摘要
The structural and operational optimization of gas-liquid stirred bioreactors presents both complexity and critical importance for enhancing mass transfer performance. This study proposes a machine learning (ML)-driven approach to identify key features and predict the volumetric mass transfer coefficient (kLa). Four ML models were adopted and compared for kLa prediction in Newtonian and non-Newtonian fluids by evaluative indices, with CatBoost and XGBoost emerging as the optimal models, respectively. Specifically, it is demonstrated that Catboost has higher prediction accuracy (AARD = 18.84%) than empirical equations by effectively incorporating multidimensional features (structural, impeller, and operational), while simultaneously extending applicability to diverse Newtonian fluids. For non-Newtonian fluids, XGBoost outperforms empirical equations by effectively incorporating fluid rheological parameters (consistency coefficient, power-law index), thereby better capturing shear-thinning behavior. Feature importance analysis further identified rotational speed (for Newtonian fluids) and liquid height (for non-Newtonian fluids) as the key features, while 2D partial dependence analysis establishes quantitative optimization ranges. This ML approach provides an efficient predictive tool for gas-liquid stirred bioreactor design and optimization.
基金
supported by the National Natural Science Foundation of China(22494713,22178160,22327809 and 22208141)
Natural Science Foundation of Jiangsu Province,China(BK20220349).
