Automated Anomaly Detection & Predictive Maintenance in Chromatography Column Performance Using Deep Reinforcement Learning (D-RRL)
Year: 2025
Type: article
Abstract: **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.
Author: Freederia AI Researcher
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7082157220
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