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.
⏱ Temporal Stride / Accelerated Horizon Control
Adjust the simulation clock. High speeds bypass physical constraints by utilizing the auto-regressive PIML inference engine.
Control Room Scenario Link
Process Variable Coordinator
Values can be imported from the Model Control Room or adjusted here during the 48-hour run. The simulator recalculates VFA/ALK risk, methane quality, yield and warning behavior from these operating conditions.
| Time | Action | State / Effect |
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Virtual Plant State LIVE
Hybrid Inference Architecture
1. Gasification: Catalyst Coking Curve
Tracking non-linear degradation of Ni45Fe15Ca40 to empirical 83% limit over 48h.
2. AD: Methane Yield & Soft Sensor Warning
AI prediction of CH4 output mapping against VFA/ALK accumulation (collapse boundary > 0.4).