摇摆
变压吸附
氧气
吸附
过程(计算)
工艺工程
真空摆动吸附
废物管理
材料科学
环境科学
核工程
机械工程
冶金
工程类
化学
计算机科学
有机化学
操作系统
作者
Hyunmin Oh,Hee Tae Beum,Suh-Young Lee,Jinsu Kim,Jungil Kim,Yongju Yun,Sang Sup Han
标识
DOI:10.1016/j.cej.2022.140432
摘要
• Bench-scale CO-VPSA experiments are conducted to validate the numerical model. • 4-cases of bed configurations are selected as operation of CO-VPSA. • The effectiveness of the recovery step was evaluated. • The unit production cost of CO of each case is compared. CO is a primary component of basic oxygen furnace gas (BOFG) and can be used for producing fuel and various value-added chemicals. It can be typically obtained from steel mill gases via separation. Herein, suitable bed operation configurations for vacuum pressure swing adsorption (VPSA) were determined based on the desired CO product purity ( PUR CO,P ) when separating CO from simulated BOFG (CO:CO 2 :N 2 :CH 4 = 65:20:10:5 mol %) using a numerical model validated with experimental data. By changing the operation steps, one case of two-bed, four-step ( 2-bed ), one case of three-bed, five-step ( 3-bed ), and two cases of four-bed, six-step ( 4-bed base and 4-bed mod ) operation configurations were considered at a setting temperature of 60 ℃, desorption pressure of 0.13 kg f cm −2 , and adsorption pressure in the range of 2.5–4.0 kg f cm −2 . The sensitivities of these four operation configurations were evaluated to compare the separation performance and economic benefits of each operation configuration. In the 2-bed case, the PUR CO,P demonstrates a separation limit (92.1–92.7 mol %). When targeting PUR CO,P ≥ 99.00 mol %, the 3-bed case presents the most favorable CO recovery values ( REC CO,P ; 92.97–94.13 %) and unit production costs of CO ( UPC CO ; 0.252–0.357 US$ Nm −3 ), whereas the 4-bed mod case presents optimal REC CO,P (82.06–91.65 %) and UPC CO values (0.273–0.406 US$ Nm −3 ) when targeting PUR CO,P ≥ 99.99 mol %, under the same operating conditions. These results indicate cost-effective CO-VPSA process configurations for enriching CO from BOFG, based on the target PUR CO,P .
科研通智能强力驱动
Strongly Powered by AbleSci AI