Predictive Analytics for Reducing Title V Deviations in Chemical Manufacturing

Abstract

The Title V operating permits require chemicals manufacturing facilities to comply with stringent air emissions limits, monitoring, recordkeeping, and reporting provisions. Despite robust Environmental Management Systems (EMS), deviations continue to occur due to manual monitoring limitations, complex regulatory obligations, and the variability of industrial processes. This study proposes a predictive analytics framework designed to anticipate Title V deviations before they occur. Using statistical modeling, machine learning (ML), historical deviation data, continuous emissions monitoring system (CEMS) inputs, and operational parameters, the framework estimates the probability of future deviations and identifies key drivers. Case simulations demonstrate that predictive modeling can reduce deviations by 30 55% by enabling proactive operational adjustments, improved recordkeeping, and targeted corrective actions. The approach is designed to complement existing facility specific permit conditions. Results show that predictive analytics can shift air compliance programs from reactive detection to proactive prevention, improving compliance consistency across multi unit chemical operations.