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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Modelling of NOx Emissions in Natural Gas Fired Hot Water Boilers
verfasst von : Pekka Kumpulainen, Timo Korpela, Yrjö Majanne, Anna Häyrinen
Erschienen in: Engineering Applications of Neural Networks
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Nitrogen oxides (NO
x
) are one of the main pollutants produced by combustion processes. New European emission regulations (IED) extent emission monitoring requirements to smaller boilers. Heating grid operators may have a notable number of such boilers and therefore appreciate affordable monitoring solutions. This paper studies several types of regression models for estimating NO
x
emissions in natural gas fired boilers. The objective is to predict the emissions utilising the existing process measurements for monitoring, without an external NO
x
analyser. The performance of linear regression is compared with three nonlinear methods: multilayer perceptron, support vector regression and fuzzy inference system. The focus is on generalisation ability. The results on the two boilers in the study suggest that linear regression and multilayer perceptron network outperform the others in predicting with new, unseen data.