OPTIMIZATION AND NEURAL MODELLING OF PULSE COMBUSTORS FOR DRYING APPLICATIONS

Results of investigations of a valved pulse combustor to choose optimal geometry, which covered measurements of the flow rates of air and fuel, pressure oscillations, including pressure amplitude and frequency and flue gas composition are presented in the paper. Experimental studies compsiring the operation of the pulse combustor coupled with a drying chamber and working separately are described. It was found that coupling of the pulse combustor with a drying chamber had no significant effect on the pulse combustion process. Smoother runs of pressure oscillations in the combustion chamber, lower noise level and slightly higher NO_x emission were observed. The velocity flow field inside the drying chamber was measured by LDA technique. Results confirmed a complex character of pulsating flow in the chamber. A large experimental data set obtained from measurements enabled developing a neural model of pulse combustion process. Artificial neural networks were trained to predict amplitudes and frequencies of pressure oscillations, temperatures in the combustion chamber and emission of toxic substances. An excellent mapping performance of the developed neural models was obtained. Due to complex character of the pulse combustion process, the application of artificial neural networks seems to be the best way to predict inlet parameters of a drying agent produced by the pulse combustor