Utilisation of non-linear modelling methods in flue-gas oxygen-content control

Abstract

<div lang="en" class="abs"><p class="abs_header">Abstract</p><p>Non-linear methods have been utilised in modelling the processes on a flue-gas oxygen-content control system of a power plant. The ultimate objective is to reduce NO_x and CO emissions by enhancing the control system. By investigating the flue-gas emission control strategy, the major factors affecting the flue-gas emissions have been determined. A simulator has been constructed, and it emulates a real process automation system and its physical processes. The process models of the simulator are: a flue-gas oxygen-content model, a secondary air flow model, a primary air flow model and a fuel feeding screw model (a fuel flow). The effort has been focused on two plant models: the flue-gas oxygen-content model and the secondary air flow model. Combustion is a non-linear, timevariant, multi-variable process with a variable delay. The secondary air model is a non-linear, timeinvariant (in principle), multi-variable system. Both phenomenological modelling (mass and energy calculations) and black-box modelling (neural networks) have been utilised in the Wiener/Hammerstein structures. It is possible to use a priori knowledge in model modifying, and therefore the model of flue-gas oxygen-content can be tuned on site. The simulator with precalculated parameters was tested in a full-scale power plant and a pilot-scale circulating fluidised bed boiler. The results in the power plant were remarkable since NO_x emissions decreased significantly without increasing CO emissions.</p></div>