Staff Publications

Staff Publications

  • external user (warningwarning)
  • Log in as
  • language uk
  • About

    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

    We have a manual that explains all the features 

Record number 346571
Title Automatic detection of exogenous respiration end-point using artificial neural network
Author(s) Bisschops, I.A.E.; Spanjers, H.; Keesman, K.J.
Source Water Science and Technology 53 (2006)4-5. - ISSN 0273-1223 - p. 273 - 281.
Department(s) Sub-department of Environmental Technology
Systems and Control Group
Publication type Refereed Article in a scientific journal
Publication year 2006
Keyword(s) water
Abstract When aerobic bacteria receive a biodegradable material such as wastewater, then respiration changes from endogenous to exogenous. The reverse occurs when biodegradation is complete. When using respirometry a respirogram is recorded showing those changes in respiration, and for an expert it is not difficult to point the moments at which they occur. The area corresponding to the exogenous respiration phase is a measure of the easily biodegradable fraction of material, also called the short-term BOD or BODST. That value, in combination with a value for COD, can be used to determine the treatability of wastewater. Respirometry can also be applied on-line, e.g. for on-line monitoring of wastewater. However, automatic detection of the end-point of exogenous respiration is difficult. The first step towards on-line monitoring of wastewater treatability is to make automatic detection of this end-point possible. In this study the use of a neural network for detection of this end-point was investigated. Results are promising; after training the neural network is able to detect the correct end-point in the majority of the studied cases
There are no comments yet. You can post the first one!
Post a comment
Please log in to use this service. Login as Wageningen University & Research user or guest user in upper right hand corner of this page.