Evaluating the impact of extreme wet weather events on biological wastewater treatment processes using modeling

Abstract

This dissertation investigates data-driven and simulation-based approaches for quantifying the resilience and sustainability of wastewater treatment operations under increasingly variable climatic conditions. The growing frequency and intensity of rainfall events threaten the continuity of biological nutrient removal processes, leading to hydraulic overloads, biomass washout, and effluent quality deterioration. To address these challenges, this work integrates machine learning, resilience quantification, and life-cycle assessment (LCA) to better understand and manage wet-weather impacts at wastewater resource recovery facilities (WRRFs).In the first chapter, we develop and implement machine-learning models for predicting wet-weather flows. Because extreme inflow events are rare and difficult to capture in conventional datasets, the study applies data-level resampling methods such as Synthetic Minority Oversampling (SMOTE) and Adaptive Synthetic Sampling (ADASYN) to improve the representation of infrequent but operationally critical high-flow conditions. Testing across two full-scale WRRFs, one served by a combined sewer system and the other by a separate sanitary system, demonstrates that the ADASYN–Random Forest configuration provides the most reliable early warnings of extreme hydraulic loads, offering actionable lead time for operators to implement flow-management strategies. Building on predictive modeling, the second component introduces a data driven framework for quantifying process resilience directly from routine operational data. Using Statistical Process Control (SPC) to identify wet-weather disturbances, three complementary metrics are derived to capture the magnitude, duration, and cumulative effect of performance degradation. Application of this framework to two nitrogen-removal systems reveals distinct differences in resilience between storm and non-storm disturbances, with integral performance loss emerging as a robust indicator of process resilience. The results from this chapter demonstrate that empirical resilience analysis can be achieved using existing monitoring data, providing utilities with an efficient diagnostic tool for assessing process resilience without the need for additional sensors or external hydrologic predictors. The final component integrates dynamic plant-wide simulation with LCA to evaluate both plant resilience and sustainability of different wet-weather management strategies, including direct wet-flow treatment, flow equalization, and targeted chemical dosing. Lifecycle inventory derived from the process model quantifies energy use, chemical consumption, and greenhouse-gas emissions under each scenario. The analysis shows that nitrous oxide (N2O) dominates the carbon footprint of the liquid treatment line, while flow equalization significantly reduces both emission variability and eutrophication potential by buffering hydraulic peaks. Importantly, the results reveal that environmental burdens are more related to cumulative performance loss (rppr) than with performance degradation (mpr), underscoring the link between operational resilience and sustainability. Collectively, this work advances our understanding of wet weather and quantifying both resilience and sustainability of plant operations for various wastewater systems. By combining predictive analytics, empirical resilience metrics, and sustainability assessment, the dissertation provides new methodological tools for operating treatment plants that are both robust to hydraulic disturbances and aligned with broader decarbonization goals.