In this work, CO₂ absorption from simulated biogas is investigated using different blends of a PZ + AMP solution in an absorption system at CO₂ partial pressures ranging between 20 and 110 kPa. The collected data were presented as CO₂ removal profiles along the packed column and were evaluated in terms of CO₂ removal efficiency (%) and average overall volumetric mass transfer coefficient in the gas phase ($\overline{K_G{a}_v}$). An increased PZ concentration in the AMP solution was found to significantly increase the CO₂ removal efficiency and $\overline{K_G{a}_v}$ values. It was observed that, when conducted at different CO₂ partial pressures, gas and liquid flow rates, and chemical concentrations, the $L_{amine}/G_{C{O}_2}$ ratio strongly influenced the process behaviour in the packed column. Additionally, the optimal inlet liquid temperature was observed to be 35 ± 2 °C in this study.

The improvement in energy efficiency is recognized as one of the significant parameters for achieving our net-zero emissions target by 2050. One exciting area for development is conventional carbon capture technologies. Current amine absorption-based systems for carbon capture operate at suboptimal conditions resulting in an efficiency loss, causing a high operational expenditure. Knowledge of qualitative and quantitative speciation of CO₂-loaded alkanolamine systems and their interactions can improve the equipment design and define optimal operating conditions. This work investigates the potential of Raman spectroscopy as an in situ monitoring tool for determining chemical species concentration in the CO₂-loaded aqueous monoethanolamine (MEA) solutions. Experimental information on chemical speciation and vapour-liquid equilibrium was collected at a range of process parameters. Then, partial least squares (PLS) regression and an artificial neural network (ANN) were applied separately to develop two Raman species calibration models where the Kent–Eisenberg model correlated the species concentrations. The data were paired and randomly distributed into calibration and test datasets. A quantitative analysis based on the coefficient of determination (R²) and root mean squared error (RMSE) was performed to select the optimal model parameters for the PLS and ANN approach. The R² values of above 0.90 are observed for both cases indicating that both regression techniques can satisfactorily predict species concentration. ANN models are slightly more accurate than PLS. However, PLS (being a white box model) allows the analysis of spectral variables using a weight plot.