Abstract Gas hydrates have significant applications in the areas of natural gas storage, desalination and gas separation. Knowledge of the thermodynamic conditions associated with hydrate formation is critical to their synthesis. Presently, we use machine learning (ML) to train and evaluate the performance of three algorithms on an experimental database (>1800 data points) to predict hydrate dissociation temperatures as a function of the constituent hydrate precursors and inhibitors. Importantly, and in contrast to most previous studies, we use thermodynamic variables such as the activity-based contribution due to electrolytes, partial pressure of individual gases, and specific gravity of the overall mixture as input features in the prediction algorithms. Using such features results in more physics-aware ML algorithms, which can capture the individual contributions of gases and electrolytes in a more fundamental manner. Three ML algorithms, Random Forest (RF), Extra Trees (ET), and Extreme Gradient Boosting (XGBoost) are employed and demonstrate excellent accuracy in their predictions of hydrate equilibrium conditions. The overall coefficient of determination (R2) percentage is greater than 97% for all the ML models. XGBoost outperforms RF and ET with the highest overall coefficient of determination (R2) and the lowest overall Average Absolute relative deviation (AARD) of 99.56% and 0.086% respectively.

中文翻译：

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存在电解质时，热力学特征驱动的基于机器学习的包合物水合物平衡预测

摘要 天然气水合物在天然气储存、海水淡化和气体分离等领域有着重要的应用。了解与水合物形成相关的热力学条件对其合成至关重要。目前，我们使用机器学习 (ML) 在实验数据库（> 1800 个数据点）上训练和评估三种算法的性能，以预测作为组成水合物前体和抑制剂的函数的水合物离解温度。重要的是，与之前的大多数研究相比，我们使用热力学变量作为预测算法中的输入特征，例如由电解质引起的基于活动的贡献、单个气体的分压和整体混合物的比重。使用这些特征会产生更多的物理感知 ML 算法，它可以以更基本的方式捕捉气体和电解质的个体贡献。采用了随机森林 (RF)、额外树 (ET) 和极限梯度增强 (XGBoost) 三种 ML 算法，它们在预测水合物平衡条件方面表现出出色的准确性。所有 ML 模型的总体决定系数 (R2) 百分比都大于 97%。XGBoost 以最高的整体决定系数 (R2) 和最低的整体平均绝对相对偏差 (AARD) 分别优于 RF 和 ET，分别为 99.56% 和 0.086%。和极限梯度提升 (XGBoost) 被采用，并在他们对水合物平衡条件的预测中表现出出色的准确性。所有 ML 模型的总体决定系数 (R2) 百分比都大于 97%。XGBoost 以最高的整体决定系数 (R2) 和最低的整体平均绝对相对偏差 (AARD) 分别优于 RF 和 ET，分别为 99.56% 和 0.086%。和极限梯度提升 (XGBoost) 被采用，并在他们对水合物平衡条件的预测中表现出出色的准确性。所有 ML 模型的总体决定系数 (R2) 百分比都大于 97%。XGBoost 以最高的整体决定系数 (R2) 和最低的整体平均绝对相对偏差 (AARD) 分别优于 RF 和 ET，分别为 99.56% 和 0.086%。