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.

使用胺的化学吸收是从富CO _2的天然气流中分离CO ₂的合适方法。即时监测胺溶剂中CO _2的浓度对于有效的化学吸收过程至关重要。诸如拉曼光谱之类的光谱技术以及多变量建模被认为是一种强大而快速的分析方法。它已被用于监测化学吸收过程中的CO ₂浓度。但是，这些研究仅限于低的CO ₂负载量（<0.5 mol _CO2 / mol_胺），不能推断为高的CO ₂负载条件。高CO下拉曼方法的评价。₂加载对于在高压气流中的应用至关重要。在本研究中，拉曼光谱技术被无创地用于监测宽范围的CO ₂装载量（0.04–1.3 mol _CO2 / mol_胺）中_胺水溶液（DEA，MDEA及其混合物）中的CO ₂浓度。偏最小二乘回归（PLSR）校准模型得到相应开发和验证。使用确定系数（R ²）和均方根误差（RMSE）报告预测精度。所有研究系统的平均验证R ² _V和RMSE _V计算为0.94和0.064 mol _CO2 / mol_胺分别。这些值表明，具有PLSR的拉曼光谱法是一种有前途的技术，可在广泛的CO ₂负载范围内监测CO ₂浓度。预期CO ₂监测的改善将提高天然气加工厂的工艺效率。