Design a bioelectrochemical resource-recovery system
Start with a wastewater stream. Get a ranked design from the lab catalog via the 5-physics-family router, open its dossier and P&ID sheet, and see a real ML prediction with conformal bounds, an OOD flag, and a prior-trust badge. Trace every number to its source artifact.
- /api/ml/predict — 15-block response
- holdout-coverage-2026-05-21.json — 940 OOS obs
- calibration.json — 517 obs, random split (not paper-disjoint)
- wastewater/*-baseline.json — 6 process-TEAs
- @messai/component-catalog — 6 lab-catalog twins
- @messai/pid-schematic — ISA-5.1 sheets per twin
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 · lab catalog designs
Pick a coupled architecture
Ranked by influent fit using the 5-physics-family router. Every card is a digital twin from the lab catalog; click one to drive every downstream panel.
Section 2b · System dossier · from the lab catalog
MFC + Anaerobic Digester (electricity from wastewater)
The Bio-Battery Train
A two-stage flowsheet that pairs an anaerobic digester (for bulk COD reduction + biogas recovery) with a downstream microbial fuel cell that polishes residual COD into direct electrical current. The AD does the thermodynamic heavy lifting; the MFC monetises what’s left as electrons rather than gas, with no biogas-handling burden on the back end.
Key differentiator
AD alone leaves 25–50 % of the influent COD behind in the digestate. An MFC polish step recovers a portion of that residual COD as electricity at electrode-surface efficiencies that exceed dark-fermentation H2 routes. Bridges the parameter-sweep page’s Butler-Volmer + Monod outputs (sweep export → MESSanUnit) directly into a full-flowsheet TEA.
How it works
Influent enters the AD; biogas exits the dome to CHP; digestate flows by gravity into the MFC anode chamber where exoelectrogens (Geobacter / mixed consortia) attach to a carbon-cloth or graphite-felt anode. Substrate oxidation releases electrons that travel through an external circuit to an air cathode (typical) and protons migrate through a separator. Power is dumped to a buck-boost charger that conditions the MFC output to grid-tie or battery voltage.
Performance envelope
Research status
MFCs remain at TRL 4–5 outside niche sensor applications. The MFC+AD combination is published (Logan group, Penn State 2010s) but no pilot plants are continuously operated. Active research: scaling anode area while preserving CE, cheaper cathode catalysts, hybrid AD-MFC operating strategies.
Key parameters
Reactor geometry
Left: cylindrical AD tank (see "wastewater-ad" for sizing). Right: rectangular MFC chamber with planar anode (left wall) and air cathode (right wall, typically Pt/C on carbon cloth or activated-carbon-on-stainless-mesh). Top: external circuit with adjustable load resistor; LED indicator on the wire visualises current.
Applications
- Wastewater treatment with energy recovery (food / dairy / brewery effluents)
- Decentralised treatment at sites without grid access
- BOD sensors at WWTP outfalls (MFC voltage tracks BOD)
- Educational / public-engagement reactors
- Combined heat (AD biogas) + power (MFC) micro-grids
Scale
Pilotscale
TRL
4–5
Active volume
0.5 – 50 m³ MFC stack downstream of a 10 000 m³ AD
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
Each lift is a fitted corpus effect with its n and 95 % CI. Stage one to see it as a ghost overlay in 3D; apply to commit it to the scenario.
live prediction unavailable — lifts shown as relative % only
Section 7b · Raise confidence
33 evidence gaps for MFC + Anaerobic Digestion
Each row is a design lever the corpus cannot yet quantify with high confidence, the study that would close it, and the literature search that looks for it.
- external resistance → power density128 papers · cross-paper · effect not distinguishable from zero · lowSuggested study: vary external resistance; hold temperature, pH, HRT, COD_in, anode material fixed; measure power density + coulombic efficiency; ≥ 3 conditions per study.openalex: external resistance "microbial fuel cell" power density
- pH → power density110 papers · cross-paper · effect not distinguishable from zero · lowSuggested study: vary anolyte pH; hold temperature, HRT, COD_in, R_ext, anode material fixed; measure power density + coulombic efficiency; ≥ 3 conditions per study.openalex: anolyte pH "microbial fuel cell" power density
- temperature → COD removal108 papers · cross-paper · effect not distinguishable from zero · mediumSuggested study: vary reactor temperature; hold pH, HRT, COD_in, R_ext, anode material fixed; measure COD removal + coulombic efficiency; ≥ 3 conditions per study.openalex: reactor temperature "microbial fuel cell" COD removal
- pH → COD removal105 papers · cross-paper · effect not distinguishable from zero · lowSuggested study: vary anolyte pH; hold temperature, HRT, COD_in, R_ext, anode material fixed; measure COD removal + coulombic efficiency; ≥ 3 conditions per study.openalex: anolyte pH "microbial fuel cell" COD removal
- influent COD → COD removal94 papers · cross-paper · effect not distinguishable from zero · lowSuggested study: vary influent COD concentration; hold temperature, pH, HRT, R_ext, anode material fixed; measure COD removal + coulombic efficiency; ≥ 3 conditions per study.openalex: influent COD concentration "microbial fuel cell" COD removal
- influent COD → coulombic efficiency62 papers · cross-paper · effect not distinguishable from zero · lowSuggested study: vary influent COD concentration; hold temperature, pH, HRT, R_ext, anode material fixed; measure coulombic efficiency + power density; ≥ 3 conditions per study.openalex: influent COD concentration "microbial fuel cell" coulombic efficiency
Section 7c · What to measure
Measurement plan for MFC + Anaerobic Digestion
Ranked from 255 MFC papers that report a recovery target: levers a study should vary, the context variables the corpus expects alongside them, and the outcomes to report. Coverage and correlation are descriptive; only the effect column is a fitted causal-grade estimate.
