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Abstract

The present invention provides a method and a machine-learning system for predicting pipeline corrosion. The pipeline corrosion prediction according to the method and system comprises steps of i) generating a predictive model (100) based on a neural network algorithm; ii) applying a supervised machine learning technique for training the predictive model (100) from step i); and iii) applying the predictive model (100) from step ii) to other sets of the input data in order to predict a depth of metal loss rate (122). The predictive model (100) includes multiple modules relevant to a water condensation rate module, a flow regime module, a corrosion rate module, and an operating data module. Each module is integrated into a concatenate layer (114) and then processing through hidden layers (116), a long short-term memory layer (118) and hidden layers (120) respectively, in order to generate the depth of metal loss rate with high accuracy.

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