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Purpose of Review <p>Global food insecurity and increasing freshwater scarcity continue to intensify as a result of population growth, urbanisation, climate variability, and the depletion of natural resources. Addressing these interconnected challenges requires a transition away from conventional, resource-intensive food systems toward sustainable protein alternatives that can deliver adequate nutrition with a reduced freshwater demand. Microalgae have emerged as a strong candidate in this context due to their rapid growth rates, broad environmental tolerance, ability to utilise carbon dioxide, and capacity to grow in brackish water, seawater, or nutrient-rich wastewater, thereby substantially lowering their blue-water footprint. While several microalgal species are already produced as single-cell protein (SCP), large-scale deployment across food, health, and industrial applications remains limited by economic, technical, and operational constraints. This review critically evaluates the potential and limitations of microalgae as a scalable solution for food and water security. Considering the increasing digitalisation of biomanufacturing, particular attention is given to the role of computational biology and artificial intelligence (AI)–enabled strategies in overcoming cultivation and process optimisation bottlenecks.</p> Recent Findings <p>Recent advances demonstrate that artificial intelligence (AI) approaches, particularly machine learning (ML), alongside Internet of Things (IoT)-based sensing, can significantly improve resource-use efficiency and nutrient recovery in microalgae production systems. In parallel, the growing application of multi-omics and systems biology tools is generating high-resolution datasets that are increasingly important for the development, validation, and deployment of robust ML models.</p> Summary <p> This review distinguishes itself from previous studies by presenting an integrated perspective that links alternative protein production with environmental sustainability, particularly within the Water–Food–Energy nexus, while systematically examining ML applications across the microalgal bioprocess value chain. Key knowledge gaps, future research priorities, and the practical challenges associated with implementing AI-driven solutions in microalgae-based systems are also critically discussed.</p> Graphical Abstract <p></p>

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Machine Learning–Enabled Microalgae Bioprocess Optimization for Alternative Foods and Water Sustainability

  • 期刊论文

  • 发表日期：

    2026年5月19日

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  • Muhammad Adnan Ul Haq,
  • Mishaal Irfan,
  • Sana Malik,
  • Nitiphong Kaewman,
  • Piroonporn Srimongkol,
  • Sirasit Srinuanpan,
  • Shaza Y. A. Qattan,
  • Ashwaq Hassan Alsabban,
  • Antonio Albuquerque,
  • Raj Boopathy,
  • Peter Ralph

摘要

Purpose of Review

Global food insecurity and increasing freshwater scarcity continue to intensify as a result of population growth, urbanisation, climate variability, and the depletion of natural resources. Addressing these interconnected challenges requires a transition away from conventional, resource-intensive food systems toward sustainable protein alternatives that can deliver adequate nutrition with a reduced freshwater demand. Microalgae have emerged as a strong candidate in this context due to their rapid growth rates, broad environmental tolerance, ability to utilise carbon dioxide, and capacity to grow in brackish water, seawater, or nutrient-rich wastewater, thereby substantially lowering their blue-water footprint. While several microalgal species are already produced as single-cell protein (SCP), large-scale deployment across food, health, and industrial applications remains limited by economic, technical, and operational constraints. This review critically evaluates the potential and limitations of microalgae as a scalable solution for food and water security. Considering the increasing digitalisation of biomanufacturing, particular attention is given to the role of computational biology and artificial intelligence (AI)–enabled strategies in overcoming cultivation and process optimisation bottlenecks.

Recent Findings

Recent advances demonstrate that artificial intelligence (AI) approaches, particularly machine learning (ML), alongside Internet of Things (IoT)-based sensing, can significantly improve resource-use efficiency and nutrient recovery in microalgae production systems. In parallel, the growing application of multi-omics and systems biology tools is generating high-resolution datasets that are increasingly important for the development, validation, and deployment of robust ML models.

Summary

This review distinguishes itself from previous studies by presenting an integrated perspective that links alternative protein production with environmental sustainability, particularly within the Water–Food–Energy nexus, while systematically examining ML applications across the microalgal bioprocess value chain. Key knowledge gaps, future research priorities, and the practical challenges associated with implementing AI-driven solutions in microalgae-based systems are also critically discussed.

Graphical Abstract