Biogas Plant Optimization — Multi-Objective Modeling

This repository contains the implementation of a multi-objective optimization (MOO) model for assessing biogas plant performance, focusing on trade-offs between total cost (TC), greenhouse gas emissions (GHG), and energy revenue (ER). The model and visualizations support a scientific article on sustainable biogas system design.

📁 Repository Contents

• MOO model.ipynb — Jupyter Notebook implementing the full MOO framework, including:

  • Definition of objective functions (cost, emissions, revenue, etc.)
  • Operational and environmental constraints
  • Optimization using evolutionary algorithms
  • Visualization of Pareto fronts in 2D and 3D

• PF_2D_subplots.pdf — Set of 2D Pareto front plots:

  • Total Cost vs. GHG Emissions
  • Total Cost vs. Energy Revenue
  • GHG Emissions vs. Energy Revenue

• PF3D_real.pdf — 3D Pareto front plot:

  • Simultaneously shows trade-offs among TC, GHG, and ER
  • Highlights a “compromise zone” of robust, balanced solutions

📌 Applications

This repository is intended for:

• Researchers in sustainable energy and waste-to-energy systems
• Developers working on optimization of resource recovery technologies
• Supporting material for a scientific publication on MOO-based scenario analysis

📄 Publication

The model, plots, and results are part of the forthcoming article:
"Multi-Objective Optimization of Biogas Systems: Techno-Economic and Environmental Trade-Offs", 2025 (under preparation).

⚙️ Requirements

To run the notebook locally, you’ll need:

• Python 3.8+
• Required libraries: numpy, scipy, matplotlib, pandas, deap (or any other MOO framework)

📜 License

This project is licensed under the MIT License. You are free to use and adapt it with appropriate credit.