Data mining application in assessment of weather-based influent scenarios for a WWTP : getting the most out of plant historical data
- Submitting institution
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The University of Warwick
- Unit of assessment
- 12 - Engineering
- Output identifier
- 10730
- Type
- D - Journal article
- DOI
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10.1007/s11270-018-4053-1
- Title of journal
- Water, Air, & Soil Pollution
- Article number
- 5
- First page
- -
- Volume
- 230
- Issue
- -
- ISSN
- 0049-6979
- Open access status
- Deposit exception
- Month of publication
- January
- Year of publication
- 2019
- URL
-
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- Supplementary information
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- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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5
- Research group(s)
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-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Proposed a time-series data mining methodology for the estimation of characteristic flow and pollutant concentrations of combined sewer overflow (CSO). Flow characteristics in CSO are crucial for optimizing the operations of Wastewater-treatment-plants (WWTP) during extreme climatic conditions. The proposed methodology has been adopted by the largest Italian WWTP during extreme weather conditions, and greater plant efficiency has been reported by the managing company, SMAT (contact: Gerardo.Scibilia@smatorino.it). This paper led to a follow-up industrially funded project to develop process optimization for SMAT plants in Italy.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
