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

doi:10.1016/j.cjche.2025.06.010

中国化学工程学报 ›› 2025, Vol. 84 ›› Issue (8): 211-226.DOI: 10.1016/j.cjche.2025.06.010

• Full Length Article • 上一篇下一篇

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

收稿日期:2024-12-08 修回日期:2025-06-19 接受日期:2025-06-26 出版日期:2025-08-28 发布日期:2025-07-02
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).

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:2025-08-28 Published:2025-07-02
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).

摘要/Abstract

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

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

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]. 中国化学工程学报, 2025, 84(8): 211-226.

参考文献

[1] Z.M. Kang, L.F. Feng, J.J. Wang, Optimization of a gas-liquid dual-impeller stirred tank based on deep learning with a small data set from CFD simulation, Ind. Eng. Chem. Res. 63 (1) (2024) 843-855.
[2] A.Q. Li, Y. Yao, X.Y. Tang, P.Q. Liu, Q. Zhang, Q. Li, P. Li, F. Zhang, Y.D. Wang, C.Y. Tao, Z.H. Liu, Experimental and computational investigation of chaotic advection mixing in laminar rectangular stirred tanks, Chem. Eng. J. 485 (2024) 149956.
[3] M. Jamshidzadeh, A. Kazemzadeh, F. Ein-Mozaffari, A. Lohi, Analysis of power consumption for gas dispersion in non-Newtonian fluids with a coaxial mixer: New correlations for Reynolds and power numbers, Chem. Eng. J. 401 (2020) 126002.
[4] A. Kazemzadeh, C. Elias, M. Tamer, A. Lohi, F. Ein-Mozaffari, Mass transfer in a single-use angled-shaft aerated stirred bioreactor applicable for animal cell culture, Chem. Eng. Sci. 219 (2020) 115606.
[5] Y. Kawase, B. Halard, M. Moo-Young, Liquid-phase mass transfer coefficients in bioreactors, Biotechnol. Bioeng. 39 (11) (1992) 1133-1140.
[6] F. Garcia-Ochoa, E. Gomez, Bioreactor scale-up and oxygen transfer rate in microbial processes: an overview, Biotechnol. Adv. 27 (2) (2009) 153-176.
[7] H. Chen, Z. Chen, X.Q. Wang, X.G. Yi, X.B. Zhang, Z.H. Luo, Mass transfer and bubble hydrodynamics in stirred tank with multiple properties fluid via a CFD-PBM method, Can. J. Chem. Eng. 102 (11) (2024) 4038-4054.
[8] N. Kumar, A. Bansal, R. Gupta, Shear rate and mass transfer coefficient in internal loop airlift reactors involving non-Newtonian fluids, Chem. Eng. Res. Des. 136 (2018) 315-323.
[9] X.M. Liu, J. Wan, J.N. Sun, L. Zhang, F. Zhang, Z.B. Zhang, X.Y. Li, Z. Zhou, Effect of bubble morphology and behavior on power consumption in non-Newtonian fluids’ aeration process, Chin. J. Chem. Eng. 65 (2024) 243-254.
[10] Y.H. Du, L.L. Tong, Y. Wang, M.Z. Liu, L. Yuan, X.Y. Mu, S.J. He, S.X. Wei, Y.D. Zhang, Z.L. Chen, Z.D. Zhang, D.S. Guo, Development of a kinetics-integrated CFD model for the industrial scale-up of DHA fermentation using Schizochytrium sp, AlChE. J. 68 (9) (2022) e17750.
[11] J.Y. Xia, G. Wang, M. Fan, M. Chen, Z.Y. Wang, Y.P. Zhuang, Understanding the scale-up of fermentation processes from the viewpoint of the flow field in bioreactors and the physiological response of strains, Chin. J. Chem. Eng. 30 (2021) 178-184.
[12] P. Moilanen, M. Laakkonen, O. Visuri, V. Alopaeus, J. Aittamaa, Modelling mass transfer in an aerated 0.2m3 vessel agitated by Rushton, Phasejet and Combijet impellers, Chem. Eng. J. 142 (1) (2008) 95-108.
[13] M.H. Xie, J.Y. Xia, Z. Zhou, G.Z. Zhou, J. Chu, Y.P. Zhuang, S.L. Zhang, H. Noorman, Power consumption, local and average volumetric mass transfer coefficient in multiple-impeller stirred bioreactors for xanthan gum solutions, Chem. Eng. Sci. 106 (2014) 144-156.
