A model-based framework for controlling activated sludge plants

TL;DR

The paper presents a general, model-based framework for advancing the control of activated sludge plants by coupling an Output MPC with a Moving-Horizon Estimator (MHE) to operate as a water resource recovery facility. It uses a discretized state-space representation derived from the Benchmark Simulation Model No. 1 (BSM1) to compute control actions over a horizon $H_c$ while estimating states and disturbances over a horizon $H_e$, all under plant constraints. The approach is demonstrated through case studies on conventional wastewater treatment and nitrogen on-demand with energy recovery, showing improved effluent quality, the ability to track variable nitrogen targets, and the potential to recover energy from sludge, albeit with trade-offs in treatment performance under stringent energy-recovery constraints. The framework offers a flexible, scalable path to operate existing ASPs as WRRFs, balancing regulatory compliance, resource recovery, and energy autonomy using model-based optimization.

Abstract

This work presents a general framework for the advanced control of a common class of activated sludge plants (ASPs). Based on a dynamic model of the process and plant sensors and actuators, we design and configure a highly customisable Output Model-Predictive Controller (Output MPC) for the flexible operation of ASPs as water resource recovery facilities. The controller consists of a i) Moving-Horizon Estimator for determining the state of the process, from plant measurements, and ii) a Model-Predictive Controller for determining the optimal actions to attain high-level operational goals. The Output MPC can be configured to satisfy the technological limits of the plant equipment, as well as operational desiderata defined by plant personnel. We consider exemplary problems and show that the framework is able to control ASPs for tasks of practical relevance, ranging from wastewater treatment subject to normative limits, to the production of an effluent with varying nitrogen content, and energy recovery.

Figures (31)

• Figure 1: Model-based control of activated sludge plants.
• Figure 2: Activated sludge plant: Conventional process layout equipped with a basic setup for measurement and control.
• Figure 3: Benchmark simulation model No. 1: Process layout equipped with a selection of measurement and actionable variables.
• Figure 4: Influent wastewater, week 2: Evolution of influent flow-rate $Q_{IN}$, oxygen demand $COD^{IN}$, and nitrogen $N_{TOT}^{IN}$.
• Figure 5: Output MPC: Main components. To follow a reference, the controller uses a state-space model and measurement data (red) to determine the controls (blue) given to the plant.