Prioritize effluent quality, operational costs or global warming? – Using predictive control of wastewater aeration for flexible management of objectives in WRRFs

• Published in: Water research (Oxford) Vol. 196; p. 116960
• Main Authors: Stentoft, P.A., Munk-Nielsen, T., Møller, J.K., Madsen, H., Valverde-Pérez, B., Mikkelsen, P.S., Vezzaro, L.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 15-05-2021
• Subjects: Activated sludge; Economic MPC; Global Warming; N2O emissions; Nitrous Oxide - analysis; Nonlinear MPC; Waste Disposal, Fluid; Waste Water; Water Resources; Activated sludge; Nonlinear MPC; N2O emissions; Economic MPC
• ISSN: 0043-1354; 1879-2448
• DOI: 10.1016/j.watres.2021.116960

•Model predictive control and stochastic differential equations control WRRF aeration.•MPC considers variable electricity prices, greenhouse gas emissions, effluent quality.•MPC optimize WRRF based on multi-objective targets (costs, climate change reduction).•High variations in electricity- price and GHG emissions increase MPC performance.
This study presents a general model predictive control (MPC) algorithm for optimizing wastewater aeration in Water Resource Recovery Facilities (WRRF) under different management objectives. The flexibility of the MPC is demonstrated by controlling a WRRF under four management objectives, aiming at minimizing: (A) effluent concentrations, (B) electricity consumption, (C) total operations costs (sum electricity costs and discharge effluent tax) or (D) global warming potential (direct and indirect nitrous oxide emissions, and indirect from electricity production) . The MPC is tested with data from the alternating WRRF in Nørre Snede (Denmark) and from the Danish electricity grid. Results showed how the four control objectives resulted in important differences in aeration patterns and in the concentration dynamics over a day. Controls B and C showed similarities when looking at total costs, while similarities in global warming potential for controls A and D suggest that improving effluent quality also reduced greenhouse gasses emissions. The MPC flexibility in handling different objectives is shown by using a combined objective function, optimizing both cost and greenhouse emissions. This shows the trade-off between the two objectives, enabling the calculation of marginal costs and thus allowing WRRF operators to carefully evaluate prioritization of management objectives. The long-term MPC performance is evaluated over 51 days covering seasonal and inter-weekly variations. On a daily basis, control A was 9–30% cheaper on average compared to controls A, D and to the current rule-based control. Similarly, control D resulted on average in 35–43% lower greenhouse gasses daily emission compared to the other controls. Difference between control performance increased for days with greater inter-diurnal variations in electricity price or greenhouse emissions from electricity production, i.e. when MPC has greater possibilities for exploiting input variations. The flexibility of the proposed MPC can easily accommodate for additional control objectives, allowing WRRF operators to quickly adapt the plant operation to new management objectives and to face new performance requirements.
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