Laccase production from Bacillus sp. BAB-4151 using artificial neural network and genetic algorithm and its application for wastewater treatment

Dye-based pollutants are frequently discharged into water bodies, negatively impacting human and environmental health. The treatment of waste-water using laccase-producing microbes generates non-toxic compounds. Higher yields of laccase at the industrial level require a potential laccase-producing bacteria and optimization of production parameters. The present study aimed to maximize the laccase yield of Bacillus sp. BAB-4151, using artificial neural network (ANN) coupled with a genetic algorithm (GA) approach. Of the six laccase-producing bacteria, Bacillus sp. BAB-4151 produced copious amounts of laccase (150 ± 2.5 UmL^-1). A further improvement in laccase yield (~35%) was obtained through ANN-GA. The laccase-rich broth was applied to wastewater treatment using a completely randomized design (CRD) using five treatments consisting of control (uninoculated), and wastewater concentrations (25%, 50%, 75%, and 100%) and the dye decolorization potential of Bacillus sp. BAB-4151 was determined based on changes in pH, Biological Oxygen Demand (BOD) and Chemical Oxygen Demand (COD) values. T3 treatment resulted in the maximum decolorization (80.13%), yielding minimum BOD (153.21 mg/L), and COD (6.53 mg/L). Thus, the laccase yield of Bacillus sp. BAB-4151 can be improved using the ANN-GA approach laccase rich broth and can be employed to mitigate dye-rich wastewater of the textile industries.