NO_x and CO prediction in fossil fuel plants by time delay neural networks

This paper presents a time delay neural network (TDNN) model designed for the prediction of nitrogen oxides (NO_x) and carbon monoxide (CO) emissions from a fossil fuel power plant. NO_x and CO emissions of the plant are determined as a function of other related time-series such as air flow rates and oxygen levels that are measured during the system operation. Correlation analysis is performed on the data to determine the location and the spread of cross-correlation between pairs of variables and this information is used to form a variable tapped delay line at the input of the network. We also introduce a neural network based preprocessor which employs an iterative regularization scheme to recover missing portions of CO data that are censored due to saturation of the measuring device. Prediction after training with the restored data set is observed to be significantly more accurate.