GFIS | Green Fuel Intelligence System

Research Context

Mechanistic models like ADM1 or CFD-DEM accurately simulate anaerobic digestion and gasification, but require massive computational resources (days of computing for seconds of simulation). GFIS solves this via a Digital Twin. By utilizing a hybrid XGBoost + LSTM architecture bounded by Physics-Informed loss functions, the digital twin decouples physical time from computational time, granting operators the ability to instantaneously forecast a complex 48-hour operational window.

PIML positioning was strengthened from the accepted BITS abstract feedback. The term is used here as Physics-Informed Machine Learning, with credit to the evaluator for sharpening GFIS from web prediction toward a physics-guided digital twin.