides, tiny fly ash particles, and air toxic emissions. US Department of Energy recently funded several projects to investigate advanced methods for reducing air pollutants from conventional coal-fired power plants. This paper takes advantage of artificial intelligence technology and develops a neural network (NN) that quickly and efficiently determines the impurities and ash forming species in coal. The NN results are compared with those from current computer-controlled scanning electron microscopy (CCSEM) methods. The developed model shows promise and has the potential to save coal-fired electric utilities millions of dollars in dealing with various coal ash problems.
ISSN:0925-2312
1872-8286
DOI:10.1016/S0925-2312(98)00083-6