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System automation for measuring coal combustion efficiency

Abstract 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 characterize the efficiency of coal burning furnaces. Traditional methods such as thermo-gravimetric analysis and loss on ignition, which are based on ash collection and subsequent analysis, proved to be tediously difficult, time consuming, and costly. Thus, 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 multi-dimensional problem. When the particles are dispersed across the test space, the instrument observed single particle counts simultaneously on the two photomultipliers. The output voltages displayed represent the intensity of horizontally and vertically polarized light, and the polarization ratio is calculated from the ratio of these voltages. It was found that the carbon-in-ash is related to the polarization ratio and the carbon mass fraction could be determined to within ± 1 per cent of the carbon content. However, if a proximate analysis is performed on the coal, the prediction could be improved to within ± 0.05 per cent using neural network simulation. The system has been tested for a range of coal ashes and proved to be efficient.