Optimization of Biological Wastewater Treatment and Biomass Production by Microalgae Desmodesmus sp.: A Comparison of Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

Labbafi, Sina; Razi Astaraei, Fatemeh

doi:10.22059/ses.2026.416097.1258

Optimization of Biological Wastewater Treatment and Biomass Production by Microalgae Desmodesmus sp.: A Comparison of Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

Articles in Press

Document Type : Original Article

Authors

Sina Labbafi ¹

Fatemeh Razi Astaraei ²

¹ School of Energy Engineering and Sustainable Resources, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, Iran

² School of Energy Engineering and Sustainable Resources, College of Interdisciplinary Science and Technology, University of Tehran, Tehran, Iran.

10.22059/ses.2026.416097.1258

Abstract

This study investigated the optimization of biological wastewater treatment and biomass production by the microalga Desmodesmus sp. while comparing two modeling approaches: Response Surface Methodology (RSM) and Artificial Neural Network (ANN). Samples were collected from Anzali Wetland, pretreated, enriched, and cultivated under controlled laboratory conditions. The operational variables included light intensity, aeration rate, pH, and retention time, which were optimized to achieve the best treatment performance. The results indicated that ANN provided more accurate predictions than RSM and was better able to capture the nonlinear relationships between process variables and responses. The optimal conditions were determined as a light intensity of 392 μ"mol photons"⋅"m" ^(-2)⋅"s" ^(-1), aeration rate of 1.37 "mg"⋅"L" ^(-1), pH 8.5, and retention time of 7.5 days. Under these conditions, the highest predicted efficiencies were 88.3% COD removal, 86.7% TN removal, 95.4% TP removal, and biomass production of 1.372 mg.L⁻¹. Overall, the findings suggest that Desmodesmus sp. can serve as an effective system for simultaneous wastewater treatment and valuable biomass production, with ANN offering superior predictive and optimization performance compared with RSM.

Keywords

Optimization

Wastewater Treatment

Microalgae

Biomass

Subjects