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<p>This research focuses on introducing the gas sweetening unit and identifying its optimal operating conditions using advanced modeling techniques. By analyzing experimental data collected over 1227 days from a gas refinery’s sweetening unit, the study investigates factors influencing energy consumption. Two methods, response surface methodology (RSM) and artificial neural networks (ANNs), were employed to model the process, with the ANNs utilizing a multilayer perceptron (MLP) and a radial basis function (RBF). The R² value for RSM was 0.930, whereas the ANN models achieved higher accuracy, with R² values of 0.981 for RBF and 0.986 for MLP. Performance metrics also favored MLP, which recorded a lower error value of 0.002 compared to RBF (0.0051), making MLP the preferred method. Using the optimized MLP model, it was predicted that with an input feed of 8.3 MMSCM, 30.3% DEA amine, and 12.7% methyl diethanol amine (MDEA), fuel consumption could be reduced to 17380.2 SCM - a saving of 12710 SCM. Finally, the accuracy of the MLP model’s predictions was validated through simulations in Aspen HYSYS, further confirming its effectiveness in optimizing energy consumption.</p>

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Optimization of energy consumption in the gas refinery sweetening unit using RSM, ANN, and Aspen HYSYS

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月5日

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  • Erfan Gholamzadeh,
  • Abolfazl Shokri,
  • Ahad Ghaemi,
  • Bahman Heydari

摘要

This research focuses on introducing the gas sweetening unit and identifying its optimal operating conditions using advanced modeling techniques. By analyzing experimental data collected over 1227 days from a gas refinery’s sweetening unit, the study investigates factors influencing energy consumption. Two methods, response surface methodology (RSM) and artificial neural networks (ANNs), were employed to model the process, with the ANNs utilizing a multilayer perceptron (MLP) and a radial basis function (RBF). The R² value for RSM was 0.930, whereas the ANN models achieved higher accuracy, with R² values of 0.981 for RBF and 0.986 for MLP. Performance metrics also favored MLP, which recorded a lower error value of 0.002 compared to RBF (0.0051), making MLP the preferred method. Using the optimized MLP model, it was predicted that with an input feed of 8.3 MMSCM, 30.3% DEA amine, and 12.7% methyl diethanol amine (MDEA), fuel consumption could be reduced to 17380.2 SCM - a saving of 12710 SCM. Finally, the accuracy of the MLP model’s predictions was validated through simulations in Aspen HYSYS, further confirming its effectiveness in optimizing energy consumption.