[Machine learning-based optimization strategies for fermentation processes].

Authors

Faman Lu, Huayao Chen, Haiyan Zhou, Yuanshan Wang, Kun Niu, Zhiqiang Liu, Yuguo Zheng

Published in

Sheng wu gong cheng xue bao = Chinese journal of biotechnology. Volume 42. Issue 9. Pages 3846-3866. Sep 25, 2026.

Abstract

Microbial fermentation technology is crucial in modern biomanufacturing processes, such as biopharmaceutical production. However, traditional fermentation process control faces bottlenecks including offline monitoring delay, reliance on empirical experience, and a lack of precise and real-time regulation. Machine learning (ML), with its powerful data extraction and predictive modeling capabilities, has emerged as a key tool for driving the transition from traditional fermentation processes to intelligent paradigms. This paper systematically reviewed the latest research progress on the closed-loop intelligent fermentation control systems, spanning from underlying data perception to high-level decision-making. First, the specific applications of ML in fermentation optimization were elaborated, with a focus on systematic modeling workflows in response to the inherent "black-box" nature and data-lag limitations of purely data-driven models. Second, by comparing the monitoring characteristics of various process analytical technologies, the critical supporting role of Raman spectroscopy in real-time fermentation monitoring was elucidated, and cutting-edge approaches employing semi-supervised learning and data augmentation strategies to address the scarcity of high-quality labeled samples were summarized. Furthermore, the ML-driven "intelligent perception-feedback closed-loop" control mechanism was thoroughly analyzed, and the transformation of fermentation control from traditional "macroscopic feeding regulation" to "microscopic metabolic precise guidance" was expounded. Finally, the future development trajectories for intelligent fermentation was envisioned from two perspectives, namely the construction of cross-genus universal predictive models and the application of digital twin technology. This review aims to provide valuable technical references for the intelligent upgrading of the biomanufacturing sector and the efficient development of fermentation processes.

PMID:
42773650
Bibliographic data and abstract were imported from PubMed on 23 Sep 2026.

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