[14] S. Bun, K. Wongwailikhit, N. Chawaloesphonsiya, J. Lohwacharin, P. Ham, P. Painmanakul, Development of modified airlift reactor (MALR) for improving oxygen transfer: optimize design and operation condition using ‘design of experiment’ methodology, Environ. Technol. 41 (20) (2020) 2670-2682.
[15] A. Pan, M.H. Xie, J.Y. Xia, J. Chu, Y.P. Zhuang, Gas-liquid mass transfer studies: The influence of single- and double-impeller configurations in stirred tanks, Korean J. Chem. Eng. 35 (1) (2018) 61-72.
[16] T.J. Bondancia, L.J. Correa, A.J.G. Cruz, A.C. Badino, L.H.C. Mattoso, J.M. Marconcini, C.S. Farinas, Enzymatic production of cellulose nanofibers and sugars in a stirred-tank reactor: determination of impeller speed, power consumption, and rheological behavior, Cellulose 25 (8) (2018) 4499-4511.
[17] Y.F. Zhou, P.X. Kang, Z.L. Huang, P. Yan, J.Y. Sun, J.D. Wang, Y.R. Yang, Experimental measurement and theoretical analysis on bubble dynamic behaviors in a gas-liquid bubble column, Chem. Eng. Sci. 211 (2020) 115295.
[18] H. Ali, J. Solsvik, Axial distributions of bubble-liquid mass transfer coefficient in laboratory-scale stirred tank with viscous Newtonian and non-Newtonian fluids, 32 (12) (2020) 123308.
[19] L. Nino, R. Gelves, J. Solsvik, Viscous effects on gas-liquid hydrodynamics for bubble size determinations in different Newtonian and non-Newtonian fluids using a CFD-PBM model, Chem. Eng. Sci. 282 (2023) 119324.
[20] S. Mishra, V. Kumar, J. Sarkar, A.S. Rathore, Mixing and mass transfer in production scale mammalian cell culture reactor using coupled CFD-species transport-PBM validation, Chem. Eng. Sci. 267 (2023) 118323.
[21] A. Rahimzadeh, F. Ein-Mozaffari, A. Lohi, New insights into the gas dispersion and mass transfer in shear-thinning fluids inside an aerated coaxial mixer via analysis of flow hydrodynamics and shear environment, Ind. Eng. Chem. Res. 61 (10) (2022) 3713-3728.
[22] X. Xu, L. Wang, H.L. Wang, H.L. Liu, Q. Yang, Circulating jet for enhancing the mass transfer in a gas-liquid stirred tank reactor, AlChE. J. 68 (1) (2022) e17392.
[23] B.Q. Liu, Q. Xiao, N. Sun, P.F. Gao, F.Y. Fan, B. Sunden, Effect of gas distributor on gas-liquid dispersion and mass transfer characteristics in stirred tank, Chem. Eng. Res. Des. 145 (2019) 314-322.
[24] S.S. de Jesus, J.M. Neto, A. Santana, R.M. Filho, Influence of impeller type on hydrodynamics and gas-liquid mass-transfer in stirred airlift bioreactor, AlChE. J. 61 (10) (2015) 3159-3171.
[25] A. Rahimzadeh, F. Ein-Mozaffari, A. Lohi, A methodical approach to scaling up an aerated coaxial mixer containing a shear-thinning fluid: effect of the fluid rheology, Ind. Eng. Chem. Res. 62 (21) (2023) 8454-8476.
[26] J.C. Gabelle, F. Augier, A. Carvalho, R. Rousset, J. Morchain, Effect of tank size on kLa and mixing time in aerated stirred reactors with non-Newtonian fluids, Can. J. Chem. Eng. 89 (5) (2011) 1139-1153.
[27] X. Xiao, J.Y. Yin, J. Xu, T. Tat, J. Chen, Advances in machine learning for wearable sensors, ACS Nano 18 (34) (2024) 22734-22751.
[28] X.Z. Zhao, H.A. Fan, G.B. Lin, Z.C. Fang, W.L. Yang, M. Li, J.H. Wang, X.Y. Lu, B.L. Li, K.J. Wu, J. Fu, Multi-objective optimization of radially stirred tank based on CFD and machine learning, AlChE. J. 70 (3) (2024) e18324.
