Waste assessment decision support systems used for domestic sewage treatment

•Decision Support System was used in evaluating the quality of sewage treated in a septic tank and a vertical flow filter.•Waste such as PET flakes, polyurethane foam trims, shredded rubber tires and wadding as a VFF filling material were analyzed.•Decision Support System (ANN preceded by PCA) prove...

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Veröffentlicht in:Journal of water process engineering 2019-10, Vol.31, p.100885, Article 100885
1. Verfasser: Dacewicz, Ewa
Format: Artikel
Sprache:eng
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Zusammenfassung:•Decision Support System was used in evaluating the quality of sewage treated in a septic tank and a vertical flow filter.•Waste such as PET flakes, polyurethane foam trims, shredded rubber tires and wadding as a VFF filling material were analyzed.•Decision Support System (ANN preceded by PCA) proved to be helpful in evaluating the quality of treated sewage.•Good agreement between the predictions of the neural model and the reduction values for the MLP 11-7-2 network was obtained. The paper discusses the use of Decision Support Systems (artificial neural networks analysis preceded by Principal Component Analysis) for the assessment of domestic sewage filtration effectiveness with four types of waste serving as filling materials in vertical flow filters. The study analyzed the effectiveness of pollution removal from wastewater by mechanically shredded waste in the form of PET flakes, polyurethane foam trims, shredded rubber tires and wadding. The organic compounds (CODcr, BOD5) removal, suspended solids, biogenic compounds (N-NH4+, PO43−) and oxygen saturation changing compared with reference sand filling was analyzed. The paper presents the proposal for the use of artificial neural networks as a tool to support decision making on the selection of waste material, filling vertical filters cooperating with the septic tank. An analysis of the functioning of the trained neural network was performed, comparing its responses with the reduction values obtained for individual fillings under changing hydraulic conditions. Generally good agreement between the predictions of the neural model and the reduction values was obtained for the MLP 11-7-2 network.
ISSN:2214-7144
2214-7144
DOI:10.1016/j.jwpe.2019.100885