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The paper describes a smart model for palletising as main intra-logistics task for product-on-pallet distribution, and its development in Logistics 4.0 framework aligned to Industry 4.0. The model is developed in holonic paradigm as a 2-layer Holonic Logistics Execution System (HLES) in semiheterarchical topology. Scheduling of logistics activities and allocation to resources are optimised for global efficiency on compact time periods. The logistics model virtualises physical entities: resources, pallets and orders as holons implemented with digital twin software, and categories of task workloads: resource health monitoring, dispatching and tracking pallet orders, re-assigning jobs at resource failure, which grants reality-awareness and robustness. Global workload optimisation uses Constraint Programming as decision making technology with ILOG optimiser engine as situation-specific solver tool in SaaS cloud model, and delegate multi-agent system (MAS) technology for intelligence distribution. The optimised objective function is a combination of palletising cost weighted by robot speed limits and pallet storage cost in payable stocks. The main constructs are exemplified and validated on a real-life structure with multiple palletising resources in which programmable logic controllers (PLC) coordinate locally the parallel execution of order holons according to their globally optimised sequence. [ABSTRACT FROM AUTHOR]

The air-drying of resin impregnated paper sheets in industrial lines, formed by a serial array of furnaces, presents a high number of different controllable operational parameters whose adjustment, usually done by the maintenance staff, leads to non-efficient configurations. A model-based numerical tool, which predicts accurately in a few seconds the evolution of the paper temperature and paper grammage along the line for a given combination of the input operational parameters (direct design), was used coupled to an optimization tool to select appropriate operational parameters (inverse design) that ensure a drying process quality (i.e., fulfills an objective grammage profile) with a minimum of energy consumption. The numerical tool was capable of selecting suitable configurations with an energy reduction of up to 50% for several tested industrial cases, making the model an essential tool in the framework of the increasingly relevant role of digital twins in industry. [ABSTRACT FROM AUTHOR]

This paper proposes a digital twin enhanced approach for optimizing collaborative production scheduling in multi-enterprise manufacturing systems. A multi-objective model is developed incorporating practical constraints such as limited time windows, different production capacities, and transportation considerations. To solve the model, an Improved Non-dominated Sorting Genetic Algorithm (INSGA-II) is designed with specialized operators and strategies. The digital twin simulation is enriched with Multi-objective Decision Making based on Interaction Structures (MDIS) to obtain higher-quality solutions. Experiments demonstrate that the MDIS digital twin approach reduces manufacturing lead times and costs while improving utilization and quality compared to standard methods. This research provides an effective optimization framework to leverage cloud manufacturing resources across organizations. [ABSTRACT FROM AUTHOR]

With the rapid development of new-generation cyber–physical system (CPS) technologies, the smart operation and management of the broad bean harvesting system are enabled. This paper proposed a smart broad bean harvesting system (SBHS) and a self-adaptive control method based on CPS technologies. Firstly, the overall architecture of the SBHS is designed, which consists of four main components, namely optimal intelligent perception environment configuration, digital twin model construction, virtual simulation and real-time optimization, self-adaptive adjustment and control. Then, three key enabling technologies are discussed in detail, namely, intelligent perception environment configuration for the SBHS, digital twin model construction for the SBHS, colored Petri net (CPN)-based self-adaptive analysis and control of the harvesting system. Lastly, a proof-of-concept experiment based on a cooperative company is presented to illustrate the main work logic and advantage of the proposed SBHS. After the edge–cloud cooperative intelligent harvesting environment is configured, the CPN model for the workflow of the SBHS is created to analyze and optimize the harvesting processes. In addition, a management and control platform are developed to further illustrate the implementation of the proposed SBHS and the self-adaptive control method. [ABSTRACT FROM AUTHOR]

Digital twin technology provides a reliable paradigm to address the high trial-and-error costs and limited perception capabilities in satellite networking. However, the dynamic constellation topology and real-time twin applications remain significant challenges in satellite network design. This paper proposes a network topology simulation approach that dynamically analyzes the inter-satellite topology based on pre-calculated ephemeris and orbital information. Furthermore, the paper introduces a digital twin algorithm based on network virtualization, cloud platform management, and software-defined networking to validate and analyze the twin requirements at different stages. Finally, a low Earth orbit (LEO) constellation twin validation environment is constructed to verify the networking protocols at various stages. The experimental results demonstrate the performance of the proposed twin systems at different stages. [ABSTRACT FROM AUTHOR]

