Advances in Digital Twin Applications for Enhancing Energy Efficiency in Industrial Environments: Challenges and Opportunities

In recent years, digital twins have emerged as effective tools for simulating, monitoring, and optimizing complex systems across a wide range of industries. These digital replicas provide a virtual representation of physical assets, processes, or systems, allowing for real-time data analysis, predictive insights, and proactive decision making. In industrial environments, where energy consumption frequently accounts for a significant portion of operational costs and environmental impact, leveraging digital twins offers a promising opportunity for improving energy efficiency. Recent advancements in this area show that digital twins can significantly enhance energy efficiency, enabling meticulous energy use analysis, predictive maintenance, and operational adjustments, yet comprehensive integration and exploitation of these technologies remain in their nascent stages.

The goal of this Research Topic is to provide a comprehensive platform for researchers, engineers, and practitioners to explore the latest advancements, challenges, and opportunities in the realm of digital twin applications specifically tailored for enhancing energy efficiency within industrial settings.

We welcome original research articles, review, and brief research reports addressing various aspects that contribute to the understanding and advancement of digital twin applications for improving energy efficiency in industrial environments. This Research Topic aims to provide a comprehensive overview of the state-of-the-art methodologies, tools, and best practices in this rapidly evolving field, fostering interdisciplinary collaboration and driving innovation towards sustainable industrial practices. We invite contributions addressing the following key areas:

• Development and implementation of digital twin frameworks for energy management in industrial processes.
• Case studies demonstrating successful applications of digital twins in optimizing energy usage across various
industrial sectors.
• Integration of advanced data analytics and machine learning techniques within digital twin platforms to forecast and optimize energy consumption.
• Real-time monitoring and control strategies facilitated by digital twins to minimize energy waste and enhance operational efficiency.
• Challenges and solutions in integrating digital twins with existing industrial infrastructure to improve energy efficiency.
• Cybersecurity and data privacy considerations in deploying digital twin solutions for energy management in industrial environments.
• Interdisciplinary approaches combining engineering, computer science, and domain-specific expertise to develop effective digital twin solutions for energy optimization.
• Scalability and interoperability challenges in deploying digital twins across diverse industrial processes and systems.
• Economic and environmental impacts of implementing digital twin technologies for energy efficiency improvement in industrial settings.
• Future directions and emerging trends in the field of digital twin applications for enhancing energy efficiency in industrial environments.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Technology and Code

Keywords: Digital Twin Industrial process, Energy Efficiency, Industrial Applications, Advanced Analytics, Operational Optimization

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.