The Development of a Neural Network Based System for the Optimal Control of Chain-Grate Stoker-Fired Boilers

Abstract

A novel Neural Network Based Controller (NNBC) was developed following a comprehensive set of experiments carried out on a pilot-scale stoker test facility at CRE Group Ltd., U.K. The NNBC mimicked the actions of an expert boiler operator, by providing ‘near optimum’ settings of coal feed and air flow, as well as ‘staging’ these parameters during load following conditions, before fine tuning the combustion air under quasi-steady-state conditions. Test results from the online implementation of the NNBC have demonstrated that improved transient and steady-state combustion conditions were attained. The prototype NNBC thus provides both stoker manufacturers and users with a means of reducing pollutant emissions, as well as improving the combustion efficiency of this type of coal firing equipment.