Optimization of Celullase Production by Molecular Identification Bacterial Isolated, PUA 18 by Using Response Simulation Modelling

Cellulase enzymes are essential for converting cellulose into glucose, enabling sustainable industrial processes. Optimising cellulase production at an industrial scale requires refining microbial strains, growth medium components, and operating conditions. Traditional One-Factor-At-A-Time (OFAT) approaches are limited in efficiency, whereas Response Surface Methodology (RSM) provides a robust statistical tool for evaluating multiple variables simultaneously. This study improved cellulase production from Bacillus subtilis PUA-18, identified through 16S rRNA sequencing, using a combined Design of Experiment (DoE) approach. Screening and optimising were applied by using Plackett Burman Design (PBD) and Box-Behnken Design (BBD), respectively. Temperature was the most influential factor, followed by KNO₃, FeSO₄, and CaCl₂. The predicted maximum cellulase activity of 0.574 U mL−1 was achieved at 40 °C, 1.45 g L−1 KNO₃, 20 mg L−1 FeSO₄, and 50 mg L−1 CaCl₂. The DoE approach yielded a 293 % improvement in cellulase activity, demonstrating its superiority over traditional methods. These findings highlight Bacillus subtilis PUA-18 as a promising cellulase producer and emphasise the critical role of advanced optimisation strategies for enhancing enzyme production, paving the way for broader industrial applications.