LLM-Driven Heuristic Synthesis for Industrial Process Control: Lessons from Hot Steel Rolling
Industrial process control demands policies that are interpretable and auditable, requirements that black-box neural policies struggle to meet. We study an LLM-driven heuristic synthesis framework for hot steel rolling, in which a language model iteratively proposes and refines human-readable Python controllers using rich behavioral feedback from a physics-based simulator. The framework combines structured strategic ideation, executable code generation, and per-component feedback across diverse operating conditions to search over control logic for height reduction, interpass time, and rolling velocity. Our first contribution is an auditable controller-synthesis pipeline for industrial pro...
| Authors | Nima H. Siboni, Seyedreza Kiamousavi, Emad Scharifi |
|---|---|
| Primary category | cs.AI |
| Published | 20 March 2026 |
| Last updated at source | 20 March 2026 |
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