Giulia De Masi

A Neural Network Predictive Model of Pipeline Internal Corrosion Profile

Internal corrosion is a crucial issue for the safe operation of oil&gas pipelines. This is a phenomenon due to interaction of different mechanisms. Water and electrochemistry, protective scales, flow velocity, steel composition and localized bacteria attacks are relevant. Despite the large number of models proposed in literature, the corrosion process is very complex and rarely reproduced by existing models. For this reason, an artificial neural network (ANN) based model is investigated, with the aim to correctly predict the presence of metal loss and corrosion rate along a pipeline. In this paper, a case study is considered, based on real field data. The model integrates the geometrical profile of a real pipeline, flow simulations and the most important deterministic corrosion models. It is shown that the ANN model outperforms the deterministic ones.

Ship motion prediction by radial basis neural networks

A radial basis function (RBF) artificial neural network (ANN) is proposed to develop a model of short term (50 seconds) prediction of vessel heave motion. This is a cutting edge topic in Ocean Engineering, since it is primary to support marine operations of vessels in harsh sea environment. The present study proposes a combined application of ANN and Hilbert transform. The time series of vessel heave motions, measured by on board Inertial Platform System, are used to train the network and to find the best configuration. The results indicate that RBF networks provide an effective and accurate tool to predict vessel motions produced by waves.