Prediction of mass transfer performance in gas-liquid stirred bioreactor using machine learning

doi:10.1016/j.cjche.2025.06.010

Chinese Journal of Chemical Engineering ›› 2025, Vol. 84 ›› Issue (8): 211-226.DOI: 10.1016/j.cjche.2025.06.010

Prediction of mass transfer performance in gas-liquid stirred bioreactor using machine learning

Feifei Chen¹, Zhenyuan Xiao¹, Zhongfan Luo¹, Peng Jiang¹, Jingjing Chen¹, Yuanhui Ji², Jiahua Zhu¹, Xiaohua Lu¹, Liwen Mu¹

1. State Key Laboratory of Material-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, Nanjing 211816, China;
2. Jiangsu Province Hi-Tech Key Laboratory for Biomedical Research, School of Chemistry and Chemical Engineering, Southeast University, Nanjing 211189, China

Received:2024-12-08 Revised:2025-06-19 Accepted:2025-06-26 Online:2025-07-02 Published:2025-08-28
Contact: Jingjing Chen,E-mail:jingjingchen@njtech.edu.cn;Liwen Mu,E-mail:lwmu@njtech.edu.cn
Supported by:
This research is supported by the National Natural Science Foundation of China (22494713, 22178160, 22327809 and 22208141), Natural Science Foundation of Jiangsu Province, China (BK20220349).

Prediction of mass transfer performance in gas-liquid stirred bioreactor using machine learning

Feifei Chen¹, Zhenyuan Xiao¹, Zhongfan Luo¹, Peng Jiang¹, Jingjing Chen¹, Yuanhui Ji², Jiahua Zhu¹, Xiaohua Lu¹, Liwen Mu¹

1. State Key Laboratory of Material-Oriented Chemical Engineering, College of Chemical Engineering, Nanjing Tech University, Nanjing 211816, China;
2. Jiangsu Province Hi-Tech Key Laboratory for Biomedical Research, School of Chemistry and Chemical Engineering, Southeast University, Nanjing 211189, China

通讯作者: Jingjing Chen,E-mail:jingjingchen@njtech.edu.cn;Liwen Mu,E-mail:lwmu@njtech.edu.cn
基金资助:
This research is supported by the National Natural Science Foundation of China (22494713, 22178160, 22327809 and 22208141), Natural Science Foundation of Jiangsu Province, China (BK20220349).

Abstract

Abstract: 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 (k_La). Four ML models were adopted and compared for k_La 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.

Key words: Machine learning, Volumetric mass transfer coefficient, Gas-liquid stirred bioreactor, Multi-parameter optimization

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 (k_La). Four ML models were adopted and compared for k_La 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.

关键词: Machine learning, Volumetric mass transfer coefficient, Gas-liquid stirred bioreactor, Multi-parameter optimization

Feifei Chen, Zhenyuan Xiao, Zhongfan Luo, Peng Jiang, Jingjing Chen, Yuanhui Ji, Jiahua Zhu, Xiaohua Lu, Liwen Mu. Prediction of mass transfer performance in gas-liquid stirred bioreactor using machine learning[J]. Chinese Journal of Chemical Engineering, 2025, 84(8): 211-226.

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Prediction of mass transfer performance in gas-liquid stirred bioreactor using machine learning

Prediction of mass transfer performance in gas-liquid stirred bioreactor using machine learning

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