Integrating response surface methodology and decision tree algorithms for valorization of cheese whey wastewater

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dc.authoridKayan, İremsu / 0009-0005-0698-6661
dc.authoridAyman Öz, Nilgün / 0000-0002-6309-0547
dc.contributor.authorKayan, İremsu
dc.contributor.authorAyman Öz, Nilgün
dc.date.accessioned2025-05-29T02:58:01Z
dc.date.available2025-05-29T02:58:01Z
dc.date.issued2025
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractRecently, the potential of microalgae in wastewater treatment has attracted attention. The goal of this study is to find optimum conditions for microalgae growth and the concentration of cheese whey wastewater (CWW) to get the best treatment efficiency by using response surface methodology (RSM) and the decision tree algorithm for different pollutant parameters. The study used reactors with different amounts of CWW and Nannochloropsis sp. to find the best concentrations for each parameter. The best concentration of CWW was found to be 8000 mgCOD/L, and the best concentration of Nannochloropsis sp. microalgae was found to be 2200 mgVS/L. It was found that Chemical Oxygen Demand (COD), Total Organic Carbon (TOC), Total Kjeldahl Nitrogen (TKN), and Orthophosphorus (Ortho-P) could be removed at different ranges, 77-96 %, 95-98 %, 51-97 %, and 60-99 % of CWW, respectively, depending on the different combinations of microalgae and CWW concentrations. The desirability values in RSM for COD, TOC, TKN, and Ortho-P parameters to be 0.99, 0.94, 0.78, and 0.63, respectively. The study suggests the marine microalgae (Nannochloropsis sp.) could be an alternative way to treat saline CWW, to create a circular economy. The machine learning (ML) method validates that RSM predictions are consistent and accurate. The results show that it is possible to combine traditional optimization methods with more advanced ML methods to facilitate the design and the operation of the treatment plants.
dc.identifier.doi10.1016/j.dwt.2025.101129
dc.identifier.issn1944-3994
dc.identifier.issn1944-3986
dc.identifier.scopus2-s2.0-105000517792
dc.identifier.scopusqualityQ3
dc.identifier.urihttps://doi.org/10.1016/j.dwt.2025.101129
dc.identifier.urihttps://hdl.handle.net/20.500.12428/30241
dc.identifier.volume322
dc.identifier.wosWOS:001456951900001
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Science Inc
dc.relation.ispartofDesalination and Water Treatment
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250529
dc.subjectCheese whey wastewater
dc.subjectMicroalgae
dc.subjectNannochloropsis sp.
dc.subjectOptimization
dc.subjectResponse surface analysis
dc.subjectDecision tree
dc.subjectMachine learning
dc.titleIntegrating response surface methodology and decision tree algorithms for valorization of cheese whey wastewater
dc.typeArticle

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