Digital Twin Convergence for Carbon-Aware Energy Management and Sustainability Optimization in Industry 4.0 Plants

Abstract

This study introduces a hybrid carbon-aware digital twin framework that integrates synchronization, multi-objective optimization, and sustainability-aware scheduling to advance energy management in Industry 4.0 environments. Unlike conventional control or predictive analytics systems, the proposed approach emphasizes adaptive alignment between physical and virtual states, real-time carbon accounting, and renewable-aware scheduling. The framework demonstrates strong scalability, interoperability, and reliability, addressing core challenges of synchronization error, computational overhead, and heterogeneous data integration. Experimental evaluations reveal significant improvements across multiple performance dimensions: energy efficiency (94%), emission reduction (91%), renewable utilization (82%), resource optimization efficiency (85%), cost reduction (79%), and decision latency reduction to 145 ms. Additionally, predictive maintenance accuracy reached 91%, system reliability 96%, and sustainability compliance 89%, surpassing existing digital twin-based or AI-driven methods. These results underscore the potential of digital twins as a convergent platform to balance productivity, cost efficiency, and sustainability while contributing to global decarbonization targets.