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GP-CH-1104YQQChemicalReady

Electrochemical CO2 reactor transport model

A segmented chemical-engineering model for studying mass transfer, CO selectivity, carbon loss, product concentration, and electrical energy in a gas-fed electrochemical CO2 reactor.

Electrochemical CO2 reactor transport model project visual
GP-CH-1104YQQ · Chemical
  • Python
  • NumPy
  • Matplotlib
  • Jupyter

Project definition

Problem statement

Gas-fed electrochemical CO2 reactors can lose selectivity when reactant delivery to the catalyst falls below the electrochemical demand. Restricting the feed can increase conversion while also increasing hydrogen dilution, carbonate loss, and energy per unit product.

The engineering problem is to compare reactor layouts while tracking charge, carbon, axial CO2 availability, outlet composition, voltage, and electrical energy together.

Project objectives

  • Model a 25 cm2 reactor with twenty axial control volumes.
  • Compare serpentine GDE, flow-through GDE, zero-gap, and staged-feed layouts.
  • Run sixty controlled cases across three inlet flows and five current densities.
  • Measure CO Faradaic efficiency, conversion, carbon efficiency, outlet CO, voltage, and specific energy.
  • Verify bounds, transport trends, segment resolution, and carbon-balance closure.

Project structure

Project components

01

Reactor layouts

Defines the transport, selectivity, carbonate, resistance, voltage, and feed-staging assumptions.

02

Axial model

Tracks bulk and catalyst-surface CO2 through twenty reactor segments.

03

Charge and carbon balance

Allocates current to CO and hydrogen and retains carbonate as a separate carbon loss.

04

Experiment

Runs all sixty controlled cases and retains the complete result matrix.

05

Evidence

Writes CSV, JSON, transport plots, performance comparisons, and balance checks.

Methodology

Project workflow

  1. 01
    Select a case

    Choose the reactor layout, inlet flow, current density, and inlet CO2 fraction.

  2. 02
    Calculate transport

    The model estimates local limiting current and catalyst-surface CO2 availability.

  3. 03
    Allocate charge

    Current is divided between CO production and hydrogen competition.

  4. 04
    Close carbon

    Unreacted CO2, CO product, and carbonate loss are retained in the balance.

  5. 05
    Compare results

    Product concentration, conversion, carbon efficiency, voltage, and energy are compared.

Demonstration scenario

The sixty-case study compares all four layouts under the same inlet flows and current densities. The best retained outlet CO concentration is 88.80 percent for the zero-gap case at 50 sccm and 200 mA/cm2, while the lowest-energy case occurs under a different condition.

Engineering

Tools and method

Tools
The project uses Python, NumPy, Matplotlib, Jupyter for subject analysis, simulation, and results.
Reactor model
Python and NumPy for axial material, charge, carbon, voltage, and energy calculations.
Experiment evidence
CSV and JSON outputs with Matplotlib figures generated directly from retained results.
Verification
Automated checks for physical bounds, directional trends, commands, evidence generation, and carbon closure.

Testing

Evaluation

Evaluation measures

  • CO Faradaic efficiency and production rate
  • Single-pass conversion and outlet CO concentration
  • Carbon efficiency and carbonate loss
  • Minimum catalyst-surface CO2 availability
  • Average cell voltage and specific electrical energy
  • Automated tests, coverage, dependency audit, and carbon residual

Project boundaries

  • Parameters are illustrative and are not fitted to a physical reactor.
  • The model is steady state, isothermal, and one dimensional.
  • It does not model pressure drop, humidity, flooding, salt precipitation, heat, degradation, or downstream separation.
  • Results are comparative engineering evidence, not reactor approval or a commercial process guarantee.

Included

  1. 01Complete Python source code
  2. 02Serpentine GDE, flow-through GDE, zero-gap, and staged-feed reactor models
  3. 03Sixty-case experiment with CSV, JSON, and eight result figures
  4. 04106-page project report in PDF and editable Word formats
  5. 0517-page setup and usage guide in PDF and editable Word formats
  6. 0644 annotated references and sourced literature material
  7. 0753 automated tests with 99 percent statement coverage

Project record

No information is collected on this page.

Permanent project ID
GP-CH-1104YQQ
Catalogued
21 Aug 2026
Completed
24 Aug 2026
Verified
24 Aug 2026
Demonstration
Included in repository

Handover

After purchase

  1. 01
    Payment is confirmed

    The project is marked unavailable and cannot be purchased again.

  2. 02
    Repository access is granted

    The buyer's submitted GitHub account receives access to the private repository.

  3. 03
    The purchase record is delivered

    The certification sheet is prepared from the reviewed buyer details and sent privately by email.