## 3 Results
### Statistical and Technological Models  
Experimental results of biocrude yield and contents of nitrogen and oxygen in the biocrude were fitted to nonlinear multiple regression analysis, assuming a second-order polynomial model. The statistical models (Equations (4)-(6)) were obtained from the coded factors (X\({}_{\text{i}}\)) on a dimensionless scale (\(-1\), \(0\), \(1\)) and provided information on the real influence of each variable within the HTL process. On the other hand, the technological models (Equations (7)-(9)) were obtained from the actual temperature (T), reaction time (t), and microalga concentration (CS) in their correspondent units within the experimental ranges studied. Statistic model:  
\[ \begin{split}\text{Y}_{\text{B}}\left(\%\right)&=29. 96+12.65\;\text{X}_{\text{T}}-0.84\;\text{X}_{\text{t}}+0.52\;\text{X}_{\text{CS }}-3.32\;\text{X}_{\text{T}}^{2}-2.38\;\text{X}_{\text{T}}\text{X}_{\text{t}} \\ &\quad+0.41\;\text{X}_{\text{T}}\text{X}_{\text{CS}}-0.65\;\text{ X}_{\text{t}}^{2}+1.05\;\text{X}_{\text{t}}\text{X}_{\text{CS}}+0.84\;\text{X}_{\text{CS}} ^{2}\;(\text{r}^{2}=0.994)\end{split}  \tag{4}\]  
\[ \begin{split}\text{C}_{\text{O}}\left(\%\right)&=5.97+0.71\;\text{X}_{\text{T}}-0.22\;\text{X}_{\text{t}}-0.031\;\text{X}_{\text{ CS}}-0.067\;\text{X}_{\text{T}}^{2}-0.34\;\text{X}_{\text{T}}\text{X}_{\text{t}} \\ &\quad-0.21\;\text{X}_{\text{T}}\text{X}_{\text{CS}}-0.61\;\text{ X}_{\text{t}}^{2}+0.24\;\text{X}_{\text{t}}\text{X}_{\text{CS}}-0.37\;\text{X}_{\text{CS}} ^{2}\;(\text{r}^{2}=0.867)\end{split}  \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Exp. ** & \(\text{X}_{\text{T}}\) & \(\text{X}_{\text{t}}\) & \(\text{X}_{\text{CS}}\) & \(\text{T}\) & \(\text{t}\) & \(\text{CS}^{\text{1}}\) & \(\text{Y}_{\text{B}}\)\({}^{\text{2}}\) & \(\text{C}_{\text{O}}\)\({}^{\text{3}}\) & \(\text{C}_{\text{N}}\)\({}^{\text{4}}\) \\  & & & & (\({}^{\circ}\text{C}\)) & **(min)** & **(wt%)** & **(wt%)** & **(wt%)** & **(wt%)** \\ \hline
1 & \(-1\) & \(1\) & \(1\) & \(200\) & \(180\) & \(50\) & \(17.40\) & \(10.01\) & \(5.46\) \\
2 & \(-1\) & \(1\) & \(-1\) & \(200\) & \(180\) & \(10\) & \(14.67\) & \(20.11\) & \(3.10\) \\
3 & \(-1\) & \(-1\) & \(1\) & \(200\) & \(10\) & \(50\) & \(13.29\) & \(12.42\) & \(4.34\) \\
4 & \(-1\) & \(-1\) & \(-1\) & \(200\) & \(10\) & \(10\) & \(19.03\) & \(15.12\) & \(4.08\) \\
5 & \(1\) & \(1\) & \(1\) & \(320\) & \(180\) & \(50\) & \(38.60\) & \(10.77\) & \(6.12\) \\
6 & \(1\) & \(1\) & \(-1\) & \(320\) & \(180\) & \(10\) & \(35.03\) & \(23.60\) & \(3.64\) \\
7 & \(1\) & \(-1\) & \(1\) & \(320\) & \(10\) & \(50\) & \(42.55\) & \(13.18\) & \(4.75\) \\
8 & \(1\) & \(-1\) & \(-1\) & \(320\) & \(10\) & \(10\) & \(42.35\) & \(15.66\) & \(2.93\) \\
9 & \(0\) & \(0\) & \(0\) & \(260\) & \(95\) & \(30\) & \(29.58\) & \(16.25\) & \(5.78\) \\
10 & \(0\) & \(0\) & \(0\) & \(260\) & \(95\) & \(30\) & \(29.35\) & \(16.25\) & \(5.78\) \\
11 & \(0\) & \(0\) & \(0\) & \(260\) & \(95\) & \(30\) & \(30.73\) & \(9.32\) & \(5.59\) \\
12 & \(-1\) & \(0\)\[ \begin{split}\text{C}_{\text{N}}\left(\%\right)=12.79-3.25\text{ }\text{X}_{\text{T}}-1.36\text{ }\text{X}_{\text{t}}+0.14\text{ }\text{X}_{\text{CS}}+4.81\text{ }\text{X}_{\text{T}}{}^{2}+2.22\text{ }\text{X}_{\text{T}}\text{X}_{\text{t}}\\ +0.31\text{ }\text{X}_{\text{T}}\text{X}_{\text{CS}}-0.10\text{ }\text{X}_{\text{t}}{}^{2}+0.37\text{ }\text{X}_{\text{t}}\text{X}_{\text{CS}}-1.92\text{ }\text{X}_{\text{CS}}{}^{2}\text{ (r}^{2}=0.743)\end{split}  \tag{6}\]  
Technological model:  
\[ \begin{split}\text{Y}_{\text{B}}\left(\%\right)=27.96+0.59\text{ T}-0.26\text{ t}+0.91\text{ CS}-8.10^{-4}\text{T}^{2}+5.10^{5}\text{T}\text{\cdot t}+3\text{\cdot}10^{4}\text{T} \text{-CS}\\ -3\text{\cdot}10^{-4}\text{t}^{2}+3\text{\cdot}10^{-3}\text{ t}\text{-CS}-0.01\text{ CS}^{2}\text{ (r}^{2}=0.994)\end{split}  \tag{7}\]  
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