At present, the informatization level in the construction process of steel structures is relatively low. Meanwhile, digital twin technology, with better interactive features, provides a new development direction for the intelligent construction of steel structures. Therefore, this paper introduces the concept of a digital twin into the steel structure construction process, analyzes the connotation and characteristics of the digital twin model, and proposes the digital twin model architecture for steel structure construction processes. Furthermore, a method for establishing a digital twin model for steel structure construction processes is presented, which includes three stages: the acquisition and transmission of physical space data, the construction of a digital twin virtual model, and information exchange in the digital twin model. Based on these concepts, this paper describes a digital twin system architecture for the steel structure construction process from the perspective of data flow in the digital twin model. Finally, with the application of information technology in the steel structure construction process of the university park library project in Xiongan New Area and the reconstruction and expansion project of the Nanchong Gaoping airport, the digital twin model and its establishment method methods are analyzed practically and demonstrated effectively in this study. [ABSTRACT FROM AUTHOR]

Based on the rapid development of intelligent technologies in recent years, the digital transformation of the whole industry and society has become increasingly important. Among them, digital twins and artificial intelligence have great potentials in improving industry processes and further enhancing productivity. This paper proposes an Intelligent Digital Twin System (IDTS) based on artificial intelligence and digital twins for the paper industry. The system includes the prediction models for the stirring speed of the dump chest, the water consumption of the deflaker, the supply air pressure of the dryer, and the exhaust air temperature of the dryer. The sensors, 5G network slices, and other equipment collect related data during the papermaking process for generating twin data, and we use the prediction models to analyze the data and monitor important indicators (stirring speeds, water consumptions, supply air pressures, and exhaust air temperatures) for the manufacturing processes, which are used to improve the energy utilization and production efficiency of the paper industry and thus facilitate cost saving. We apply this intelligent digital twin system and its associated prediction models to an actual paper manufacturing factory and show their effectiveness by improving the operational efficiency and saving labor and maintenance costs. [ABSTRACT FROM AUTHOR]

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The concept of digital twins in bridge engineering is still vague and even confused with the Bridge Information Model (BrIM). Therefore, this study provides a detailed review of 42 papers related to digital twins in bridge engineering, focusing on a proper definition, key features and creation techniques for bridge digital twin (BDT). The paper also compares BDT and BrIM from the perspectives of their elements, features, fidelity, services provided, and degree of development. The applications of BDT at different life cycle stages are identified, and the related technologies are analyzed in detail. The results show that the research clusters of BDT are divided into geometric model generation, finite element model updating, and management and are focused on the operation and maintenance phase while lacking attention in the design and construction phase. Besides, a reference framework of BDT based on the life cycle of bridges is proposed, and directions for future research are suggested. [ABSTRACT FROM AUTHOR]

Occupational safety and health is among the most challenging issues in many industrial workplaces, in that various factors can cause occupational illness and injury. Robotics, automation, and other state-of-the-art technologies represent risks that can cause further injuries and accidents. However, the tools currently used to assess risks in workplaces require manual work and are highly subjective. These tools include checklists and work assessments conducted by experts. Modern Industry 4.0 technologies such as a digital twin, a computerized representation in the digital world of a physical asset in the real world, can be used to provide a safe and healthy work environment to human workers and can reduce occupational injuries and accidents. These digital twins should be designed to collect, process, and analyze data about human workers. The problem is that building a human-focused digital twin is quite challenging and requires the integration of various modern hardware and software components. This paper aims to provide a brief survey of recent research papers on digital twins, focusing on occupational safety and health applications, which is considered an emerging research area. The authors focus on enabling technologies for human data acquisition and human representation in a virtual environment, on data processing procedures, and on the objectives of such applications. Additionally, this paper discusses the limitations of existing studies and proposes future research directions. [ABSTRACT FROM AUTHOR]

