Artificial neural network models for the prediction of CO2 solubility in aqueous amine solutions

CO2 equilibrium solubility is an important parameter used to evaluate the performance of absorption solvents in CO2 capture processes. Back-propagation neural networks (BPNN) and radial basis function neural networks (RBFNN) were proposed to predict the CO2 solubility in 12 known amine solutions. Both of the models were firstly conducted in monoethanolamine, diethanolmine and methyldiethanolamine solutions to evaluate their effectiveness, and were then applied in nine other amine solutions to further verify their adaptability. The results showed that both BPNN and RBFNN models provided excellent agreements with the experimental values for all the amine solutions with average absolute relative errors and root mean square errors less than 10%. A comparison between the predicted results and those of the eight published models showed that the proposed ANN models performed better than the literature models. Furthermore, scalability analysis was carried out to evaluate the adaptability of BPNN and RBFNN models in terms of the wide input parameter ranges. (C) 2015 Elsevier Ltd. All rights reserved.

二氧化碳平衡溶解度是用于评估二氧化碳捕集过程中吸收溶剂性能的一个重要参数。提出了反向传播神经网络（BPNN）和径向基函数神经网络（RBFNN）来预测二氧化碳在12种已知胺溶液中的溶解度。这两种模型首先在一乙醇胺、二乙醇胺和甲基二乙醇胺溶液中进行评估其有效性，然后应用于其他9种胺溶液以进一步验证其适应性。结果表明，对于所有胺溶液，BPNN和RBFNN模型与实验值都高度吻合，平均绝对相对误差和均方根误差均小于10%。将预测结果与8种已发表模型的结果进行比较，表明所提出的人工神经网络模型比文献中的模型表现更好。此外，进行了可扩展性分析，以评估BPNN和RBFNN模型在较宽输入参数范围方面的适应性。（C）2015爱思唯尔有限公司。保留所有权利。