This research integrated domain knowledge of chemical kinetics with neural ordinary differential equations (Neural ODEs) to model hydrocracking reactions in petroleum refining. The PINO model was trained on synthetic and industrial data, capturing non-linear dynamics and temperature-dependent behaviors of multi-lump reaction networks. By incorporating Arrhenius kinetics into the training process, the model achieved both physical consistency and high predictive accuracy. (Souvik)
Publication(s):
- Ta, S., Samavedham, L., Ray, A., and Liu, T. Physics—Informed neural ordinary differential equations for hydrocracking kinetics modeling and system identification. 2026, 167(1), DOI: 10.1016/j.engappai.2026.113808