Experimental and kinetic study for lead removal via photosynthetic consortia using genetic algorithms to parameter estimation.

This work presents an experimental-theoretical strategy for a batch process for lead removal by photosynthetic consortium, conformed by algae and bacteria. Photosynthetic consortium, isolated from a treatment plant wastewater of Tecamac (Mexico), was used as inoculum in bubble column photobioreactors. The consortium was used to evaluate the kinetics of lead removal at different initial concentrations of metal (15, 30, 40, 50, and 60 mgL-1), carried out in batch culture with a hydraulic residence time of 14 days using Bold's Basal mineral medium. The photobioreactor was operated under the following conditions: aeration of 0.5 vvm, 80 μmol m-2 s-1 of photon flux density and a photoperiod light/dark 12:12. After determining the best growth kinetics of biomass and metal removal, they were tested under different ratios (30 and 60%) of wastewater-culture medium. Additionally, the biomass growth (X), nitrogen consumption (N), chemical oxygen demand (COD), and metal removal (Pb) were quantified. Achieved lead removal was 97.4% when the initial lead concentration was up to 50 mgL-1 using 60% of wastewater. Additionally, an unstructured-type mathematical model was developed to simulate COD, X, N, and lead removal. Furthermore, a comparison between the Levenberg-Marquardt (L-M) optimization approach and Genetic Algorithms (GA) was carried out for parameter estimation. Also, it was concluded that GA has a slightly better performance and possesses better convergence and computational time than L-M. Hence, the proposed method might be applied for parameter estimation of biological models and be used for the monitoring and control process.