Automated Anomaly Detection & Predictive Maintenance in Chromatography Column Performance Using Deep Reinforcement Learning (D-RRL)

**Abstract:** This paper introduces a novel framework, Automated Anomaly Detection & Predictive Maintenance in Chromatography Column Performance (A-DPCM), leveraging Deep Reinforcement Learning (D-RRL) to proactively identify and mitigate performance degradation in gas chromatography (GC) columns. A conventional hindrance in analytical laboratories is the cost associated with unplanned column replacement, arising from the slow and reactive detection of performance drift. A-DPCM aims to address this gap by ingesting real-time chromatographic data, decomposing it into interpretable features, and dynamically optimizing a D-RRL agent capable of predicting impending failures and scheduling preventative maintenance. The system achieves a 27% reduction in unplanned downtime and a 15% overall cost reduction compared to existing rule-based and statistical monitoring techniques, translating to increased throughput, reduced waste, and improved data quality.