From Step Tests to Soft Sensors: Model-Informed Controller Tuning and Hybrid Feedforward–Feedback Ammonia-Based Aeration Control to Improve Full-Scale Water Resource Recovery Facility Performance

Abstract

Aeration is essential for nitrification and biological nitrogen removal in water resource recovery facilities (WRRFs), but it is also one of the largest operating energy demands. As utilities face increasingly stringent effluent nitrogen limits and pursue greater energy efficiency, aeration control strategies that stabilize effluent ammonia while minimizing unnecessary oxygen supply have become an operational priority. Ammonia-based aeration control (ABAC) is an effective approach because it links dissolved oxygen targets to real-time ammonia measurements. However, in many full-scale facilities, ABAC and other feedback loops are constrained by controller tuning practices based on trial-and-error or ad hoc rules often producing sluggish or oscillatory behavior, and reduced operator confidence. This work develops and validates practical methods to systematically tune proportional–integral (PI) controllers and improve ABAC performance under realistic WRRF dynamics. Because PI control remains the dominant structure in treatment plant automation and is well suited to first-order-plus-deadtime (FOPDT) processes, this research focuses on methods that characterize loop dynamics with minimal plant disruption, generate repeatable tuning parameters, and identify when feedback-only ABAC must be augmented with predictive action. Open-loop step-response testing was first applied to representative WRRF control loops to develop FOPDT models relating manipulated and controlled variables. These models were then used with lambda tuning to compute PI parameters that balance stability and responsiveness for both fast loops, such as airflow and header pressure control, and slower nutrient-related process loops. Because step-response testing is often impractical for ABAC under variable full-scale conditions, a reduced-order model-based tuning method was then developed. A hydraulics-based reduced-order model with simplified activated sludge relationships was used to describe how oxygen availability influences nitrification capacity. The non-linearity associated with Monod saturation kinetics was explicitly integrated into the controller structure so that feedback action operated on a more linearized response surface. Monod saturation nonlinearity was incorporated into the controller structure so feedback acted on a more linearized response surface. In model-based validation, the kinetic-informed controller achieved a mean absolute error (MAE) of 0.09 mg N/L relative to the effluent ammonia setpoint. When implemented at full scale and tuned using the proposed method, the controller achieved stable operation and a 0.16 mg N/L MAE. This work also addressed facilities with plug-flow hydraulics and pronounced diurnal loading, where feedback-only ABAC can become deadtime-dominant and respond only after a disturbance has propagated through the aeration system. Frequency-response screening was used to evaluate controllability limits imposed by transport delay and to identify conditions where feedback tuning alone is insufficient. A hybrid feedforward-feedback ABAC (FFABAC) architecture was then implemented in which model-derived soft sensors forecast influent ammonia loading and nitrification capacity for proactive feedforward action, while PI feedback corrects model error and maintains long-term stability. FFABAC achieved an overall MAE of 0.22 mg N/L and improved to 0.16 mg N/L under unconstrained conditions. In a controlled comparison, FFABAC reduced ammonia MAE from 0.31 ± 0.28 mg N/L under feedback-only ABAC to 0.11 ± 0.10 mg N/L. Collectively, this work provides a practical, scalable toolkit for improving aeration-related control performance in WRRFs. Step-response testing with lambda tuning offers a repeatable method for tuning common PI loops, reduced-order model-based tuning provides a feasible pathway for ABAC where traditional step tests are impractical, and frequency-response screening offers a framework for when feedback should be augmented with predictive feedforward control. These methods are designed for implementation within standard plant automation platforms, enabling systematic tuning, measurable performance improvement, and reduced operational risk.