## 6 Conclusions  
In this paper, two machine learning models are developed to forecast the concentration of pentanes (C5) in the LPG debutanization process. The first one is a regression model that provides pentane concentration estimates. The second one is a classifier that predicts whether the pentane concentration levels exceed allowed thresholds. Both models were designed to provide real-time forecasts based on sensor data. The advantages of the models are that only two sensors are required (temperature and pressure sensors, located at two distinct points at the top of the debutanizer column). Both models were compared against several baseline models, and machine learning models developed based on algorithms cited in the literature.  
Our experiments show that the best results for the pentanes concentration estimation was obtained with a voting regressor, trained with historical data of the debutanizer unit. The model surpasses the performance achieved by a baseline predicting the pentane concentration as a median of past values and a linear regressor predicting the concentration from raw sensor values. When predicting the off-specification detection, best results were achieved with a CatBoost classifier trained with a focal loss over the data of both debutanizer units considered in this research. The model achieved an AUC ROC of 0.7670. In both cases, the addition of data from another debutanizer unit boosted the learning and consequent performance of most of the models.  
In addition to the aforementioned models, we developed a prototype dashboard that allows visualization of relevant information regarding feature selection, features relevance to the model, and sensor reading values within which the model was trained. Such a dashboard is useful to assess strengths, limitations and improvement opportunities regarding the developed models.  
We envision several directions for future research. First, we would like to extend these experiments to a broader range of debutanizer units. Secondly, we would like to compare the current approach to more complex settings, where a broader range of sensors is available. Finally, we consider that this approach can be applied in other industries using distillation processes and where soft sensors predicting specific substance concentrations are helpful.  
Conceptualization, J.M.R.; methodology, J.M.R.; software, J.M.R., E.T., A.K. and N.S.; validation, J.M.R. and A.K.; formal analysis, J.M.R. and A.K.; investigation, J.M.R., N.S.; resources, M.K.O., D.A.Y., A.K. and G.A., D.M., B.F.; data curation, A.K., N.S., J.M.R. and E.T.; writing--original draft preparation, J.M.R.; writing--review and editing, J.M.R., A.K., G.A., J.L., P.E., I.M., B.F. and D.M.; visualization, J.M.R.; supervision, K.K., A.K., G.A., B.F. and D.M.; project administration, G.A., B.F. and D.M.; funding acquisition, P.E., I.M., G.A., B.F. and D.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Slovenian Research Agency and European Union's Horizon 2020 program project FACTLOG under grant agreement number H2020-869951.  
Not applicable.  
Figure 12: The bullet chart summarizes the sensor values distribution: three segments, Q1, Q2 + Q3, and Q4 mark values related to the quartiles; a dark vertical bar within the Q2 + Q3 segment represents the median value, while the dark horizontal bar within the bullet chart informs the mean reading value.