Precise control of water and wastewater treatment systems with non-ideal heterogeneous mixing models and high-fidelity sensing

Abstract Non-ideal heterogeneous mixing models are developed and incorporated within advanced closed-loop control strategies utilizing high-resolution sensing to maximize the resiliency and minimize the energy consumption of water treatment processes with intelligent model-based decision-making approaches. The proposed non-ideal heterogeneous mixing models capture continuity (heat and mass conservation), yet are extremely simple with few parameters, so they lend themselves to fast online prediction (with extrapolation capabilities) and regular recalibration. Further, they are more accurate than computational fluid dynamics (CFD) (60% less error) and symbolic regression data-driven models (73% less error). Real-time high-resolution sensor data are collected for observing spatiotemporal responses of state variables (conductivity, pH, and temperature) to transient influent shocks. Deterministic global dynamic optimization is used for training and recalibration of the non-ideal heterogeneous mixing models to guarantee the best-possible fits to the sensor data. The models are then deployed within standard model-predictive control and two economic model-predictive control strategies to demonstrate model-based decision-making for disturbance rejection and optimal operation of aeration in a continuous flow nitrification system utilizing high-resolution sensor data from several spatial positions. The new technology platform, consisting of high-resolution sensors, non-ideal heterogeneous mixing modeling, deterministic global dynamic optimization, and model-predictive control, offers superior performance over current approaches in water and wastewater treatment processes.