As amply acknowledged, operational errors can be identified as one of the most common cause of plant equipment deterioration, consequently the operational control exerts a determining role in managing and slowing down the effects of aging and enhancing safety. Given the ongoing trend towards advanced sensors and real-time performance monitoring, novel approaches represent an up-to-date research topic in changing risk environments. In designing and implementing reliable operational control systems, based on data-driven models and machine learning for predicting the system behaviour, one of the critical issues to deal with is the co-existence of Boolean elements (e.g. failure of instruments) and analogical elements (deviation of process variables). Starting from this observation, this paper outlines a hybrid system consisting of a hierarchical predictive network, where the input of the analogical elements are the predicted values of the process variables obtained by deep learning neural networks (soft sensors). The combination of the two approaches allows integrating Boolean events and process variables in an overall predictive dynamic model. In order to verify the actual capability of the system, a pilot application to a hydrocarbon storage park is considered. Upon optimal training sets, the predictive system allows obtaining quasi real time predictions with an overall accuracy attained in the case study higher than 98% over the whole simulation test series.