Methodology to develop Digital Twins for energy efficient customizable IoT-Products

Through the development of digital twins, the Industrial Internet of Things (IIoT) has fundamentally changed how the physical and digital worlds are integrated. The purpose of this study is to examine how digital twins can improve production control in smart consumer electronics factories. In particular, the study examines how well digital twins work as a production control tool and suggests a novel strategy that combines real-time data, sophisticated analytics, and predictive capabilities. The proposed method uses a digital twin-based methodology to model and simulate the behavior of the production system in real-time, enabling the early identification of potential problems and proactive decision-making. The proposed method optimizes resource utilization, lowers energy consumption, and adapts to changing conditions by using cutting-edge algorithms and machine-learning techniques. However, existing approaches face limitations in energy efficiency, scalability, reliability, robustness, cost-effectiveness, and interoperability. Addressing these challenges is essential to advance production control in smart consumer electronics factories.

The convergence of Industrial Internet of Things (IIoT) and digital twin technology offers new paradigms for process automation and control. This paper presents an integrated IIoT and digital twin framework for intelligent control of a gas–liquid separation unit with interacting flow, pressure, and differential pressure loops. A comprehensive dynamic model of the three-loop separator process is developed, linearized, and validated. Classical stability analyses using the Routh–Hurwitz criterion and Nyquist plots are employed to ensure stability of the control system. Decentralized multi-loop proportional–integral–derivative (PID) controllers are designed and optimized using the Integral Absolute Error (IAE) performance index. A digital twin of the separator is implemented to run in parallel with the physical process, synchronized via a Kalman filter to real-time sensor data for state estimation and anomaly detection. The digital twin also incorporates structured singular value (μ) analysis to assess robust stability under model uncertainties. The system architecture is realized with low-cost hardware (Arduino Mega 2560, MicroMotion Coriolis flowmeter, pneumatic control valves, DAC104S085 digital-to-analog converter, and ENC28J60 Ethernet module) and software tools (Proteus VSM 8.4 for simulation, VB.Net 2022 version based human–machine interface, and ML.Net 2022 version for predictive analytics). Experimental results demonstrate improved control performance with reduced overshoot and faster settling times, confirming the effectiveness of the IIoT–digital twin integration in handling loop interactions and disturbances. The discussion includes a comparative analysis with conventional control and outlines how advanced strategies such as model predictive control (MPC) can further augment the proposed approach. This work provides a practical pathway for applying IIoT and digital twins to industrial process control, with implications for enhanced autonomy, reliability, and efficiency in oil and gas operations.

This study uses a wind turbine case study as a subdomain of Industrial Internet of Things (IIoT) to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real-time condition monitoring, predictive analytics, and health management of selected components of wind turbines in a wind farm. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing, and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by machine learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as condition monitoring and predictive maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real-time sensor data and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations.

This chapter aims to explore the managerial aspects of implementing artificial intelligence (AI) governance and digital twins (DTs) within the context of Industrial Internet of Things (IIoT). It focuses on establishing ethical and secure cybersecurity frameworks to enhance operational efficiency and protect critical infrastructure. The focus is on conducting systematic literature review. It begins with an overview of AI governance principles and the role of DTs in IIoT. The research then delves into the challenges and best practices for integrating these technologies, emphasising the importance of ethical considerations and robust cybersecurity measures. The study reveals that effective AI governance and the use of DTs can significantly improve the security and operational performance of IIoT systems. However, the integration of these technologies requires careful planning and adherence to ethical standards. For managers in the industrial sector, this chapter provides actionable insights into developing and implementing ethical and secure AI governance frameworks. It highlights the importance of fostering a culture of cybersecurity awareness and ethical responsibility, which are essential for safeguarding DTs and IIoT systems against cyber threats. This chapter contributes to the emerging discourse on AI governance and DTs by offering a managerial perspective on the ethical and secure deployment of these technologies in IIoT. It fills a gap in existing literature by providing a comprehensive analysis of the interplay between AI, DTs, and cybersecurity, and offering practical recommendations for industry leaders.

