Efficient production of pullulan by Aureobasidium pullulans using a multi-objective optimization strategy with...

Area of Science:

• Biotechnology
• Microbial Fermentation
• Biopolymer Production

Background:

• Efficient pullulan production is crucial for various industrial applications.
• Traditional optimization methods often struggle with multi-objective challenges in fermentation.

Purpose of the Study:

• To develop a hybrid optimization strategy for enhancing pullulan production and molecular weight.
• To integrate orthogonal experimental design (OED), backpropagation artificial neural network (BP-ANN), and a genetic algorithm (NSGA-II).

Main Methods:

• Utilized maltodextrin as a carbon source for Aureobasidium pullulans fermentation.
• Employed OED to identify key factors affecting pullulan production and molecular weight.
• Developed a BP-ANN model for predicting production and molecular weight, integrated with NSGA-II for multi-objective optimization.

Main Results:

• OED indicated MgSO4·7H2O and pH significantly impact production and molecular weight, respectively.
• The BP-ANN model demonstrated high accuracy (goodness-of-fit > 0.980) for both parameters.
• The hybrid BP-ANN-NSGA-II approach achieved a 6.89% increase in production, 368.97% increase in molecular weight, and 42.49% cost reduction.

Conclusions:

• The hybrid optimization strategy effectively balances pullulan production, molecular weight, and cost.
• Multi-objective optimization significantly outperforms single-objective approaches for pullulan fermentation.
• This integrated method provides a robust framework for improving biopolymer fermentation processes.