Fluid catalytic cracking (FCC) is one of the most important processes in the renewable
energy as well as petrochemical industries. The prediction and understanding of the FCC
performance in a real industrial environment is still challenging, as this is a highly complex
process affected by many extremely non-linear and interrelated factors. In this paper, a novel
hybrid predictive framework for FCC is developed by integrating a data-driven deep neural
network with a physically meaningful lumped kinetic model, powered by orders of magnitude …