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

Apr 2, 2025Sheng wu gong cheng xue bao = Chinese journal of biotechnology

Improving fermentation using real-time monitoring, artificial intelligence, and digital twin technology in smart biomanufacturing

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Circadian Biology on OpenScience ↗PubMed ↗DOI ↗

Abstract

Biomanufacturing faces challenges in optimizing and scaling up fermentation processes.

Real-time sensing technologies provide critical data for characterizing microbial metabolic states during fermentation.
AI-driven optimization strategies enhance the regulation of feeding and process parameters compared to traditional methods.
Digital twin technology combines real-time data with multi-scale models for improved lifecycle optimization and process scale-up.
Future advancements in intelligent sensing and digital twin systems may accelerate the industrialization of synthetic biology innovations.

AI simplified

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.

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

Abstract

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