Application of artificial intelligence in bioenergy supply chain management from feedstock collection to power generation

Artificial Intelligence is transforming the future of bioenergy supply chains, ranging from intelligent systems at feedstock collection levels to those at power generation. This extensive review offers a comprehensive history of Artificial Intelligence applications for optimizing efficiency, sustainability, and supply chain choices at all levels of the bioenergy supply chain. It also reveals how machine learning algorithms, prediction algorithms, and real-time analytics are being applied to streamline biomass collection, preprocessing, logistics, and conversion operations. Verified prominent innovations from relevant literatures from 2020 to 2025 include Artificial Intelligence based predictive maintenance, reducing downtime at bioenergy plants by 20 to 30% and up to 15% biomass conversion efficiency enhancement using adaptive control systems. Intelligent biomass haulage routing resulted in 10 to 25% fuel savings, reduced carbon emissions by 12% and feedstock classification accuracy up to 90% using high-end image recognition and sensor fusion. Artificial Intelligent sinventory systems also increased feedstock utilization by 18%, energy demand forecast models improved forecast accuracy by 25 to 40%, alongside optimized resource allocation and grid resilience. The findings from this paper benchmarks interdisciplinary coordination, suitable data infrastructures and regulatory support as driving forces to scaling Artificial Intelligent applications in bioenergy sectors. While reconstructing conventional supply systems using intelligent automation, Artificial Intelligence has been confirmed one foundation stone upon which to scale clean energy agendas around the world.