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GP-CH-121TMRXChemicalReady

IDAES carbon-capture parameter estimation

A chemical-engineering parameter-estimation study for a reduced MEA carbon-capture absorber, with held-out validation, bootstrap uncertainty, and operating sensitivity analysis.

IDAES carbon-capture parameter estimation project visual
GP-CH-121TMRX · Chemical
  • Python
  • IDAES
  • Pyomo
  • NumPy
  • SciPy
  • Pandas
  • Matplotlib
  • Jupyter

Project definition

Problem statement

Reduced absorber models contain empirical corrections for transfer, equilibrium loading, and thermal response. These parameters can compensate for one another unless campaigns provide enough independent information.

The engineering problem is to estimate the corrections from controlled campaign responses, test prediction on held-out conditions, quantify uncertainty, and state where the reduced model remains valid.

Project objectives

  • Model counter-current carbon dioxide absorption across twenty-four axial segments.
  • Estimate mass-transfer, loading-offset, and heat-response corrections from twelve campaigns.
  • Predict four held-out campaigns without using them in the fitting objective.
  • Quantify uncertainty with 240 campaign bootstrap refits and local Jacobian conditioning.
  • Evaluate eighty operating cases across lean loading, liquid-to-gas ratio, and inlet carbon dioxide fraction.

Project structure

Project components

01

Absorber model

Marches gas composition, solvent loading, and temperature through the segmented column.

02

IDAES parameter layer

Declares bounded model parameters through an IDAES and Pyomo compatible container.

03

Estimator

Fits three corrections with weighted nonlinear least squares and reports Jacobian conditioning.

04

Uncertainty study

Resamples complete campaigns and refits the model 240 times.

05

Evidence pipeline

Writes retained CSV, JSON, PNG, and SVG results for validation and reporting.

Methodology

Project workflow

  1. 01
    Prepare campaigns

    The study loads sixteen deterministic synthetic campaigns with declared response uncertainty.

  2. 02
    Fit parameters

    Only the twelve K campaigns contribute to the weighted objective.

  3. 03
    Validate

    The four V campaigns are predicted after fitting and retain separate error metrics.

  4. 04
    Estimate uncertainty

    Campaign bootstrap intervals and parameter correlations expose weak information and bound contact.

  5. 05
    Map operation

    The eighty-case matrix compares capture, rich loading, outlet temperature, and a duty proxy.

Demonstration scenario

The released study fits twelve absorber campaigns and predicts four held-out campaigns. It then performs 240 campaign bootstrap refits and maps eighty combinations of solvent loading, liquid-to-gas ratio, and inlet carbon dioxide fraction.

Engineering

Tools and method

Tools
The project uses Python, IDAES, Pyomo, NumPy, SciPy, Pandas, Matplotlib, Jupyter for subject analysis, simulation, and results.
Process model
Python and NumPy for segment balances, equilibrium opposition, transfer, solvent loading, and heat response.
Estimation
SciPy nonlinear least squares with explicit response weights, bounds, residuals, and Jacobian analysis.
IDAES integration
IDAES and Pyomo provide the declared engineering parameter container and compatibility layer.
Engineering evidence
Pandas and Matplotlib generate campaign tables, uncertainty evidence, sensitivity matrices, and eight figures.
Verification
Automated tests cover model behavior, estimation, IDAES integration, study outputs, and commands.

Testing

Evaluation

Evaluation measures

  • Estimation and held-out RMSE for capture, rich loading, and outlet temperature
  • Recovery of the three generating corrections
  • Bootstrap percentile intervals and parameter correlation
  • Jacobian singular values and condition number
  • Directional response across the eighty-case operating matrix
  • Automated tests, coverage, resolver checks, dependency audit, and artifact validation

Project boundaries

  • The prepared observations are deterministic synthetic data.
  • The model is steady state, reduced order, and intended for parameter-estimation education.
  • The regeneration value is a transparent duty proxy, not a measured reboiler duty.
  • Hydraulics, rigorous electrolyte thermodynamics, degradation, corrosion, emissions, equipment sizing, safety, and plant economics require separate specialist work.

Included

  1. 01Complete Python source code
  2. 02Reduced twenty-four-segment MEA absorber model
  3. 03Twelve estimation and four held-out validation campaigns
  4. 04240 bootstrap refits and eighty sensitivity cases
  5. 05Eight result figures and complete CSV evidence
  6. 0671-page project report in PDF and editable Word formats
  7. 0715-page setup and usage guide in PDF and editable Word formats
  8. 0836 annotated references and two sourced literature figures
  9. 0921 automated tests with 100 percent statement coverage

Project record

No information is collected on this page.

Permanent project ID
GP-CH-121TMRX
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.