Speaker
Description
Advanced process simulation is often locked behind proprietary software creating barriers to students, educators, researchers, and small organizations. DWSIM shows that high quality process engineering can be done with a mature community driven open source platform. Together with Python’s scientific ecosystem, it enables reproducible workflows for simulation, automation, optimization, and data-driven engineering.
In this session you will see the complete engineering workflow—from building a process flowsheet to automating simulations and preparing models for AI-assisted analysis. Participants will start with a process model in DWSIM and will learn how to define thermodynamic packages, setup unit operations and validate the simulation results. The process then moves on to Python, where simulations can be run automatically, operating conditions can be changed in a planned way, and engineering datasets can be made for sensitivity analysis and optimization. Finally, the session will discuss how to use these datasets to promote machine learning and AI applications with transparent and reproducible computational practices.
This presentation shows how open source tools work hand-in-hand to deliver useful engineering solutions rather than just talking about software features. Participants will learn how to integrate DWSIM with Python for use in research, education and industrial problem solving, with workflows that are open, extensible and without proprietary licensing restrictions.
If you’re a researcher, educator, student, or practicing engineer, this session provides a practical roadmap for adopting open source process simulation and building scalable engineering workflows that combine process modeling, automation, optimization, and emerging AI techniques.
Session author's bio
Dr. Nitin Dutt Chaturvedi is an Assistant Professor at the Indian Institute of Technology (IIT) Patna, India. His research focuses on process systems engineering, process simulation, mathematical optimization, artificial intelligence, decision support systems, and sustainable engineering. He works on integrating open-source computational tools with data-driven methods to develop transparent, reproducible, and scalable engineering workflows.
Dr. Chaturvedi actively applies open-source technologies, including DWSIM and Python, for process modeling, optimization, and engineering education. His research interests include AI-assisted process engineering, multi-criteria decision-making, digital engineering, and sustainable process design. He has authored numerous peer-reviewed publications and is committed to promoting accessible, reproducible, and collaborative engineering research through open-source software and modern computational techniques.
| In Person Attendance | Remote |
|---|---|
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| Social Media | https://www.linkedin.com/in/nitin-dutt-chaturvedi-7b320715/ |
| Level of Difficulty | Beginner |
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