Integrating Machine Learning and Multi-Objective Optimization in Biofuel Systems: A Review

Abstract

The optimization of biofuel production involves balancing multiple conflicting objectives such as yield maximization, cost minimization, and environmental impact reduction. Recent studies have explored various multi-objective optimization (MOO) techniques integrated with machine learning (ML) models to enhance process efficiency. This review synthesizes key advancements in biofuel optimization, highlighting the use of techniques such as Artificial Neural Networks (ANN), Genetic Algorithms (GA), Non-Dominated Sorting Genetic Algorithm II (NSGA-II), and Response Surface Methodology (RSM). Studies have leveraged hybrid models, including Convolutional Neural Network - Gated Recurrent Unit (CNN-GRU) networks for emission control and Neutrosophic Fuzzy Optimization (NFO) for uncertainty handling. While existing models demonstrate improvements in predictive accuracy and optimization effectiveness, challenges remain in model generalization, computational complexity, and real-time adaptability. Future research directions include expanding datasets, incorporating adaptive optimization strategies, integrating uncertainty quantification, and refining hybrid modeling approaches for robust decision-making in biofuel production systems.