Sulphide capacity prediction of molten slags by using a neural network approach

In the present study, the neural network approach was applied for the estimation of sulfide capacities (Cs) in binary and multi-component melts at different temperatures. The calculated results obtained using neural network computation were plotted against the experimental values for comparison comparative purposes. Besides, iso-sulfide capacity contours on liquid regions of some ternary melt phase diagrams were generated and plotted by using neural network model results. It was found that calculated results obtained through neural network computation agree very well with the experimental results and more precise than those of some models.