GREENFUEL INTELLIGENCE SYSTEM
Intelligence for
circular energy.
From the reactor.
To the connected city.
Bring process science, machine learning and inspectable simulation into one clearer view of bioenergy.
Understand the inputs.
Keep the evidence. See the possibilities.
Understand what drives the process.
Connect feed assumptions, reactor conditions and a modelled response.
Conceptual facility illustration · not a live plant feed
Process science + Applied intelligence + Inspectable simulation
The GFIS approach ↗MEET THE SOFTWARE
One place to follow
the whole process.
GFIS Synthesis brings the facility into focus. Explore the plant, investigate a scenario and retain the reasoning behind an experiment.
GFIS Synthesis
THREE PERSPECTIVES. ONE SYSTEM.
Think across scales.
Understand the signal.
Chronological model comparisons, dataset provenance and physics-aware interpretation.
Explore the facility.
Assembly, digestion, a separate thermal line, storage and useful-energy scenarios.
Connect the infrastructure.
Inspect plant portfolios, city networks and clearly scoped planning assumptions.
EVIDENCE BEFORE ASSUMPTION
A useful model
must earn its place.
Persistence. XGBoost. LSTM. Compare on a chronological basis, keep methane separate from total biogas, and preserve the result—even when a simpler baseline wins.
Explore the scientific foundation →Illustrative chronology · not a plotted experimental result
Persistence
Last observed value
02XGBoost
Past-only features
03LSTM
Sequence memory
Traceable configuration and retained predictions.
THE ENGINEERING JOURNEYA FOUNDATION WORTH KEEPING
Built through curiosity.
Carried forward
by engineering.
The DIPEX journey and M.Tech research are part of GFIS’s foundation. The original workbench, dissertation and evidence readers remain accessible—not lost in a redesign.
Follow the GFIS story →YOUR NEXT QUESTION