Development of a multi-objective optimisation approach for the assessment of techno-economical and environmental performances of pipeline-based Carbon Capture and Storage (CCS) systems

It is widely recognized that Carbon Capture and Storage (CCS) may be playing a key role in decarbonising industrial and power generation processes and meeting net zero emission targets by 2050. However, the successful large-scale development of CCS is still hindered by a wide lack of reliable techno-economic and environmental assessment methodologies. In the present work, a CFD code developed by UCL Chemical Engineering, named PipeTech, was first used to simulate CO2 pipeline transport performances with respect to variations of main design and operational parameters. Based on these results, a multi-objective optimisation methodology for CCS pipelines is proposed to minimise economic and environmental footprints while reliably simulating fluid flow behaviour. Then, the Decision Maker (DM) is supposed to select one or more trade-offs that best suit his preferences. Three potentially most effective solutions were determined by applying the mathematical Level Diagrams technique. Uncertainty affecting CO2 transport cost modelling is assessed by employing a Monte Carlo simulation approach, which also provides the DM with a probabilistic tool to compare the different alternatives. Lastly, a quantitative methodology is applied to convert multi-criteria optimisation results in impact indicators weighting economic and environmental issues. Results were then compared with those given by the single minimisation of two additional indices. These are presented as a “CO2 avoided transport cost”, which considers an apparent transported flow rate decreased by emitted GHG expressed as CO2 equivalents, and as a “market-based transport cost”, which applies a carbon pricing to the greenhouse gas emissions according to carbon markets. Overall, this approach provided a route to flexibly weigh environmental and economic concerns according to DM preferences, whereas the two aggregated indices give a lower visibility to the environmental footprint of the system.