Featured Application: Application of the digital twin concept to a specific scenario of the lift-and-transverse garage. Starting from the design phase to the final field planning and debugging. It not only verifies the feasibility of equipment solutions using virtual models of digital twins but also optimizes design solutions, identifies problems, and solves them. The operational test results of the virtual model can also fully demonstrate the effectiveness of the proposed digital twin model and related design methods, which provide an important reference for the design optimization and operation of future stereo garages. In addition, compared with the traditional development process, the virtual simulation design and debugging method can not only shorten the development cycle and debugging cost but also accelerate the development progress. This new method provides a brand new idea for the development of the enterprise and also makes a positive contribution to promoting the modernization of urban parking lots. In the face of the challenges of limited urban space and the continuous increase of vehicles, stereo garages have been widely used as a solution in cities. In order to improve the automation and intelligence level of the stereo garage, this paper applies the digital twin technology to the lift-and-transverse stereo garage. A five-dimensional model of a digital twin has been developed based on an actual stereo garage. Combined with S7-PLCSIM Advanced, Botu TIA Portal, and NX MCD to build a virtual simulation platform, realizing the virtual simulation design and debugging of the digital twin-based stereo garage. This approach allows to test and optimize operational processes without relying on physical equipment, reducing labor and field debugging costs, shortening deployment cycles, and significantly reducing development costs. In addition, MCD allows real-time monitoring and control of security risks and failures detected. Finally, the feasibility of the virtual simulation and debugging scheme based on digital twin is verified by comparing the operation data of the virtual model and the actual stereo garage, which provides new ideas for the intelligent development of stereo garage but also can be used as an important reference for the development of equipment in other areas. [ABSTRACT FROM AUTHOR]

Climate-change-induced unpredictable weather patterns are adversely affecting global agricultural productivity, posing a significant threat to sustainability and food security, particularly in developing regions. Wealthier nations can invest substantially in measures to mitigate climate change's impact on food production, but economically disadvantaged countries face challenges due to limited resources and heightened susceptibility to climate change. To enhance climate resilience in agriculture, technological solutions such as the Internet of Things (IoT) are being explored. This paper introduces a digital twin as a technological solution for monitoring and controlling temperatures in a greenhouse tunnel situated in Stellenbosch, South Africa. The study incorporates an aeroponics trial within the tunnel, analysing temperature variations caused by the fan and wet wall temperature regulatory systems. The research develops an analytical model and employs a support vector regression algorithm as an empirical model, successfully achieving accurate predictions. The analytical model demonstrated a root mean square error (RMSE) of 2.93 °C and an R 2 value of 0.8, while the empirical model outperformed it with an RMSE of 1.76 °C and an R 2 value of 0.9 for a one-hour-ahead simulation. Potential applications and future work using these modelling techniques are then discussed. [ABSTRACT FROM AUTHOR]

The growing demand for high-quality steel across various industries has led to an increasing need for superior-grade steel. The quality of slab ingots is a pivotal factor influencing the final quality of steel production. However, the current level of intelligence in the steelmaking industry's processes is relatively insufficient. Consequently, slab ingot quality inspection is characterized by high-temperature risks and imprecision. The positional accuracy of quality detection is inadequate, and the precise quantification of slab ingot production and quality remains challenging. This paper proposes a digital twin (DT)-based monitoring system for the slab ingot production process that integrates DT technology with slab ingot process detection. A neural network is introduced for defect identification to ensure precise defect localization and efficient recognition. Concurrently, environmental production factors are considered, leading to the introduction of a defect prediction module. The effectiveness of this system is validated through experimental verification. [ABSTRACT FROM AUTHOR]

The complexity of industrial challenges is emerging together with the exponentially developing information and communication technologies (ICT) that provide several implementation approaches for systems engineering. It is difficult for a single company to follow technological development and remain a pioneer in every topic. Still, the industrial challenges require experts in each technology. Therefore, collaboration among technology providers is an opportunity to gather all resources and competences needed for full delivery. A joint offering (JO) is a solution, that is, co‐created in collaboration between two or more actors that usually have complementary technological skills or value‐creation logics. A JO has several doubts relating to the use case (UC) in hand, the feasibility of the joint solution to be co‐created, the resources and skills needed to deliver the joint solution, the partners, the business value creation, the elements of the contract and the ownership of the outcome, just to mention a few. The Joint Offering Evaluation Framework (JOEF) uncovers these issues and supports decision‐making before the development of a JO starts. The JOEF comprises the Joint Offering Playbook and the Business Value Toolset (BVT). The Playbook offers seven viewpoints with checklists and tools for IT solution providers considering collaboration and co‐creation for a solution that they cannot deliver or sell alone. The BVT evaluates and illustrates the business value of a JO from the viewpoints of both creation and delivery, together with value capture and assessment. The JOEF was piloted with a digital twin (DT) UC from the pulp and paper industry. [ABSTRACT FROM AUTHOR]

