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GP-BT-0535M8HBiotechnologyReady

Bioreactor oxygen-transfer and scale-up model

A completed stirred-tank bioreactor study comparing oxygen transfer, mixing, power, heat release, fed-batch intensity, and productivity from 2 to 2,000 L.

Bioreactor oxygen-transfer and scale-up model project visual
GP-BT-0535M8H · Biotechnology
  • Python
  • SciPy
  • NumPy
  • Pandas
  • Matplotlib
  • Jupyter

Project definition

Problem statement

A bioreactor operating point that works at laboratory scale may not preserve oxygen transfer, mixing, heat removal, gas flow, and mechanical limits at pilot or production scale.

The engineering problem is to compare common scale-up rules under one declared biological model and show their competing effects instead of treating kLa as the only design target.

Project objectives

  • Build geometrically similar stirred tanks at 2, 20, 200, and 2,000 L.
  • Compare constant tip speed, constant power per volume, constant kLa, and a balanced strategy.
  • Test conservative, reference, and aggressive fed-batch regimes.
  • Calculate biomass, substrate, product, dissolved oxygen, temperature, volume, OTR, OUR, and heat release over 36 hours.
  • Measure oxygen limitation, cooling limitation, mixing time, mechanical demand, and final productivity.

Project structure

Project components

01

Vessel model

Calculates tank and impeller geometry, heat-transfer area, gas velocity, tip speed, power density, clean-liquid kLa, and mixing time.

02

Scale-up strategies

Selects operating points under constant-tip-speed, constant-power, constant-kLa, and balanced rules.

03

Fed-batch model

Solves coupled biomass, substrate, product, dissolved-oxygen, temperature, and liquid-volume balances.

04

Experiment

Runs all combinations of four scales, four strategies, and three feed regimes.

05

Evidence

Retains case metrics, five-minute time series, operating points, structured summaries, and eight labelled figures.

Methodology

Project workflow

  1. 01
    Select scale

    Choose a 2, 20, 200, or 2,000 L geometrically similar vessel.

  2. 02
    Select strategy

    Apply one declared scale-up rule within tip-speed, power, and aeration limits.

  3. 03
    Select feed

    Choose the conservative, reference, or aggressive fed-batch profile.

  4. 04
    Run the model

    The solver calculates the coupled biological, oxygen-transfer, and thermal response for 36 hours.

  5. 05
    Compare evidence

    Tables and figures show productivity, oxygen limitation, temperature, mixing, power, gas flow, and scale tradeoffs.

Demonstration scenario

At 2,000 L with the reference feed, constant tip speed produces 7.01 g/L product. Constant power per volume produces 15.16 g/L but reaches 32.36 C and needs much greater mechanical power. The balanced case holds clean-liquid kLa near 294 per hour with lower aeration, allowing the student to defend the tradeoff instead of naming one universal best rule.

Engineering

Tools and method

Tools
The project uses Python, SciPy, NumPy, Pandas, Matplotlib, Jupyter for subject analysis, simulation, and results.
Engineering calculations
Python implements geometric similarity, impeller power, gas velocity, kLa, mixing-time, and heat-transfer relations.
Dynamic simulation
SciPy integrates the six-state fed-batch model at every selected operating point.
Experiment design
A controlled 48-case matrix separates the effects of scale, operating strategy, and feed intensity.
Evidence
Pandas, JSON, and Matplotlib retain all comparison tables, time histories, summaries, and figures.
Verification
Automated tests cover equations, constraints, physical limits, case counts, reproducibility, and figures.

Testing

Evaluation

Evaluation measures

  • Clean and effective kLa across scale
  • Tip speed, power per volume, gas flow, and mixing time
  • Final biomass, substrate, product concentration, and product mass
  • Minimum dissolved oxygen and oxygen-limited hours
  • Maximum temperature, heat release, and cooling-limited hours
  • OTR to OUR margin through the fed-batch run

Project boundaries

  • The model uses generic declared teaching assumptions and is not calibrated for a named organism, strain, medium, product, impeller, or sparger.
  • Clean-liquid kLa correlations do not replace measured oxygen-transfer tests in the real broth and vessel.
  • The lumped model cannot resolve local oxygen, substrate, temperature, shear, bubble-size, or mixing gradients.
  • Real scale-up requires laboratory and pilot data, equipment limits, contamination controls, safety review, and qualified bioprocess engineering judgement.

Included

  1. 01Complete bioreactor scale-up model and Python study workflow
  2. 02Forty-eight completed dynamic simulation cases
  3. 03Four vessel scales, four scale-up strategies, and three feed regimes
  4. 04CSV, JSON, and eight result figures
  5. 05Twenty-nine automated tests with 100 percent combined coverage
  6. 06Complete project files, calculations, results, and analysis material in a private GitHub repository
  7. 0798-page project documentation in PDF and editable Word formats
  8. 0821-page setup and usage guide in PDF and editable Word formats
  9. 09Forty-five annotated references

Project record

No information is collected on this page.

Permanent project ID
GP-BT-0535M8H
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.