Bio-degradation of Bisphenol A by Pseudomonas aeruginosa PAb1 isolated from effluent of thermal paper industry: Kinetic modeling and process optimization. Issue 1 (January 2018) - Vijayalakshmi, V.; Senthilkumar, P.; Mophin-Kani, K.; Sivamani, S.; Sivarajasekar, N.; Vasantharaj, S.
Abstract: A bacterium isolated from effluent of thermal paper industry and identified as Pseudomonas aeruginosa (PAb1) based on 16SrRNA gene sequence analysis which could grow on basal mineral salt medium upon bisphenol A, which functions as an exclusive carbon source. Physicochemical variables of thermal paper industry effluent noted were significantly greater than the typical limit due to pollution of the acquiring water systems. The mathematic kinetic models like Monod, Moser and Tesier models were applied for batch fermentation of bisphenol A degradation in basal salt medium and the half saturation coefficient (KS ) and the regression coefficient R 2 using Monad, Moser and Tesier kinetic models registered as 9.947 g/L, 12.46 g/L and 14.14 g/L and 0.91, 0.94 and 0.84 respectively. Besides, the utmost specific growth rate μmax was witnessed as 0.841 h −1 for the P. aeruginosa (PAb1) regarding BPA degradation. Metabolic intermediates like phenol, acetophenone, and hydroquinone and p -hydroxybenzoic acid were also determined through the degradation process by GC-MS. The metabolic pathway of BPA degradation by the bacterial isolates was also designed in today's analysis. A probabilistic statistical model originated using Box-Behnken response surface methodology and process variables were optimized by nonlinear optimization. Graphical abstract: Highlights: Pseudomonas aeruginosa (PAb1) isolated from effluent of thermal paper industry and characterized. The mathematic kinetic models were applied for batch fermentation of bisphenol A degradation. Metabolic intermediates identified during the degradation process by GC-MS and the metabolic pathway was also designed. A probabilistic model was developed using Box-Behnken response surface methodology and process variables were optimized by nonlinear optimization.