Advancing algal biofuel production through data-driven insights: A comprehensive review of machine learning applications

Abstract

This paper examines machine learning (ML)'s contemporary applications in biofuel production, emphasizing microalgae-based bioenergy systems. The study aims to explore various aspects of ML integration in the biofuel production process, including microalgae detection, classification, growth phase optimization, and dataset quality and quantity considerations. The research methodology is in a detailed literature review of current ML models and their applications in biofuel production. It covers bioenergy systems, microalgae detection, growth phase optimization, dataset quality, ML applications in microalgal biorefineries, and the advantages and disadvantages of ML models over first-principle models. The analysis highlights the challenges and implications of utilizing smaller datasets in biofuel production models and investigates the impact of dataset quality and quantity on ML model performance. Despite sparse datasets, the findings offer insights into leveraging ML techniques for improved efficiency and sustainability in microalgae-based biofuel production systems.