CO₂ solubility in trisodium phosphate (TSP) and its mixed solutions is a crucial information for CO₂ absorption and utilization. However, with limited experimental data and large variations of experimental conditions, intrinsic trends of CO₂ solubility under a specific set of conditions are difficult to be determined without comprehensive experiments. To address this, here, a machine learning based data-mining is proven a powerful method to explore the intrinsic trends of CO₂ solubility trained from 299 data groups extracted from previous experimental literatures. A generalized machine learning input representation method was applied, for the first time, by quantifying the types and concentrations of the blended solutions. With a general regression neural network (GRNN) as the algorithm, we found that the intrinsic trends of CO₂ solubility could be well-fitted with a limited amount of experimental data, having the average root mean square error (RMSE) lower than 0.038 mol CO₂/mol solution. More importantly, it is shown that with a generalized input representation, machine learning can mine the relationships between CO₂ solubility and various experimental conditions, which could help to better understand the intrinsic trends of CO₂ solubility in blended solutions.

CO ₂在磷酸三钠（TSP）及其混合溶液中的溶解度是吸收和利用CO ₂的关键信息。然而，由于有限的实验数据和实验条件的较大变化，如果不进行全面的实验，则很难确定在特定条件下的CO ₂溶解度的内在趋势。为了解决这个问题，在这里，基于机器学习的数据挖掘被证明是探索CO ₂内在趋势的有力方法。从以前的实验文献中提取的299个数据组训练了溶解度。通过量化混合溶液的类型和浓度，首次应用了通用的机器学习输入表示方法。使用通用回归神经网络（GRNN）作为算法，我们发现在有限的实验数据的情况下，CO ₂溶解度的内在趋势可以很好地拟合，其平均均方根误差（RMSE）低于0.038 mol。 CO ₂ /摩尔溶液。更重要的是，它表明，通过广义输入表示，机器学习可以挖掘CO ₂溶解度与各种实验条件之间的关系，这有助于更好地了解CO的内在趋势。₂在混合溶液中的溶解度。