A Novel Neural Network Based Modeling for Control of NO_x Emission in Power Plant

Abstract

A novel neural network based modeling for non-linear model identification technique is proposed. It combines a nonlinear steady state model with a linear one, to describe the disturbance and dynamics in the coal-fired power plant. The modeling and training algorithm is used to develop a model of nitrogen oxides (NOx) emitted from the process where one-step ahead optimal prediction formula are developed. Two cases show that the resulting model provides a better prediction of NOx and fitting capabilities.

Analysis Technology of Environmental Monitoring Data Based on Internet of Things Environment and Improved Neural Network Algorithm

“…Through the monitoring of the quality of the atmospheric environment, it can be controlled in real time, which guides the industrial production and the construction of related pollution prevention facilities (Rodgers et al, 2015;Lochner et al, 2011;Zhang et al, 2018a). At present, almost all atmospheric environmental monitoring data is used to prepare environmental reports such as daily newspapers and annual reports (Arsie et al, 2010;Marino et al, 2017) while the value of data needs to be further explored (Li et al, 2014). For example, through historical monitoring data, the future trend of atmospheric environmental quality can be predicted so as to better guide people's production activities (Chien et al, 2005(Chien et al, , 2010.…”