AI-Driven Algae Biorefineries: A New Era for Sustainable Bioeconomy

Abstract

Purpose of Review

The urgent need for a transition toward a sustainable and greener future is underscored by the projected risk of a global temperature increase of up to 3 °C above pre-industrial levels, driven by rising carbon emissions. Algae biorefineries offer a promising solution to these challenges. However, the high production and downstream processing costs continue to hinder successful commercialization. This review provides a comprehensive overview of several AI-driven innovations: phenotypic screening for strain improvement, monitoring of environmental parameters, machine learning for optimizing dewatering efficiency, predictive modeling for algae growth and yield, and AI-controlled drying systems for biomass preservation.

Recent Findings

Integrating machine learning algorithms and predictive modeling can transform algae cultivation by automating the process with continuous monitoring and feedback systems, significantly reducing labor costs while enhancing process economics and efficiency. Accurate prediction of optimal harvesting times can further decrease harvesting costs, address key scalability issues, and facilitate the broader commercialization of algae for diverse biotechnological applications.

Summary

In the future, smart biorefineries that integrate artificial intelligence into algae production facilities will be pivotal in enhancing process efficiency and economics within circular and sustainable frameworks. While AI continues to impact various fields to ease human effort, ethical considerations must remain central to its use, especially as this sector grows rapidly.

Graphical Abstract