Abstract
In the case of big energy boilers energy efficiency is usually determined with the application of the indirect method. Flue gas losses and unburnt combustible losses have a significant influence on the boiler's efficiency. To estimate these losses the knowledge of the operating parameters influence on the flue gases temperature and the content of combustible particles in the solid combustion products is necessary. A hybrid model of a boiler developed with the application of both analytical modelling and artificial intelligence is described. The analytical part of the model includes the balance equations. The empirical models express the dependence of the flue gas temperature and the mass fraction of the unburnt combustibles in solid combustion products on the operating parameters of a boiler. The empirical models have been worked out by means of neural and regression modelling.
| Original language | English |
|---|---|
| Pages (from-to) | 1107-1113 |
| Number of pages | 7 |
| Journal | Energy |
| Volume | 35 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - Feb 2010 |
Keywords
- Empirical modelling
- Energy losses
- Neural network
- Steam boiler
ASJC Scopus subject areas
- Civil and Structural Engineering
- Building and Construction
- Pollution
- Mechanical Engineering
- Industrial and Manufacturing Engineering
- Electrical and Electronic Engineering
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