Prediction on the Seasonal Behavior of Hydrogen Sulfide Using a Neural Network Model

Abstract

Models to predict seasonal hydrogen sulfide (H₂S) concentrations were constructed using neural networks. To this end, two types of generalized regression neural networks and radial basis function networks are considered and optimized. The input data for H₂S were collected from August 2005 to Fall 2006 from a huge industrial complex located in Ansan City, Korea. Three types of seasonal groupings were prepared and one optimized model is built for each dataset. These optimized models were then used for the analysis of the sensitivity and main effect of the parameters. H₂S was noted to be very sensitive to rainfall during the spring and summer. In the autumn, its sensitivity showed a strong dependency on wind speed and pressure. Pressure was identified as the most influential parameter during the spring and summer. In the autumn, relative humidity overwhelmingly affected H₂S. It was noted that H₂S maintained an inverse relationship with a number of parameters (e.g., radiation, wind speed, or dew-point temperature). In contrast, it exhibited a declining trend with a decrease in pressure. An increase in radiation was likely to decrease during spring and summer, but the opposite trend was predicted for the autumn. The overall results of this study thus suggest that the behavior of H₂S can be accounted for by a diverse combination of meteorological parameters across seasons.