A data-driven methodology to support pump performance analysis and energy efficiency optimization in Waste Water Treatment Plants. (15th December 2017) - Torregrossa, Dario; Hansen, Joachim; Hernández-Sancho, Francesc; Cornelissen, Alex; Schutz, Georges; Leopold, Ulrich
Highlights: A novel approach for the efficient management of wastewater pumps is presented. This approach couples fuzzy logic and data-mining to reduce the pump energy consumption. This approach enables to monitor the pumps and provide case-specific suggestions. Short-term and long-term phenomena were identified and separately monitored. Flow-related issues and early-stage failures were detected. Abstract: Studies and publications from the past ten years demonstrate that generally the energy efficiency of Waste Water Treatment Plants (WWTPs) is unsatisfactory. In this domain, efficient pump energy management can generate economic and environmental benefits. Although the availability of on-line sensors can provide high-frequency information about pump systems, at best, energy assessment is carried out a few times a year using aggregated data. Consequently, pump inefficiencies are normally detected late and the comprehension of pump system dynamics is often not satisfactory. In this paper, a data-driven methodology to support the daily energy decision-making is presented. This innovative approach, based on fuzzy logic, supports plant managers with detailed information about pump performance, and provides case-based suggestions to reduce the pump system energy consumption and extend pump life spans. A case study, performed on a WWTP in Germany, shows that it is possible to identify energy inefficiencies and case-based solutions to reduce the pump energy consumption by 18.5%.