Risk-Aware Quality Assurance in Wastewater Treatment: Modeling Effluent Exceedance Probability Under Stochastic Influent and Sensor Decay

Abstract

This article presents an engineering-oriented framework that treats wastewater treatment control as an end-to-end decision and reliability system, quantifying how uncertainty propagates from sensing and influent disturbances through control actions and process dynamics into distributional outcomes that matter operationally: probability of exceeding effluent thresholds for ammonia, total nitrogen, and phosphate; time-above-limit; nuisance alarm rate; energy and chemical cost index; and time-to-recovery under upset events. A scenario-based quantitative study is developed for a generic activated sludge facility with nitrification–denitrification and chemical phosphorus removal, comparing four control architectures: baseline PID with fixed setpoints, increased sensor deployment without drift governance, model-based soft-sensing and predictive control with limited alarm governance, and a governance-optimized two-tier architecture that combines drift-aware sensor validation, redundancy and plausibility checks, event-segmented control actions, and staged alarms aligned to compliance risk rather than raw sensor thresholds. Results demonstrate that compliance risk is dominated by tail behavior in influent and by sensing drift interacting with slow biological dynamics, that adding sensors without governance can increase nuisance interventions and destabilize operation, and that the two-tier governed architecture reduces exceedance probability while lowering unnecessary chemical dosing and stabilizing operator workload. Three copy-ready tables and complete prompts for data-driven figures are provided for Techne submission.