middoe 0.0.19

Model Identification, Discrimination, and Design of Experiments.

MIDDoE: Model-(based) Identification, Discrimination, and Design of Experiments

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🌍 About MIDDoE

MIDDoE is an open-source Python package designed to streamline model identification for dynamic lumped models. Developed to address gaps in existing tools, MIDDoE offers a structured framework integrating:

✅ Model Identification
✅ Model Discrimination
✅ Experimental Design

With its flexible and user-friendly design, MIDDoE ensures practical usability across various scientific disciplines.

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✨ Key Features

✅ Comprehensive Workflow — A structured framework that covers all essential steps in model identification.
✅ Flexible Integration — Supports external simulators while offering built-in options.
✅ Adaptable Design — Easily accommodates physical constraints for practical applications.
✅ Accessible Framework — Uses NumPy-based structures for improved generality and minimal dependencies.
✅ User-Friendly Interface — Designed for use beyond traditional process systems engineering applications.

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⚙️ Functionalities

MIDDoE offers a wide range of numerical capabilities to support model identification, including:

🔍 Sensitivity Analysis — Identifies key parameters influencing model behaviour.
📊 Estimability Analysis — Determines which parameters can be reliably estimated.
📈 Parameter Estimation — Estimates model parameters based on experimental data.
📉 Uncertainty Analysis — Evaluates confidence in model predictions.
🧪 MBDoE for Model Discrimination (MBDoE-MD) — Optimises experiments to distinguish between competing models.
🎯 MBDoE for Parameter Precision (MBDoE-PP) — Designs experiments to improve parameter precision.
🧪 Model Validation — Assesses predictive accuracy using independent data.

Additional service functionalities include:

• 📂 Data Handling
• 📑 Plotting and Reporting
• 🧬 In-silico Data Generation

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🧪 Applications

MIDDoE has been successfully applied across various domains, including:

• 💊 Pharmaceutical systems
• 🧫 Biological processes
• 🪨 Mineral systems
• ⚗️ Chemical processes

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🚀 Installation

MIDDoE can be installed via PyPI or by cloning the repository:

PyPI Installation

pip install middoe

Git Clone

git clone https://github.com/zuhairblr/middoe.git

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📚 Tutorials and Examples

MIDDoE provides a comprehensive set of tutorials and case studies demonstrating its application in:

• 📋 Pharmaceutical Systems
• 🧬 Biological Processes
• 🪨 Mineral Systems
• ⚗️ Chemical Processes

📝 Documentation will be available soon to guide users through package functionalities.

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💬 Getting Help

For support and community interaction:

• 🏷️ Use the #MIDDoE tag on StackOverflow.
• 📧 Contact the development team:
  • Zuhair Tabrizi — [e-mail]
  • Dr. Elena Barbera — [e-mail]
  • Dr. Wilson Ricardo Leal Da Silva — [e-mail]
  • Prof. Fabrizio Bezzo — [e-mail]

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👨‍💻 Developers

We welcome contributions! If you'd like to improve MIDDoE, report issues, or suggest new features, please visit the GitHub repository for guidelines.

Contributing Terms

By contributing to MIDDoE, you agree to the following terms:
1️⃣ Your contributions are submitted under the MIT License.
2️⃣ You confirm that you have the rights to submit these contributions.

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🛡️ License

MIDDoE is licensed under the MIT License. See the LICENSE file for more details.

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🙏 Acknowledgements

This work is part of the CO2Valorize project, funded by the European Union’s Horizon Europe research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 101073547.

MIDDoE is a collaborative effort between:

• 🏫 The Computer-Aided Process Engineering (CAPE) lab at the University of Padova, Italy
• 🏢 The Green Innovation team at FLSmidth Cement, Denmark

Special thanks to the research community for their valuable contributions and feedback.

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💻 Developed with ❤️ by the MIDDoE team