Trends in Using Microalgae as a Green Energy Source: Conventional, Machine Learning, and Hybrid Modeling Methods

This study analyzes, quantifies, and maps, from a bibliometric perspective, scientific research on microalgae energy production. It includes traditional simulation, machine learning, and hybrid approaches, covering 500 original articles from 2005 to 2024 in Scopus. We used Biblioshiny 4.1.2 software in RStudio 4.3.0 to categorize and evaluate the contributions of authors and journals. The studied field underwent an exponential growth in publications from 2004 to 2022, with an average annual increase of approximately 21%. Moreover, recent research focuses on photobioreactors, computational fluid dynamics, carbon dioxide capture, bio-oils, biodiesel, and hydrothermal liquefaction, increasingly integrating machine learning algorithms and hybrid methods. Since 2020, we have identified a clear trend toward combining modeling approaches to predict and improve energy efficiency, particularly for biodiesel, bio-derived hydrogen, and crude bio-oil produced via pyrolysis or hydrothermal liquefaction, which is often influenced by factors such as light, carbon dioxide, nutrients, and blending operations. Finally, recent advancements involve combining physical models with data to enable real-time optimization and control, supporting microalgae-based circular biorefining strategies. This review serves as a guide for future research in green energy materials and process modeling, inspiring colleagues to explore new ways for microalgae energy production and modeling.