Event-driven model predictive control of sewage pumping stations for sulfide mitigation in sewer networks

Abstract

Chemicals such as Mg(OH)2 and iron salts are widely dosed to sewage for mitigating sulfide-induced corrosion and odour problems in sewer networks. The chemical dosing rate is usually not automatically controlled but profiled based on experience of operators, often resulting in over- or under-dosing. Even though on-line control algorithms for chemical dosing in single pipes have been developed recently, network-wide control algorithms are currently not available. The key challenge is that a sewer network is typically wide-spread comprising many interconnected sewer pipes and pumping stations, making network-wide sulfide mitigation with a relatively limited number of dosing points challenging. In this paper, we propose and demonstrate an Event-driven Model Predictive Control (EMPC) methodology, which controls the flows of sewage streams containing the dosed chemical to ensure desirable distribution of the dosed chemical throughout the pipe sections of interests. First of all, a network-state model is proposed to predict the chemical concentration in a network. An EMPC algorithm is then designed to coordinate sewage pumping station operations to ensure desirable chemical distribution in the network. The performance of the proposed control methodology is demonstrated by applying the designed algorithm to a real sewer network simulated with the well-established SeweX model using real sewage flow and characteristics data. The EMPC strategy significantly improved the sulfide mitigation performance with the same chemical consumption, compared to the current practice.

Keywords: ARMA; Chemical dosing; Model predictive control; Modelling; Sewer; Sulfide.