Parameter optimization in designing a rational capacitive deionization (CDI) process is usually performed to achieve both high electrosorption capacity and speed. This necessitates a clear understanding of system behavior and discriminating the features' role on desalination capacity from its dynamic. Machine learning (ML) modeling is widely employed for understanding various systems' behavior as an alternative for physics-based extrapolation models. Herein, various ML models are implemented with reasonable accuracies to unveil CDI electrode and operational features' local and global impacts on equilibrium desalination capacity, speed, and duration. Electrode specific surface area and electrolyte ionic concentration are determined to play the most significant roles in CDI by synergistically enhancing desalination capacity and speed. Increasing electrode micropore volume is detected to inhibit desalination and make ion removal sluggish. According to the established models, electrode nitrogen content extends desalination capacity without improving its dynamic. In addition, unlike the complex impacts from electrodes oxygen content on desalination capacity, it is shown that electrode oxygen content clearly elongates desalination time. This study demonstrates the strong abilities of the established ML models in explaining the underlying complex mechanisms in the CDI process.

通常在设计合理的电容去离子 (CDI) 工艺时进行参数优化，以实现高电吸附容量和速度。这需要清楚地了解系统行为，并从其动态中区分特征对淡化能力的作用。机器学习 (ML) 建模被广泛用于理解各种系统的行为，作为基于物理的外推模型的替代方法。在此，以合理的精度实施了各种 ML 模型，以揭示 CDI 电极和操作特征对平衡脱盐能力、速度和持续时间的局部和全局影响。通过协同提高脱盐能力和速度，确定电极比表面积和电解质离子浓度在 CDI 中发挥最重要的作用。检测增加电极微孔体积以抑制脱盐并使离子去除缓慢。根据已建立的模型，电极含氮量在不改善其动态的情况下扩展了脱盐能力。此外，与电极氧含量对脱盐能力的复杂影响不同，电极氧含量明显延长了脱盐时间。这项研究证明了已建立的 ML 模型在解释 CDI 过程中潜在的复杂机制方面的强大能力。与电极氧含量对脱盐能力的复杂影响不同，电极氧含量明显延长了脱盐时间。这项研究证明了已建立的 ML 模型在解释 CDI 过程中潜在的复杂机制方面的强大能力。与电极氧含量对脱盐能力的复杂影响不同，电极氧含量明显延长了脱盐时间。这项研究证明了已建立的 ML 模型在解释 CDI 过程中潜在的复杂机制方面的强大能力。