Application of GA-Optimized ANNs to Predict the Water Content, CO₂ and H₂S Absorption Capacity of Diethanolamine (DEA) in Khangiran Gas Sweetening Plant

In this work, with the aim of accurate prediction of water content, H₂S and CO₂ absorption capacity of diethanolamine (DEA) solvent in Khangiran gas sweetening plant, an artificial neural network (ANN) model of feed-forward multilayer perceptron, with the learning algorithm of Levenberg–Marquardt has been developed. The training, validation, and testing of the ANN model, respectively, was performed using 70, 15, and 15% of all of the gathered operation data. An optimization procedure on the basis of the genetic algorithm (GA) was implemented to select the optimum ANN architecture. Accordingly, a three layer feed-forward neural network with Levenberg–Marquardt back-propagation training algorithm was designed and developed. The structure of the model comprised of 12 variables as inputs and three as outputs, 13 neurons in the hidden layer, the log-sigmoid transfer function in the hidden layer, and the output layer containing linear transfer function. The results, based on statistical analysis, showed very little difference between the predicted and actual operation data with a remarkably low mean square error (MSE) value and a coefficient of determination (R2) value approaching one. The mentioned factors are strong indicators of the proposed model’s high accuracy in predicting the output variables.