Majid Hosseini, Seyed Javad Amirfakhri, Reza Ghiaasiaan
Biofuels have the potential to improve the sustainability of transportation fuels. Production systems for biofuels are complex, and data-driven artificial intelligence (AI) modeling offers advanced capabilities for prediction, optimization, and quality control. However, large-scale applications of AI in biofuels production remain in their early stages compared to laboratory research. This article discusses how AI-based modeling is used for various bioconversion technologies in the effort to improve their efficiency, dependability, and management of biofuels. Furthermore, the applications of AI technology in various types of biofuels, including biodiesel, bioethanol, biobutanol, biomethanol, biohydrogen, biogas, and algal biofuels, are critically discussed. The benefits and drawbacks of applying AI-based modeling to manage, optimize, control, monitor, and predict biofuels yields are comprehensively investigated. Additionally, the use of fuzzy logic, genetic algorithms (GAs), artificial neural networks (ANNs), expert systems (ES), adaptive neuro-fuzzy inference system (ANFIS), hybrid AI techniques, and other AI-based methods to increase biofuels production yield and quality, as well as end-user system performance, are thoroughly reviewed. Finally, a concise summary of the present state of research milestones is provided, and most recent state-of-the-art studies are discussed. The technology readiness level analysis indicates that integrating AI with established technologies is the most effective commercial approach.