Real-Time Process Monitoring of CO₂ Capture by Aqueous AMP-PZ Using Chemometrics: Pilot Plant Demonstration

Abstract

A combination of analytical instrumentation and multivariate statistics is widely applied to improve in-line process monitoring. Currently, postcombustion CO2 capture (PCC) technology often involves the use of multiamine based chemical reagents for carbon dioxide removal from flue gas. The CO2 capture efficiency and overall process performance may be improved by introduction of the chemometrics analytical methods for flexible and reliable process monitoring. In this study, six variables were measured (conductivity, pH, density, speed of sound, refractive index, and near-infrared absorbance spectra). A compact data-collecting chemometric setup was constructed and installed at an industrial pilot plant for real-case testing. This setup was applied to the characterization of CO2 absorption into aqueous 2-amino-2-methyl-1-propanol (AMP) activated by piperazine (PZ) as the absorption agent. A partial least-squares (PLS) regression model was calibrated and validated based on the measurements conducted in the laboratory environment. The developed approach was applied to predict the concentrations of AMP, PZ, and CO2 with accuracies of ±2.1%, ± 3.5%, and ±4.3%, respectively. The model was constructed to include the temperature dependency in order to make it insensitive to operational temperature fluctuations during a CO2 capture process. The setup and model have been tested for almost 850 hours of in-line measurements at a postcombustion CO2 capture pilot plant. To provide validation of the chemometrics approach, an off-line analysis of the samples has been conducted. The results of the validation technique benchmarking appear to be consistent with values predicted in-line, with average deviations of ±1.8%, ± 1.3%, and ±3.9% for the concentrations of AMP, PZ, and CO2, respectively.

Deep learning for industrial processes: Forecasting amine emissions from a carbon capture plant

“…[2][3][4][5] This requires tools that fully capture the dynamic and multivariate behaviour of the plant away from its steady-state operation. The classical analysis techniques, such as response function fits, 6,7 or chemometrics approaches 8 give some insights into the typical response to the different perturbations. However, these techniques cannot take the full multivariate, non-linear nature of the time-dependent behaviour of a complex plant into account.…”

Dynamic analysis of carbon dioxide desorption with 2‐amino‐2‐methyl‐1‐propanol and piperazine blends

“…In addition, this technique does not need sample preparation, such as dilution that may alter the chemical equilibria. Moreover, a multivariate regression method such as a PLS model significantly reduces the interference of compounds 32–35 …”

Application of Raman spectroscopy to real-time monitoring of CO2 capture at PACT pilot plant; Part 1: Plant operational data

“…They predicted the concentrations of MDEA, PZ, and CO2 using real-time measurements of solvent properties which were density, pH, conductivity, sound velocity, refractive index and NIR absorption (Kachko et al, 2016a). A similar approach was used to predict AMP, PZ and CO2 in another pilot plant test (Kachko et al, 2015). During long term pilot demonstrations at TCM CO2 capture pilot plant in Norway, CO2 loadings and solvent concentration were mainly followed on a daily basis with manual samples and analysis and they also used online analyzers such as conductivity, density and pH to make correlations to CO2 loading and solvent strength (Andersson et al, 2013;Flø et al, 2017;Montañés et al, 2017).…”