Emerging technologies such as digital twins and 6th Generation mobile networks (6G) have accelerated the realization of edge intelligence in Industrial Internet of Things (IIoT). The integration of digital twin and 6G bridges the physical system with digital space and enables robust instant wireless connectivity. With increasing concerns on data privacy, federated learning has been regarded as a promising solution for deploying distributed data processing and learning in wireless networks. However, unreliable communication channels, limited resources, and lack of trust among users, hinder the effective application of federated learning in IIoT. In this paper, we introduce the Digital Twin Wireless Networks (DTWN) by incorporating digital twins into wireless networks, to migrate real-time data processing and computation to the edge plane. Then, we propose a blockchain empowered federated learning framework running in the DTWN for collaborative computing, which improves the reliability and security of the system, and enhances data privacy. Moreover, to balance the learning accuracy and time cost of the proposed scheme, we formulate an optimization problem for edge association by jointly considering digital twin association, training data batch size, and bandwidth allocation. We exploit multi-agent reinforcement learning to find an optimal solution to the problem. Numerical results on real-world dataset show that the proposed scheme yields improved efficiency and reduced cost compared to benchmark learning method.

Rapid decarbonization is fast becoming the primary environmental and sustainability initiative for many economic sectors. Industry consumes more than 30 % of all primary energy in the United States and accounts for nearly 25 % of all greenhouse gas (GHG) emissions. More than 70 % of energy consumed by the industrial sector is related to thermal processes, which are also the largest contributors of carbon emissions, overwhelmingly due to the combustion of fossil fuels. Thermal process intensification (TPI) seeks to dramatically improve the energy performance of thermal systems through technology pillars focusing on alternative energy sources and processes, supplemental technologies, and waste heat management. The impacts of TPI have significant overlap with the goals of industrial decarbonization (ID) that seeks to phase out all GHG emissions from industrial activities. Emerging supplemental technologies such as smart manufacturing (SM) and the industrial internet of things (IoT) enable significant opportunities for the optimization of manufacturing processes. Combining strategies for TPI and ID with SM and IoT can open and enhance existing opportunities for saving time and energy via approaches such as tighter control of temperature zones, better adjustment of thermal systems for variations in production levels and feedstock properties, and increased process throughput. Data collected by smart processes will also enable new advanced solutions such as digital twins and machine learning algorithms to further improve thermal system savings. This paper examines the individual pathways of TPI, ID, and SM and how the combination of all three can accelerate energy and GHG reductions.

ABSTRACTThe fourth industrial revolution has driven the emergence of Digital Twins (DTs) and Industrial Internet of Things (IIoT) in manufacturing. However, the use of different definition has led to varied interpretations and inconsistent understanding of DTs. Thus, by exploring the gap between theoretical frameworks and practical implementations of IIoT‐based DTs in manufacturing, this paper aims to shed light on the DT phenomenon by considering the historical evolution and fundamental concepts of IIoT‐based DTs. Therefore, a systematic literature review was conducted to assess the ambiguity concerning DTs, particularly in distinguishing architectures and types. Therefore, this paper identifies IIoT‐based DTs in manufacturing by reviewing application‐oriented literature. As a result of a subsequent classification, this paper proposes a hierarchical classification based on communication dynamics (i.e., Uni‐directional and Bi‐directional) and information processing (i.e., use or non‐use of machine learning). Conclusively, this study proposes a comprehensive classification approach for IIoT‐based DTs and thus contributes to a more consistent understanding of the DT phenomenon. Moreover, this paper discusses key findings, as well as implications for research and practice. Finally potential avenues for future research are derived and the limitations of this study are discussed.

