Environmental process optimisation of an adsorption-based direct air carbon capture and storage system

Abstract

Adsorption-based direct air carbon capture and storage (DACCS) removes CO₂ from the atmosphere, thus helping to limit anthropogenic climate change below 2 °C when employed on a large scale. However, DACCS is energy- and cost-intense. To reduce DACCS's energy demand and costs, a major focus in research is process optimisation. The optimisation task requires sound key performance indicators (KPIs) as objectives that should reflect the purpose of DACCS, i.e., to provide net negative CO₂ emissions via carbon dioxide removal. Currently used KPIs for process optimisation are the specific energy demand, the specific exergy demand, or the equivalent shaft work. However, these energy-related KPIs neglect life-cycle greenhouse gas (GHG) emissions from DACCS, caused for example, by energy consumption or plant construction. Neglecting these GHG emissions can lead to suboptimal processes in the sense of not realising the full carbon removal potential of DACCS. Therefore, we extended a detailed dynamic DACCS model to cover all life-cycle GHG emissions, enabling us to employ the climate-benefit metrics carbon removal efficiency (CRE) and carbon removal rate (CRR) as KPIs for in-depth process analyses and process optimisation. We assessed how using different KPIs for process optimisation affects the carbon removal potential of DACCS. For this purpose, we used the extended DACCS model and optimised the process for different KPIs and plant productivity, which resulted in Pareto frontiers. We found that using CRE as KPI instead of specific energy demand increased CRE by up to 4% at the same plant productivity. More importantly, at the same CRE, the plant productivity can be significantly increased when using CRE as a KPI. In addition, we demonstrated that expanding a detailed DACCS process model with life-cycle GHG emissions and the associated provision of the climate-benefit metrics CRE and CRR as KPIs provides new insights that improve our knowledge about optimal DACCS process designs. Overall, we showed that choosing a climate-benefit metric as KPI for process optimisation is imperative for realising the full carbon removal potential of DACCS.