MESSAI · microbial electrochemical systems AI
MESSAI predicts what a microbial electrochemical system will recover from your effluent — and proves it with the largest curated MES corpus, real ML calibration, and honest scope limits. Built for the waste-stream operators looking to adopt MES and the researchers who study it.
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Two front doors · one platform
If you operate a waste stream
Pick a waste stream → see predicted recovery
Six industrial archetypes + custom influent characterisation. Live ML predictions with 95% CI.
Honest TEA + LCA per archetype
CapEx, OpEx, LCOE, LCO-water scaled with flow. GWP + fossil energy from peer-reviewed baselines.
5-physics-family routing
MFC / MEC / MDC / MES / MNRC / MMRC / MBES auto-routed by influent characteristics.
If you research MES
23,480 papers · 195,846 measurements
Two-tier extraction (paper header + per-measurement conditions). Every row carries DOI + snippet + confidence.
706 canonical parameters, FK-linked DAG
2,812 mechanistic edges with literature support. Multi-axis taxonomy (17 primary types × subtypes × combinations).
Per-class predictors + conformal bounds
97.98% OOS coverage at the 95% target on 940 held-out observations. Hierarchical priors fit via PyMC NUTS.
Whitepaper · 2026-05
How 5-physics-family routing, conformal calibration, and a FK-linked parameter DAG turn the noisiest part of bioelectrochemistry into something you can quote, build, and underwrite.
Inside a MFC
The fundamental two-chamber architecture every model on this site is built from. Drag to inspect.
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