Digital Twin and Big Data Analytics for Energy-Efficient Smart Manufacturing in Industry 4.0 Environments

Abstract

The demand for energy efficiency, sustainability, and resilience, as well as the operational complexity of intelligent manufacturing, has led to the need for more intelligent solutions as the manufacturing sector evolves towards Industry 4.0. Traditional manufacturing systems implement static monitoring systems with reactive control, which leads to excessive energy waste, unplanned downtimes, and increased carbon emissions. This study formulates a framework to address obstacles to energy-efficient smart manufacturing through the use of Digital Twin and Big Data Analytics integrated with real-time, predictive, adaptive energy management systems and intelligent manufacturing systems. The intended design of the constructed system uses a high-fidelity digital twin of the architecture of the created system, which integrates the Industrial Internet of Things (IIoT) with cloud/edge computing to continuously replicate the manufacturing shop floor. Production, machine usage, and energy management are optimized through the use of machine learning-based analytics, which comprise temporal forecasting and anomaly detection. The integrated system improves energy flexibility and reduces peak demand through the coordinated management of renewable energy sources, battery energy storage systems (BESS), and electric vehicle (EV) charging loads. A multi-objective optimization approach is used to minimize energy costs, carbon emissions, and equipment downtimes concurrently. When experimental validation was performed in the areas of energy consumption, peak load, production downtime, and carbon emissions within the context of the smart factory dataset, improvements were seen in the areas of energy consumption (22 to 28%), peak load (18%), production downtime (25%), and carbon emissions (30%) as opposed to the baseline measurement of rule-based management techniques. In the area of predictive maintenance, an overall equipment effectiveness was achieved through a 92% accuracy in fault detection. These results demonstrate the potential of digital twins and big data analytics combined to facilitate management of predictive, real-time, control, and sustainable operations. The proposed model illustrates the potential for smart factories to remain relevant and transition to sustainable operations within the integrated intelligent energy ecosystems of the future.