Energy-efficient machine learning approaches for enhanced biofuel (Biodiesel) production: A review

Abstract

Biodiesel represents a sustainable alternative to fossil fuels and aligns with long-term decarbonization goals. Integrating economic efficiency with sustainability requires a reinvention of process technology and artificial intelligence (AI)-based advanced prediction tools to maximize performance and minimize costs and carbon footprint. Under conservative estimates of 1% efficiency enhancement in an AI-driven optimization process, a 100-million kg/year microalgal biodiesel plant could save 3.6-5.7 million MJ/year of energy, corresponding to 457-719 tonnes of CO2eq reductions. However, these transitions become challenging due to the significant energy consumption required for AI modeling of nonlinear, complex datasets in energy production systems. This review focuses on current progress in designing energy-efficient AI models using green computing systems to advance smart energy systems. Four essential integrated pillars have been identified and discussed for implementation in an AI model framework that minimizes environmental impacts in biofuel synthesis. These pillars are Real-Time Process Optimization and Control, Predictive Intelligence and Resilience, Sustainable and Scalable Architecture, and Trustworthy and Compliant AI. A four-stage roadmap has been proposed to develop a low-energy framework for process optimization and predictive control. This roadmap includes moving from basic data infrastructure and pilot trials to the integration of key performance indicator dashboards, a human-assisted, semi-automated co-pilot phase, and, finally, a fully autonomous, closed-loop control system. Ultimately, AI tools offer a pathway to lower their own carbon footprint by following a clear roadmap and turning traditional production systems into next-generation biodiesel manufacturing platforms through cutting-edge technologies and a focus on creating incremental value through continual validation.