Non-invasive monitoring of CO₂ concentration in aqueous diethanolamine (DEA), methyldiethanolamine (MDEA) and their blends in high CO₂ loading region using Raman spectroscopy and partial least square regression (PLSR)

Chemical absorption using amines is a suitable method to separate CO₂ from CO₂ rich natural gas stream. An instantaneous monitoring of CO₂ concentration in amine solvent is essential for an efficient chemical absorption process. A spectroscopic technique such as Raman spectroscopy along with multivariate modeling is considered as a robust and fast analytical method. It has been applied to monitor CO₂ concentration in a chemical absorption process. However, these studies are limited to low CO₂ loadings (<0.5 mol_CO2/mol_amine) and cannot be extrapolated to high CO₂ loading conditions. The evaluation of Raman method at high CO₂ loading is essential for the application at high pressure gas streams. In the present study, Raman spectroscopy is non-invasively applied to monitor CO₂ concentration in aqueous amines (DEA, MDEA, and their blends) over a wide range of CO₂ loadings (0.04–1.3 mol_CO2/mol_amine). The partial least square regression (PLSR) calibration models are developed and validated accordingly. The prediction accuracy is reported using determination coefficient (R²) and root mean square error (RMSE). The average validation R²_V and RMSE_V for all the studied systems are calculated as 0.94 and 0.064 mol_CO2/mol_amine respectively. These values show that Raman spectroscopy with PLSR is a promising technique to monitor CO₂ concentration for a wide range of CO₂ loading. The improvement in CO₂ monitoring is expected to enhance the process efficiency of natural gas processing plants.