Post‐combustion CO₂ capture is one strategy of greenhouse gases mitigation. Ammonia is a useful option as CO₂ absorbent and an alternative to conventional amine‐based solutions. This study deals with CO₂ capture by ammonia aqueous solution in a co‐current two‐phase flow by utilizing a T‐shaped microchannel. Three parameters of temperature, gas flow rate, and ammonia concentration were considered as the main parameters affecting the CO₂ capture efficiency. A response surface methodology based on central composite design (CCD) was used to model the CO₂ capture efficiency as output in terms of the aforementioned input variables. CCD suggested a quadratic model to fit the experimental data. The model validation was implemented by ANOVA. All statistic tools including correlation coefficient, P‐value, and F‐value of the model, and P‐value of lack‐of‐fit confirmed that the prediction model was significant. It was deduced from F‐values that the importance of the input variables followed the sequence of ammonia concentration > gas flow rate > temperature. Ammonia concentration was the most effective input variable because there was a direct correlation between ammonia concentration and the number of absorption sites in the liquid phase. Numerical optimization predicted the best output of 96.48% CO₂ capture under the following optimum conditions: temperature of 20.00°C, gas flow rate of 110.59 mL/min, and ammonia concentration of 0.1382 mL/mL (13.82 vol%). The average CO₂ capture of 95.42% obtained at the input conditions indicates the accuracy of the prediction model.

中文翻译：

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微通道中氨水溶液捕获CO2的响应面建模

燃烧后的CO ₂捕集是缓解温室气体的一种策略。氨水作为CO ₂吸收剂是一种有用的选择，并且是常规基于胺的溶液的替代品。这项研究涉及利用T形微通道在并流两相流中通过氨水溶液捕获CO ₂。温度，气体流速和氨浓度这三个参数被认为是影响CO ₂捕集效率的主要参数。使用基于中央复合设计（CCD）的响应面方法对CO ₂进行建模根据上述输入变量捕获效率作为输出。CCD建议使用二次模型来拟合实验数据。通过ANOVA进行模型验证。所有的统计工具，包括模型的相关系数，P值和F值，以及缺乏拟合的P值，都证实了预测模型的有效性。从F值得出，输入变量的重要性遵循氨浓度>气体流速>温度的顺序。氨浓度是最有效的输入变量，因为氨浓度与液相中吸收位点的数量之间存在直接的关系。数值优化预测最佳产量为96.48％CO ₂在以下最佳条件下捕集：温度20.00°C，气体流速110.59 mL / min，氨气浓度0.1382 mL / mL（13.82 vol％）。在输入条件下获得的平均CO ₂捕获率为95.42％，表明预测模型的准确性。