A Neural Network Model for Predicting NOx at the Mělník 1

Ivo Bukovský, Michal Kolovratník

Abstract


This paper presents a non-conventional dynamic neural network that was designed for real time prediction of NOx at the coal powder power plant Mělnik 1, and results on real data are shown and discussed. The paper also presents the signal preprocessing techniques, the input-reconfigurable architecture, and the learning algorithm of the proposed neural network, which was designed to handle the non-stationarity of the burning process as well as individual failures of the measured variables. The advantages of our designed neural network over conventional neural networks are discussed.

Keywords


dynamic neural networks; prediction; NOx emissions; signal processing

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This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN 1210-2709 (Print)
ISSN 1805-2363 (Online)
Published by the Czech Technical University in Prague