This work conducts a comprehensive modeling study on the viscosity, density, heat capacity, and surface tension of ionic liquid (IL)-IL binary mixtures by combining the group contribution (GC) method with three machine learning algorithms: artificial neural network, XGBoost, and LightGBM. A large number of experimental data from reliable open sources is exhaustively collected to train, validate, and test the proposed ML-based GC models. Furthermore, the Shapley Additive Explanations technique is employed to quantify the influential factors behind all the studied properties. Finally, these ML-based GC models are sequentially integrated into computer-aided mixed solvent design, process design, and optimization through an industrial case study of recovering hydrogen from raw coke oven gas. Optimization results demonstrate their high computational efficiency and integrability in solvent and process design, while also highlighting the significant potential of IL-IL binary mixtures in practical applications.

中文翻译：

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离子液体二元混合物：机器学习辅助建模、溶剂定制、工艺设计和优化

该工作通过将基团贡献（GC）方法与三种机器学习算法（人工神经网络、XGBoost、和 LightGBM。详尽地收集了来自可靠开源的大量实验数据，以训练、验证和测试所提出的基于 ML 的 GC 模型。此外，采用沙普利加性解释技术来量化所有研究特性背后的影响因素。最后，通过从焦炉煤气中回收氢气的工业案例研究，这些基于机器学习的气相色谱模型依次集成到计算机辅助混合溶剂设计、工艺设计和优化中。优化结果证明了它们在溶剂和工艺设计中的高计算效率和可集成性，同时也凸显了 IL-IL 二元混合物在实际应用中的巨大潜力。