This study shows a mathematical and statistical analysis to generate models based on multiple linear regression (MLR) and regression trees (RT) that allow a reliable prediction of the Mass Yield (MY) and the Higher Heating Value (HHV) of the final solid product obtained by Hydrothermal Carbonization, called hydrochar. MLR models were obtained for lignocellulosic and non-lignocellulosic biomass using a set of experimental data with more than 500 points collected from the literature. A new approach based on dimensionless groups of variables that describe the composition of biomass and operational conditions was used. The analysis for each equation indicated that the MY depends on the process conditions and the biomass composition, which is proportional to the Polarity Index (IP) and Reactive Index (IR) values. On the other hand, the severity factor (log Ro) and the initial calorific value (HHVo) were the main factors for the HHV, but also the raw biomass composition (IP and H/C ratio) had an opposite and equal significant effect. For these equations, the results indicated an adjusted R² (R²_a) of about 0.90 and an average RMSE of 6% and 1.7 MJ/kg for MY and HHV, respectively.

Besides, explanatory variables were analyzed by their Relative Importance for the RT models. The severity factor (65%) and the IR (18%) were the most decisive variable in the MY prediction. The R² and RMSE were 0.73 and 2%, respectively. For HHV, the variables with the most significant impact were the HHVo (33%), the log Ro (24%), and the IP (22%). In this case, the R² and RMSE were 0.87 and 0.68 MJ/kg, respectively. Therefore, the model equations obtained are a powerful tool to predict the mass yield and the energetic value of the hydrochar before developing an experimental study.

这项研究显示了数学和统计分析，以基于多重线性回归（MLR）和回归树（RT）生成模型，从而可以可靠地预测最终固体产品的质量产率（MY）和较高的热值（HHV）通过热液碳化获得的，称为水焦。使用一组实验数据获得了木质纤维素和非木质纤维素生物质的MLR模型，并从文献中收集了500多个点。使用了一种基于无量纲变量组的新方法，该变量组描述了生物质的组成和运行条件。对每个方程的分析表明，MY取决于工艺条件和生物质组成，其与极性指数（IP）和反应指数（IR）值成比例。另一方面，严重性因子（log Ro）和初始热值（HHVo）是影响HHV的主要因素，但原始生物量组成（IP和H / C比）的作用却相反且相等。对于这些方程，结果表明调整后的R²（R ² _a）约为0.90，MY和HHV的平均RMSE分别为6％和1.7 MJ / kg。

此外，通过RT模型的相对重要性分析了解释变量。在MY预测中，严重性因子（65％）和IR（18％）是最决定性的变量。R ²和RMSE分别为0.73和2％。对于HHV，影响最大的变量是HHVo（33％），log Ro（24％）和IP（22％）。在这种情况下，R ²和RMSE分别为0.87和0.68 MJ / kg。因此，在进行实验研究之前，获得的模型方程是预测水煤的质量产率和能量值的有力工具。