[Discussion] Testing RL on industrial control: We engineered a physics-informed batch reactor dataset/environment because real SCADA logs are inaccessible.

Finding high-quality, cascading failure logs from real manufacturing to train continuous control RL agents is practically impossible due to proprietary air-gaps. Most open-source datasets are just Gaussian noise, which doesn’t respect the physical invariants needed for realistic state-transition dynamics.

I’ve been experimenting with building a hybrid LLM-Physics simulation of a liquid-phase exothermic batch reactor to generate high-fidelity telemetry, and I’d love to get this community’s thoughts on the methodology for industrial environment design.

**How we structured the state dynamics for RL:**

* **Episodic Boundaries:** Every batch is tagged with a `Reactor_Run_ID` so you can easily parse the data into discrete training episodes.

* **Thermodynamic Guardrails:** Modeled exact mass balance and Arrhenius-based reaction kinetics so the state transitions (temperature, pressure, concentration) are physically accurate based on the coolant flow actions.

* **Non-Stationary Dynamics:** Injected dynamic fault modes like Exothermic Runaway (cooling failures) and mixing loss to test how policies handle sudden, non-linear shifts in the environment.

* **Missing State Variables:** Simulated a 99-minute telemetry dropout (MCAR) to test POMDP (Partially Observable Markov Decision Process) handling and imputation.

I uploaded a 5,000-minute sample output of the telemetry (CC BY-NC 4.0) and my baseline EDA notebook to Hugging Face so people can poke holes in the simulation: https://huggingface.co/datasets/AIMindTeams/synthetic-chemical-reactor-50k-sample

For those working in continuous control or industrial RL, how are you handling the lack of edge-case failure data? Are you building your own simulators from scratch, or relying on heavy augmentation of nominal data?