In stationary power generation units such as distributed remote site power systems and ship power systems, diesel engine generator systems are essential for supplying electricity. This paper proposes a digital twin diesel generator system for teaching and research purposes. A five-layer resilient architecture, including a web interface layer, server cluster layer, real-time data layer, controller layer, and equipment layer, is proposed in this paper. Based on the resilient architecture, users are able to build, implement and monitor the digital twin through web interfaces. Apart from MATLAB/Simulink, a modeling tool called M2PLink is developed to allow users to create mathematical models using a block diagram editor similar to Simulink. Various basic blocks for control systems are provided for users to form sophisticated models. These models are converted into executable codes which are downloaded to the simulator in the controller layer, where the real-time simulations are implemented. A web-based real-time monitoring interface with many widgets such as charts, oscilloscopes, and three-dimensional (3D) animation is also provided for users to customize their monitoring interface. All the signals can be traced and all the parameters can be tuned in the monitoring interface. The users are able to interact with the digital twin just like they do with the real system. The proposed system can not only be used for research such as digital twin-assisted real-time online monitoring but also for educational purposes, which is not only cost-effective but can also ensure the safety of the user as well as the equipment. [ABSTRACT FROM AUTHOR]

To address the low level of intelligence and low utilization of logs in current rotary cutting equipment, this paper proposes a digital twin-based system for optimizing the rotary cutting of logs using a five-dimensional model of digital twins. The system features a log perception platform to capture three-dimensional point cloud data, outlining the logs' contours. Utilizing the Delaunay3D algorithm, this model performs a three-dimensional reconstruction of the log point cloud, constructing a precise digital twin. Feature information is extracted from the point cloud using the least squares method. Processing parameters, determined through the kinematic model, are verified in rotary cutting simulations via Bool operations. The system's efficacy has been substantiated through experimental validation, demonstrating its capability to output specific processing schemes for irregular logs and to verify these through simulation. This approach notably improves log recovery rates, decreasing volume error from 12.8% to 2.7% and recovery rate error from 23.5% to 5.7% The results validate the efficacy of the proposed digital twin system in optimizing the rotary cutting process, demonstrating its capability not only to enhance the utilization rate of log resources but also to improve the economic efficiency of the factory, thereby facilitating industrial development. [ABSTRACT FROM AUTHOR]

OLED device performance is intricately linked to microscopic processes influenced by molecular properties, creating a complex interdependence. Two key material properties in the design of balanced devices are ionization potential (IP) and electron affinities (EA). In this work we discuss how measured quantities relate to relevant energy levels in OLEDs. We present a simulation approach to compute IP and EA, benchmarked against experimental data, to shed light on the complexity of these properties. The thereby derived understanding has the potential to improve material alignment in OLED devices, ultimately improving efficiency. [ABSTRACT FROM AUTHOR]

Wind and photovoltaic (PV) power forecasting are crucial for improving the operational efficiency of power systems and building smart power systems. However, the uncertainty and instability of factors affecting renewable power generation pose challenges to power system operations. To address this, this paper proposes a digital twin-based method for predicting wind and PV power. By utilizing digital twin technology, this approach provides a highly realistic simulation environment that enables accurate monitoring, optimal control, and decision support for power system operations. Furthermore, a digital twin platform for the AI (Artificial Intelligence) Grid is established, allowing real-time monitoring, and ensuring the safe, reliable, and stable operation of the grid. Additionally, a deep learning-based model WPNet is developed to predict wind and PV power at specific future time points. Four datasets are constructed based on weather conditions and historical wind and PV power data from the Flanders and Wallonia regions. The prediction models presented in this paper demonstrate excellent performance on these datasets, achieving mean square error (MSE) values of 0.001399, 0.001833, 0.000704, and 0.002708; mean absolute error (MAE) values of 0.025164, 0.027854, 0.018592, and 0.033501; and root mean square error (RMSE) values of 0.037409, 0.042808, 0.026541, and 0.052042, respectively. [ABSTRACT FROM AUTHOR]

