Waste assessment decision support systems used for domestic sewage treatment

Ewa Dacewicz

Abstract

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.
Author Ewa Dacewicz (FoEEaLS / DoSEaWM)
Ewa Dacewicz,,
- Department of Sanitary Engineering and Water Management
Journal seriesJournal of Water Process Engineering, ISSN 2214-7144, e-ISSN 2214-7144, (N/A 100 pkt)
Issue year2019
Vol31
No100885
Pages1-7
Publication size in sheets0.5
Keywords in EnglishDecision Support System, ANN, Waste, PET, Polyurethane foam, Rubber tires, Treatment process
ASJC Classification1305 Biotechnology; 1508 Process Chemistry and Technology; 2213 Safety, Risk, Reliability and Quality; 2311 Waste Management and Disposal
DOIDOI:10.1016/j.jwpe.2019.100885
Internal identifierWIŚIG/2019/54
Languageen angielski
Score (nominal)100
Score sourcejournalList
Publication indicators WoS Citations = 0; Scopus SNIP (Source Normalised Impact per Paper): 2018 = 1.165; WoS Impact Factor: 2018 = 3.173 (2) - 2018=3.688 (5)
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