Energy digital twins in smart manufacturing systems: A case study

Biopharma Is Going Digital … Bit by Bit

Migrating the industry and society into digital area have gradually been increased in recent years. Digitalization has made our life more efficient and faster. As a result of digitalization, the Fourth Industrial Revolution, also known as Industry 4.0, was born. The Fourth Industrial Revolution, like previous revolutions, aims to facilitate life and improve the quality of life. Two terms are especially important for digitalization. These terms are Industry 4.0 and Internet of Things (IoT). Industry 4.0 and the IoT focus on the networking and automation of devices both used human being and industry. Digitalization and IoT technologies play an important role in improving energy efficiency. Digital twin (DT) technology is one of the most important key of Industry 4.0 technologies under digitalization. DT represents a digital copy of a physical system or entity. Physical objects and DT models created interact with each other. DT technologies have a wide range from manufacturing to healthcare sectors and from maritime transportation to the heavy industry. DT provides many conveniences for industries to grow with digitalization. Meanwhile, DT draws attention with its development in the academic field. In this study, an overview of DT is presented, a DT roadmap is proposed, some algorithms emerging in the concept of DT for industrial processes are explained and a case study is analyzed. This roadmap shows different methods to be applied in order to make accurate forecasting for the future applications and sustainable manufacturing with DT. As a result, DT concept is thought to attract much attention in the near future.

Leveraging digital twin into dynamic production scheduling: A review

In industry 4.0/5.0, Smart manufacturing systems are typically complex systems that can be adjusted to deal with demand fluctuation through reconfigurable software/hardware. The emergence of digital twin as enabler technology can promote the operation efficiency of smart manufacturing systems. However, the modelling processes of digital twin of smart manufacturing systems is inefficient due to their complicated structures in general. Besides, the frequent reconfiguration of smart manufacturing systems and the differentiation among smart manufacturing systems will increase the burden of digital twin modelling. Therefore, a reconfigurable modelling method for digital twin of smart manufacturing systems is proposed in this paper. First, a reconfigurable modelling framework based on open architecture is proposed to provide basic model scenario for digital twin of smart manufacturing systems. Second, the reconfiguration of digital twin is elaborated from three dimensions, including reconfigurable virtual model, reconfigurable mapping, and reconfigurable scene. Finally, a case study is given to demonstrate the effectiveness of the proposed method.

As the Internet of Things (IoT) revolutionizes intelligent environments, energy efficiency remains a critical challenge. Traditional IoT-based energy management systems rely on predefined rule-based automation, which lacks adaptability to dynamic environmental conditions. This study proposes an Agentic AIoT system that integrates AI Agents, Large Language Models (LLMs), and Digital Twin technology to enhance IoT decision-making and optimize energy consumption in smart homes. Unlike conventional IoT frameworks, our system dynamically analyzes contextual factors—including real-time environmental conditions, user behaviors, and external data sources (e.g., weather forecasts and occupancy schedules)—to generate optimal energy-saving decisions autonomously. The proposed system consists of three key components: (1) Information Modules, which aggregate multi-source data beyond traditional IoT sensor inputs and construct a Digital Twin model for real-time environment simulation; (2) AI Agents & LLMs, which process contextual data and generate adaptive energy-saving strategies through intelligent reasoning; and (3) Automated Decision Execution, which seamlessly integrates optimized actions into IoT operations. By incorporating Digital Twin technology, the system can simulate various energy consumption scenarios, predict future power demands, and refine decision-making through AI-driven iterative learning. Experimental results demonstrate that our AIoT system achieves a 47.77% reduction in energy consumption compared to non-IoT environments while enhancing automation, adaptability, and user-centric control. Unlike conventional rule-based IoT solutions, integrating Digital Twin with LLM-powered AI Agents enables real-time context-aware decision-making and predictive energy optimization. This study contributes to intelligent IoT applications, Digital Twin-driven energy management, and AI-enhanced automation. Future research will focus on refining AI-driven autonomous decision-making, improving Digital Twin’s predictive accuracy, and further quantifying the impact of LLM-powered AIoT systems on energy efficiency. Our findings highlight the trans-formative potential of LLM-integrated AIoT with Digital Twin technology in revolutionizing energy management for intelligent environments while ensuring seamless interoperability with existing IoT infrastructures.

