Improved deinking and biobleaching efficiency of enzyme consortium from Thermomyces lanuginosus VAPS25 using genetic Algorithm-Artificial neural network based tools

• Published in: Bioresource technology 2022-04, Vol.349, p.126846-126846, Article 126846
• Main Authors: Dixit, Mandeep, Kumar Gupta, Guddu, Yadav, Monika, Chhabra, Deepak, Kumar Kapoor, Rajeev, Pathak, Puneet, Bhardwaj, Nishi K., Shukla, Pratyoosh
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Cellulase; Deinking; Enzyme consortium; Eurotiales; GA-ANN; Neural Networks, Computer; Submerged fermentation; Thermomyces lanuginosus VAPS25
• ISSN: 0960-8524; 1873-2976
• DOI: 10.1016/j.biortech.2022.126846

[Display omitted] •A distinctive thermostable enzyme consortia from T. lanuginosus VAPS25 is described.•Improved deinking efficiency of waste paper through enzyme consortium is reported.•The GA-ANN based tools gave substantial enzyme production and improved deinking.•The deinking efficiency was increased to 72.7% by using this enzyme consortia.•The significant improvement in the bio-bleached paper strength is also reported. The present study reports the combined enzymatic production efficiency of thermophilic fungus Thermomyces lanuginosus VAPS25 using a combinatory artificial intelligence-based tool, resulting in 2.7 IU/ml, 5.2 IU/ml, and 18.85 U/ml activity of endoglucanase, amylase, and lipase, respectively with good thermostability at 90 °C (pH 8–10). Interestingly, the metal ions viz. Cu2+ and Mg2+ increased the endoglucanase activity to 5 folds, i.e.,5.6 IU/ml compared to control. Further, the amylase and lipase activity was also enhanced by Fe2+ and Co2+ to 5.4 IU/ml and 19.57 U/ml, respectively. Additionally, the deinking efficiency was improved by 68.9%, 42.7%, and 52.8% by endoglucanase, amylase, and lipase, respectively, while the consortium increased the deinking efficiency to 72.7%. The bio-bleached paper strength parameters such as burst index, breaking length, tear index, and tensile index of sheets were significantly improved by 1.38%, 13.54%, 7.54%, and 20.88%, respectively. These enzymes at an industrial scale would help develop an economical paper recycling process.