The use of a radial basis neural network and genetic algorithm for improving the efficiency of laccase-mediated dye decolourization

A radial basis function neural network (RBF) and genetic algorithm (GA) were applied to improve the efficiency of the oxidative decolourization of the recalcitrant dye Reactive Black 5 (RB 5) by a technical laccase (Trametes spp.) and the natural mediator acetosyringone (ACS). The decolourization of RB 5 in aqueous solution was studied with a 34 factorial design including different levels of laccase (2, 100, 200 U L−1), acetosyringone (5, 50, 100 μM), pH value (3, 4.5, 6) and incubation time (10, 20, 30 min). The generated RBF network was mathematically evaluated by several statistical indices and revealed better results than a classical quadratic response surface (RS) model. The experimental data showed that within 10 min of incubation time a complete decolourization (>90%) was achieved by using the highest amount of laccase (200 U L−1) and acetosyringone (100 μM) at pH 6. By applying the RBF-GA methodology, the efficiency of the laccase-mediated decolourization was improved by minimising the required amount of laccase and acetosyringone by 25% and 21.7% respectively. Complete decolourization (>90%) was obtained within 10 min at the GA-optimised process conditions of laccase (150 U L−1) and acetosyringone (78.3 μM) at pH 5.67. These results illustrate that the RBF-GA methodology could be a powerful technique during scale-up studies.

► Development of a RBF-GA hybrid model for laccase-mediated dye decolourization.
► The most important process parameters are identified and optimal regulated.
► The RBF-GA approach improved the efficiency of the decolourization process.
► The important cost factors are minimised.