Energy saving in WWTP: Daily benchmarking under uncertainty and data availability limitations.
Efficient management of Waste Water Treatment Plants (WWTPs) can produce significant environmental and economic benefits. Energy benchmarking can be used to compare WWTPs, identify targets and use these to improve their performance. Different authors have performed benchmark analysis on monthly or yearly basis but their approaches suffer from a time lag between an event, its detection, interpretation and potential actions. The availability of on-line measurement data on many WWTPs should theoretically enable the decrease of the management response time by daily benchmarking. Unfortunately this approach is often impossible because of limited data availability. This paper proposes a methodology to perform a daily benchmark analysis under database limitations. The methodology has been applied to the Energy Online System (EOS) developed in the framework of the project "INNERS" (INNovative Energy Recovery Strategies in the urban water cycle). EOS calculates a set of Key Performance Indicators (KPIs) for the evaluation of energy and process performances. In EOS, the energy KPIs take in consideration the pollutant load in order to enable the comparison between different plants. For example, EOS does not analyse the energy consumption but the energy consumption on pollutant load. This approach enables the comparison of performances for plants with different loads or for a single plant under different load conditions. The energy consumption is measured by on-line sensors, while the pollutant load is measured in the laboratory approximately every 14 days. Consequently, the unavailability of the water quality parameters is the limiting factor in calculating energy KPIs. In this paper, in order to overcome this limitation, the authors have developed a methodology to estimate the required parameters and manage the uncertainty in the estimation. By coupling the parameter estimation with an interval based benchmark approach, the authors propose an effective, fast and reproducible way to manage infrequent inlet measurements. Its use enables benchmarking on a daily basis and prepares the ground for further investigation.
Luxembourg Institute of Science and Technology (LIST), 5, avenue des Hauts-Fourneaux, 4362 Esch-sur-Alzette, Luxembourg. Electronic address: dario.torregrossa@list.lu.
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Torregrossa
RTC4Water s.a.r.l, 9, av. des Hauts-Fournaux, 4362 Esch-Sur-Alzette (Belval), Luxembourg.
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Schutz
RTC4Water s.a.r.l, 9, av. des Hauts-Fournaux, 4362 Esch-Sur-Alzette (Belval), Luxembourg.
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Cornelissen
Estructura Económica, Universitat de València, Avda dels Tarongers, s/n, 46022 Valencia, Spain.
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Hernández-Sancho
Université du Luxembourg, 6 rue Richard Coudenhove-Kalergi, L-1359, Luxembourg.
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Hansen
D000465:Algorithms; D019985:Benchmarking; D057919:Biological Oxygen Demand Analysis; D003247:Conservation of Natural Resources; D017143:Fuzzy Logic; D000069550:Machine Learning; D016571:Neural Networks, Computer; D012044:Regression Analysis; D013696:Temperature; D035501:Uncertainty; D014865:Waste Disposal, Fluid
Artificial Intelligence (AI); Chemical Oxygen Demand (COD); Energy Benchmarking; Key Performance Indicators (KPI); Waste Water Treatment Plant (WWTP)