Process Control Reliability in Wastewater Treatment: Data-Driven Monitoring of Nutrient Removal, Aeration Efficiency, and Compliance Risk Under Sensor Drift and Influent Variability

Abstract

This article develops an applied engineering framework for monitoring and controlling activated sludge nutrient removal with an emphasis on compliance risk, aeration energy efficiency, and sensor reliability. The framework integrates mass-balance-informed indicators, statistical drift detection for key online sensors, and a tiered alarm strategy that distinguishes transient disturbances from sustained process deterioration. A generic case-based evaluation is presented for nitrification and denitrification performance under influent ammonia shocks, dissolved oxygen control variability, and sensor drift scenarios, and performance is evaluated using probability of limit exceedance, time-to-detection, and energy-normalized removal efficiency. Results show that dissolved oxygen and ammonia sensor drift can create false confidence or false alarms depending on control logic, that aeration dominates energy consumption and must be governed by risk-aware setpoints rather than fixed targets, and that combining online monitoring with periodic laboratory validation improves reliability by preventing drift from being absorbed as “normal.” The paper provides copy-ready KPI tables and figure prompts for OJS submission and plant reporting, supporting implementation in diverse plant contexts.