Abstract:
Bulk cargos maritime transportation represents significant risks to the vessel, its crew, and the environment, and is duly regulated by IMO (International Maritime Organization), who created the IMSBC (International Maritime Solid Bulk Cargoes Code) and suggested to governmental institutions to use it directly or as a foundation to their national regulations. According to IMSBC, bulk cargoes susceptible to liquefaction are those which can be liquefied if loaded with moisture above its TML (Transportable Moisture Limit). The application of empirical models for moisture prediction takes huge importance in this context, supporting due time decisions to guarantee the overall safety cargo and regulatory requirements compliance. Over 1200 iron ore fines cargoes, with chemical quality, moisture and size distribution were used for the modeling and four different models were developed to moisture prediction (time series, regression, and artificial neural network). The obtained results showed artificial neural network models were able to explain higher variance, over 70%, thus being more suitable to industrial application.