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

Kayan I., AYMAN ÖZ N.

Journal of Chemical Technology and Biotechnology, vol.100, no.6, pp.1244-1256, 2025 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 100 Issue: 6
  • Publication Date: 2025
  • Doi Number: 10.1002/jctb.7856
  • Journal Name: Journal of Chemical Technology and Biotechnology
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Chemical Abstracts Core, Chimica, Communication Abstracts, Compendex, Computer & Applied Sciences, Food Science & Technology Abstracts, INSPEC, Metadex, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Page Numbers: pp.1244-1256
  • Keywords: decision tree, microalgae, municipal wastewater, Nannochloropsis sp., optimization, response surface methodology
  • Çanakkale Onsekiz Mart University Affiliated: Yes

Abstract

BACKGROUND: 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).