Neural Modeling of the Selective Noncatalytic Reduction Of Nitric-Oxide in a laboratory Reactor And A Full-Scale Plant

A. B. Bendtsen, K. Dam-Johansen, N. Jensen, M. Jodal, T. Lauridsen

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Neural networks have been used for estimating multivariate models of the selective non-catalytic reduction (SNR) of NO with NH3. A neural network model accurately fitted the data from well-defined laboratory experiments; this model predicted NH3 and NO(x) concentrations at the reactor outlet as functions of a wide variety of parameters. The control scheme of a commercial SNR process was optimised by a different neural model, designed from experimental measurements of NH3 and NO(x) emissions as functions of various operational parameters- Discrete prediction of NH3 was necessary because of limited analytical accuracy. The results demonstrate the power of neural networks in the static input/output modelling of non-linear processes, either generating an accurate model from an accurate set of data or extracting an adequate model from a noisy set of data.
Original languageEnglish
JournalJournal of the Energy Institute
Volume66
Issue number466
Pages (from-to)40-46
ISSN0144-2600
Publication statusPublished - 1993
Externally publishedYes

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