The level of nitrogen oxides in atmosphere has been increasing in the last century, mainly due to human activities. Unfortunately nitrogen oxides have a number of negative effects on air quality: they contribute to photochemical smog, visibility reduction, acid rain and also have a negative impact on human health. In the paper a novel strategy to improve the estimation of nitrogen oxides emissions produced by chimneys of refineries is proposed. In particular nonlinear models, obtained by using MLPs neural networks, which are being a commonly used tool in processing data acquired in petrochemical processes, are proposed. The performance of the proposed model with respect to both traditional heuristic models and linear models are described
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