BioSTEAM Lignocellulosic Biorefinery Uncertainty Study
A chemical-engineering study of corn-stover ethanol production using the released BioSTEAM and Bioindustrial-Park process, deterministic cases, and uncertainty analysis.
Project definition
Problem statement
Cellulosic ethanol performance depends on feedstock cost, plant scale, enzyme demand, sugar conversion, operating time, and financing. Studying one assumption at a time can miss the combined range of possible results.
The engineering problem is to reproduce a published corn-stover biorefinery, keep the process state consistent between cases, and measure how process and economic assumptions affect production, yield, cost, electricity, and water.
Project objectives
- Reproduce the released BioSTEAM corn-stover ethanol process.
- Calculate the baseline material, utility, production, capital, operating-cost, and MESP results.
- Run 21 deterministic cases across eight selected inputs.
- Run 500 Latin hypercube uncertainty cases with a fixed seed.
- Rank the inputs affecting MESP, ethanol yield, production, capital, electricity, and water.
- Retain complete numerical evidence, figures, tests, references, and editable documentation.
Project structure
Project components
Process model
Loads the released 68-unit corn-stover biorefinery and clears recycle state before every case.
Case model
Applies validated scale, feed price, enzyme, conversion, uptime, and IRR inputs.
Economic calculation
Solves minimum ethanol selling price and retains capital and annual operating cost.
Uncertainty study
Runs 500 fixed-seed Latin hypercube cases and calculates percentiles and rank sensitivity.
Evidence builder
Writes CSV, JSON, PNG, SVG, Word, and PDF outputs from the released study.
Methodology
Project workflow
- 01Load the released model
BioSTEAM and Bioindustrial-Park create the published corn-stover process.
- 02Reset the process state
Recycle streams and solver cache are cleared before each simulation.
- 03Apply the case inputs
The selected scale, cost, conversion, operating, and financing values are applied.
- 04Run and retain results
The process is simulated, MESP is solved, and all declared results are saved.
- 05Analyse the experiment
Deterministic response, uncertainty intervals, tradeoffs, and rank sensitivity are compared.
Demonstration scenario
The released baseline produces 227.183 million litres of ethanol per year at 324.857 litres per dry tonne and a MESP of 0.5199 USD/L. The 500-case experiment gives a MESP range of 0.4360 to 0.8308 USD/L and identifies feedstock price as the strongest selected MESP driver.
Engineering
Tools and method
- Tools
- The project uses Python 3.12, BioSTEAM 2.53.11, Bioindustrial-Park 2.35.1, ThermoSTEAM 0.53.5, NumPy, SciPy, Pandas, Matplotlib for subject analysis, simulation, and results.
- Process simulation
- BioSTEAM 2.53.11 and Bioindustrial-Park 2.35.1 for the released corn-stover flowsheet and techno-economic model.
- Experiment design
- Python, NumPy, and SciPy for validated cases, Latin hypercube sampling, and rank correlation.
- Data and figures
- Pandas and Matplotlib for retained result tables and fourteen labelled figures.
- Verification
- Automated tests cover validation, clean-state simulation, statistics, commands, outputs, and repeatability.
- Documentation
- Project documentation and setup guide in fixed PDF and editable Word formats.
Testing
Evaluation
Evaluation measures
- Minimum ethanol selling price in USD/L
- Annual ethanol production and litres per dry tonne
- Total capital investment and annual operating cost
- Net electricity export and makeup-water intensity
- Uncertainty minimum, percentiles, median, mean, and maximum
- Rank sensitivity across eight inputs and seven outputs
- Automated tests, full coverage, dependency audit, and repository validation
Project boundaries
- The process is a steady-state conceptual simulation based on a published corn-stover configuration.
- Conversions are specified inputs and are not predicted from reaction kinetics or measured feedstock data.
- Costs use model correlations and are not current vendor quotations, market forecasts, or a bankable feasibility study.
- The project does not include detailed equipment design, dynamic operation, logistics, permits, safety design, or full environmental assessment.
Included
- 01Complete Python engineering source code
- 02Released 68-unit corn-stover ethanol process model
- 0321 deterministic and 500 uncertainty cases
- 04Complete CSV and JSON process and economic results
- 0514 project figures in PNG and SVG formats
- 0673-page project documentation in PDF and editable Word formats
- 078-page setup and usage guide in PDF and editable Word formats
- 0850 annotated references and three sourced literature images
- 0937 automated tests with 100 percent statement and branch coverage
Project record
No information is collected on this page.
- Permanent project ID
- GP-CH-0HZFW99
- Catalogued
- 21 Aug 2026
- Completed
- 26 Aug 2026
- Verified
- 26 Aug 2026
- Demonstration
- Included in repository
Handover
After purchase
- 01Payment is confirmed
The project is marked unavailable and cannot be purchased again.
- 02Repository access is granted
The buyer's submitted GitHub account receives access to the private repository.
- 03The purchase record is delivered
The certification sheet is prepared from the reviewed buyer details and sent privately by email.