Design of neural network for manipulating gas refinery sweetening regenerator column outputs

Abstract In this study, a new approach for the prediction collection outputs of regenerator column in gas sweetening plant is suggested. The experimental input data, including inlet temperatures of reflux, difference between inlet and outlet condenser temperatures, amount of H 2 O and inlet amine temperatures and outlet down temperature of tower and amount of reflux as outputs have been used to create artificial neural network (ANN) models. The testing results from the model are in good agreement with the experimental data. The new proposed method was evaluated by a case study in HASHEMI NEJAD gas refinery in KHORASAN of Iran. Design of topology and parameters of the neural networks as decision variables was done by trial and error, high performance efficiency networks was obtained to predict the output parameters of regenerator column.

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