A disturbance rejection based neural network algorithm for control of air pollution emissions

A novel neural network algorithm for training a model of a nonlinear systems that is significantly affected by unmeasured disturbances is presented. In this paper, the algorithm is used to develop a model of nitrogen oxides (NOx) emitted from a coal-fired, power plant. The NOx emissions are affected by unmeasured disturbances such as those caused by changes in fuel characteristics and ambient conditions. The resulting NOx model is subsequently used in a control system for reduction of NOx emissions, therefore, increased accuracy of the model leads to improved verification and validation of the control system. Two examples illustrate that the resulting model provides a better prediction of NOx emitted from coal fired, power plants.