A New System Based on the Use of Neural Networks and Database for Monitoring Coal Combustion Efficiency

Monitoring the combustion process for electricity generation using coal as a primary resource is of a major concern to the pertinent industries, power generation companies in particular. The carbon content of fly ash is indicative of the combustion efficiency. The determination of this parameter is useful to characterise the efficiency of coal burning furnaces. Traditional methods such as Thermogrametric Analysis (TGA) and Loss on Ignition which are based on ash collection and subsequent analysis, proved to be tediously difficult, time consuming and costly. Thus, a need for a new technology was inevitable and needed to monitor the process in a more efficient method yielding a better exploitation of the resources at a lower cost. The main aim of this work is to introduce a new automated system which can be bolted onto a furnace and work online. The system consists of three main components, namely, a laser instrument for signal acquisition, a neural network tool for training, learning and simulation, and a database system for storage and retrieval. The components have been designed, adapted and tuned for knowledge acquisition of this multidimensional problem. The system has been tested for a range of coal ashes and proved to be efficient.