Welcome to the official GitHub organization for Process Intelligence Research (https://www.pi-research.org/) — where cutting-edge machine learning meets chemical engineering.
We focus on creating intelligent, data-driven solutions for modern process industries. Our mission is to bridge the gap between AI and chemical engineering through open science, practical tools, and high-impact research.
- AI for Engineering: Developing AI-powered tools for process optimization, simulation, and design.
- Digitization of Engineering Documents: Automating interpretation and correction of P&IDs and other technical diagrams.
- Hybrid Modeling: Combining mechanistic models with machine learning for accurate, robust predictions.
- Digital Twins: Creating real-time, adaptive models for complex chemical processes.
See a list of our reserach projects here: https://www.pi-research.org/research/research_projects/
| Project | Description |
|---|---|
| ReLU_ANN_MILP | Generate mixed-integer linear programming models of trained artificial neural networks using ReLU activation functions. |
| SFILES2 | Convert between PFDs/P&IDs and SFILES 2.0 strings for process documentation digitization. |
| AI-in-Bio-Chemical-Engineering-Lecture-Coding | Python code examples used in lectures on AI applications in biochemical engineering. |
| ChemEngKG_kgtool | Python package for accessing the Chemical Engineering Knowledge Graph (ChemEngKG). |
| computational_practicum_lecture_coding | Python code files used as examples during computational practicum lectures. |
Process Intelligence Research is a university-based research group composed of:
- Dr. Artur M. Schweidtmann
- PhD candidates and postdoctoral researchers
- Master and Bachelor students
See the complete team at: https://www.pi-research.org/people/
For general inquiries and collaboration proposals:
We believe in open science. You can:
- ⭐ Star your favorite projects
- 🐛 Report issues or bugs
- 📬 Submit pull requests
- 📢 Cite our research and tools in your work
Copyright (C) 2025 Artur Schweidtmann TU Delft. All rights reserved.
Advancing chemical engineering through data and intelligence.
