Modeling and optimization of docosahexaenoic acid production by Schizochytrium sp. based on kinetic modeling and...

Docosahexaenoic acid (DHA), an essential ω-3 polyunsaturated fatty acid, is efficiently biosynthesized by Schizochytrium sp., yet its bioprocess optimization remains constrained by dynamic interdependencies between cultivation parameters and metabolic shifts. This study establishes a framework integrating kinetic modeling and machine learning to improve DHA production. Kinetic models based on Logistic and Luedeking-Piret equations were utilized to describe dynamic biomass, lipid and DHA production. An artificial neural network (ANN) trained on fermentation data predicted biomass and DHA yield, while genetic algorithm (GA) optimization elevated predictive accuracy (R² = 0.988) and overcame local optimization. The ANN-GA model identified optimal three-stage control strategy, experimentally validating a 10.4 % increase in DHA yield (45.13 g/L) compared to optimal training data. By combining kinetic models and the ANN-GA model, this study provided a scalable framework for improving DHA production and reducing experimental costs.