References of "Torregrossa, Dario"
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See detailSK-DSSy: how to integrate the YouTube platform in a cooperative decision support?
Torregrossa, Dario; Hansen, Joachim UL

Book published by Springer Science & Business Media B.V. - ICDSST 2018. Lecture Notes in Business Information Processing, vol 313. Springer, Cham (2018)

In wastewater treatment plants (WWTPs) domain, the decision support tools are nowadays necessary in order to efficiently process the large databases generated with on-line sensors. In this paper a ... [more ▼]

In wastewater treatment plants (WWTPs) domain, the decision support tools are nowadays necessary in order to efficiently process the large databases generated with on-line sensors. In this paper a cooperative decision support system (DSS) is presented. This DSS uses a KPI-based fuzzy logic engine to analyse the plant performance and identify the operational conditions that occur in the plants. Then, it associates the detected operational conditions with YouTube pages in which videos are uploaded to provide details and propose suggestions. The YouTube platform is then used to share and validate suggestions by means of the comment functions and the 'likes'. This approach is innovative, free of costs, and useful for plant managers that can rely on a user-friendly platform. [less ▲]

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See detailA data-driven methodology to support pump performance analysis and energy effiency optimization in wastewater treatment plants
Torregrossa, Dario; Hansen, Joachim UL; Hernandez-Sancho, Francesc et al

in Applied Energy (2017), 208

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 ... [more ▼]

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%. [less ▲]

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See detailEnergy saving in wastewater treatment plants: A plant-generic cooperative decision support system
Torregrossa, Dario; Hernández-Sancho; Hansen, Joachim UL et al

in Journal of Cleaner Production (2017)

In Europe, the analysis of Waste Water Treatment Plants (WWTPs) shows a significant energy efficiency potential (up to 25%). Optimistically, plant managers assess their plant efficiency once or twice per ... [more ▼]

In Europe, the analysis of Waste Water Treatment Plants (WWTPs) shows a significant energy efficiency potential (up to 25%). Optimistically, plant managers assess their plant efficiency once or twice per year. Consequently, the time gap between an inefficiency and its detection produces avoidable operational costs. Although the installation of multiple on-line sensors can provide detailed energy information, for a human operator it is unrealistic to analyse the produced data in a satisfactory time-scale. This paper proposes a cooperative tool for energy saving that remotely accesses and evaluates WWTP databases to produce daily energy assessment reports. The novelty of this decision support tool lies in the original combination of: key performance indicators, daily benchmarking, expert knowledge, scenario analysis, fuzzy logic and shared knowledge. In this paper, the Shared Knowledge Decision Support System (SK-DSS) concept is presented and the methodology demonstrated and validated on the energy consumption of biological aeration systems. [less ▲]

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See detailPump Efficiency Analysis of Waste Water Treatment Plants: A Data Mining Approach Using Signal Decomposition for Decision Making
Torregrossa, Dario; Hansen, Joachim UL; Cornelissen, Alex et al

in Lecture Notes in Computer Science (2017)

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See detailA Tool for Energy Management and Cost Assessment of Pumps in Waste Water Treatment Plant
Torregrossa, Dario; Leopold, Ulrich; Hernández-Sancho, Francesc et al

in Data, Information and Knowledge Visualization in Decision Support Systems, 282 (2017)

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See detailEnergy saving in WWTP: Daily benchmarking under uncertainty and data availability limitations
Torregrossa, Dario; Schutz, George; Cornelissen, Alex et al

in Environmental Research (2016)

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 ... [more ▼]

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 frame work 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. [less ▲]

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