In this work, the density, viscosity, and specific heat capacity of pure 1-dimethylamino-2-propanol (1DMA2P) as well as aqueous unloaded and CO₂-loaded 1DMA2P solution (with a CO₂ loading of 0.04–0.70 mol CO₂/mol amine) were measured over the 1DMA2P concentration range of 0.5–3.0 mol/L and temperature range of 293–323 K. The observed experimental results of these thermophysical properties of the 1DMA2P-H₂O-CO₂ system were correlated using empirical models as well as artificial neural network (ANN) models (namely, back-propagation neural network [BPNN] and radial basis function neural network [RBFNN] models). It was found that the developed BPNN and RBFNN models could predict the experimental results of 1DMA2P-H₂O-CO₂ better than correlations using empirical models. The results could be treated as one of the accurate and potential methods to predict the physical properties for aqueous amine CO₂ absorption systems.

中文翻译：

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1DMA2P-CO2-H2O体系物理性质关联的AI模型

在这项工作中，纯 1-二甲氨基-2-丙醇 (1DMA2P) 以及未负载和负载 CO ₂的1DMA2P 水溶液的密度、粘度和比热容（CO ₂负载量为 0.04–0.70 mol CO ₂ /mol amine) 在 0.5–3.0 mol/L 的 1DMA2P 浓度范围和 293–323 K 的温度范围内测量。1DMA2P-H ₂ O-CO _{2的这些热物理性质的观察实验结果}系统使用经验模型以及人工神经网络 (ANN) 模型（即反向传播神经网络 [BPNN] 和径向基函数神经网络 [RBFNN] 模型）进行关联。发现所开发的 BPNN 和 RBFNN 模型可以比使用经验模型的相关性更好地预测 1DMA2P-H ₂ O-CO _{2的实验结果。}该结果可被视为预测含水胺CO ₂吸收系统物理性质的准确且潜在的方法之一。