Digital equivalence is the main objective of Digital Twin (DT)s, and simulation is an integral part. DTs reach beyond traditional simulation with the help of real-time synchronization through Industrial Internet of Things (IIoT) technologies. Simulation supports off-line experimentations and planning, while DTs offer synchronous execution and modification. DTs help to understand “what may happen”. Also, they present “what is happening” and its management methodologies. In this paper, we present building aspects and an integration approach for a Digital Twin based Discrete-Event Simulation model. Our approach utilizes a data-driven agent-based simulation within a DT framework. It presents an integration layer that provides two essential features: reconfiguration and state initialization. It gives simulation models configurability and integrity that are required for operating within a DT. Our proposed approach is presented through a use case of a semiconductor manufacturing system. The proposed integration layer extends the usability of current Discrete-Event Simulation (DES) for a DT within a Cyber-Physical Production System.

Introduction. Digitalization of the construction industry, including individual housing construction, is becoming a global trend. Digital Twin (DT) technology allows to create a virtual copy of a physical object and synchronize it with real data online. Aim. To analyze the use of digital twins in private housing construction, identify the challenges of digitalization, and determine promising areas of development based on foreign research and case studies. Materials and methods. The article is based on foreign research analyzing the technological architecture of a digital twin and the necessary software and hardware components for its creation. Results. The analysis shows that creating a digital twin requires a carefully collected database (DataRoom), the integration of sensors, IoT devices, and analytical tools, as well as real-time data synchronization and AI model training based on the owners' behavior. Cloud platforms provide scalability for the system, but there are also barriers to implementation. Practical significance. The article shows that digital twins of private homes are becoming part of the smart home concept, improving the operation and interaction of residents. It offers recommendations for real-time data processing, optimizing operational modes, adapting control to the behavior of owners, and intelligently predicting consequences. Conclusions. The digital twin of a private house occupies an important place in the technological and social context, transforming approaches to construction and resident interaction. It combines the functions of a passport, a manager, and an analyst, making the house smart, adaptive, and secure, capable of responding to internal and external challenges, acting as a "digital shadow".

Digital Twins (DTs) have become a pivotal technology for enhancing the understanding, monitoring, and ultimately autonomous piloting of systems across various domains, including large-scale critical infrastructures such as smart electricity networks. The development of DTs necessitates developing diverse services that utilize different models and a digital shadow, which encompasses both real-time and historical data from the physical counterpart. The extensive scale of large infrastructures presents significant challenges, including managing numerous parameters, heterogeneous data, and the complex computations required, particularly with the increased use of AI algorithms. Current technologies, built by stacking multiple databases and using general-purpose languages, are inadequate for efficiently implementing digital twin services that need runtime reactivity. This tool demonstration paper introduces GreyCat, a framework designed for the development of digital twins over large-scale digital shadows. GreyCat combines imperative object-oriented programming, database persistent indexes, and scalable memory management to facilitate the creation of comprehensive and efficient digital twins. We demonstrate the ease use of GreyCat through simple examples and showcase its effectiveness in constructing the national digital twin of Luxembourg's electricity grid, which is currently operational and managing billions of data points. Reflecting on the development of GreyCat over the past years, we discuss the main lessons learned and identify open questions for future digital twin development frameworks.

The pharmaceutical industry faces unique challenges, including strict regulatory demands, complex production processes, and the need for precise quality control. This research introduces Smart Pharma TwinNet, a system that uses AI-powered digital twins and Industrial Internet of Things (IIoT) technology to make pharmaceutical manufacturing more efficient, adaptive, and intelligent. A digital twin is a virtual model of a physical asset that updates in real time with data from sensors and other sources. In Smart Pharma TwinNet, digital twins monitor equipment health and predict potential breakdowns, helping manufacturers perform maintenance before issues occur. This predictive maintenance approach reduces unexpected downtime, lowers costs, and extends the life of expensive machinery. Smart Pharma TwinNet also improves quality control by allowing real- time monitoring of production conditions, which helps catch any issues early and ensures consistent product quality. Additionally, the system promotes resource optimization by reducing waste and energy use, supporting a more sustainable manufacturing process. This paper discusses the design and technology behind Smart Pharma TwinNet, including the IIoT and AI tools used to build a network of digital twins. Our experiments show that this approach enhances production efficiency and accuracy while creating a scalable and sustainable solution for the pharmaceutical industry.

