Gas Sweetening units can drift off-spec quickly—and often without a clear, consistent warning. In this talk, CSV Midstream will show how they organized their operational data in the PI/AVEVA Historian and used it to learn the signatures that precede GSU off-spec events. They translated those signatures into reliable, real-time pattern detectors that notify operators early—so they can intervene before amine quality, contactor performance, or regeneration conditions trigger a failure. They'll walk through the end-to-end approach: curating tags and context, labeling historical failure windows, extracting robust features, validating repeatability (minimizing false positives and misses), and deploying alerts into operations with clear operator playbooks. Attendees will leave with a practical blueprint for turning historian data into prevention—improving uptime, product quality, and emissions performance while building trust in data-driven decisions.

Click here to get the presentation slides.