Estimation of NOx Emissions in Thermal Power Plants using Eng-Genes Neural Networks
| Authors: | Li Kang, Queen's University Belfast, United Kingdom
Peng Jian-xun, Queen's University Belfast, United Kingdom
Irwin George W., Queen's University Belfast, United Kingdom
Piroddi Luigi, Politecnico di Milano, Italy
Spinelli William, Politecnico di Milano, Italy |
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| Topic: | 6.3 Power Plants and Power Systems |
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| Session: | Thermal Power Plant Control |
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| Keywords: | power generation, pollution, estimation, neural networks, genetic algorithms |
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Abstract
This paper investigates neural network based estimation of NOx emissions in a thermal power plant, fed with both oil and methane fuels. Two types of neural network namely a novel ‘eng-genes’ architecture and a Multilayer Perceptron (MLP) have been developed, both being optimised using genetic algorithms. Due to the local nature of the NOx generation process, operational information on the burner cells of the combustion chamber has been considered. Neural networks, with different numbers of hidden nodes have been tested on a set of three-dimensional data of the simulated combustion chamber. It is shown that, the proposed 'eng-genes' neural network can produce accurate estimations with better generalisation performance than MLP.
