Application of Raman spectroscopy to real-time monitoring of CO2 capture at PACT pilot plant; Part 1: Plant operational data. (April 2020) - Akram, Muhammad; Jinadasa, M.H. Wathsala N.; Tait, Paul; Lucquiaud, Mathieu; Milkowski, Kris; Szuhanszki, Janos; Jens, Klaus-Joachim; Halstensen, Maths; Pourkashanian, Mohammed
Highlights: Raman spectroscopy is employed for real time monitoring of CO2 capture plant. A multivariate regression model was used to determine rich and lean CO2 loadings. Reliability of the Raman predictions are confirmed with the titration measurements. The Raman predictions models are not affected with accelerated solvent degradation caused by high conc. of SO2 . It is demonstrated that the technology is a step closer towards predictive control of CO2 capture plants. Abstract: Process analyzers for in-situ monitoring give advantages over the traditional analytical methods such as their fast response, multi-chemical information from a single measurement unit, minimal errors in sample handing and ability to use for process control. This study discusses the suitability of Raman spectroscopy as a process analytical tool for in-situ monitoring of CO2 capture using aqueous monoethanolamine (MEA) solution by presenting its performance during a 3-day test campaign at PACT pilot plant in Sheffield, UK. Two Raman immersion probes were installed on lean and rich streams for real time measurements. A multivariate regression model was used to determine the CO2 loading. The plant performance is described in detail by comparing the CO2 loading in each solvent stream at different process conditions. The study shows that the predicted CO2 loading recorded an acceptable agreement with the offline measurements. The findings from this study suggest that Raman Spectroscopy has the capability to follow changes in process variables and can be employed for real time monitoring and control of the CO2 capture process. In addition, these predictions can be used to optimize process parameters; to generate data to use as inputs for thermodynamic models, plant design and scale-up scenarios.