Industry 4.0 has emerged together with relevant technological tools that have enabled the rise of this new industrial paradigm. One of the main employed tools is Machine Learning techniques, which allow us to extract knowledge from raw data and, therefore, devise intelligent strategies or systems to improve actual industrial processes. In this regard, this paper focuses on the development of a prediction system based on Random Forest (RF) to estimate Pentane concentration in advance. The proposed system is validated offline with more than a year of data and is also tested online in an Energy plant of the Basque Country. Validation results show acceptable outcomes for supporting the operator’s decision-making with a tool that infers Pentane concentration in Butane 400 min in advance and, therefore, the quality of the obtained product.