Author: Davide Carecci
Initial commit: 07.01.2026
⚠️ This repo uses Git LFS - do not download as ZIP (see Notes section of this README). ⚠️ The general_utils library contains also a user manual to setup VSCode/Python for non-expert users and the list of the required standard Python libraries (download it from the GitHub repository).

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BIOGoAlS.agri-AcoDM is a comprehensive high-fidelity model tool designed for scenario analysis, off-line optimization of the diet input mix to maximize the plant performance (e.g. enhence biomethane production) and to mimic a real plant in the numerical validation of 'lower-level' controllers.

The tool is used primarily to:

• Conduct the off-line optimization of the diet input mix to set the reference controller's setpoints for the experimental campaigns
• Validate in simulation the BIOGoAlS.Select tool
• Provide an informative "synthetic" dataset for the 'first-stage' calibration of the reduced-order/control-oriented model used in the BIOGoAlS.Twin tool
• Validate in simulation the BIOGoAlS.Twin tool

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Documentation

• OpenModelica Software User Manual
  User manual to start work with the agriAcoDM.mo library in OpenModelica and the Modelica language in general.

• OpenModelica Compiler Software User Manual
  List of commands that can be user when calling the OpenModelica Compiler (OMC) from Python (used in almost all the notebooks by the modelica_integrator.py function, present in the general_utils library).

• Model calibration Pseudo-Code
  Simple conceptual guidance to clarify the work pipeline to be carried out when calibrating the model (adapt it to a specific case-study).

• Model calibration Visual Scheme
  Simple conceptual visual representation to clarify the work pipeline to be carried out when calibrating the model (adapt it to a specific case-study).

• agri-AcoDM Visual Scheme
  Simple conceptual visual representation to clarify the main variables and processes included in the agri-AcoDM model.

• Feedstock characterization Visual Scheme
  Simple conceptual visual representation to clarify the main approch to feedstock characterization used in the agri-AcoDM model.

• BIOGOALS.agriAcoDM User Manual
  Comprehensive documentation of the repository structure and main scripts:

  • /agriAcoDM.mo
  • /Diet_optimization/Optimization_diet.ipynb
  • /Integration/Intgrate.ipynb
  • /Parameter_estimation/Parameter_estimation.ipynb
  • /Parameter_estimation/Parameter_uncertainty_linear.ipynb
  • /Sensitivity_ParameterSweep/Parameter_sweep.ipynb
  • /Sensitivity_ParameterSweep/Sensitivity_local_OAT.ipynb
  • /Sensitivity_ParameterSweep/Sensitivity_global_Sobol.ipynb

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Inside the /CombiTimeTables folder:

• Jupyter Notebook to convert data stored in a CSV file to proper TXT format, for it to be read inside the models of the agriAcoDM.mo Modelica library.

• Examples of TXT files used inside the agriAcoDM.mo.

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Inside the /Diet_optimization folder:

• Jupyter Notebook to run an off-line optimization of the diet input mix and, thus, to extract the reference controller's setpoints.

• Example of notebook's output.

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Inside the /Integration folder:

• Jupyter Notebook to simply integrate whatever model inside the agriAcoDM.mo library, extract, save and plot the dynamic trajectories of interest.

• Example of notebook's output.

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Inside the /Parameter_estimation folder:

• Jupyter Notebook to estimate the parameters of a model inside the agriAcoDM.mo library.

• Jupyter Notebooks to quantify and propagate the uncertainty of the parameter estimates.

• Example of notebook's output.

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Inside the /Sensitivity_ParameterSweep folder:

• Jupyter Notebook to simply conduct a 'parameter sweep'/Monte Carlo analysis (i.e. multiple model's simulations) of a model inside the agriAcoDM.mo library.

• Jupyter Notebooks to simply conduct sensitivity and collinearity analyses (Parameter Subset Selection (PSS)) of some model's outputs to some model's parameters (practical identifiability analysis).

• Example of notebook's output.

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Inside the /Data folder:

• Example of process data required to run the notebooks.

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Notes

• Markdown links (./folder/file) are used for proper GitHub rendering.

• Python 3.10+ is required.

• Future work are needed to combine the results of the nonlinear constrained off-line optimization of the diet with the ones of the BIOGoAlS.TE-LP tool (simplified techno-economic/supply-chain prospective).

• The off-line optimization of the diet done with the Optimization_diet.ipynb notebook can be easily extended from the maximization of biomethane production to maximization of the plant profit (techno-economic framework).

• Extensive use of the custom functions present inside the general_utils library (download it from GitHub repository).

• For further details, please refer also to the PhD thesis of the author (visit POLItesi or request a copy to davide.carecci@polimi.it).

• ⚠️ Important: Git LFS Required This repository uses Git LFS (Large File Storage). ❌ Do NOT download the project using "Download ZIP" — files will be incomplete. ✅ Instead, clone the repository:

  git clone <repo-url> cd <repo> git lfs pull

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© 2026 Davide Carecci — All rights reserved.