Digital Twin-Driven Optimization Of Bioenergy Production From Waste Materials

Abstract

This study explores the transformative role of digital twin technology in optimizing bioenergy production, focusing on its applications in real-time monitoring, process simulation, predictive maintenance, and hybrid energy systems. Utilizing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, a total of 94 peer-reviewed articles were systematically identified, screened, and analyzed to ensure a comprehensive and rigorous review. The findings highlight the advancements in digital twin technology, including its integration with IoT-enabled sensors and machine learning algorithms, which enable dynamic system optimization and predictive capabilities. The study also examines the role of digital twins in hybrid energy systems, demonstrating their potential to enhance energy efficiency by up to 30% through seamless integration with solar and wind energy. Despite these advancements, the review identifies critical challenges, such as high computational demands, data integration issues, and economic and policy barriers, which limit the scalability and widespread adoption of digital twins in industrial bioenergy applications. This study contributes to the growing body of knowledge by offering a comprehensive synthesis of existing research, identifying key gaps, and emphasizing the need for interdisciplinary collaboration and policy support to fully harness the potential of digital twins in achieving sustainable energy goal.