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A data analysis approach to evaluate the impact of the capacity utilization on the energy consumption of wastewater treatment plants
•The inaccurate estimation of design inflow increases the WWTPs energy consumption.•The energy costs due to a not optimal WWTP capacity utilization are investigated.•A new methodology to quantify these energy costs is presented.•This methodology offers decision-making support for WWTP energy optimiz...
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Published in: | Sustainable cities and society 2019-02, Vol.45, p.307-313 |
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Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | •The inaccurate estimation of design inflow increases the WWTPs energy consumption.•The energy costs due to a not optimal WWTP capacity utilization are investigated.•A new methodology to quantify these energy costs is presented.•This methodology offers decision-making support for WWTP energy optimization.•This methodology is very profitable for designers, managers and public authorities.
The reduction of energy consumption in Waste Water Treatment Plants (WWTPs) is a challenge for the scientific community and for the public authorities. A source of excessive energy cost is the mismatching between operational and design inflow (i.e. capacity utilization): this issue is very relevant above all in areas with high demographic seasonality. Consequently, it is really important to have operational decision criteria to evaluate the impact of a low capacity utilization on energy consumption. In order to provide the scientific community and the plant managers with an adequate criterion, we propose a user-friendly methodology to identify critical conditions of capacity utilization. The novelty of this paper relies upon the combination of a parametric approach and statistical analysis. This methodology has a practical value for plant managers and designers that can improve their comprehension of plant performances by using a user-friendly decision-making criterion. |
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ISSN: | 2210-6707 2210-6715 |
DOI: | 10.1016/j.scs.2018.11.036 |