Abstract: Optimizing the operation of coal-fired power plants to reduce nitrogen oxide (NO_x) emissions requires accurate modeling of the NO_x emission process. The careful selection of input parameters not only forms the basis of accurate modeling, but can also be used to reduce the complexity of the model. The present study employs the least squares support vector machine-supervised learning method to model NO_x emissions based on historical real time data obtained from a 1000-MW once-through boiler. The initial input parameters are determined by expert knowledge and operational experience, while the final input parameters are obtained by sensitivity analysis, where the variation in model accuracy for a given set of data is analyzed as one or several input parameters are successively omitted from the calculations, while retaining all other parameters. Here, model accuracy is evaluated according to the mean relative error (MRE). This process reduces the parameters required for NO_x emission modeling from an initial number of 33 to 7, while the corresponding MRE is reduced from 3.09% to 2.23%. Moreover, a correlation of 0.9566 between predicted and measured values was obtained by applying the model with just these seven input parameters to a validation dataset. As such, the proposed method for selecting input parameters serves as a reference for related studies.

Key words: Nitrogen oxide (NO_x); Coal-fired boiler; Least squares support vector machine; Input parameters; Sensitivity analysis

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