Neural network modelling of a depollution process

dc.contributorSteyer, J.P., Chemical Engineering Department, CUCEI, Universidad de Guadalajara, Guadalajara, Mexico; Pelayo-Ortiz, C., Chemical Engineering Department, CUCEI, Universidad de Guadalajara, Guadalajara, Mexico; González-Alvarez, V., Chemical Engineering Department, CUCEI, Universidad de Guadalajara, Guadalajara, Mexico; Bonnet, B., Chemical Engineering Department, CUCEI, Universidad de Guadalajara, Guadalajara, Mexico; Bories, A., Chemical Engineering Department, CUCEI, Universidad de Guadalajara, Guadalajara, Mexico
dc.creatorSteyer, J.P.
dc.creatorPelayo-Ortiz, C.
dc.creatorGonzalez-Alvarez, V.
dc.creatorBonnet, B.
dc.creatorBories, A.
dc.date.accessioned2015-09-15T18:28:40Z
dc.date.accessioned2022-11-02T15:43:08Z
dc.date.available2015-09-15T18:28:40Z
dc.date.available2022-11-02T15:43:08Z
dc.date.created2015-09-15T18:28:40Z
dc.date.issued2000
dc.identifierhttp://hdl.handle.net/20.500.12104/43072
dc.identifierhttp://www.scopus.com/inward/record.url?eid=2-s2.0-2442430566&partnerID=40&md5=f330e202c44e901a9c44835c070aeaf0
dc.identifier10.1007/s004490070001
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5017579
dc.description.abstractIn this paper an artificial neural network is developed to model a new depollution process that uses sequential cultures of anaerobic bacteria and yeasts to efficiently remove both carbon and nitrogen from wastewaters. A set of batch experimental runs are used to train and test various neural network topologies. It is shown that the neural network accurately tracks the dynamics of the biological species of the yeast reactor in the process and account for the influence of butyric acid, ammonia and pH on the overall efficiency of purification.
dc.relationScopus
dc.relationWOS
dc.relationBioprocess Engineering
dc.relation23
dc.relation6
dc.relation727
dc.relation730
dc.titleNeural network modelling of a depollution process
dc.typeArticle


Este ítem pertenece a la siguiente institución