Publication:
Prediction model development for petroleum refinery wastewater treatment

Simple item page
dc.citedby14
dc.contributor.authorHayder G.en_US
dc.contributor.authorRamli M.Z.en_US
dc.contributor.authorMalek M.A.en_US
dc.contributor.authorKhamis A.en_US
dc.contributor.authorHilmin N.M.en_US
dc.contributor.authorid56239664100en_US
dc.contributor.authorid57195984780en_US
dc.contributor.authorid55636320055en_US
dc.contributor.authorid12783981300en_US
dc.contributor.authorid56226266400en_US
dc.date.accessioned2023-05-16T02:46:29Z
dc.date.available2023-05-16T02:46:29Z
dc.date.issued2014
dc.description.abstractMulti-stage biological treatment of petroleum refinery wastewater using different biological conditions (anaerobic-anoxic-aerobic) has many advantages over other biological methods. It can result in maximum treatment for type of complex wastewater. In this study, raw data obtained from two multi-stage biological reactors (MSBR) used for treatment of different loads of petroleum refinery wastewater was used for developing mathematical model that could predict the process trend. The data consists of 160 entries and were gathered over approximately 180 days from two MSBR reactors that were continuously operated in parallel. A Matlab code was written with two configurations of artificial neural network. The configurations were compared and different number of neurons at the hidden layer were tested for optimum model that represent the process behavior under different loads. The tangent sigmoid transfer function (Tansig) at hidden layer and a linear transfer function (Purelin) at output layer with 6 neurons were selected as the optimum best model. The model was then used for prediction; highest removal efficiency observed was 98% which was repeatedly recorded for various loads. Effluent concentration below 100. mg/L as chemical oxygen demand (COD) was recorded for influent concentration ranged between 900 and 3600. mg COD/L. © 2014 Elsevier Ltd.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.jwpe.2014.08.006
dc.identifier.epage5
dc.identifier.issueC
dc.identifier.scopus2-s2.0-84926381475
dc.identifier.spage1
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84926381475&doi=10.1016%2fj.jwpe.2014.08.006&partnerID=40&md5=4e3c6a805ae22726cfee0829aeddb4e0
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/21989
dc.identifier.volume4
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleJournal of Water Process Engineering
dc.titlePrediction model development for petroleum refinery wastewater treatmenten_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS