Assessment of weather-based influent scenarios for a WWTP : application of a pattern recognition technique
- Author
- Sina Borzooei (UGent) , Gisele Helena Barboni Miranda (UGent) , Ramesh Teegavarapu, Gerardo Scibilia, Lorenza Meucci and Maria Chiara Zanetti
- Organization
- Abstract
- 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-NH4) 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.
- Keywords
- WATER-QUALITY, GROUNDWATER CONTAMINATION, CLUSTER, MODEL, METHODOLOGY, SIMULATION, Wastewater treatment plant (WWTP), Influent data, K-means clustering, Climatic data, Python (TM)
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8769731
- MLA
- Borzooei, Sina, et al. “Assessment of Weather-Based Influent Scenarios for a WWTP : Application of a Pattern Recognition Technique.” JOURNAL OF ENVIRONMENTAL MANAGEMENT, vol. 242, 2019, pp. 450–56, doi:10.1016/j.jenvman.2019.04.083.
- APA
- Borzooei, S., Barboni Miranda, G. H., Teegavarapu, R., Scibilia, G., Meucci, L., & Zanetti, M. C. (2019). Assessment of weather-based influent scenarios for a WWTP : application of a pattern recognition technique. JOURNAL OF ENVIRONMENTAL MANAGEMENT, 242, 450–456. https://doi.org/10.1016/j.jenvman.2019.04.083
- Chicago author-date
- Borzooei, Sina, Gisele Helena Barboni Miranda, Ramesh Teegavarapu, Gerardo Scibilia, Lorenza Meucci, and Maria Chiara Zanetti. 2019. “Assessment of Weather-Based Influent Scenarios for a WWTP : Application of a Pattern Recognition Technique.” JOURNAL OF ENVIRONMENTAL MANAGEMENT 242: 450–56. https://doi.org/10.1016/j.jenvman.2019.04.083.
- Chicago author-date (all authors)
- Borzooei, Sina, Gisele Helena Barboni Miranda, Ramesh Teegavarapu, Gerardo Scibilia, Lorenza Meucci, and Maria Chiara Zanetti. 2019. “Assessment of Weather-Based Influent Scenarios for a WWTP : Application of a Pattern Recognition Technique.” JOURNAL OF ENVIRONMENTAL MANAGEMENT 242: 450–456. doi:10.1016/j.jenvman.2019.04.083.
- Vancouver
- 1.Borzooei S, Barboni Miranda GH, Teegavarapu R, Scibilia G, Meucci L, Zanetti MC. Assessment of weather-based influent scenarios for a WWTP : application of a pattern recognition technique. JOURNAL OF ENVIRONMENTAL MANAGEMENT. 2019;242:450–6.
- IEEE
- [1]S. Borzooei, G. H. Barboni Miranda, R. Teegavarapu, G. Scibilia, L. Meucci, and M. C. Zanetti, “Assessment of weather-based influent scenarios for a WWTP : application of a pattern recognition technique,” JOURNAL OF ENVIRONMENTAL MANAGEMENT, vol. 242, pp. 450–456, 2019.
@article{8769731,
abstract = {{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-NH4) 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.}},
author = {{Borzooei, Sina and Barboni Miranda, Gisele Helena and Teegavarapu, Ramesh and Scibilia, Gerardo and Meucci, Lorenza and Zanetti, Maria Chiara}},
issn = {{0301-4797}},
journal = {{JOURNAL OF ENVIRONMENTAL MANAGEMENT}},
keywords = {{WATER-QUALITY,GROUNDWATER CONTAMINATION,CLUSTER,MODEL,METHODOLOGY,SIMULATION,Wastewater treatment plant (WWTP),Influent data,K-means clustering,Climatic data,Python (TM)}},
language = {{eng}},
pages = {{450--456}},
title = {{Assessment of weather-based influent scenarios for a WWTP : application of a pattern recognition technique}},
url = {{http://doi.org/10.1016/j.jenvman.2019.04.083}},
volume = {{242}},
year = {{2019}},
}
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