Five AI use cases purpose-built for SRF's fluorochemical and specialty chemical operations — from yield prediction to regulatory compliance.
Each use case is deployable independently. Together they form a closed-loop intelligence platform across SRF's chemical businesses.
Soft-sensor models predict purity, moisture content, and GC assay for fluorochemical products every 5 minutes — without waiting for offline lab results at batch end. Operators receive correction windows while the reaction is still controllable.
Reinforcement learning models analyse reaction conditions — temperature profiles, catalyst loading, residence time — and recommend real-time parameter adjustments to maximise yield across fluorination and chloromethane reactors.
Vibration, temperature, and flow sensor fusion models detect early-stage equipment degradation in compressors, heat exchangers, and reactor agitators. Condition-based maintenance replaces calendar-based shutdowns, slashing unplanned stoppages by 40%.
Demand forecasting models ingest global refrigerant pricing signals, customer order patterns, and seasonal HVAC demand cycles to optimise raw material procurement and finished-goods inventory across SRF's 90+ export markets.
LLM-powered agents continuously monitor REACH, F-Gas, ODS, and agrochemical registration changes across 90+ export markets. Auto-generate SDS documents, flag regulatory gaps, and manage product registration workflows — cutting compliance overhead by 60%.
AI use cases are mapped to the specific processes, products, and regulatory environments of each SRF chemical vertical.
Indicative for a mid-scale SRF chemical plant · 50,000 MT/year capacity · Numbers scale proportionally.