Dynamic inferential NO x emission prediction model with delay estimation for SCR de-NO x process in coal-fired power plants.

ABSTRACT: The selective catalytic reduction (SCR) decomposition of nitrogen oxide (de-NO x ) process in coal-fired power plants not only displays nonlinearity, large inertia and time variation but also a lag in NO x analysis; hence, it is difficult to obtain an accurate model that can be used to control NH3 injection during changes in the operating state. In this work, a novel dynamic inferential model with delay estimation was proposed for NO x emission prediction. First, k-nearest neighbour mutual information was used to estimate the time delay of the descriptor variables, followed by reconstruction of the phase space of the model data. Second, multi-scale wavelet kernel partial least square was used to improve the prediction ability, and this was followed by verification using benchmark dataset e

SUBMITTER: Yan L 

PROVIDER: S-EPMC7062099 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature