In this paper, we develop machine learning models for a real-world use case, based on sensor data provided by a Tupras ([[https://www.tupras.com.tr](https://www.tupras.com.tr)]([https://www.tupras.com.tr](https://www.tupras.com.tr)) (accessed on 23 October 2021)) refinery. By examining the actual process in the use case, we found that different debutanizer columns have different features because of their different designs. Moreover, only a few sensors are in the debutanizer column. Most sensor data corresponded to the pipping system that connected the debutanizer column with the condensation unit and the units that follow. We used several debutanizer unit diagrams to understand where the sensors are located, and which sensors are close to the distillation column exit. Temperature and pressure conditions are identified by the ones near the column exit, and hence the first ones placed in the pipes close to the related exit but before the condensation unit. We assume such data provides good insight on how operating conditions relate to extracted samples and measured composition. Furthermore, we observed that there are some cases where both temperature and pressure sensors exist for any given point in the debutanizercolumn, but at least one of them exists. Considering these limitations, machine learning models are developed to predict C5 content based on the inputs of two sensors (one pressure sensor and one temperature sensor). Finally, we develop two machine learning models that provide predictions based on the data from these two sensors for independent estimate: (i) one that predicts the expected amount of C5 in the LPG; and (ii) one that forecasts whether C5 content is off spec (higher than 2%). The contribution of this paper is the use of operational temperature and pressure sensor data to develop:  
1. a machine-learning model to predict C5 content in LPG stream;
2. a machine-learning model to predict if C5 content exceeds specification levels  
Machine-learning models built using data from a few sensors can be more easily applied to a broad range of debutanizer columns since they impose fewer restrictions on the number of input data sources required to provide forecasts. Thus, we consider that a major strength of our approach is the fact that it relies only on data of two sensors, one measuring pressure and the second one measuring temperature in the debutanizer column-both placed at separate locations within the column. Along with the development of the aforementioned models, we also provide a prototype dashboard, which provides global explanations to understand which features were considered most relevant during the feature selection, and which features were considered relevant by the forecasting model. In addition, we provide insights on the sensor value distribution in the training set, to understand the model operational limitations. To evaluate our models, we have used three metrics: two for measuring regression features and one for measuring the classification features. We assess the regression model performance with the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). The MAE is not sensitive to outliers and can thus provide a reasonable estimate of the model performance for normal C5 levels. The RMSE penalizes large errors and thus better indicates if out-of-spec measurements were predicted adequately. The classification model performance is measured with the Area Under the Receiver Operating Characteristic Curve (AUC ROC [1]). AUC ROC is invariant to _a priori_ class probabilities, referring to a relevant property when measuring model discrimination power in an imbalanced dataset. After evaluating the models, results show that our approach is applied to effectively provide real-time C5 content predictions in the LPG debutanization process of our given use case. The rest of this paper is structured as follows: Section 2 presents related work.