Supersystem digital twin-driven framework for new product conceptual design

In an era where the resilient planning of cities is mainly done by a multitude of digital tools for thinking, predicting, building, and observing, the data used remains globally in a static condition[1], however the city and its environment are in a perpetual state of change and must include dynamic data for being as close to reality as possible[2].Coming from the industrial world, the emerging concept of the "Digital Twin" (DT) is positioned as a tool linking the 3D digital model and the implementation of continuous and dynamic information (from sensors among others), allowing to introduce the notion of piloting and maintenance of doubled physical objects[3]. Applied at different scales and functions the "DT" becomes a unique database allowing both the simulation of different climate risk scenarios and the real-time assessment of the existing environment[4].Based on the existing literature[5] and on experimental projects such as European or National "DT" projects designed for resilient planning such as LEAD, DUET or DIAMS[6], this communication will first focus on the analysis of different dynamic and static environmental indicators used (definitions and management of the data). In a second step, we will concentrate on the different actors of these "DT" by questioning their expectations and needs (visualizations and simulations of the different impacts due to climate change as well as the maintenance of real subjects). But also by analyzing their processes of use to understand the advantages and the limits noticed during their experimentations. Finally, we will ask ourselves how this tool could evolve thanks to the feedback from these tests.The aim is to evaluate the role that the "DT" can have on the dynamic and resilient planning of territories and conversely the impacts of programming on the use of this tool. Keywords: Digital Twin, dynamic planning, urban resiliency Références : [1] Ramalho, Cristina E., et Richard J. Hobbs. “Time for a Change: Dynamic Urban Ecology” Trends in Ecology & Evolution 27, no 3 (2012): 179‑88. https://doi.org/10.1016/j.tree.2011.10.008. [2] Chunyang He et al., “Alternative Future Analysis for Assessing the Potential Impact of Climate Change on Urban Landscape Dynamics ”, Science of The Total Environment 532 (1 novembre 2015): 48‑60, https://doi.org/10.1016/j.scitotenv.2015.05.103. [3] A. Fuller, Z. Fan, C. Day and C. Barlow, "Digital Twin: Enabling Technologies, Challenges and Open Research," in IEEE Access, vol. 8, pp. 108952-108971, (2020), doi: 10.1109/ACCESS.2020.2998358. [4] Guillaud, M., & Chéreau, M. (2022). "3. Des outils techniques accessibles et des données ouvertes ". In Inventer les villes durables (p. 119‑133). Dunod. https://www.cairn.info/inventer-les-villes-durables--9782100834204-p-119.htm [5] Shahat, Ehab, Chang T. Hyun, et Chunho Yeom. « City Digital Twin Potentials: A Review and Research Agenda ». Sustainability 13, no 6 (janvier 2021): 3386. https://doi.org/10.3390/su13063386. [6]    Objectives Lead Project. (2023). https://www.leadproject.eu/objectives/  About digital urban european twins. (s. d.). DUET. https://www.digitalurbantwins.com/digitaltwindemo  Planification urbaine pour les collectivités pilotes DIAMS. (s. d.). https://www.airdiams.eu/planification-urbaine-pour-les-collectivites-pilotes

As a key part of digital transformation, digital twin is an important concept for achieving smart manufacturing. However, there remains many challenges about the digital twin concept and how it can be implemented in real manufacturing systems, especially among small and medium-sized enterprises. Relevant standards that provide common terminology and implementation guidelines can help address these challenges. This paper discusses the concept, definitions, applications, and recent standardization efforts of digital twins. In particular, International Organization for Standardization (ISO)/Draft International Standard (DIS) 23247, Digital Twin Framework for Manufacturing, is introduced. The scenarios of three digital-twin use cases in manufacturing are explored in depth and the procedures of implementing such use cases according to the ISO/DIS 23247 framework are presented. The designs of these use case scenarios can be implemented at the National Institute of Standards and Technology (NIST) Smart Manufacturing Systems (SMS) Test Bed or on any manufacturing shop floor that has similar maturity level of digital transformation. In addition, the procedures of design and implementation of ISO/DIS 23247 can be used for developing additional applications of digital twins in manufacturing.

Propelled by recent advances in Mobile Edge Computing (MEC) and the Internet of Things (IoT), the digital twin technique has been envisioned as a de-facto driving force to bridge the virtual and physical worlds through creating digital portrayals of physical objects. In virtue of the flourishing of edge intelligence and abundant IoT data, data-driven modelling facilitates the implementation and maintenance of digital twins, where simulations of physical objects are usually performed based on Deep Neural Networks (DNNs). A significant advantage of adopting digital twins is to enable decisive prediction on the behaviours of objects in near future without waiting for that really happen. To provide accurate predictions, it is vital to keep each digital twin synchronized with its physical object in real-time. However, it is challenging to maintain the real-time synchronization between a digital twin and its physical object due to the dynamics of physical objects and sensing data drift over time, i.e., the live data from a physical object diverge from the model training data of its digital twin. To address this critical issue, continual learning is a promising solution to retrain models of digital twins incrementally. In this paper, we investigate digital twin synchronization issues via continual learning in an MEC environment, with the aim to maximize the total utility gain, i.e., the enhanced model accuracy. We study two novel optimization problems: the static digital twin synchronization problem per time slot and the dynamic digital twin synchronization problem for a finite time horizon. We first formulate an Integer Linear Program (ILP) solution for the static digital twin synchronization problem when the problem size is small; otherwise, we develop a randomized approximation algorithm at the expense of bounded resource violations for it. We also devise a deterministic approximation algorithm with guaranteed performance for a special case of the static digital twin synchronization problem. We thirdly consider the dynamic digital twin synchronization problem by proposing an efficient online algorithm for it. Finally, we evaluate the performance of the proposed algorithms for continuous digital twin synchronization through simulations. Simulation results show that the proposed algorithms are promising, outperforming counterpart benchmarks by no less than 13.2%, in terms of the total utility gain.

