Data-Driven Framework to Quantify Nitrogen Process Resilience at Wastewater Treatment Plants

Abstract Wastewater treatment plants are routinely and increasingly impacted by abnormal influent flow variations, driven by extreme weather and aging infrastructure. This study introduces a data-driven framework that integrates statistical process control (SPC) with resilience assessment to quantify system response to flow anomalies. The approach was applied to two full-scale treatment facilities in the Mid-Atlantic U.S., both served by combined sewer systems. SPC, implemented using eight rule-based criteria, detected deviations in influent flow that were subsequently evaluated for effects on nitrogen process resilience. Resilience was quantified using three complementary metrics: maximum performance reduction (mpr), recovery time (tr), and recovery phase performance reduction (rppr). At Plant I, flow anomalies were associated with substantial degradation in nitrogen removal performance, suggesting gaps in rainfall representation and internal operational impacts. At Plant II, only storm-related anomalies occurred, with tr varying widely among events. The results demonstrate that influent flow deviations and effluent quality data can be leveraged to interpret process disruptions. The framework provides a scalable postevent analysis tool for utilities and underscores the need for real-time detection to improve nitrogen process resilience.