Abstract
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.
Highlights
Carbon dioxide (CO2 ) is deemed a global environmental concern due to its role in global warming and climate change as a greenhouse gas [1,2]
Pre‐installed with SpectraWiz software serves to process the data 2 gases were introduced into the reaction vessel and the aqueous solution was continuously stirred from the Raman spectroscopy unit
The Raman spectra and respective species concentration were acquired through the vapour-liquid equilibrium (VLE) experiment of the CO2 -MEA-H2 O system at varying total MEA concentration
Summary
Carbon dioxide (CO2 ) is deemed a global environmental concern due to its role in global warming and climate change as a greenhouse gas [1,2]. ANN is a non-linear regression technique by virtue of utilizing a non-linear activation function This capability can potentially improve measurement accuracy made by a process analyser compared to PCR and even PLS-based calibration model in real-world application [30,31,32]. Raman spectroscopy for monitoring species concentration in CO2 -loaded aqueous DEA systems with a CO2 loading range of up to 0.98, utilizing the PLS regression technique for developing a calibration model [36]. Raman species calibration models for CO2 -loaded aqueous MEA system at 3, 4 and 5 molar MEA concentrations were developed using PLS regression and ANN. The smoothed and centred Raman spectra together with the respective species concentration data were utilized for developing the Raman calibration models using PLS regression and ANN techniques. Comparisons were made between the developed models based on the predictive performance by evaluating the coefficient of determination (R2 ) and root mean squared error (RMSE)
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