[29] P.L. Barros, F. Ein-Mozaffari, A. Lohi, S. Upreti, Exploiting the prediction of mass transfer performance in aerated coaxial mixers containing biopolymer solutions using empirical correlations and neural networks, Can. J. Chem. Eng. 103 (3) (2025) 1258-1275.
[30] I. Andrade Cruz, W. Chuenchart, F. Long, K.C. Surendra, L. Renata Santos Andrade, M. Bilal, H. Liu, R. Tavares Figueiredo, S.K. Khanal, L. Fernando Romanholo Ferreira, Application of machine learning in anaerobic digestion: Perspectives and challenges, Bioresour. Technol. 345 (2022) 126433.
[31] A. Shehadeh, O. Alshboul, R.E. Al Mamlook, O. Hamedat, Machine learning models for predicting the residual value of heavy construction equipment: an evaluation of modified decision tree, LightGBM, and XGBoost regression, Autom. Constr. 129 (2021) 103827.
[32] L.T. Zhu, X.Z. Chen, B. Ouyang, W.C. Yan, H. Lei, Z. Chen, Z.H. Luo, Review of machine learning for hydrodynamics, transport, and reactions in multiphase flows and reactors, Ind. Eng. Chem. Res. 61 (28) (2022) 9901-9949.
[33] S.L. Brunton, B.R. Noack, P. Koumoutsakos, Machine learning for fluid mechanics, Annu. Rev. Fluid Mech. 52 (2020) 477-508.
[34] R. Chen, L.L. Luo, X.D. Wang, B.B. Ren, D.K. Guo, S. Zhu, Knowing the unknowns: Network traffic detection with open-set semi-supervised learning, Comput. Netw. 251 (2024) 110630.
[35] T.Z. Si, F.Z. He, Z. Zhang, Y.S. Duan, Hybrid contrastive learning for unsupervised person re-identification, IEEE Trans. Multimed. 25 (2022) 4323-4334.
[36] X.S. Feng, H. Zahed, J. Onwuka, M.E.J. Callister, M. Johansson, R. Etzioni, H.A. Robbins, Cancer stage compared with mortality as end points in randomized clinical trials of cancer screening, Jama 331 (22) (2024) 1910.
[37] S. Ali Shah, S.T. Ai, H.X. Yuan, Predicting water level fluctuations in glacier-fed lakes by ensembling individual models into a quad-meta model, eng appl comput fluid Mech 19 (1) (2025), https://doi.org/10.1080/19942060.2024.2449124.
[38] Proceedings of 3rd international conference on document analysis and recognition, Proceedings of 3rd International Conference on Document Analysis and Recognition. August 14-16, 1995, Montreal, QC, Canada. IEEE, (2002) iii.
[39] L. Breiman, Random forests, Machine Learning 45 (2001) 5-32.
[40] Q.L. Lu, H. Zhang, R. Fan, Y.H. Wan, J.Q. Luo, Machine learning-based Bayesian optimization facilitates ultrafiltration process design for efficient protein purification, Sep. Purif. Technol. 363 (2025) 132122.
[41] M.F. Mahmoud, M. Arabi, S. Pallickara, Harnessing ensemble Machine learning models for improved salinity prediction in large river basin scales, J. Hydrol. 652 (2025) 132691.
[42] H. Lamane, L. Mouhir, R. Moussadek, B. Baghdad, O. Kisi, A. El Bilali, Interpreting machine learning models based on SHAP values in predicting suspended sediment concentration, Int. J. Sediment Res. 40 (1) (2025) 91-107.
[43] L. Shen, Y.P. Jin, A.X. Pan, K. Wang, R.Z. Ye, Y.K. Lin, S. Anwar, W.C. Xia, M. Zhou, X.G. Guo, Machine learning-based predictive models for perioperative major adverse cardiovascular events in patients with stable coronary artery disease undergoing noncardiac surgery, Comput. Methods Programs Biomed. 260 (2025) 108561.
[44] B. Yuan, Z.L. Yang, P. Wu, X.B. Yin, C.J. Liu, F.J. Sun, J. He, W. Jiang, Efficient treatment of chromium-containing wastewater based on auxiliary intelligent model with rapid-response adsorbents, Sep. Purif. Technol. 363 (2025) 132037.