The purpose of this paper is to analyse the use of machine learning in smart manufacturing, describing techniques, technologies, industries, and purposes associated with industrial applications. We conducted a systematic literature review using Scopus, in which 26,032 documents were found. After applying quality criteria, 107 articles were analysed. The main findings show that machinery was the industry subsector with the major implementations regarding machine learning; process improvement is the main concern (interest) of all implementations; random forest was the most specific machine learning technique used; and diverse technologies associated with this context were identified such as: the industrial internet of things, digital twin, sensor technologies (soft, optical, barometric, ultrasonic), software technologies (Python, MATLAB, LabView, Google AutoML Platform) and equipment technologies (robotic, PLC, CNC). Most fault detection machine learning applications were focused on predictive maintenance, specifically in mechanical equipment (bearings, machines in general, and assembly lines). This study presents a novel taxonomy that identifies 85 specific machine-learning techniques used in smart manufacturing.

The industrial landscape is about to undergo a revolution thanks to the convergence of emerging technologies. Specifically, the integration of blockchain with the digital twin-enabled industrial internet of things (IIoT) within mixed reality environments has the potential to do just that. This chapter presents a thorough analysis of the applications, advantages, and difficulties of various technologies while examining their potential for synergy. The digital twin provides real-time data monitoring, analysis, and predictive maintenance capabilities. The industrial internet of things establishes connections between tangible objects and sensors, enabling smooth communication and interchange of data. This chapter investigates the use of mixed reality (MR) technology to integrate blockchain technology with the IIoT that is enabled by digital twins. The potential for improving data security, trust, and transparency in industrial applications through the integration of blockchain with IIoT and MR could aid in the development of the Industry 4.0 paradigm.

Digital Twin Technology — A bibliometric study of top research articles based on Local Citation Score

The development and progress in information and communication technologies will transform traditional products into smart products and allow to offer novel smart services [1]. Herein, the digital twin (DT) concept is regarded as a key technology to create value with smart services [2]. Although the research and applications of DTs emerge continuously many concerns are to be scrutinized [3]. The lack of a shared conceptual framework for DTs with an unambiguous terminology [4] complicates cross-functional discussions. Therefore, a systematization of the main dimensions of DTs is proposed in the form of an ontology and a conceptual framework thereof derived. The research questions addressed in this paper are a) «Which dimensions are used to classify and structure DTs in academic literature?», b) «What are the fundamental differences or specifications within these dimensions?» and c) «How do these different specifications relate to each other?» The focus of the research is on the objective to find classification systematics that are a) representing the entire spectrum of DTs, b) universally valid in all DT related domains and c) applicable in research and practice. A systematic literature review on the relevant aspects of DTs was conducted and the findings iteratively advanced within workshop sessions with academic experts. DTs are considered as integrators of physical and digital worlds as well as internal and external value creation. Further, the creation of DTs requires per definition the use of digital data. Hence, the proposed ontology and conceptual framework for DTs include the following main dimensions to consider for every DT: Data resources, external value creation and internal value creation. The main subdimensions of the data resources are the data sources to obtain the data, the data categories and the data formats. The main subdimension of the external value creation are the attributes of the services as the basis of the value propositions, the level of smartness of the connected products and the actors on the different levels of the ecosystem. The main subdimensions of the internal value creation are the lifecycle phases of products, the product management levels and the different generations of both. The proposed ontology and conceptual framework support researchers and practitioners in positioning and structuring their intended DT activities and communicating them to internal and external stakeholders. The holistic view on the data resource dimension further allows to easily deduct the needed data for certain applications or deduct possible applications from already available data.