An Artificial Neural Network (ANN) Modelling Approach for Evaluating Turbidity Properties of Paper Recycling Wastewater

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dc.authorid0000-0002-1748-9354
dc.authorid0000-0002-6311-8634
dc.contributor.authorKardes, Serkan
dc.contributor.authorOzkan, Ugur
dc.contributor.authorBayram, Okan
dc.contributor.authorSahin, Halil Turgut
dc.date.accessioned2026-02-08T15:15:48Z
dc.date.available2026-02-08T15:15:48Z
dc.date.issued2024
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractA pre-treatment process was evaluated in this work for wastewater from precipitation of contaminants through centrifugation. Artificial neural networks (ANNs) were used to analyze and optimize the turbidity values. Thirty experimental runs were utilized including microwave (MW) power, duration, centrifuge time, and centrifuge speed as input variables, generated by the Central Composite Full Design (CCFD) approach. The experimental turbidity ranged from 8.1 to 19.7 NTU, while predicted values ranged from 8.4 to 19.7 NTU by ANN. The ANN model showed a robust prediction performance with low mean squared error values during training and testing. Moreover, high R2 values showed a remarkable agreement between the experimental observations and ANN predictions. The results obtained from the input values (A:150.00, B:60.00, C:15.00, D:30.00) of sample 2, which gave the lowest turbidity value, showed the most removal of pollution. The results obtained from the input values (A:250.00, B:60.00, C:7.00, D:20.00) of sample 30, which gave the highest turbidity value, showed the least removal of pollution. The results showed that increasing RPM and time of the centrifugation process significantly affected the removal of pollution in wastewater.
dc.identifier.doi10.15376/biores.19.3.5003-5018
dc.identifier.endpage5018
dc.identifier.issn1930-2126
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85196421302
dc.identifier.scopusqualityQ3
dc.identifier.startpage5003
dc.identifier.urihttps://doi.org/10.15376/biores.19.3.5003-5018
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5973
dc.identifier.volume19
dc.identifier.wosWOS:001259919000032
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherNorth Carolina State Univ Dept Wood & Paper Sci
dc.relation.ispartofBioresources
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzWOS_KA_20260207
dc.subjectPaper recycling
dc.subjectWastewater
dc.subjectTurbidity
dc.subjectArtificial neural network
dc.subjectMicrowave technology
dc.titleAn Artificial Neural Network (ANN) Modelling Approach for Evaluating Turbidity Properties of Paper Recycling Wastewater
dc.typeArticle

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