Carbelim
Microalgae Carbon Capture CalculatorSequestration Estimator
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Live0.71 t CO₂/yr
🌿Adjust any parameter — outputs update in real time.
Real-timev1.0OD·V·F·η

Live Results

Updates live on every input change
Annual CO₂ Sequestration
712.5
kg CO₂ per year
0.71tonnes/yr
Equivalent to offsetting 4,292 km of passenger-car driving
Monthly Capture
59.4
kg / month
Daily Capture
2.16
kg / day
Daily Biomass Yield
1.18
kg dry mass / day
Net Capture Factor
0.238
kg CO₂ · OD⁻¹·L⁻¹·yr⁻¹
System Summary

A 1,000 L flat-panel PBR at OD 3.00, operating under the Standard scenario for 330 days/yr at 95% efficiency, will sequester approximately 0.71 t CO₂ annually — yielding around 1.18 kg of dry algal biomass per operating day.

01

Sensitivity analysis

OD vs Annual CO₂ CaptureVolume & Days held constant
Volume vs Annual CO₂ CaptureOD & Days held constant · log scale
02

Methodology & assumptions

1 · Core Model Equation

YCO₂ (kg yr⁻¹) = OD × V (L) × F (kg · OD⁻¹ · L⁻¹ · yr⁻¹) × η
ODOptical density at 680 nm — the chlorophyll a absorption peak. Used as a linear proxy for volumetric biomass concentration under Beer–Lambert conditions (valid for OD₆₈₀ 0–10 in well-mixed cultures).[1, 4]
VWorking volume of the flat-panel photobioreactor in litres.
FEmpirical capture factor — maps OD-normalised volumetric productivity to annual CO₂ sequestration. Calibrated from published industrial-scale flat-panel PBR productivity data.[2, 3, 5]
ηSystem efficiency (0–1). Accounts for scheduled downtime, harvest losses, evaporation, self-shading at high OD, and respiratory CO₂ release (typically 15–25% of gross fixation).[2, 8]

2 · Capture-Factor Scenarios

F is derived from reported areal and volumetric productivities for flat-panel PBRs, converted to OD-normalised units using typical path-lengths (2–5 cm) and published dry-weight/OD calibration data.[2, 3, 5] The three scenarios bracket the performance range documented in peer-reviewed pilot and industrial studies:

ScenarioF valueProductivity basis & conditionsRefs
Conservative0.18PAR <150 µmol m⁻² s⁻¹, suboptimal CO₂ supply, non-optimised wild-type strains; corresponds to ~0.10–0.18 g·L⁻¹·d⁻¹ dry-weight productivity[2, 6]
Standard0.25Typical industrial flat-panel with CO₂ sparging, ~200 µmol m⁻² s⁻¹ PAR, controlled pH 7–8; ~0.18–0.25 g·L⁻¹·d⁻¹[3, 5]
Optimised0.30High-PAR (>300 µmol m⁻² s⁻¹), 5–10% CO₂-enriched sparging, selected/engineered strain, automated pH control; ~0.25–0.35 g·L⁻¹·d⁻¹[3, 7]

3 · CO₂ Fixation Stoichiometry

τ = Cfrac × (MCO₂ / MC) = 0.50 × (44.01 / 12.01) = 1.83 kg CO₂ kg⁻¹ dry biomass

Microalgal dry biomass contains approximately 48–52% carbon by mass across commonly cultivated genera — Chlorella, Scenedesmus, Nannochloropsis, and Spirulina.[1, 8] The representative empirical formula CH₁.₈O₀.₅N₀.₂ implies a carbon mass fraction of 48.8%, yielding τ ≈ 1.79–1.83 kg CO₂/kg.[9] The value τ = 1.83 (50% C assumption) is the de facto standard adopted in microalgae life-cycle assessment and techno-economic literature.[8, 9]

The η term accounts for respiratory CO₂ release (≈15–25% of gross fixation), so the model output represents net CO₂ sequestration, not gross photosynthetic fixation.

4 · Operating Envelope & Constraints

  • OD₆₈₀ 0–10: Chlorophyll a absorbs maximally near 680 nm; the Beer–Lambert linear relationship between OD and biomass concentration holds in well-stirred suspensions up to OD₆₈₀ ≈ 10. Beyond this threshold, mutual shading limits productive culture depth and the linear model overestimates CO₂ fixation.[4]
  • PAR ≥ 200 µmol photons m⁻² s⁻¹: Light saturation for photoautotrophic growth typically falls in the range 150–350 µmol m⁻² s⁻¹ depending on species and acclimation state. Below this threshold, photosynthetic rate and CO₂ fixation drop non-linearly (photolimitation regime).[5, 6]
  • Flat-panel geometry: Flat-panel PBRs offer surface-to-volume ratios of 80–300 m² m⁻³, outperforming tubular reactors (30–80 m² m⁻³) and open raceways (<10 m² m⁻³), enabling the higher volumetric productivities underpinning F.[5, 7]
  • Geometry correction ±10–20%: Switching to tubular or open-raceway systems typically reduces volumetric productivity by 10–30% due to longer light-path lengths and lower illuminated volume fraction.[6, 7]
  • System efficiency η: The default 95% represents an idealised controlled indoor system. Outdoor systems under variable irradiance typically achieve η ≈ 60–80%; factoring in seasonal variation and non-productive periods (cleaning, harvest, maintenance) brings realistic annual η to 70–90%.[2, 3]