[45] M. Maraschin, T.D. Metzka Lanzanova, N.P. Goncalves Salau, Predictions of heat of combustion and formation by interpretable machine learning algorithms, Fuel 390 (2025) 134699.
[46] M.F. Tahir, M.Z. Yousaf, A. Tzes, M.S. El Moursi, T.H.M. El-Fouly, Enhanced solar photovoltaic power prediction using diverse machine learning algorithms with hyperparameter optimization, Renew. Sustain. Energy Rev. 200 (2024) 114581.
[47] A. Altmann, L. Tolosi, O. Sander, T. Lengauer, Permutation importance: a corrected feature importance measure, Bioinformatics 26 (10) (2010) 1340-1347.
[48] P. Jiang, J. Fan, L. Li, C.H. Wang, S.J. Tao, T. Ji, L.W. Mu, X.H. Lu, J.H. Zhu, A hybrid approach combining mechanism-guided data augmentation and machine learning for biomass pyrolysis, Chem. Eng. Sci. 296 (2024) 120227.
[49] P. Jiang, C.H. Wang, J. Fan, T. Ji, L.W. Mu, X.H. Lu, J.H. Zhu, Hybrid residual modelling of biomass pyrolysis, Chem. Eng. Sci. 293 (2024) 120096.
[50] H. Mesghali, B. Akhlaghi, N. Gozalpour, J. Mohammadpour, F. Salehi, R. Abbassi, Predicting maximum pitting corrosion depth in buried transmission pipelines: Insights from tree-based machine learning and identification of influential factors, Process. Saf. Environ. Prot. 187 (2024) 1269-1285.
[51] Z.G. Li, H.Z. Shi, X. Yang, H. Tang, Investigating the nonlinear relationship between surface solar radiation and its influencing factors in North China Plain using interpretable machine learning, Atmos. Res. 280 (2022) 106406.
[52] J. Li, L.J. Pan, M. Suvarna, Y.W. Tong, X.N. Wang, Fuel properties of hydrochar and pyrochar: prediction and exploration with machine learning, Appl. Energy 269 (2020) 115166.
[53] B.Y. Liang, H.Y. Liu, E.L. Cressey, C.Y. Xu, L. Shi, L. Wang, J.Y. Dai, Z. Wang, J. Wang, Uncertainty of partial dependence relationship between climate and vegetation growth calculated by machine learning models, Remote. Sens. 15 (11) (2023) 2920.
[54] Q.Y. Zhao, T. Hastie, Causal interpretations of black-box models, J. Bus. Econ. Stat. 2019 (2019) 10.1080/07350015.2019.1624293.
[55] B. Triveni, B. Vishwanadham, T. Madhavi, S. Venkateshwar, Mixing studies of non-Newtonian fluids in an anchor agitated vessel, Chem. Eng. Res. Des. 88 (7) (2010) 809-818.
[56] J.M.T. Vasconcelos, S.C.P. Orvalho, A.M.A.F. Rodrigues, S.S. Alves, Effect of blade shape on the performance of six-bladed disk turbine impellers, Ind. Eng. Chem. Res. 39 (1) (2000) 203-213.
[57] R. Petricek, T. Moucha, F.J. Rejl, L. Valenz, J. Haidl, C. Tereza, Volumetric mass transfer coefficient, power input and gas hold-up in viscous liquid in mechanically agitated fermenters. Measurements and scale-up, Int. J. Heat Mass Transf. 124 (2018) 1117-1135.
[58] F. Haseidl, J. Pottbacker, O. Hinrichsen, Gas-Liquid mass transfer in a rotor-stator spinning disc reactor: Experimental study and correlation, Chem. Eng. Process. Process. Intensif. 104 (2016) 181-189.
[59] H. Ali, S. Zhu, J. Solsvik, Effects of geometric parameters on volumetric mass transfer coefficient of non-Newtonian fluids in stirred tanks, Int. J. Chem. React. Eng. 20 (7) (2022) 697-711.
[60] S.H. Zhou, Y. Li, X.Y. Zhang, Optimization modeling of anti - breast cancer candidate drugs, Biotechnol. Genet. Eng. Rev. 40 (2) (2024) 1334-1352.
[61] X.Y. Zhang, P.C. Xiao, Y.Z. Yang, Y.J. Cheng, B. Chen, D.Z. Gao, W.R. Liu, Z.W. Huang, Remaining useful life estimation using CNN-XGB with extended time window, IEEE Access 7 (2019) 154386-154397.
