部分负荷运行条件下碳捕集过程的设计
Design of Carbon Capture Processes Under Part-load Operating Conditions
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中文总结 AI 辅助
研究在部分负荷运行条件下碳捕集过程的设计问题，采用随机优化及数据驱动平衡模型方法，考虑典型煤电厂部分负荷运行数据表示可变烟气条件，可降成本，强调设计阶段应考虑烟气条件变化性。
中文摘要 AI 辅助
基于溶剂的碳捕集可减少因持续依赖化石发电厂提供稳定电力而产生的二氧化碳排放。这些捕集过程通过溶剂从烟气中去除二氧化碳。通过过程系统优化进行精心设计可限制碳捕集的总成本，碳捕集既耗费资金又消耗能源。由于可调度发电厂需满足不同的负荷需求，设计过程需考虑不同的运行点。然而，在多个运行点上优化设计会产生高计算复杂度，这就是实际设计通常基于单个运行点的原因。在此，我们通过随机优化确定最优碳捕集过程设计，通过吸收和解吸过程的数据驱动平衡模型方法降低计算复杂度。我们基于一个典型煤电厂的部分负荷运行数据来表示可变的烟气条件。在设计中考虑这种变化性可大幅减小设备尺寸并使总厂成本降低6 - 9%，代价是运行成本有所增加，从而使碳捕集总成本降低0.7 - 1.7%。鉴于碳捕集的资本密集度，因此在设计阶段应考虑烟气条件的变化性，特别是在用于负荷跟踪的电厂中部署捕集时。
英文摘要
Solvent-based carbon capture can reduce CO2 emissions resulting from a continued reliance on fossil power plants for firm power. These capture processes remove CO2 from flue gases via a solvent. Careful design via process systems optimization can limit the overall cost of carbon capture, which is both capital- and energy intensive. As dispatchable power plants operate to meet varying load demand, the design process needs to account for varying operating points. However, optimizing the design over multiple operating points yields high computational complexity, which is why designs are often based on a single operating point in practice. Here, we identify optimal carbon capture process designs via stochastic optimization, reducing computational complexity through a data-driven approach-to-equilibrium model of the absorption and desorption processes. We represent variable flue gas conditions based on part-load operation data of a representative coal power plant. Accounting for this variability in the design substantially reduces equipment size and total plant cost by 6-9 % at the expense higher operating costs, yielding a reduction in total cost of carbon capture by 0.7-1.7 %. Given the capital intensity of carbon capture, variability of flue gas conditions therefore should be considered at the design stage, particularly if capture is deployed on plants subject to load following.