5 · CO₂ Emission Equivalence Factor

Captured CO₂ is expressed as an equivalent passenger-car driving distance offset using:

doffset (km) = YCO₂ (kg) / 0.166 kg km⁻¹

The factor 0.166 kg CO₂ km⁻¹ (= 166 g CO₂ km⁻¹) is derived from the IEA Global Fuel Economy Initiative 2021 report, which documents a global average rated CO₂ intensity of 167 g km⁻¹ for new light-duty vehicle registrations in 2019 (petrol, diesel, and hybrid combined).[10] This figure is illustrative: regional averages vary significantly — EU 2022 fleet average ≈ 120 g km⁻¹; US 2022 ≈ 215 g km⁻¹.

6 · Model Scope & Limitations

  • OD as biomass proxy: OD₆₈₀ is sensitive to cell size, pigmentation, and suspended debris. A strain-specific OD-to-dry-weight calibration curve is required for quantitative pilot validation.
  • Linear volume scaling: The model assumes constant CO₂ uptake per unit OD per unit volume. Real systems experience light attenuation at high volume with fixed illumination area — estimates for V > 2,000 L with fixed lighting should be treated as upper bounds without proportional illumination scaling.
  • F calibration uncertainty: Published F values carry ±20–30% inter-study variability due to differences in strain, reactor geometry, and local climate. Pilot-scale experimental determination of F for the specific strain–reactor combination is recommended before commercial sizing.
  • Steady-state assumption: The model assumes continuous or semi-continuous operation at constant OD. Batch or fed-batch systems will yield lower effective annual capture due to lag and stationary phases.
All estimates carry ±20–30% uncertainty at laboratory/pilot scale. This tool is intended for preliminary sizing and scenario comparison — not for bankable yield guarantees. Independent pilot-scale validation is recommended before commercial investment.

References

  1. Chisti, Y. (2007). Biodiesel from microalgae. Biotechnology Advances, 25(3), 294–306. https://doi.org/10.1016/j.biotechadv.2007.02.001
  2. Acién, F.G., Fernández, J.M., Magán, J.J., & Molina, E. (2012). Production cost of a real microalgae production plant and strategies to reduce it. Biotechnology Advances, 30(6), 1344–1353. https://doi.org/10.1016/j.biotechadv.2012.02.005
  3. Slegers, P.M., Wijffels, R.H., van Straten, G., & van Boxtel, A.J.B. (2011). Design scenarios for flat panel photobioreactors. Applied Energy, 88(10), 3342–3353. https://doi.org/10.1016/j.apenergy.2010.12.037
  4. Ugwu, C.U., Aoyagi, H., & Uchiyama, H. (2008). Photobioreactors for mass cultivation of algae. Bioresource Technology, 99(10), 4021–4028. https://doi.org/10.1016/j.biortech.2007.01.046
  5. Posten, C. (2009). Design principles of photo-bioreactors for cultivation of microalgae. Engineering in Life Sciences, 9(3), 165–177. https://doi.org/10.1002/elsc.200900003
  6. Wijffels, R.H., & Barbosa, M.J. (2010). An Outlook on Microalgal Biofuels. Science, 329(5993), 796–799. https://doi.org/10.1126/science.1189003
  7. Molina, E., Fernández, J., Acién, F.G., & Chisti, Y. (2001). Tubular photobioreactor design for algal cultures. Journal of Biotechnology, 92(2), 113–131. https://doi.org/10.1016/S0168-1656(01)00353-4
  8. Lardon, L., Hélias, A., Sialve, B., Steyer, J.-P., & Bernard, O. (2009). Life-Cycle Assessment of Biodiesel Production from Microalgae. Environmental Science & Technology, 43(17), 6475–6481. https://doi.org/10.1021/es900705j
  9. Williams, P.J. le B., & Laurens, L.M.L. (2010). Microalgae as biodiesel & biomass feedstocks: Review & analysis of the biochemistry, energetics & economics. Energy & Environmental Science, 3(5), 554–590. https://doi.org/10.1039/b924978h
  10. IEA (2021). Global Fuel Economy Initiative 2021. International Energy Agency, Paris. https://www.iea.org/reports/global-fuel-economy-initiative-2021