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

Rauf Tavakoli, Puyan Bakhshi, Meysam Mirarab, Khalil Shahbazi

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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.
Original languageEnglish
Pages (from-to)995–1004
Number of pages10
JournalTheoretical Foundations of Chemical Engineering
Issue number5
Publication statusPublished - 14 Dec 2020


  • absorption
  • artificial neural network
  • diethanolamine
  • gas sweetening
  • genetic algorithm

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)


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