[62] D. Gomez-Diaz, J.M. Navaza, Analysis of carbon dioxide gas/liquid mass transfer in aerated stirred vessels using non-Newtonian media, J. Chem. Technol. Biotechnol. 79 (10) (2004) 1105-1112.
[63] V. Cappello, C. Plais, C. Vial, F. Augier, Bubble size and liquid-side mass transfer coefficient measurements in aerated stirred tank reactors with non-Newtonian liquids, Chem. Eng. Sci. 211 (2020) 115280.
[64] S.R. Lone, V. Kumar, J.R. Seay, D.L. Englert, H.T. Hwang, Mass transfer and rheological characteristics in a stirred tank bioreactor for cultivation of escherichia coli BL21, Biotechnol. Bioprocess Eng. 25 (5) (2020) 766-776.
[65] A.K. Pandey, J. Park, J. Ko, H.H. Joo, T. Raj, L.K. Singh, N. Singh, S.H. Kim, Machine learning in fermentative biohydrogen production: Advantages, challenges, and applications, Bioresour. Technol. 370 (2023) 128502.
[66] E. Dumont, Mass transfer in Multiphasic gas/liquid/liquid systems. KLa determination using the effectiveness-number of transfer unit method, Processes. 6(9) (2018) 156.
[67] D.L. Li, W. Chen, Effects of impeller types on gas-liquid mixing and oxygen mass transfer in aerated stirred reactors, Process. Saf. Environ. Prot. 158 (2022) 360-373.
[68] A. Ordaz, I. Figueroa-Gonzalez, P. San-Valero, C. Gabaldon, G. Quijano, Effect of the height-to-diameter ratio on the mass transfer and mixing performance of a biotrickling filter, J. Chem. Technol. Biotechnol. 93 (1) (2018) 121-126.
[69] M.R. Valverde, R. Bettega, A.C. Badino, Numerical evaluation of mass transfer coefficient in stirred tank reactors with non-Newtonian fluid, Theor. Found. Chem. Eng. 50 (6) (2016) 945-958.
[70] A. Rahimzadeh, F. Ein-Mozaffari, A. Lohi, Development of a scale-up strategy for an aerated coaxial mixer containing a non-Newtonian fluid: a mass transfer approach, Phys Fluids. 35(7) (2023) 073103.
[71] L. Shu, M.J. Yang, H. Zhao, T.F. Li, L. Yang, X. Zou, Y.W. Li, Process optimization in a stirred tank bioreactor based on CFD-Taguchi method: a case study, J. Clean. Prod. 230 (2019) 1074-1084.
[72] G.M. Mule, R. Lohia, A.A. Kulkarni, Effect of number of branches on the performance of fractal impeller in a stirred tank: Mixing and hydrodynamics, Chem. Eng. Res. Des. 108 (2016) 164-175.
[73] H.N. Wang, X.X. Duan, X. Feng, Z.S. Mao, C. Yang, Effect of impeller type and scale-up on spatial distribution of shear rate in a stirred tank, Chin. J. Chem. Eng. 42 (2022) 351-363.
[74] S. Salehi, A. Heydarinasab, F.P. Shariati, A.T. Nakhjiri, K. Abdollahi, Parametric numerical study and optimization of mass transfer and bubble size distribution in a gas-liquid stirred tank bioreactor equipped with Rushton turbine using computational fluid dynamics, Int. J. Chem. React. Eng. 19 (10) (2021) 1115-1131.
[75] P. Lins Barros, F. Ein-Mozaffari, A. Lohi, Gas dispersion in non-newtonian fluids with mechanically agitated systems: A Review, Processes 10(2) (2022) 275.
[76] T. Kumaresan, J.B. Joshi, Effect of impeller design on the flow pattern and mixing in stirred tanks, Chem. Eng. J. 115 (3) (2006) 173-193.
[77] A. Gabriele, A.W. Nienow, M.J.H. Simmons, Use of angle resolved PIV to estimate local specific energy dissipation rates for up- and down-pumping pitched blade agitators in a stirred tank, Chem. Eng. Sci. 64 (1) (2009) 126-143.
[78] M. Moayeri Kashani, S.H. Lai, S. Ibrahim, P. Moradi Bargani, Design factors affecting the dynamic performance of soil suspension in an agitated, baffled tank, Chin. J. Chem. Eng. 24 (12) (2016) 1664-1673.

