In this research, a new set of experimental data for CO₂ solubility in aqueous blended amine solvents were investigated experimentally over the CO₂ partial pressure range from 8 to 100 kPa at 40 °C and were compared with the benchmark aqueous 30 wt.% MEA solution. This work developed two multilayer neural network models named models A and B, for predicting the CO₂ solubility in various aqueous blended amine solvents including 36 wt.% MDEA + 17 wt.% PZ, 24 wt.% MDEA + 26 wt.% PZ, and 6 wt.% MEA + 25 wt.% MDEA + 17 wt.% PZ. Models A and B were developed by using Levenberg–Marquardt back propagation algorithm with 427 and 301 of reliable experimental data sets gathered from the published data, respectively. The results indicate that the high accuracy prediction of the CO₂ solubility in Methyldiethanolamine/Piperazine (MDEA/PZ) blends could be obtained by the network developed by Tan-sigmoid transfer function with two hidden layers consist of eight and four neurons, while the network developed by Tan-sigmoid transfer function with three hidden layers consist of 20, 10, and five neurons provided the highest accuracy for predicting the CO₂ solubility in MEA/MDEA/PZ blends comparing to other model structures. The comparison results show that the neural network modeling provided more closer predictions to the experimental results than the simulator and other thermodynamic models when predicting the CO₂ equilibrium solubility in blended amine solvents. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd.

中文翻译：

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用于预测 MDEA/PZ 和 MEA/MDEA/PZ 混合物中 CO2 溶解度的实验研究和开发多层神经网络模型

在这项研究中，在8 至 100 kPa的 CO ₂分压范围内，在 40 °C 下对 CO ₂在水性混合胺溶剂中溶解度的一组新实验数据进行了实验研究，并与基准水性 30 wt.% MEA 进行了比较解决方案。这项工作开发了两个名为模型 A 和 B 的多层神经网络模型，用于预测 CO ₂在各种水性混合胺溶剂中的溶解度，包括 36 wt.% MDEA + 17 wt.% PZ、24 wt.% MDEA + 26 wt.% PZ 和 6 wt.% MEA + 25 wt.% MDEA + 17 wt.% PZ . 模型 A 和 B 是通过使用 Levenberg-Marquardt 反向传播算法开发的，分别从已发布的数据中收集了 427 和 301 个可靠的实验数据集。结果表明，由具有两个隐藏层的 Tan-sigmoid 传递函数开发的网络可以实现对甲基二乙醇胺/哌嗪 (MDEA/PZ) 混合物中CO ₂溶解度的高精度预测，而网络由 Tan-sigmoid 传递函数开发，具有三个隐藏层，由 20、10 和 5 个神经元组成，为预测 CO ₂提供了最高准确度与其他模型结构相比，在 MEA/MDEA/PZ 混合物中的溶解度。比较结果表明，在预测混合胺溶剂中的 CO ₂平衡溶解度时，神经网络模型比模拟器和其他热力学模型对实验结果提供了更接近的预测。© 2021 化学工业协会和 John Wiley & Sons, Ltd.