Digital twin-driven architecture for AIoT-based energy service provision and optimal energy trading between smart nanogrids

Digital twins for electro-physical, chemical, and photonic processes

Analysing the synergies between Multi-agent Systems and Digital Twins: A systematic literature review

In the past decades, explosive numbers of Internet of Things (IoT) devices (objects) have been connected to the Internet, which enable users to access, control, and monitor their surrounding phenomenons at anytime and anywhere. To provide seamless interactions between the cyber world and the real world, Digital twins (DTs) of objects (IoT devices) are key enablers for real time monitoring, behaviour simulations, and predictive decisions on objects. Compared to centralized cloud computing, mobile edge computing (MEC) has been envisioning as a promising paradigm for low latency IoT applications. Accelerating the usage of DTs in MEC networks will bring unprecedented benefits to diverse services, through the co-evolution between physical objects and their virtual DTs, and DT-assisted service provisioning has attracted increasing attention recently. In this article, we consider novel DT placement and migration problems in an MEC network with the mobility assumption of objects and users, by jointly considering the freshness of DT data and the service cost of users requesting for DT data. To this end, we first propose an algorithm for the DT placement problem with the aim to minimize the sum of the DT update cost of objects and the total service cost of users requesting for DT data, through efficient DT placements and resource allocation to process user requests. We then devise an approximation algorithm with a provable approximation ratio for a special case of the DT placement problem when each user requests the DT data of only one object. Meanwhile, considering the mobility of users and objects, we devise an online, two-layer scheduling algorithm for DT migrations to further reduce the total service cost of users within a given finite time horizon. We finally evaluate the performance of the proposed algorithms through experimental simulations. The simulation results show that the proposed algorithms are promising.

The article demonstrates the applications, essence and possibilities of digital twin technology and explains the advantages of digital twin technology and potential challenges. A digital twin is a digital copy that can be used to simulate the status of a physical object or system. The integration of digital twin technologies with the Internet of things, Big Data and Artificial intelligence offers innovative solutions for quicker detection of arisen problems (or problems to arise) related to changes in a real physical object and for making relevant decisions. The development of such solutions in the healthcare is crucial for preserving human health and providing people with higher-quality medical care. It is possible to get an early diagnosis of the disease and the selection of a more effective treatment method on the produced digital twin by transferring the patient’s physical characteristics and changes in his/her body to the digital environment. The article analyzes medical digital twins, categorizing their benefits into patient health, cost reduction, self-management, and other benefits. In order to ensure adaptability and effectiveness in disease therapy and healthcare management decision-making, the patientoriented ontology of healthcare is examined, and a four-level ontological model is suggested to create its digital twin. The creation of a patient-oriented digital twin of healthcare requires the creation of a digital twin of existing physical objects at each level of its ontological model. The creation of digital twin in healthcare opens wide opportunities for making decisions on provision of safe and high-quality medical care to patients.

Digital Twins (DT) have become valuable tools of industry 4.0 architectures, providing a cyber-physical link of the increasingly complex systems found in modern society. This link has proven useful in modern manufacturing and distribution methods, enabling instantaneous monitoring and optimization of processes. A key benefit of synchronous monitoring and management of systems is the resultant reduction of carbon emissions. It is expected that the gains attained in other industries through DT use can be applied to the Built Environment (BE), transforming the sector, and building on the foundations laid by Building Information Modelling (BIM). Facility Management (FM) is of interest for DT application due to the complexity of modern FM systems and increasing pressures for improved energy efficiency of assets. Current trends in research highlight the following challenges: Anomaly detection, limited scope, integration of artificial intelligence (AI), standardisation, and practical application. AI offers the possibility to automate the detection and management of anomalies within building systems, enabling automated decision-making. If DTs are to contribute to improved energy efficiency; the building blocks of the Twin must be targeted towards promoting Net Zero principles. This paper posits that DTs should be created towards specific objectives, and that if done correctly these will transfer from the cyber to physical system. This paper presents a conceptual framework for a Net Zero DT based automated decision-making system. The benefits and challenges of BE DTs and the value of specific needs generation were explored through bibliographic literature review, resulting in creation of a novel Net Zero predictive maintenance framework. Key findings include a novel framework, insights into Net Zero DT principles, and key challenges faced by these systems. These new findings contribute to knowledge of the field by providing a basis for efficient DT design, laying a path towards a more sustainable BE.