Determination of the relationship between sewage odour and BOD by neural networks [An article from: Environmental Modelling and Software]
Book Details
Author(s)G. Onkal-Engin, I. Demir, S.N. Engin
PublisherElsevier
ISBN / ASINB000RR80ZI
ISBN-13978B000RR80Z8
AvailabilityAvailable for download now
MarketplaceUnited States 🇺🇸
Description
This digital document is a journal article from Environmental Modelling and Software, published by Elsevier in . The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
Sewage treatment works are one of the major sources that cause atmospheric odour pollution. Due to the increase in environmental concerns, there is a growing number of complaints on odour nuisance. In order to determine the boundaries of legal standards, reliable and efficient odour measurement methods need to be defined. An electronic nose was used for the purpose of characterising sewage odours. Samples collected at different locations of a wastewater treatment plant were classified using an Artificial Neural Network (ANN) trained with a back-propagation algorithm. Additionally, the same method was used to determine the relation between sewage sample odours and their related Biochemical Oxygen Demand (BOD) values. The overall results have indicated that ANNs can be used to classify the sewage samples collected from different locations of a wastewater treatment plant. Moreover, the electronic nose output could be used as an indicator in monitoring the biochemical activities of wastewaters.
Description:
Sewage treatment works are one of the major sources that cause atmospheric odour pollution. Due to the increase in environmental concerns, there is a growing number of complaints on odour nuisance. In order to determine the boundaries of legal standards, reliable and efficient odour measurement methods need to be defined. An electronic nose was used for the purpose of characterising sewage odours. Samples collected at different locations of a wastewater treatment plant were classified using an Artificial Neural Network (ANN) trained with a back-propagation algorithm. Additionally, the same method was used to determine the relation between sewage sample odours and their related Biochemical Oxygen Demand (BOD) values. The overall results have indicated that ANNs can be used to classify the sewage samples collected from different locations of a wastewater treatment plant. Moreover, the electronic nose output could be used as an indicator in monitoring the biochemical activities of wastewaters.
