Application of internal model control methods to industrial combustion

This paper outlines the application of neural networks based IMC methods for estimation/control of important input and output variables of a 0.5 MW laboratory scale industrial furnace. The application involves inputs such as the airflow rate, swirl number and momentum ratio. The outputs include emission levels of oxides of nitrogen especially nitric oxide. The response to step and staircase inputs has been analysed. The results have been compared with standard linear quadratic controller. The control output of the IMC methods has resulted in almost similar steady state error performance to the linear quadratic regulator. Although the development process of the IMC method might take longer time because of the training and data arrangement but has the capability of readjustment after being developed.