Estimation of Dilution Factor for Moving Cruise Ships by Artificial Neural Networks


Şahin V., Bilgili L., Vardar N.

WATER, AIR AND SOIL POLLUTION : AN INTERNATIONAL JOURNAL OF ENVIRONMENTAL POLLUTION, vol.233, no.235, pp.1-16, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 233 Issue: 235
  • Publication Date: 2022
  • Doi Number: 10.1007/s11270-022-05701-x
  • Journal Name: WATER, AIR AND SOIL POLLUTION : AN INTERNATIONAL JOURNAL OF ENVIRONMENTAL POLLUTION
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Agricultural & Environmental Science Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), Artic & Antarctic Regions, BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Chemical Abstracts Core, Chimica, Compendex, EMBASE, Environment Index, Geobase, Greenfile, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Page Numbers: pp.1-16
  • Keywords: Ship sewage, Dilution factor, Artificial neural networks, Sustainable development goals
  • Van Yüzüncü Yıl University Affiliated: Yes

Abstract

Although domestic wastewater originating from ships is discharged to the sea after being treated in the treatment system, it cannot meet the wastewater concentration standards determined by the authorities in terms of some pollutant concentrations. This problem is more important on cruise ships, which can carry much more people than other commercial ships. After the wastewater treated in the treatment system on the ship is discharged to the sea, it is subjected to a secondary natural treatment due to the turbulence that occurs on the ship's trail. This phenomenon, called dilution, helps the pollutant concentrations in high concentrations to reach the wastewater standards determined by the authorities in a short time. The magnitude of this dilution is called the dilution factor. In this study, gross ton, deadweight ton, passenger number, freeboard, engine power, propeller number, and block coefficient data of a total of 1942 passenger ships, 941 of which were small and 1041 of which were large passenger ships, were used in artificial neural networks to determine which parameter was more effective in calculating the dilution factor. Engine power and gross ton value were determined as the most effective parameters for the dilution factor, and it was seen that by using these parameters alone in artificial neural networks, the dilution factor could be successfully predicted regardless of whether the ship was small or large. Finally, the effect of dilution was assessed in terms of sustainable development goals and life cycle perspective.