Machine Learning Optimization of Multi-Feedstock Biofuel Production

Abstract

The transition toward sustainable and renewable energy sources has positioned biofuels as a critical component in global decarbonization strategies. While traditional biofuel production relies on single feedstocks such as corn, sugarcane, or microalgae, these processes face substantial limitations in scalability, land use competition, and vulnerability to seasonal variations. Multi-feedstock biofuel systems—integrating lignocellulosic biomass, microalgae, and industrial organic waste—offer significant advantages in terms of energy yield, waste reduction, and process flexibility. However, the heterogeneous and variable nature of such feedstocks complicates process modeling, control, and optimization using conventional methods.

Machine Learning (ML) presents powerful tools to address these challenges by enabling predictive modeling, dynamic optimization, and real-time decision support across the biofuel production chain. This research paper provides an in-depth analysis of ML-driven optimization for multi-feedstock biofuel systems. It includes a comprehensive literature review, a detailed methodological framework for ML-based predictive analytics, data handling strategies, model development, and evaluation procedures. Synthetic but realistic datasets are used to illustrate model training, feature engineering, optimization workflows, and performance assessment. Results demonstrate that ML models ensemble, reinforcement learning strategies, and hybrid AI-control architectures significantly enhance yield prediction accuracy, improve energy efficiency, and reduce process variability. Although challenges such as data scarcity, model interpretability, and industrial integration persist, this study highlights the transformative potential of ML for industrial-scale multi-feedstock biofuel production and outlines directions for future research toward autonomous smart biorefineries.

Cite as:

Dr. Kondekal Manjunatha. (2025). Machine Learning Optimization of Multi-Feedstock Biofuel Production. Machine Learning Optimization of Multi-Feedstock Biofuel Production, 1(3), 8–12. 

https://doi.org/10.5281/zenodo.17878586