Prediction of Chemical Inhibitors Efficiency for Reducing Deposition Thickness Using Artificial Neural Network

Prediction of efficiency of chemical inhibitors to mitigation of deposition thickness is a key to developing crude oil transportation process. In this work, a feed-forward artificial neural network (ANN) algorithm has been applied to predict the influence of the mitigation effect of ethylene-co-vinyl acetate (EVA) copolymer and its combination with chloroform (C), acetone (A), P-xylene (PX), and petroleum ether (PE) on the deposition thickness in the pipeline. An optimized three-layer feed-forward ANN model using properties of the oil pipeline such as: inlet oil temperature, environmental (coolant mixture) temperature, oil Reynolds numbers; properties of injected inhibitor such as molecular weight, boiling point, and amount of injection; and time is presented. Different networks are considered and trained using 62661 data sets; the accuracy of the network is validated by 20888 testing data sets. To verify the network generalization, 29 different experiment data sets of four different set of inhibitors hav...