## 5 Experiments and Results
### Regression Models  
When implementing the experiments for the regression models presented in Section 4.4.1, we measured MAE and RMSE metrics. We present the results in Tables 4 and 5. From Experiment 1, we observed the best overall performance was achieved with _Model 4 (VR)_, which demonstrates the best performance for all the scenarios except for one (RMSE for _Unit B_), where it achieved the second-best prediction. This performance was nearly matched by _Baseline 1 (C5 median)_, which achieved the best performance in three cases: _Unit B_, and MAE for _Unit A_. We consider the _Model 2 (SVR)_ was the third-best model among the evaluated ones, achieving the second-best prediction in all cases, except for MAE at Unit A, where it matched the best performance displayed by _Model 4 (VR)_ and _Baseline 1 (C5 median)_. Moreover, we found the _Model 1 (LiR)_ demonstrated a significantly worse performance, which we attribute to the feature selection. We ground this conclusion on the fact that a better result was obtained by _Baseline (LiR)_, and while some improvement was observed when augmenting the data in Experiment 2, it did not match the performance of the rest of the models. The best overall performance for Experiment 2 was achieved by _Model 4 (VR)_, which achieved the best performance at _Unit A_, and second-best for _Unit B_. We consider the overall second-best performance was achieved by _Baseline 1 (C5 median)_, which had the best performance in _Unit B_, and second-best considering MAE at _Unit A_. Figure 10: We pose two experiments: Experiment 1 trains models only with data of the debutanizer unit we aim to predict for (**A**), while Experiment 2 enriches the training set with data from other debutanizer unit (**B**). When comparing results from both experiments, we observed _Model 4 (VR)_ displayed the best performance, surpassing the _Baseline 1 (C5 median)_. _Model 4 (VR)_ is used to predict the C5 peaks providing good forecasts for low C5 levels, which reflects on the close results when compared to _Baseline 1 (C5 median)_. Although the SVR algorithm is frequently reported in the literature, it achieved the third-best performance in Experiment 1 and degraded in Experiment 2. Using data from both units to train the models improved the performance of _Baseline 2 (LiR)_ and _Model 4 (VR)_ in all cases. It also improved the performance of _Model 1 (LiR)_ for _Unit B_, and degraded the performance of _Model 3 (MLPR)_ in all cases, except when measuring RMSE for _Unit B_. Finally, we assessed which features were considered most informative by the feature selection criteria for both experiments. We found that from Experiment 1, the most relevant features were the pressure sensor readings, features from _Feature Group 6_ (ratio between expected T1 temperature and P1 pressure, for the given LPG mixtures--see Table 3), and categorical features indicating whether the pressure sensor readings are below 7.14 kg/cm\({}^{2}\), or above 7.63 kg/cm\({}^{2}\). However, the set of relevant features in Experiment 2 changed. Among the most important ones, we found those from _Feature Group 2_ (expected mixture saturation vapor pressure for considering temperature T2--see Table 3), and those from _Feature Group 6_. Pressure measures were still considered relevant in Experiment 2, but their importance faded in the presence of the ones mentioned above.