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.

中文翻译：

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基于吸附的直接空气碳捕获和储存系统的环境过程优化

基于吸附的直接空气碳捕获和储存（DACCS）可去除大气中的CO ₂，​​因此在大规模使用时有助于将人为气候变化限制在2°C以下。然而，DACCS 是能源和成本密集型的​​。为了降低 DACCS 的能源需求和成本，研究的一个主要重点是流程优化。优化任务需要健全的关键绩效指标(KPI)作为目标，其应反映DACCS的目的，即通过二氧化碳去除提供净负CO ₂排放。目前用于流程优化的 KPI 是特定能源需求、特定火用需求或等效轴功。然而，这些与能源相关的 KPI 忽略了 DACCS 的生命周期温室气体 (GHG) 排放，例如由能源消耗或工厂建设引起的温室气体 (GHG) 排放。忽视这些温室气体排放可能会导致流程不理想，无法实现 DACCS 的全部碳去除潜力。因此，我们扩展了详细的动态DACCS模型以覆盖所有生命周期的温室气体排放，使我们能够采用气候效益指标碳去除效率（CRE）和碳去除率（CRR）作为KPI进行深入的过程分析和处理优化。我们评估了使用不同的 KPI 进行流程优化如何影响 DACCS 的碳去除潜力。为此，我们使用了扩展的 DACCS 模型，并针对不同的 KPI 和工厂生产力优化了流程，从而产生了帕累托前沿。我们发现，在工厂生产率相同的情况下，使用 CRE 作为 KPI 而不是特定能源需求，可将 CRE 提高高达 4%。更重要的是，在相同的CRE下，使用CRE作为KPI可以显着提高工厂的生产率。此外，我们还证明，通过生命周期温室气体排放扩展详细的 DACCS 流程模型以及将气候效益指标 CRE 和 CRR 作为 KPI 的相关规定，可以提供新的见解，从而提高我们对最佳 DACCS 流程设计的了解。总体而言，我们表明，选择气候效益指标作为流程优化的 KPI 对于实现 DACCS 的全部碳去除潜力至关重要。