Deployment of A Fuel Oil Blending Viscosity Inferential – A Comparison of Conventional and Machine Learning Models

This manuscript describes an inferential to estimate fuel oil viscosity on real-time basis using a hybrid modeling approach. Various conventional and machine learning approaches were compared to select the best model. The deployed inferential can be used as an advisory tool (open-loop) for refinery personnel to capture sub-optimal components distribution across the fuel oil network, or it can be implemented as part of the blending system for closed-loop control applications. A modern inferential framework was established which allows deployment of conventional or modern machine learning models in the plant control network.