HS3 as a novel solvent for carbon capture: Model validation and an industrial case study with comparison against 30 wt% MEA

Abstract

CO2 capture is currently the most mature technological solution to reduce the environmental footprint of several emitters. However, the characterization of innovative blends with reduced energy demand and environmental impact is nowadays a challenge to increase the profitability and deployment of carbon capture on an industrial scale. This work investigates an innovative non-proprietary amine blend, called HS3. This article presents the development of a full model (including thermodynamics, kinetics, and mass transfer) and its validation with pilot-scale data covering temperature, CO2 concentration, and capture rate ranges of interest for industrial applications. The model predicts the main process Key Performance Indicators, such as CO2 captured, stripped flow, and cycling capacity, with deviations from the measurements lower than 7%. Then, the validated model is exploited for sizing and designing a carbon capture process from the flue gas generated within an oil refinery. A special focus is devoted to energy integration and process optimization by means of a sensitivity analysis. Eventually, HS3 performances are compared to benchmark MEA in terms of energy requirements and unit operation sizing for the same case study. Results show that HS3 can reduce the specific reboiler duty (MJ/kg CO2 captured) and the required solvent flow per unit of flue gas (kg/kg) by 21% and 19%, respectively. Examples of comprehensive models developed starting from lab-scale testing up to the validation on a pilot scale are still limited in the literature. The validation in a semi-industrial pilot is needed to fully understand the solvent properties including, for instance, drawbacks which cannot be detected on a small scale. Needless to mention, reliable models are needed for consistent scale-up, techno-economic assessment, and LCA analysis. Thus, this present presents a model validation using semi-industrial pilot data and measurements, which is not common in the literature.