*2.2. Data Analysis*

The observed relationships between the considered dependent and independent variables are highly linear as shown in Figures 1 and 2. Therefore, ordinary least squares regression (OLS), which is a linear regression method, was employed for analyzing the data to model the C and H content, and HHV of torrefied biomass. The assumptions made for the OLS were that the errors in the resulting prediction: (1) have an expected mean value of zero, (2) have the same variance, and (3) are not correlated with each other [45]. The correlations are developed for the HHV, carbon and hydrogen yields by excluding the nitrogen and oxygen contents as the nitrogen is assumed to remain in the solid and the oxygen content can be determined by difference. Three dimensionless parameters Cr, Hr and HHVrdefined in Equations (7)–(9), are introduced for the regression analysis.

$$\mathbf{C\_r = \frac{\mathbf{C} \times \mathbf{Y\_s}}{100 \times \mathbf{C\_0}}} \tag{7}$$

$$\mathbf{H\_{r}} = \frac{\mathbf{H} \times \mathbf{Y\_{s}}}{100 \times \mathbf{H\_{0}}} \tag{8}$$

$$\text{HHV}\_{\text{r}} = \frac{\text{HHV} \times \text{Y}\_{\text{s}}}{100 \times \text{HHV}\_{\text{o}}} \tag{9}$$

where the subscript "o" refers to the raw biomass.


**Table 2.** Summary of the collected data (dry and ash free) categorized by the wood type.
