The aim of this study is to investigate the effects of key operating parameters of packed absorption columns on the performance of mass transfer considering the volumetric overall mass transfer coefficient (${{\mathrm{K}}_{\mathrm{G}}\mathrm{a}}_{\mathrm{V}}$). The effects were studied for CO₂ absorption by using 4-diethylamino-2-butanol (DEAB) and N,N-Diethylethanolamine (DEEA) mixed with monoethanolamine (MEA) as novel amine solutions. In doing so, an optimization–simulation framework was developed based on the two-film theory model, thermodynamic model, multi-layer perceptron neural network (MLPNN), and statistical technique. To predict the CO₂ loading as one of the parameters in input of MLPNN model, the Deshmukh–Mather model, as an electrolyte thermodynamic model, was developed for CO₂ + DEAB + H₂O and CO₂ + DEEA + MEA + H₂O systems. The effect of enhancement factor on the ${{\mathrm{K}}_{\mathrm{G}}\mathrm{a}}_{\mathrm{V}}$ was considered based on the series resistances model including pseudo-first order enhancemnt factor and instantaneous enhancemnt factor. To optimize and rank the key operating factors that simultaneously affect the ${{\mathrm{K}}_{\mathrm{G}}\mathrm{a}}_{\mathrm{V}}$, the Taguchi method was used. Statistical indices showed that our model could efficiently predict the experimental data with AARDs of 5.6%, 0.43% and 4.96%, respectively, for ${{\mathrm{K}}_{\mathrm{G}}\mathrm{a}}_{\mathrm{V}}$ data, CO₂ loading data of DEAB and DEEA + MEA. A significant order of process variables affecting the ${{\mathrm{K}}_{\mathrm{G}}\mathrm{a}}_{\mathrm{V}}$ values was as CO₂ mole fraction > amine temperature > amine flow rate > gas temperature > packing type > CO₂ loading > amine concentration. Moreover, the sensitivity analysis results showed that by increasing the CO₂ mole fraction in gas feed, gas temperature, and CO₂ loading, the ${{\mathrm{K}}_{\mathrm{G}}\mathrm{a}}_{\mathrm{V}}$ values decreased, and by increasing the amine concentration, amine flow rate, and amine temperature, the ${{\mathrm{K}}_{\mathrm{G}}\mathrm{a}}_{\mathrm{V}}$ values increased.

这项研究的目的是研究考虑到总体积传质系数（％）的填料吸收塔关键操作参数对传质性能的影响。${{\mathrm{ķ}}_{\mathrm{G}}\mathrm{一个}}_{\mathrm{伏特}}$）。通过使用4-二乙基氨基-2-丁醇（DEAB）和N，N-二乙基乙醇胺（DEEA）与单乙醇胺（MEA）混合作为新型胺溶液，研究了吸收CO ₂的效果。为此，基于两层膜理论模型，热力学模型，多层感知器神经网络（MLPNN）和统计技术，开发了优化模拟框架。为了预测作为MLPNN模型输入参数之一的CO ₂负荷，开发了Deshmukh–Mather模型作为电解质热力学模型，用于CO ₂  + DEAB + H ₂ O和CO ₂  + DEEA + MEA + H ₂ O系统。增强因子对${{\mathrm{ķ}}_{\mathrm{G}}\mathrm{一个}}_{\mathrm{伏特}}$基于包括伪一阶增强因子和瞬时增强因子在内的串联电阻模型来考虑。优化并排序同时影响发动机运行的关键操作因素${{\mathrm{ķ}}_{\mathrm{G}}\mathrm{一个}}_{\mathrm{伏特}}$，使用了田口法。统计指标表明，我们的模型可以有效地预测实验数据，AARDs分别为5.6％，0.43％和4.96％。${{\mathrm{ķ}}_{\mathrm{G}}\mathrm{一个}}_{\mathrm{伏特}}$数据，DEAB和DEEA + MEA的CO ₂装载数据。影响变量的过程变量的重要顺序${{\mathrm{ķ}}_{\mathrm{G}}\mathrm{一个}}_{\mathrm{伏特}}$值是CO ₂摩尔分数>胺温度>胺流速>气体温度>填充类型> CO ₂负载量>胺浓度。此外，灵敏度分析结果表明，通过增加进料气体中的CO ₂摩尔分数，气体温度和CO ₂负载量，可以${{\mathrm{ķ}}_{\mathrm{G}}\mathrm{一个}}_{\mathrm{伏特}}$ 值降低，并且通过增加胺浓度，胺流速和胺温度， ${{\mathrm{ķ}}_{\mathrm{G}}\mathrm{一个}}_{\mathrm{伏特}}$ 值增加。