A dual approach using response surface methodology and machine learning for optimization and enhancement of microalgae-based municipal wastewater treatment

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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.accessioned2025-05-29T02:58:10Z
dc.date.available2025-05-29T02:58:10Z
dc.date.issued2025
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.descriptionThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution andreproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
dc.description.abstractBACKGROUND Municipal wastewater comprises both organic and inorganic contaminants. Especially in rural areas, conventional municipal treatment plants primarily focus on carbon removal; therefore, nutrient removal should be prioritized for preventing environmental pollution. Mixotrophic microalgae such as Nannochloropsis sp. have significant potential for both carbon and nutrient removal. However, microalgae-based wastewater systems can be affected by many parameters and, using response surface methodology and decision tree, a machine learning model can help to determine the optimal conditions for the systems to operate more efficiently. RESULTS The optimal removal conditions were determined by response surface methodology to be a light period of 21 h at an intensity of 8000 lx and a temperature value of 30 °C. Under these optimal conditions, the respective removal efficiency for chemical oxygen demand, total organic carbon, total Kjeldahl nitrogen, and orthophosphate was 53%, 34%, 87%, and 70%, respectively. Chlorophyll-a concentration increased by as much as 49%. Real municipal wastewater was used instead of synthetic wastewater, yielding closer approximations to real situations. CONCLUSION The present study has underscored innovative, data-driven approaches as core in ensuring sustainable wastewater management and sets a useful framework for future research, which could be done with the aim of refining the methods to enhance efficiency in treatment. © 2025 The Author(s). Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI).
dc.identifier.doi10.1002/jctb.7856
dc.identifier.endpage1256
dc.identifier.issn0268-2575
dc.identifier.issn1097-4660
dc.identifier.issue6
dc.identifier.scopus2-s2.0-105001541298
dc.identifier.scopusqualityQ1
dc.identifier.startpage1244
dc.identifier.urihttps://doi.org/10.1002/jctb.7856
dc.identifier.urihttps://hdl.handle.net/20.500.12428/30297
dc.identifier.volume100
dc.identifier.wosWOS:001452547100001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley
dc.relation.ispartofJournal of Chemical Technology and Biotechnology
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250529
dc.subjectmunicipal wastewater
dc.subjectmicroalgae
dc.subjectNannochloropsis sp.
dc.subjectresponse surface methodology
dc.subjectdecision tree
dc.subjectoptimization
dc.titleA dual approach using response surface methodology and machine learning for optimization and enhancement of microalgae-based municipal wastewater treatment
dc.typeArticle

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