Workshop Materials

Welcome

Introduction

Welcome to the workshop on LLMs and Agents for Process Industries. This page is your single entry point to everything you'll need for the hands-on session and beyond — starter notebooks, datasets, slide decks, and curated reading.

Our aim for the day is modest but concrete. You'll build a couple of small LLM-agent prototypes on realistic process-engineering tasks, deliberately break them to see where they fail, and leave with enough intuition to evaluate claims about this technology rather than take them on trust.

You don't need deep Python experience or prior exposure to LLMs. The morning sessions cover the necessary background, and the afternoon exercises are designed to be completed collaboratively. Observers are welcome — code if you like, watch if you prefer.

All workshop materials, including notebooks, slides, and datasets, are hosted on the workshop Dropbox folder linked below.

Before you arrive

Checklist

Bring

A laptop (required — you will work directly with LLM APIs on Python notebooks during the session).
Ability to connect to the workshop Wi-Fi.

Provided on the day

Jupyter notebooks and starter code.
Access to LLMs for the duration of the session.
Sample datasets: simplified P&IDs, alarm lists, maintenance logs.
Offline backup — pre-recorded demos and local notebooks using open-source models — in case of connectivity issues.

Resources

All notebooks, slide decks, and datasets for the day live in a single shared Dropbox folder. Open it below in a new tab; you can browse and download individual files, or download the whole folder as a zip.

Workshop Dropbox folder

Everything for the workshop, including morning and afternoon slide decks, hands-on notebooks, sample datasets, and reading pointers.

Open workshop materials on Dropbox

Reading list

Key papers

A short, focused list if you'd like to prime yourself before the workshop. All are linked directly to arXiv or publisher copies.

Balhorn, L. S., Caballero, M., Schweidtmann, A. M. (2024). Toward autocorrection of chemical process flowsheets using large language models. Computer Aided Chemical Engineering, Vol. 53, 3109–3114. Elsevier. Preprint: arXiv:2312.02873
Du, W., Yang, S. (2025). The potential and challenges of large language model agent systems in chemical process simulation. Frontiers of Chemical Science and Engineering, 19(10), 99.
Gill, M. S., Vyas, J., Markaj, A., Gehlhoff, F., Mercangöz, M. (2025). Leveraging LLM agents and digital twins for fault handling in process plants. arXiv:2505.02076
Hirtreiter, E., Schulze Balhorn, L., Schweidtmann, A. M. (2024). Toward automatic generation of control structures for process flow diagrams with large language models. AIChE Journal, 70(1), e18259.
Qaid, H. A. et al. (2024). FD-LLM: Large language model for fault diagnosis of machines. arXiv:2412.01218
Schulze Balhorn, L. et al. (2024). Graph-to-SFILES: Control structure prediction from process topologies using generative artificial intelligence. arXiv:2412.00508
Venkatasubramanian, V. (2019). The promise of artificial intelligence in chemical engineering: Is it here, finally? AIChE Journal, 65(2), 466–478.
Vyas, J., Mercangöz, M. (2024). Autonomous industrial control using an agentic framework with large language models. arXiv:2411.05904
Woo, T., Kim, S., Tariq, S. et al. (2025). Leveraging generative AI and large language models for process systems engineering: a state-of-the-art review. Korean Journal of Chemical Engineering, 42, 2787–2808.
Xia, Y., Dittler, D., Jazdi, N., Chen, H., Weyrich, M. (2024). LLM experiments with simulation: Large language model multi-agent system for process simulation parametrization in digital twins. IEEE ETFA (Best Paper Award).
Xia, Y., Jazdi, N., Zhang, J., Shah, C., Weyrich, M. (2024). Control industrial automation system with large language model agents. arXiv:2409.18009

Questions before the workshop? Email bhushan.gopaluni@ubc.ca.

Hands-on notebooks, slides, and reading list

Enter the workshop passcode