Predicting CO2 equilibrium solubility in various amine-CO2 systems using an artificial neural network model

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Energy and AI Pub Date : 2024-09-16 DOI:10.1016/j.egyai.2024.100426
Apri Wahyudi , Uthaiporn Suriyapraphadilok
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Abstract

Three proposed reaction mechanisms can occur in an amine-CO2 system: either zwitterionic or termolecular mechanisms for primary/secondary amines and base-catalyzed hydration for tertiary amines. The intricacy of this system hinders the construction of a general model for all types of amines. This study attempts to build an artificial neural network model that predicts the equilibrium solubility of any nonblended aqueous amine-CO2 system under given operating conditions, regardless of the reaction mechanism. This is a novel approach that has not yet been reported. The amines were characterized using molecular descriptors derived from COSMO theory through density functional theory calculations to incorporate molecular structures as model features. Our model achieved performance metrics (R2) of 0.9645 and 0.9481 for the training and validation sets, respectively. For unfamiliar amines that were absent in both the training and validation sets, our model achieved an R2 of 0.8601. Model benchmarking was performed using a previously established thermodynamic model. Interpretations of the model are also provided based on the chosen features. This study also offers exploratory insight into how the molecular structure and operating conditions affect the CO2 equilibrium solubility in amines. The model developed in this study has the potential to reduce the solvent screening time in determining appropriate amines for larger-scale applications.

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利用人工神经网络模型预测各种胺-CO2 系统中的二氧化碳平衡溶解度
在胺-CO2 系统中可能会出现三种拟议的反应机制:伯胺/叔胺的齐聚物机制或分子机制,叔胺的碱催化水合机制。该系统的复杂性阻碍了为所有类型的胺构建通用模型。本研究试图建立一个人工神经网络模型,以预测任何非混合水胺-CO2 系统在给定操作条件下的平衡溶解度,而不论反应机理如何。这是一种尚未报道过的新方法。通过密度泛函理论计算,使用从 COSMO 理论得出的分子描述符对胺进行了表征,并将分子结构作为模型特征。我们的模型在训练集和验证集上的性能指标(R2)分别为 0.9645 和 0.9481。对于训练集和验证集中都不存在的陌生胺,我们的模型达到了 0.8601 的 R2。我们使用以前建立的热力学模型对模型进行了基准测试。此外,还根据所选特征对模型进行了解释。本研究还对分子结构和操作条件如何影响二氧化碳在胺中的平衡溶解度提供了探索性的见解。本研究中开发的模型有可能缩短溶剂筛选时间,为更大规模的应用确定合适的胺。
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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
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