These “self-driving” bioreactor sensors are forging new fermentation frontiers

Ubiquitous in biomanufacturing, fermentation processes are complex and unpredictable when compared to traditional chemical-processing operations. From changing cell-growth rates to drifts in environmental conditions, typical control approaches can only react to predetermined setpoints rather than reliably maximize product output. A group of researchers have applied reinforcement learning (RL) to maximize production yields in fed-batch fermentation, moving beyond setpoint-based constraints on process optimization.

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