Intensification of CO2 capture using aqueous diethylenetriamine (DETA) solution from simulated flue gas in a rotating packed bed. (15th December 2018)

• Record Type: Journal Article
• Authors: Sheng, Miaopeng; Xie, Chenxia; Zeng, Xiaofei; Sun, Baochang; Zhang, Liangliang; Chu, Guangwen; Luo, Yong; Chen, Jian-Feng; Zou, Haikui
• Is Part Of: Fuel. Volume 234(2018)
• Page Start: 1518
• Page End: 1527
• Publication Date: 2018-12-15
• Subjects: CO2 capture -- Process intensification -- Diethylenetriamine -- Rotating packed bed -- Back-Propagation Neural Network model; Fuel -- Periodicals; Coal -- Periodicals; Coal; Fuel; Periodicals; 662.6
• DOI: 10.1016/j.fuel.2018.07.136
• Languages: English
• ISSNs: 0016-2361

Abstract

Highlights: PI technology comprising DETA solvent and RPB was proposed to intensify CO2 capture. Experimental and model studies on CO2 capture into DETA solvent in RPB was presented. RPB can enhance CO2 removal performance as compared to conventional packed column. A Back-Propagation Neural Network (BPNN) model was developed for predicting K G a v . Abstract: Coal still has a vital role in power generation, and coal-fired power plants are considered to be a main source of CO2 emission. This work proposed a process intensification (PI) technology, combining the highly efficient diethylenetriamine (DETA) solvent for CO2 absorption and the PI device of rotating packed bed (RPB) for enhancing gas-liquid mass transfer, to improve CO2 capture performance. Experimental study was conducted in a lab-scale RPB, and the dependences of CO2 removal performance on various operation conditions were systematically investigated. It was founded that CO2 loading has a vital effect on removal efficiency, and increasing rotation speed and solvent flow rate is beneficial to CO2 removal. The comparison of mass transfer performance between RPB and packed column (PC) demonstrated that the gas retention time in RPB with a value of 1.5 s is far shorter than that in PC under the similar operation conditions, which means RPB possesses a great advantage of shrinking mass-transfer device's size for the CO2 capture process. Additionally, a Back-Propagation Neural Network (BPNN) model was developed for predicting the value of overall volumetric mass-transfer coefficient ( K G a v ), and the predicted values agree well with experimental data with a satisfactory average absolute relative derivation (AARD) of 7.85%. These results demonstrated that this PI technology is expected to be a competitive candidate for improving CO2 capture performance from flue gas.