Assessment of weather-based influent scenarios for a WWTP: Application of a pattern recognition technique.
This study proposes an integrated approach by combining a pattern recognition technique and a process simulation model, to assess the impact of various climatic conditions on influent characteristics of the largest Italian wastewater treatment plant (WWTP) at Castiglione Torinese. Eight years (viz....
| Published in: | Journal of Environmental Management Vol. 242; pp. 450 - 457 |
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| Main Authors: | , , , , , |
| Format: | Article |
| Published: |
Academic Press Inc.
Jul2019
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=136417774&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 136417774 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 03014797 EMJ jtl: Journal of Environmental Management issn: 03014797 maglogo: N pubinfo: dt: Jul2019 vid: 242 pid: 735 pub: Academic Press Inc. artinfo: ui: 136417774 10.1016/j.jenvman.2019.04.083 ppf: 450 ppct: 7 formats: tig: atl: Assessment of weather-based influent scenarios for a WWTP: Application of a pattern recognition technique. aug: au: Borzooei, Sina Miranda, Gisele H.B. Teegavarapu, Ramesh Scibilia, Gerardo Meucci, Lorenza Zanetti, Maria Chiara affil: Department of Environment, Land and Infrastructure Engineering (DIATI), Politecnico di Torino, Corso Duca Degli Abruzzi, 10129, Torino, TO, Italy Institute of Mathematics and Computer Science, University of São Paulo, Trabalhador São-carlense Av., 400, Pq. Arnold Schmidt, São Carlos, SP, Brazil Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000, Gent, Belgium Department of Civil, Environmental and Geomatics Engineering, Florida Atlantic University, 777 Glades Rd, Boca Raton, FL, 33431, USA SMAT (Società Metropolitana Acque Torino) Research Center, Corso Unità D'Italia 235/3, 10127, Torino, TO, Italy su: Pattern perception Total suspended solids Sewage disposal plants Inceptisols K-means clustering Chemical oxygen demand sug: subj: Sewage Treatment Facilities Water and Sewer Line and Related Structures Construction Waste treatment and disposal Solid Waste Landfill Pattern perception Total suspended solids Sewage disposal plants Inceptisols K-means clustering Chemical oxygen demand keyword: Climatic data Influent data Python™ Wastewater treatment plant (WWTP) Climatic data Influent data Python™ Wastewater treatment plant (WWTP) ab: This study proposes an integrated approach by combining a pattern recognition technique and a process simulation model, to assess the impact of various climatic conditions on influent characteristics of the largest Italian wastewater treatment plant (WWTP) at Castiglione Torinese. Eight years (viz. 2009–2016) of historical influent data namely influent flow rate (Q in), chemical oxygen demand (COD), ammonium (N-NH 4) and total suspended solids (TSS), in addition to two climatic attributes, average temperature and daily mean precipitation rates (P I) from the plant catchment area, are evaluated in this study. Following the outlier removal and missing-data imputation, five influent climate-based scenarios are identified by K -means clustering approach. Statistical characteristics of clustered observations are further investigated. Finally, to demonstrate that the proposed approach could improve the process control and efficiency, a process simulation model was developed and calibrated. Steady-state simulations were conducted, and the performance of the plant was studied under five influent scenarios. Further, an optimization scenario-based method was conducted to improve the energy consumption of the plant while meeting effluent requirements. The results indicate that with the adaptation of suitable aeration strategies for each of the influent scenarios, 10–40% energy saving can be achieved while meeting effluent requirements. • An integrated approach is proposed to investigate the impact of climatic variations on the performance of a WWTP. • Data pre-processing and clustering methods are performed on eight years of historical influent and climatic data. • Five influent climate-based scenarios are identified by K-means clustering approach. • The process simulation model is developed and calibrated for steady state condition. • Aeration energy optimization measures are proposed for each of the obtained influent climate-based scenarios. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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