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Can this recover value from my waste stream — and is it real?
MESSAI turns thousands of peer-reviewed studies into calibrated models so an industrial or commercial team can evaluate microbial electrochemical systems for wastewater treatment, resource recovery, and hydrogen — with honest uncertainty and a first-pass TEA — before spending capital on a pilot.
Grounded in the literature · honest uncertainty · no black boxes
What you get out of it
Outcome 01
See the recovery — and the honest economics
Enter your influent and get a predicted recovery balance (water, electricity, biogas, H₂, struvite, NH₃) with a 25-year mini-TEA and LCA for your stream. Numbers are literature midpoints, not a contracted quote — you see exactly where each one comes from.
Outcome 02
De-risk before capex
Every prediction ships with conformal uncertainty bounds, an out-of-distribution flag, and a prior-trust badge. You see where the model is confident and where it is extrapolating — so a pilot decision rests on calibrated evidence, not a single point estimate.
Outcome 03
Standardize on evidence, not vendor claims
Recovery and performance ranges trace to thousands of peer-reviewed measurements with published calibration coverage. Compare architectures on a common, auditable basis instead of taking a supplier deck at face value.
Live demo · your stream
Drive it on a stream like yours
Start with a wastewater stream, get a ranked system suggestion, and see a real ML prediction with conformal bounds, an out-of-distribution flag, and a prior-trust badge — then a recovery balance, calibration receipts, and a 25-year mini-TEA.
Illustrative — not design numbers
Every value below is a literature-midpoint estimate with published uncertainty, shown so you can judge whether the recovery is real for your case. Site engineering typically adds 20–40%; these are not a contracted quote or a stamped design. The calibration and scope-limit panels at the bottom show exactly where the models are — and aren't — trustworthy.
Section 1 · Influent characterisation
Define the wastewater stream
Six industrial archetypes or set parameters within their literature ranges. Each slider shows hard bounds, typical band, sub-ranges by facility type, and the active preset's target value.
Section 2 · System suggestion
Pick a coupled architecture
Ranked by influent fit using the 5-physics-family router. Click a card to drive every downstream panel.
loading 3D reactor scene…
Section 4 · Live prediction · /api/ml/predict
Real ML stack, honest bounds
Section 5 · Recovery balance
What this system actually recovers
Daily mass + energy flux from first-principles balance on the influent and the live prediction. Hover any band for source.
- · Using literature-midpoint fallback for power density.
- · COD removal from archetype default (80%).
Section 7 · Design recommendations
Apply a recommended change
Stage a recommendation to see it as a ghost overlay in 3D. Apply to commit it to the scenario.
Section 8 · Sensitivity ladder
Which knob moves the needle
Within-paper Bayesian effects from within-paper-effects.json. Each β is the population-level effect on the target after partialling out paper-level confounds. Sign matters — green increases, red decreases.
Mechanistic closure · direction-validated levers
Does the physics closure predict the direction (sign) of a within-design change better than a coin flip (50%)? These are the only axes that validated. Direction agreement only — not magnitude.
Not shown: current↔substrate and current↔pH are anti-skillful (the closure predicts the wrong direction), so we exclude them rather than surface a misleading bar. The richer interactive version lives at /lab/design.
Section 9 · Calibration · honest caveats
What this model is — and isn't — calibrated for
What we don't claim
- •MDC, MES, MNRC, MMRC, MBES are not in the 97.98% OOS holdout — analytical predictors only.
- •TEA numbers are literature midpoints; not a contracted quote. Site engineering adds 20–40%.
- •COD removal predictions are coarse for high-strength industrial streams with toxic shocks.
- •No predictions of micropollutant removal, antibiotic resistance, or pathogen fate.
- •Long-term biofouling, electrode degradation > 12 months are absent from the corpus.
Section 10 · Mini-TEA · MFC + Anaerobic Digestion
What it costs
From the archetype's Process-TEA baseline. Discount rate 0.1, lifetime 15 yr. Honest disclaimer: literature midpoints, not a contracted quote.
- · Electrode capex $1,200/m² (carbon-cloth, Logan 2008 inflated).
- · Performance inputs are stub literature midpoints — wire to a sweep export for real numbers.
- · Higher 10 % discount rate reflects MFC scale-up risk (Santoro 2017).
Every number traces to a source file · no black boxes · audit dashboard · full technical demo
Who it's for
Wastewater utilities & industrial dischargers
Turn a treatment cost centre into an energy- and nutrient-recovery opportunity — and quantify it before committing.
Resource-recovery & circular-economy teams
Screen struvite, ammonia, metals, and water reuse pathways against the evidence for a given feedstock.
Hydrogen & biogas developers
Assess MEC-H₂ and MFC/MEC + anaerobic-digestion routes for low-strength or recalcitrant effluent.
Process & TEA engineers
Get a defensible starting flowsheet and capex/opex band to build your own detailed model on top of.
Typical streams
The demo ships six commonly-cited industrial and municipal wastewater archetypes as starting points — each calibrated to published characterisations. Load one, or dial in your own.
Brewery effluent
High-strength, well-cited Logan anchor
Municipal wastewater
Largest TAM, low-grade energy + nutrient recovery
Dairy processing
Fats + proteins, balanced C:N
Landfill leachate
Toxic, recalcitrant — MEC-H₂ candidate
Blackwater (decentralized)
High nutrients, struvite + NH₃ recovery target
Pharmaceutical effluent
High-COD, micropollutant polishing
Get started
Bring us your stream. We'll show you what the evidence says it can recover.
Share an influent characterisation and target outcomes, and we'll walk you through a calibrated recovery + TEA assessment and where a pilot would de-risk fastest.
founders@messai.io