Optimization of endoglucanase-lipase-amylase enzyme consortium from Thermomyces lanuginosus VAPS25 using Multi-Objective genetic algorithm and their bio-deinking applications

In this study, the enzyme consortium of endoglucanase, lipase, and amylase was obtained and optimized using artificial intelligence-based tools. After optimization using a multi-objective genetic algorithm and artificial neural network, the enzyme activity was 8.8 IU/g, 153.68 U/g, and 19.2 IU/g for endoglucanase, lipase, and amylase, respectively, using Thermomyces lanuginosus VAPS25. The highest enzyme activity was obtained at parameters 77.69% moisture content, 52.7 °C temperature, 98 h, and 3.1 eucalyptus leaves: wheat bran ratio. The endoglucanase-lipase-amylase (END-LIP-AMY) enzyme consortium showed reliable characteristics in terms of catalytic activity at 50–80 °C and pH 6.0–9.0. The increase in deinking efficiency of 27.8% and 11.1% were obtained compared to control for mixed office waste and old newspaper, respectively, using the enzyme consortium. The surface chemical composition and fiber morphology of deinked pulp was investigated using Attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) and Scanning electron microscopy (SEM).