Rigid Bioplastics with Preserved Natural Structures Offer Greater Resource Efficiency and Cost-Competitiveness

Process models used to generate the results for Figure 2, 4 and 5 of "Rigid Bioplastics with Preserved Natural Structures Offer Greater Resource Efficiency and Cost-Competitiveness".

• Aspen_Plus_V11_models: process flowsheet simulations saved as back-up files (.bkp) developped on Aspen Plus V11. A licence is required to open and run the simulation files.
• Capital_costs: Excel spreadsheet with equipment sizing and costing, and cost linearisation.
• Datasets_costs_ecoinvent.xlsx: Excel spreadsheet with tabulated cost factors and life cycle inventories (Tables S19-25).
• OSMOSE_Fig2: Lua scripts to run single-objective optimisation. A valid AMPL licence is required to run the script. The OSMOSE infrastructure must be installed locally or accessed via a server before running the files. OSMOSE is an in-house platform developped at IPESE, EPFL Valais-Wallis, Swizterland. Reach out to the group to request an access to the tool. 
  • ET: Lua scripts with mass and energy balances for the models considered to generate the results from Figure 2. The files can be read with VS Code without any licence.
  • project: Lua scripts connecting ET files and lauching the optimiser.
• OSMOSE_Fig4: Same comments as for OSMOSE_Fig2 but with the models and objective functions required to generated the results from Figure 4.
• OSMOSE_Fig5: Lua scripts to run multi-objective optimisation.
  • data: Empty Excel file where the results are automatically written.
• Sensitivity_analysis_Fig2: Jupyter notebook and associated Excel file to perform a univariate sensitivity analysis on the cost and impact factor estimates used to generate Figure 2. The code to make the Tornado plots is also included.
• Uncertainty_analysis_Fig4: Jupyter notebook and associated Excel file to perform a stochastic uncertainty analysis on the cost and impact factor estimates used to generate Figures 4 and 6. The notebook first creates triangular probability density functions and samples 10000 times.