Industrial collaborative robots play an essential role in smart manufacturing because they improve productivity while also ensuring workplace safety. However, the development of prognostic and health management systems to ensure the reliability of these robots has been a major challenge due to the lack of fault data. This paper proposed a digital twin scheme based on the fusion of the robot kinematic and dynamic models' information down to the powertrains (i.e., the joints motor, and gear) along with the control algorithms and uncertainty accommodation based upon deep learning. The presented digital twin concept has the potential to propel simulation-based fault prediction. We also highlight and discuss challenges and opportunities around the development of the hybrid digital twin for condition monitoring of industrial collaborative robots. [ABSTRACT FROM AUTHOR]

The paper describes the digital twin of a Li-ion battery cell based on the MATLAB/Simulink generic model. The digital twin is based on measured data for constant current/constant voltage charging and discharging cycles with State of Health (SoH) up to 79%, also including fast charging. Mathematical equations used for the digital twin are obtained by 3D data fitting of measured SoH, battery capacity, and battery cell current. The input to the proposed digital twin is only the measured battery cell current, and its output includes State of Charge (SoC), SoH, and battery cell voltage. The designed digital twin is tested and compared with MATLAB/Simulink generic model and battery cell measurements for constant discharging current and dynamically generated discharging current profile. The results show significant improvement in the generic MATLAB/Simulink model. [ABSTRACT FROM AUTHOR]

Digital Twins, which are virtual representations of physical systems mirroring their behavior, enable real-time monitoring, analysis, and optimization. Understanding and identifying the temporal dependencies included in the multivariate time series data that characterize the behavior of the system are crucial for improving the effectiveness of Digital Twins. Long Short-Term Memory (LSTM) networks have been used to represent complex temporal dependencies and identify long-term links in the Industrial Internet of Things (IIoT). This paper proposed a Digital Twin temporal dependency technique using LSTM to capture the long-term dependencies in IIoT time series data, estimate the lag between the input and intended output, and handle missing data. Autocorrelation analysis showed the lagged links between variables, aiding in the discovery of temporal dependencies. The system evaluated the LSTM model by providing it with a set of previous observations and asking it to forecast the value at future time steps. We conducted a comparison between our model and six baseline models, utilizing both the Smart Water Treatment (SWaT) and Building Automation Transaction (BATADAL) datasets. Our model's effectiveness in capturing temporal dependencies was assessed through the analysis of the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF). The results of our experiments demonstrate that our enhanced model achieved a better long-term prediction performance. [ABSTRACT FROM AUTHOR]

In order to reduce the repeated work intensity of drilling personnel and the risks faced by high-risk environments during oilfield exploitation, an intelligent manipulator is introduced into the drilling platform, and a dynamic target tracking control method of the manipulator based on deep reinforcement learning is proposed in this paper. In this paper The digital twin of the manipulator is constructed in the virtual drilling simulation environment, and multi-agent parallel training is carried out using the machine learning agents(ML-Agents) framework and proximal policy optimization(PPO) algorithm. The ideal training algorithm model is deployed to the virtual manipulator on the virtual drilling platform, and the virtual and real manipulator can track the dynamic target synchronously through serial communication. The experimental results indicate that synchronous tracking of dynamic targets with virtual and real robotic arms is feasible and accurate, providing a new approach for the digital twin application in the oil and gas industry. [ABSTRACT FROM AUTHOR]

In traditional construction safety assessment, it is difficult to describe the safety status of different construction stages. To solve this problem, this paper proposes a digital twin modeling theory for construction safety assessment. Firstly, this paper analyzes the requirements of a digital twin model. Secondly, the required information is collected by IoT. Finally, the DT model is established based on the collected information. This DT model analyzes the collected information by ML, which aims to conducting the assessments of construction safety. To verify this method, this paper analyzes the vault settlement during tunnel construction. The analysis results show that the DT model can predict the settlement value with high accuracy. Moreover, the safety state is assessed dynamically based on the settlement value by DT. [ABSTRACT FROM AUTHOR]