Adaptive Low-Carbon Bioethanol Energy Management through Knowledge Graph–Digital Twin Frameworks

Abstract

Lignocellulosic bioethanol can reduce dependence on fossil liquid fuels, but its low-carbon performance is strongly constrained by process-energy demand. Energy use is shaped by coupled variations in feedstock quality, pretreatment response, fermentation performance, separation efficiency, equipment condition, and utility integration. Existing energy-balance, process-simulation, techno-economic, life-cycle, and data-driven studies have provided important methods for quantifying energy use, cost, emissions, and process bottlenecks, but their outputs often remain distributed across separate models and assessment boundaries. This fragmentation limits the ability to explain why energy intensity changes during operation and which intervention should be prioritized. A knowledge graph–digital twin approach is proposed to connect semantic representation with dynamic process interpretation for low-carbon bioethanol energy management. Feedstock, process, equipment, energy, sustainability, and decision entities are organized into traceable relationships, while a literature-derived metric system links process variables, sustainability indicators, equipment-state descriptors, data-readiness metrics, and heat-integration information. Economic feasibility is further considered by relating implementation and life-cycle costs to measurable operational benefits and staged deployment decisions. The framework reframes bioethanol energy analysis from retrospective accounting toward explainable, updatable, and decision-oriented energy management.