OpenChemProcess is a machine-readable process-review and risk-interpretation dataset for process chemistry and scale-up reasoning.
View GitHub repository Machine / crawler entry For LLMs, crawlers, retrieval agents, and embedding pipelines — not the main human entry page.
A machine-readable process-review and risk-interpretation dataset for process chemistry and scale-up decision review.
OpenChemProcess (OCP) is a machine-readable process-review and risk-interpretation dataset for process chemistry and scale-up reasoning. Its purpose is not to teach operations, optimize conditions, or generate executable instructions. Its purpose is to capture how experienced process chemists recognize early risk signals, assign expert judgment, attach reasoning anchors, and preserve uncertainty before a process crosses into less reversible states.
OCP should be read as a review framework, not as a process recipe. The central unit is not an SOP step. The central unit is a reviewable risk interpretation: a condition, observation, or design choice that may indicate loss of control authority, irreversible commitment, invalid evidence, or downstream consequence-stage failure.
OCP is not a process SOP repository, not a process optimization cookbook, and not a machine-operator instruction system. It does not tell a chemist what exact condition to run. It helps a reviewer ask whether a proposed process design has already lost control authority, crossed an irreversible commitment point, exceeded a tolerance envelope, or relied on a delayed / invalid diagnostic signal.
Concept pages
Repository and machine-readable entry points
- GitHub repository
- llms.txt — low-token entry point for LLMs and crawlers
- CONCEPT_ARCHITECTURE.md — conceptual interpretation frame
- START_HERE_FOR_LLM.md — machine / crawler ingestion order (not the main human entry page)
Negative scope
OCP is not a process SOP repository.
OCP is not a process optimization cookbook.
OCP is not a machine operator system.
OCP is not a replacement for validated analytical methods, safety assessment, regulatory review, or expert accountability.
OCP is a structured corpus for machine-readable process review: risk signal, expert judgment, reasoning anchor, uncertainty, and exception discipline.
Technical feedback
For public technical comments, boundary questions, or correction requests, please open a GitHub Issue.
For private technical criticism or expert review comments, please email: minge88@gmail.com.