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GP-CH-16WO5JWChemicalReady

Deep-Eutectic-Solvent Selection for Carbon Capture and Separation Processes

Examine how water content and reporting basis affect a carbon-capture solvent comparison.

Deep-Eutectic-Solvent Selection for Carbon Capture and Separation Processes project visual
GP-CH-16WO5JW · Chemical
  • Python 3.12
  • Matplotlib

Software compatibility

Read without installing software

Word, PDF and editable slides explain the study. Optional Python 3.12 calculations use the standard library. Matplotlib is needed only to regenerate figures. Docker is optional. No web framework, API key, paid simulator or laboratory is required.

Project definition

Problem statement

Capture per kilogram of aqueous solution and capture per kilogram of nominal dry formulation use different denominators. Comparing them directly can reverse the apparent direction of a result.

Solvent selection also needs phase, transport, cyclic loading and regeneration evidence. A high uptake value alone cannot resolve those questions.

Project objectives

  • Review solvent-selection literature and preserve source-access limitations.
  • Compare initial-solution, nominal-dry and common cyclic-loading bases.
  • Reanalyse published viscosity and density data at matched conditions.
  • Explain ideal and nonideal eutectic behaviour using hypothetical binary models.
  • Evaluate explicit heat terms without hiding regeneration assumptions.
  • Record formulation-specific evidence gaps without inventing favourable scores.

Project structure

Project components

01

Literature review

Fourteen annotated sources cover capture, phase behaviour, transport, regeneration and formulation-specific risk evidence.

02

Loading comparison

Published uptake observations are compared on explicit mass bases, keeping nominal water assumptions visible.

03

Phase equilibrium

Three hypothetical regular-solution cases illustrate ideal eutectics and additional nonideal depression.

04

Transport and heat

Matched property calculations and a 240-case thermal grid expose the assumptions behind circulation and regeneration.

05

Evidence assessment

Eight qualitative cards connect supported observations, remaining gaps and useful further work.

Methodology

Project workflow

  1. 01
    Define the comparison

    Identify composition, water convention, loading denominator and experimental conditions.

  2. 02
    Read the sources

    Follow the source matrix and distinguish inspected methods from abstract-only evidence.

  3. 03
    Reproduce the results

    Run the offline checks for published-input calculations and separately labelled hypothetical cases.

  4. 04
    Explore assumptions

    Examine how phase interactions, working loading and heat recovery affect the stated model.

  5. 05
    Discuss the evidence

    Explain what the comparison supports and what further measurements would be needed.

Demonstration scenario

A nominal 10 wt% water case changes by -8.824% on the initial-solution loading basis and +1.307% after nominal-dry normalization. The arithmetic reversal illustrates why denominators matter; it does not establish chemical improvement or statistical significance.

Engineering

Tools and method

Tools
The project uses Python 3.12, Matplotlib for subject analysis, simulation, and results.
Engineering study
The documentation develops composition, loading, phase equilibrium, transport, regeneration and uncertainty theory.
Optional calculations
Standard-library Python reproduces seven numerical outputs without a cloud service or process simulator.
Verification
Independent analytical fixtures, implicit-equation residuals and invalid-input tests check the implementation.
Editable material
Word documents, native presentation data and SVG figures support supervised extension. Recheck static contents after repagination.

Testing

Evaluation

Evaluation measures

  • Source-table transcription and provenance
  • Capacity normalization and matched-condition comparisons
  • Independent phase-equilibrium residuals and limiting cases
  • Restricted laminar-flow power ratios
  • Common-basis working loading and thermal-accounting totals
  • Evidence gaps, uncertainty limits and consistency across deliverables

Project boundaries

  • Published measurements retain their attribution. No new laboratory experiment is claimed.
  • Phase parameters and thermal cases are hypothetical, not measured properties of a named candidate.
  • The hydraulic model assumes fully developed Newtonian laminar flow, fixed pipe geometry and pump efficiency.
  • The study does not identify a universally best solvent or certify chemical safety, industrial performance or lifecycle benefit.
  • The source photograph has an unresolved temperature label that remains disclosed.
  • Native Microsoft Word and PowerPoint rendering has not been tested.

Included

  1. 0175-page project documentation in PDF and editable Word formats
  2. 02Six-page student guide in PDF and editable Word formats
  3. 0324-slide editable presentation with native tables, charts and source notes
  4. 0414 annotated references, source matrix and an attributed literature photograph
  5. 05Ten report tables, five figures and native editable equations
  6. 06120 published property values and five attributed capacity observations
  7. 07Eight evidence assessments, three hypothetical phase cases and 240 thermal scenarios
  8. 08Complete Python source, 167 automated tests and offline reproduction

Project record

No information is collected on this page.

Permanent project ID
GP-CH-16WO5JW
Catalogued
21 Aug 2026
Completed
06 Sept 2026
Verified
06 Sept 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.