CO₂ conditioning: process optimisation, thermodynamic modelling, and measurement of impurities

The carbon capture utilisation and storage (CCUS) value chain has been identified as a key pathway for mitigating anthropogenic CO₂ emissions, especially for hard-to-abate sectors. This thesis focuses on a key segment of the CCUS value chain - CO₂ conditioning - which is the interface between CO₂ capture and transport. The conditioning process ensures that compositional and pressure specifications of transportation, utilisation and storage facilities are met. The objectives of this study are to optimise the conditioning process, improve the thermodynamic modelling of common impurities in the CCUS value chain, and finally develop techniques towards the real-time monitoring of impurities in CO₂-rich streams.

To maximise economies of scale, the CCUS value chain requires the sharing of infrastructure, such as a network of transportation pipelines linking multiple CO₂ point sources and sinks. However, the development of this infrastructure faces multiple uncertainties, due to the nascency of the large-scale deployment of CCUS. These uncertainties include the number of future pipeline users, the amount of CO₂ to be transported and the capacity of CO₂ storage sinks. Therefore, in optimising the CO₂ conditioning process to minimise energy consumption and capital requirement, process flexibility was also evaluated, considering the uncertainty in pipeline flowrate. Different permutations of conventional multistage compression, and subcritical liquefaction and pumping of CO₂ process schemes were considered. The outcome of this investigation provides a basis for the integrated design of CO₂ conditioning processes and the sizing of shared CO₂ pipelines. Upon considering various process parameters, this work reveals that using low-temperature cooling water is the most critical variable to enable the least energy-intensive and flexible CO₂ conditioning processes.

Impurities pose significant challenges to the CCUS value chain. They can influence the phase equilibria and thermophysical properties of CO₂. Therefore, accurate thermodynamic models are required to model and design various elements of the CCUS value chain. This thesis further presents an improvement in the thermodynamic modelling of a key class of common impurities in the CCUS value chain – sulphur-containing compounds (SO₂, H₂S, COS). This class of impurities often has a strict specification at CO₂ storage facilities. By considering these compounds as self-associating, this work provides a successful predictive approach using the Cubic Plus Association equation of state, to model the phase equilibria of sulphurcompounds in mixtures relevant to CCUS. The predictive approach is validated against experimental data for mixtures of these sulphur-containing compounds with CO2, and with other common impurities such as CH₄, and glycols.

Furthermore, this thesis sheds light on how impurities can be monitored in the CCUS value chain. An experimental investigation is conducted involving the use of in-situ Raman spectroscopy to measure the water content of CO₂, and those of N₂ and H₂, which are common CCUS impurities. The experimental investigation is performed at pressures ranging from 5 to 15 MPa. This work highlights and demonstrates the inherent constraints of conventional quantitative methods of Raman spectroscopy for the determination of gas water content. Thus, in this thesis, a novel approach which uses water-only Raman spectra features for gas water content determination is proposed. This proposed method is validated at a wide range of pressure and temperature conditions, water content levels, and different binary systems. The developed approach allows for the generation of a new set of experimental data describing the phase equilibria of the investigated gases with water. Immediate applications of the proposed quantitative approach would be in the monitoring of gas dehydration processes.