Abstract
Microalgal treatment can couple pharmaceutical attenuation with organic-matter and nutrient removal, but the operating windows for saline and freshwater species remain insufficiently defined. This laboratory study combined a central composite design, dual-detector confirmation of cortisone acetate (CA) removal, biomass mass balance, and artificial neural network (ANN) modelling and genetic algorithm (GA) optimisation to evaluate Tetraselmis chui (T. chui) and Scenedesmus obtusus (S. obtusus) under salinity, pH, and treatment-time variation. Cortisone acetate was evaluated with T. chui, whereas biochemical oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) were measured for both species. At 30.00 g/L salinity, pH 8.00, and 10.00 days, T. chui achieved 96.62% CA removal and 95.69–96.91% removal of the four water-quality parameters. At 0.00 g/L salinity, pH 7.00, and 10.00 days, S. obtusus achieved 96.25–98.41% removal of BOD, COD, TN, and TP. Species-specific ANN models accurately reproduced the experimental response surfaces (R² = 0.997 for T. chui and 0.978 for S. obtusus), and the GA identified species-specific optima within the experimental ranges. The integrated experimental–analytical–modelling framework identifies salinity as the principal operational control while distinguishing the saline niche of T. chui from the freshwater niche of S. obtusus.
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Funding
This research was supported by the Vice-Chancellor for Research and Technology Affairs of Shiraz University of Medical Sciences (Grant Number: 29974), Shiraz, Iran.
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All experiments were conducted in strict compliance with institutional safety and ethical guidelines to ensure the integrity and responsibility of the research process. The study was approved by the Ethics Committee of Shiraz University of Medical Sciences (approval code: IR.SUMS.SCHEANUT.REC.1403.005). No hazardous materials or procedures were involved in this study, and all experimental protocols were designed to minimise potential risks to researchers and the environment. Additionally, data collection and analysis adhered to ethical principles, ensuring the accuracy, transparency, and reproducibility of the results. These measures underscore the commitment of the research team to uphold the highest ethical standards throughout the study.
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Derakhshan, Z., Tavakoli, F., Feilizadeh, M. et al. ANN modelling and GA optimisation of microalgal removal of cortisone acetate, organic matter and nutrients.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-73487-7
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DOI: https://doi.org/10.1038/s41598-026-73487-7
Keywords
- Artificial neural network
- Central composite design
- Cortisone acetate
- Genetic algorithm
- Microalgae
- Nutrient removal
- Wastewater treatment
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