科微学术

生物工程学报

2025年4月5日 11:11 星期六
首页 > 过刊浏览>2025年第41卷第3期 >1179-1196. DOI:10.13345/j.cjb.250032
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智能生物制造之发酵过程优化：在线检测、人工智能与数字孪生技术
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山西省重点研发计划(202202140601018)


Optimization of fermentation processes in intelligent biomanufacturing: on online monitoring, artificial intelligence, and digital twin technologies
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    摘要:

    生物制造作为新兴产业，其核心挑战在于实现发酵过程的精准优化与高效放大。本文聚焦发酵过程的关键环节——实时感知与智能控制，系统综述了在线检测技术、人工智能驱动的优化策略及数字孪生技术的应用进展。首先，从常规参数(温度、pH、溶解氧)到高级传感技术(在线活细胞传感、光谱分析、尾气监测)的在线检测手段，为实时获取微生物代谢状态提供了数据基础。其次，传统基于专家经验的静态控制逐步向人工智能驱动的动态优化演进，机器学习(如人工神经网络、支持向量机)与遗传算法等技术的整合显著提升了补料策略与工艺参数的调控效率。最后，数字孪生技术通过融合实时传感数据与多尺度模型(细胞代谢动力学与反应器流场模拟)，为发酵过程的全生命周期优化与理性放大提供了新范式。未来，基于智能感知与数字孪生的闭环控制系统将加速合成生物学成果的产业化，推动生物制造向高效、智能、可持续方向迈进。

    Abstract:

    As a strategic emerging industry, biomanufacturing faces core challenges in achieving precise optimization and efficient scale-up of fermentation processes. This review focuses on two critical aspects of fermentation—real-time sensing and intelligent control—and systematically summarizes the advancements in online monitoring technologies, artificial intelligence (AI)-driven optimization strategies, and digital twin applications. First, online monitoring technologies, ranging from conventional parameters (e.g., temperature, pH, and dissolved oxygen) to advanced sensing systems (e.g., online viable cell sensors, spectroscopy, and exhaust gas analysis), provide a data foundation for real-time microbial metabolic state characterization. Second, conventional static control relying on expert experience is evolving toward AI-driven dynamic optimization. The integration of machine learning technologies (e.g., artificial neural networks and support vector machines) and genetic algorithms significantly enhances the regulation efficiency of feeding strategies and process parameters. Finally, digital twin technology, integrating real-time sensing data with multi-scale models (e.g., cellular metabolic kinetics and reactor hydrodynamics), offers a novel paradigm for lifecycle optimization and rational scale-up of fermentation. Future advancements in closed-loop control systems based on intelligent sensing and digital twin are expected to accelerate the industrialization of innovative achievements in synthetic biology and drive biomanufacturing toward higher efficiency, intelligence, and sustainability.

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夏建业,龙东娇,陈敏,陈安祥. 智能生物制造之发酵过程优化：在线检测、人工智能与数字孪生技术[J]. 生物工程学报, 2025, 41(3): 1179-1196

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  • 收稿日期:2025-01-12
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