Design of Artificial Neural Network for Prediction of Hydrogen Sulfide and Carbon Dioxide Concentrations in a Natural Gas Sweetening Plant

Abstract

Gas sweetening is a fundamental step in gas treatment processes for environmental and safety concerns. One of the most extensively used and largely recognized solvents for gas sweetening is methyl diethanolamine (MDEA). One of the most crucial metrics for measuring the effectiveness of gas treatment units is the amount of acid gas that has been treated with MDEA solution. As a result, it should be regularly monitored to avoid operational issues in downstream processes and excessive energy consumption. In this study, the Artificial Neural Network (ANN) approach was followed to predict the hydrogen sulfide (H2S) and carbon dioxide (CO2) sour gases concentrations of sweetening process. The model was built using dataset gathered from a real operation plant in Iraq, collected from February 2019 to February 2020, and used as input to the neural network. The data include H2S and CO2 concentrations of the feed gas, temperature, pressure, and flow rate of the unit. The designed ANN model showed good accuracy in modeling the process under investigation, even for a wide range of parameter variability. The testing outcomes demonstrated a high coefficient of determination (R2) of greater than 0.99, while the overall training performance showed a low mean squared error of less than 0.0003.

Keywords

• absorption
• mdea
• ann modeling
• natural gas dataset
• sour gases
• sweetening process