The emission of CO₂ is leading to serious global climate change, which has attracted increasing attention. In this work, a rotating packed bed (RPB) was employed as a highly effective reactor to intensify the CO₂ absorption in an alkanolamine solution that mainly contained 2-(2-aminoethylamino)ethanol (AEEA). The effects of important operating conditions, such as high gravity level, amine solvent concentration, gas/liquid flow ratio, CO₂ inlet concentration, absorption temperature, and CO₂ loading in the amine solvent, on the gas-phase volumetric mass-transfer coefficient (K_Ga) and CO₂ capture efficiency were investigated. The results indicated that the high gravity level and CO₂ inlet concentration had significant effects on K_Ga, and the experimental value of K_Ga was found to be about 1.42–2.86 kmol·m^–3·h^–1·kPa^–1 in the RPB, which is an order of magnitude higher than that in a conventional packed column. Furthermore, an artificial neural network (ANN) model was applied to predict the mass-transfer performance. The values predicted using the ANN model were in good agreement with the experimental data (±10%).

中文翻译：

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旋转填充床中2-（2-氨基乙基氨基）乙醇溶液吸收CO _2的传质性能

CO ₂的排放导致严重的全球气候变化，引起了越来越多的关注。在这项工作中，旋转填充床（RPB）被用作高效反应器，以增强主要包含2-（2-氨基乙基氨基）乙醇（AEEA）的链烷醇胺溶液中的CO ₂吸收。重要操作条件（如高重力水平，胺溶剂浓度，气/液流量比，CO ₂入口浓度，吸收温度和胺溶剂中的CO ₂负载）对气相体积传质系数的影响（K _G a）和CO ₂捕获效率进行了调查。结果表明，高重力水平和CO ₂入口浓度对显著效果ķ _ģ一个，和实验值ķ _ģ一个被发现是大约1.42-2.86千摩尔·^米-3 ·H ^-1 ·帕^-1 RPB中的浓度比常规填充塔高一个数量级。此外，将人工神经网络（ANN）模型用于预测传质性能。使用ANN模型预测的值与实验数据非常吻合（±10％）。