High investment and operating costs are two barriers hindering the commercial deployment of solvent-based post combustion CO₂ capture (PCC) technology in power industry. Since upstream power plant are always in flexible operation, conventional approaches which carry out the design based on a fixed condition fail to achieve the best overall performance. To this end, this paper proposes a simultaneous size and operation parameters optimization strategy for the PCC process, in which the flexible operation of the CFPP is taken into account during the design stage. The technique of particle swarm optimization (PSO) is used in a hierarchical manner to find the design and operating conditions of the PCC with minimal total annualized cost. To further explore the optimization potential, the average CO₂ capture level in one day is taken as the environmental constraint rather than the conventional instantaneous one. The proposed approach is tested on the PCC design for an existing 660MW supercritical coal-fired power plant. The optimization results of four different design approaches are compared with the economic, efficiency and environmental (3E) performance being fully assessed. The simulation results show that by using the proposed optimization method, 14.45% reduction in TAC can be attained compared with the conventional approach.

高投资和运营成本是阻碍电力行业中溶剂基燃烧后CO ₂捕集（PCC）技术在商业上推广的两个障碍。由于上游发电厂始终处于灵活运行状态，因此基于固定条件进行设计的常规方法无法获得最佳的总体性能。为此，本文提出了一种针对PCC过程的同时尺寸和操作参数优化策略，其中在设计阶段考虑了CFPP的灵活操作。粒子群优化（PSO）技术以分层方式使用，以最低的总年度成本找到PCC的设计和运行条件。为了进一步探索优化潜力，平均CO ₂一天中的捕获水平被视为环境限制，而不是常规的瞬时限制。在现有660兆瓦超临界燃煤电厂的PCC设计上对提议的方法进行了测试。比较了四种不同设计方法的优化结果，并全面评估了其经济，效率和环境（3E）性能。仿真结果表明，与传统方法相比，所提出的优化方法可将TAC降低14.45％。