Estimation of $NOx$ Emissions in Thermal Power Plants Using Neural Networks

In this paper a neural network-based strategy is proposed for the estimation of the $NOx$ emissions in thermal power plants, fed with both oil and methane fuel. A detailed analysis based on a three-dimensional simulator of the combustion chamber has pointed out the local nature of the $NOx$ generation process, which takes place mainly in the burners’ zones. This fact has been suitably exploited in developing a compound estimation procedure, which makes use of the trained neural network together with a classical one-dimensional model of the chamber. Two different learning procedures have been investigated, both based on the external inputs to the burners and a suitable mean cell temperature, while using local and global $NOx$ flow rates as learning signals, respectively. The approach has been assessed with respect to both simulated and experimental data.