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Biology-aware scaling of microalgal biofuels: bioprocess constraints and data-centric digital twins
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Microalgal biofuels remain a promising route for renewable fuel production, carbon utilization, wastewater valorization, and the development of a circular bioeconomy. However, their industrial deployment is still limited by the difficulty of translating laboratory performance into robust, economically viable, and environmentally sustainable large-scale systems. This review presents a biology-aware and data-centric perspective that connects biological, engineering, operational, techno-economic, and computational dimensions to address industrial microalgal biofuel scale-up. It focuses on process observability, soft sensors, and the data requirements needed to transform microalgal cultivation into a measurable, modellable, and controllable bioprocess. It further evaluates artificial intelligence, hybrid modeling, uncertainty-aware digital twins, and decision-support systems for improving monitoring, prediction, optimization, and scale-up. Rather than presenting digital twins as autonomous solutions, it demonstrates that their value lies in integrating biological knowledge, heterogeneous data, mechanistic understanding, and uncertainty estimation within human-in-the-loop decision-support frameworks. The available evidence indicates that biological constraints, limited process observability, and insufficient integration of validated data remain the principal barriers to industrial implementation. In contrast, biology-aware, hybrid, and uncertainty-aware digital twins represent the most realistic direction for near-term deployment. Finally, techno-economic and life-cycle implications are discussed to highlight that industrial viability will depend not on a single technological breakthrough, but on the convergence of robust strains, resource-efficient cultivation, circular biorefineries, validated data infrastructures, and biology-informed AI tools. A roadmap for 2026–2036 is proposed to guide the transition of microalgal biofuels from laboratory promise toward industrial relevance. The integrated perspective presented here can guide future research and support the sustainable industrial deployment of microalgal biofuel systems.
Title: Biology-aware scaling of microalgal biofuels: bioprocess constraints and data-centric digital twins
Description:
Microalgal biofuels remain a promising route for renewable fuel production, carbon utilization, wastewater valorization, and the development of a circular bioeconomy.
However, their industrial deployment is still limited by the difficulty of translating laboratory performance into robust, economically viable, and environmentally sustainable large-scale systems.
This review presents a biology-aware and data-centric perspective that connects biological, engineering, operational, techno-economic, and computational dimensions to address industrial microalgal biofuel scale-up.
It focuses on process observability, soft sensors, and the data requirements needed to transform microalgal cultivation into a measurable, modellable, and controllable bioprocess.
It further evaluates artificial intelligence, hybrid modeling, uncertainty-aware digital twins, and decision-support systems for improving monitoring, prediction, optimization, and scale-up.
Rather than presenting digital twins as autonomous solutions, it demonstrates that their value lies in integrating biological knowledge, heterogeneous data, mechanistic understanding, and uncertainty estimation within human-in-the-loop decision-support frameworks.
The available evidence indicates that biological constraints, limited process observability, and insufficient integration of validated data remain the principal barriers to industrial implementation.
In contrast, biology-aware, hybrid, and uncertainty-aware digital twins represent the most realistic direction for near-term deployment.
Finally, techno-economic and life-cycle implications are discussed to highlight that industrial viability will depend not on a single technological breakthrough, but on the convergence of robust strains, resource-efficient cultivation, circular biorefineries, validated data infrastructures, and biology-informed AI tools.
A roadmap for 2026–2036 is proposed to guide the transition of microalgal biofuels from laboratory promise toward industrial relevance.
The integrated perspective presented here can guide future research and support the sustainable industrial deployment of microalgal biofuel systems.
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