Torrefaction is a promising technology to improve fuel properties of biomass. However, torrefied char does not show densified energy content compared with coal and typically requires densification such as pelletization. Several key parameters including pelletization and torrefaction conditions were investigated in order to determine the effects on energy density of torrefied char pellets. In this study, multivariate analyses were performed using three independent variables, torrefaction temperature, pelletization temperature, and pelletization pressure; and five dependent variables: mass density, durability, volume expansion, higher heating value (HHV), and energy density (ED). The variation and correlation of the dependent variables were evaluated using principal component analysis (PCA). Additionally, multivariate linear regression was performed to correlate independent variables and dependent variable responses using an adjusted sum of squares method and a two-sided, 90% confidence interval for the mean response; the model was fitted with statistically significant linear, quadratic, and interaction terms. The statistical analysis showed that all independent parameters are statistically significant for ED with the largest model variation from experimental data being 1.44%. Response curve of the regression analysis showed that mass density, HHV, and energy density model have the highest output predictability with higher R² values of 95.3%, 98.1%, and 74.1%, respectively.

烘焙是改善生物质燃料性质的有前途的技术。但是，与煤相比，焙烧的焦炭未显示出致密的能量含量，通常需要致密化（例如造粒）。研究了包括制粒和烘焙​​条件在内的几个关键参数，以确定对烘焙焦炭颗粒能量密度的影响。在这项研究中，使用三个独立变量进行了多变量分析，分别是烘焙温度，制粒温度和制粒压力。和五个因变量：质量密度，耐用性，体积膨胀，较高的热值（HHV）和能量密度（ED）。使用主成分分析（PCA）评估因变量的变化和相关性。另外，使用调整后的平方和方法和均值响应的两侧为90％的置信区间，进行多元线性回归以将自变量和因变量响应相关联。该模型装有统计上显着的线性，二次和相互作用项。统计分析表明，所有独立参数对于ED具有统计学意义，与实验数据相比最大的模型变化为1.44％。回归分析的响应曲线显示，质量密度，HHV和能量密度模型具有最高的输出可预测性，而较高 该模型装有统计上显着的线性，二次和相互作用项。统计分析表明，所有独立参数对于ED具有统计学意义，与实验数据相比最大的模型变化为1.44％。回归分析的响应曲线显示，质量密度，HHV和能量密度模型具有最高的输出可预测性，而较高 该模型装有统计上显着的线性，二次和相互作用项。统计分析表明，所有独立参数对于ED具有统计学意义，与实验数据相比最大的模型变化为1.44％。回归分析的响应曲线显示，质量密度，HHV和能量密度模型具有最高的输出可预测性，而较高R ²值分别为95.3％，98.1％和74.1％。