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

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	Ali Tarik Karagoz, Omar Alqusair, Chao Liu, Jie Li. Advances in conceptual process design: From conventional strategies to AI-assisted methods[J]. 中国化学工程学报, 2025, 84(8): 60-76.
[2]	Dian Zhang, Bo Ouyang, Zheng-Hong Luo. Reaction process optimization based on interpretable machine learning and metaheuristic optimization algorithms[J]. 中国化学工程学报, 2025, 84(8): 77-85.
[3]	Zhi Ma, Peng Cui, Xu Wang, Lanyu Li, Haoxiang Xu, Adrian Fisher, Daojian Cheng. The integration of artificial intelligence and high-throughput experiments: An innovative driving force in catalyst design[J]. 中国化学工程学报, 2025, 84(8): 117-132.
[4]	Jianfeng Jiao, Xi Gao, Jie Li. Pure component property estimation framework using explainable machine learning methods[J]. 中国化学工程学报, 2025, 84(8): 158-178.
[5]	Haijun Feng, Li Jiajia, Zhou Jian. Prediction of ionic liquid toxicity by interpretable machine learning[J]. 中国化学工程学报, 2025, 84(8): 201-210.
[6]	Yuan Tian, Honghua Zhang, Yueyang Qiao, Han Yang, Yanrong Liu, Xiaoyan Ji. Intelligent prediction of ionic liquids and deep eutectic solvents by machine learning[J]. 中国化学工程学报, 2025, 84(8): 227-243.
[7]	Yongqi Pan, Yazi Yu, Lijie Wang, Guogang Hu, Yujun Wang, Guangsheng Luo. Intelligent chemical synthesis based on microchemical engineering technology[J]. 中国化学工程学报, 2025, 84(8): 274-288.
[8]	Zhe Ding, Li Guo, Fang Bai, Chao Hua, Ping Lu, Jinyi Chen. Toward the rational design for low-temperature hydrogenation of silicon tetrachloride: Mechanism and data-driven interpretable descriptor[J]. 中国化学工程学报, 2025, 79(3): 172-184.
[9]	Mingqi Jiang, Zhuo Wang, Zhijian Sun, Jian Wang. A parallel chemical reaction optimization method based on preference-based multi-objective expected improvement[J]. 中国化学工程学报, 2025, 78(2): 82-92.
[10]	Yixin Wei, Leyu Shan, Tong Qiu, Diannan Lu, Zheng Liu. Machine learning-assisted retrosynthesis planning: Current status and future prospects[J]. 中国化学工程学报, 2025, 77(1): 273-292.
[11]	Yingxue Fu, Xinyan Liu, Jingzi Gao, Yang Lei, Yuqiu Chen, Xiangping Zhang. Machine learning models for the density and heat capacity of ionic liquid-water binary mixtures[J]. 中国化学工程学报, 2024, 73(9): 244-255.
[12]	Kai Ge, Yiping Huang, Yuanhui Ji. Machine learning with active pharmaceutical ingredient/polymer interaction mechanism: Prediction for complex phase behaviors of pharmaceuticals and formulations[J]. 中国化学工程学报, 2024, 66(2): 263-272.
[13]	Denglong Ma, Ruitao Wu, Zekang Li, Kang Cen, Jianmin Gao, Zaoxiao Zhang. A new method to forecast multi-time scale load of natural gas based on augmentation data-machine learning model[J]. 中国化学工程学报, 2022, 48(8): 166-175.
[14]	Tong Qin, Zhenhao Xi, Ling Zhao, Weikang Yuan. Monte Carlo simulation of sequential structure control of AN-MA-IA aqueous copolymerization by different operation modes[J]. 中国化学工程学报, 2022, 46(6): 231-242.
[15]	Weizhou Jiao, Xingyue Wei, Shengjuan Shao, Youzhi Liu. Catalytic decomposition and mass transfer of aqueous ozone promoted by Fe-Mn-Cu/γ-Al₂O₃ in a rotating packed bed[J]. 中国化学工程学报, 2022, 45(5): 133-142.