| parameter | role | reported in | fitted effect | gaps it closes | ρ vs target (pooled) |
|---|---|---|---|---|---|
| anode material | lever | 0% · 0 | +0.017 → power density · low · n=5 | 64 · hold in 64 | — |
| hydraulic retention timeh | lever | 12% · 30 | −0.029 → COD removal · low · n=43 | 15 · hold in 113 | -0.25 (11) |
| pH | lever | 33% · 85 | −0.086 → ammonium removal · medium · n=10 | 14 · hold in 114 | +0.55 (15) |
| temperature°C | lever | 22% · 56 | +0.0087 → COD removal · medium · n=108 | 14 · hold in 114 | -0.25 (103) |
| COD Concentrationmg/L | lever | 34% · 86 | +4.6e-7 → COD removal · low · n=94 | 13 · hold in 115 | +0.43 (69) |
| External LoadΩ | lever | 28% · 71 | −0.0000037 → coulombic efficiency · medium · n=60 | 8 · hold in 120 | +0.78 (8) |
| Power DensitymW/cm² | covariate | 54% · 137 | — | — | +0.74 (30) |
| voltageV | covariate | 44% · 111 | — | — | +0.48 (13) |
| Open Circuit VoltageV | covariate | 35% · 90 | — | — | -0.45 (10) |
| Current DensitymA/cm² | covariate | 34% · 87 | — | — | +0.56 (26) |
| Reactor VolumemL | covariate | 31% · 79 | — | — | -0.20 (73) |
| Internal ResistanceΩ | covariate | 30% · 77 | — | — | -0.71 (12) |
| Cod Removal% | outcome | 83% · 211 | — | — | — |
| coulombic efficiency% | outcome | 43% · 110 | — | — | — |
| Ammonium-N Removed% | outcome | 9% · 22 | — | — | — |
| Phosphorus Removal% | outcome | 4% · 10 | — | — | — |
source: apps/web/public/data/computed/research/measure-priorities.json · 2026-09-12
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
⚠ Conformal coverage guarantee is provisional
The split-conformal q̂ were calibrated against holdout-priors-2026-05-21, but the served predictor is v2-sota-2026-05-15. The interval math (log-space + physical clamp) is correct, but the marginal-coverage guarantee holds only once the constants are regenerated against the served fit. Every /api/ml/predict response carries this same version_check.ok = false.
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).
Section 11 · For industrial partners · your stream in, value out
What your stream could become
Pick the row that matches what you discharge or emit. Each wastewater row is a modeled system with a published baseline and selects that design above; each prediction comes with an interval and an honest flag when your stream sits outside the data we have.
| Your input | System | What comes out | Notes |
|---|---|---|---|
MFC + anaerobic digestion The Bio-Battery Train · TRL 4–5 | waterelectricitybiogas | Brewery, dairy, food processing. Most data-rich archetype. | |
MEC + anaerobic digestion The Hydrogen Train · TRL 3–4 | waterH₂biogas | ~0.5 V applied; hydrogen instead of power. | |
MFC + struvite side-stream The Phosphorus Mine · TRL 8–9 | waterelectricitystruvite (P) | MgCl₂ dosing; 75 % P recovery in the baseline model. | |
MFC + NH₃ stripping The Ammonia Stripper · TRL 8–9 | waterelectricityNH₃ (N) | N-fertilizer feedstock; 65 % N recovery in the baseline model. | |
MFC + electrochemical polishing The Color Eater · TRL 7–8 | waterelectricitymetals | Mining, plating, electronics effluent. | |
Conventional activated sludge The Industry Workhorse · TRL 9 | water only | The incumbent baseline every case is priced against. | |
| Ambient air ↗ | Bipolar-membrane electrodialysis DAC | CO₂ streamcompressed CO₂ | Presets at 0.15 t/yr bench, 1 kt/yr pilot, 100 kt/yr commercial. Same core, different inlet. |
| Mine ventilation methane ↗ | Methanotrophic biofilter | CH₄ oxidized | Early model, not yet calibrated. Ask if relevant. |
What a feasibility study looks like
Six to eight weeks from stream data to a decision-grade answer. The output is not a brochure; it is the number, its interval, the plant economics, and the one or two bench measurements that would move the answer most.
Weeks 1–2
Characterize
You share flow, COD, N, P, metals, temperature, and what you pay today. We map it to the corpus and flag where your stream is outside the data.
Weeks 2–4
Route and predict
The stream is routed to the right physics family and archetype. Every output carries a calibrated interval and a prior-trust badge.
Weeks 4–6
Price it
CAPEX, OPEX, levelized cost or net present value over 25 years, against your incumbent. Cost inputs are labeled literature or assumed, line by line.
Weeks 6–8
Name the test
A ranked measurement plan: the one or two bench measurements that would move the answer most, with the variance each one explains.
What we will tell you before you ask
- Waste-to-value predictions are strongest for MFC systems, where held-out coverage was measured. Sparse archetypes carry a visible out-of-distribution flag in the prediction strip above.
- Recovery fractions in the balance are baseline-model constants, not measurements of your stream: 75 % P as struvite and 65 % N as NH₃ until a partner sample replaces them.
- Cost inputs are labeled literature or assumed, line by line in the mini-TEA. A partner quote replaces an assumption with a fact.
- The DAC and mine-methane models have not been calibrated against a running plant. They are listed so you know they exist, and they say so on their own pages.
Partners are the calibration. The first studies are priced accordingly.
Start with one stream
Send a flow rate and a lab sheet. Within a week you get a routed archetype, a first interval, and a scope for the study.