Modeling and Optimization of Ketoprofen Bioremediation Process by a Freshwater Microalgal Consortium

This study optimized the ketoprofen (KTP) bioremediation, a non-steroidal anti-inflammatory drug (NSAID), using a microalgal consortium (TWB) isolated from thermal springs in Bejaia, Algeria. Conducted in a batch reactor under controlled light, temperature, and nutrient conditions, the experiment evaluated microalgal growth and KTP removal efficiency. Three parameters—pH, initial KTP concentration (C_O), and nitrogen source concentration (No, as (NH₄)₂SO₄)—were varied in a two-level full factorial design (FFD). Linear models derived from FFD and regression analysis (RA) were compared based on multicollinearity (VIF), predictive ability (R²pred), residual autocorrelation (Durbin-Watson), and lack of fit. Analysis of variance (ANOVA) performed with Minitab 17 validated all regression equations (p < 0.05). RA models excelled in prediction (R²pred up to 97.98%), while FFD models facilitated effect interpretation due to low multicollinearity (VIF = 1). pH and nitrogen positively influenced growth and KTP removal, whereas KTP concentration had a negative effect. The KTP-nitrogen interaction significantly reduced both responses in high-KTP media. The maximum KTP removal efficiency of 97.4% was achieved at Co = 12.4 mg L⁻¹ and No = 2 g L⁻¹. These findings highlight the potential of microalgal consortia for sustainable pharmaceutical wastewater treatment, offering a cost-effective and eco-friendly alternative to conventional methods. Future research should focus on scaling up this process and elucidating the biochemical pathways involved in KTP degradation.