scholarly journals Greenhouse industry 4.0 – digital twin technology for commercial greenhouses

2021 ◽  
Vol 4 (S2) ◽  
Author(s):  
Daniel Anthony Howard ◽  
Zheng Ma ◽  
Christian Veje ◽  
Anders Clausen ◽  
Jesper Mazanti Aaslyng ◽  
...  
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Industry 4.0 ◽  
Influential Factors ◽  
Sensor Data ◽  
Greenhouse Production ◽  
Production Chain ◽  
Improve Energy Efficiency ◽  
Digital Twin ◽  
Digital Twins

AbstractThe project aims to create a Greenhouse Industry 4.0 Digital Twin software platform for combining the Industry 4.0 technologies (IoT, AI, Big Data, cloud computing, and Digital Twins) as integrated parts of the greenhouse production systems. The integration provides a new disruptive approach for vertical integration and optimization of the greenhouse production processes to improve energy efficiency, production throughput, and productivity without compromising product quality or sustainability. Applying the Industry 4.0 Digital Twin concept to the Danish horticulture greenhouse industry provides digital models for simulating and evaluating the physical greenhouse facility’s performance. A Digital Twin combines modeling, AI, and Big Data analytics with IoT and traditional sensor data from the production and cloud-based enterprise data to predict how the physical twin will perform under varying operational conditions. The Digital Twins support the co-optimization of the production schedule, energy consumption, and labor cost by considering influential factors, including production deadlines, quality grading, heating, artificial lighting, energy prices (gas and electricity), and weather forecasts. The ecosystem of digital twins extends the state-of-the-art by adopting a scalable distributed approach of “system of systems” that interconnects Digital Twins in a production facility. A collection of specialized Digital Twins are linked together to describe and simulate all aspects of the production chain, such as overall production capacity, energy consumption, delivery dates, and supply processes. The contribution of this project is to develop an ecosystem of digital twins that collectively capture the behavior of an industrial greenhouse facility. The ecosystem will enable the industrial greenhouse facilities to become increasingly active participants in the electricity grid.

Digital Twins: Review and Challenges

2021 ◽  
Vol 21 (3) ◽  
Author(s):  
Maria G. Juarez ◽  
Vicente J. Botti ◽  
Adriana S. Giret
Keyword(s):  
Computer Science ◽  
Industry 4.0 ◽  
Multi Agent System ◽  
Agent System ◽  
Digital Twin ◽  
Digital Twins ◽  
Science Approach ◽  
Multi Agent ◽  
Current Operating

Abstract With the arises of Industry 4.0, numerous concepts have emerged; one of the main concepts is the digital twin (DT). DT is being widely used nowadays, however, as there are several uses in the existing literature; the understanding of the concept and its functioning can be diffuse. The main goal of this paper is to provide a review of the existing literature to clarify the concept, operation, and main characteristics of DT, to introduce the most current operating, communication, and usage trends related to this technology, and to present the performance of the synergy between DT and multi-agent system (MAS) technologies through a computer science approach.

Digital Twins for Cities: A State of the Art Review

2020 ◽  
Vol 46 (4) ◽  
pp. 547-573
Author(s):  
Bernd Ketzler ◽  
Vasilis Naserentin ◽  
Fabio Latino ◽  
Christopher Zangelidis ◽  
Liane Thuvander ◽  
...  
Keyword(s):  
Grey Literature ◽  
Physical Models ◽  
Global Perspective ◽  
Sensor Data ◽  
Dominant Term ◽  
Civic Society ◽  
Digital Twin ◽  
3D City Model ◽  
Digital Twins ◽  
City Model

During the last decades, a variety of digital tools have been developed to support both the planning and management of cities, as well as the inclusion of civic society. Here, the concept of a Digital Twin – which is rapidly emerging throughout many disciplines due to advances in technology, computational capacities and availability of large amounts of data – plays an important role. In short, a digital twin is a living virtual model, a connected digital representation of a physical system and has been a central concept in the manufacturing industry for the past decades. In this article, we review the terminology of digital twins for cities and identify commonalities and relations to the more established term 3D city models. Our findings indicate an increasing use of the term digital twin in academic literature, both in general and in the context of cities and the built environment. We find that while there is as yet no consensus on the exact definition of what constitutes a digital twin, it is increasingly being used to describe something that is more than a 3D city model (including, e.g. semantic data, real-time sensor data, physical models, and simulations). At the same time, the term has not yet replaced the term 3D city model as the most dominant term in the 3D GIS domain. By looking at grey literature we discuss how digital twins for cities are implemented in practice and present examples of digital twins in a global perspective. Further, we discuss some of the application areas and potential challenges for future development and implementation of digital twins for cities. We conclude that there are significant opportunities for up-scaling digital twins, with the potential to bring benefits to the city and its citizens and clients.

Online (Remote) Teaching for Laboratory Based Courses Using “Digital Twins” of the Experiments

2021 ◽  
Author(s):  
Sabri Deniz ◽  
Ulf Christian Müller ◽  
Ivo Steiner ◽  
Thomas Sergi
Keyword(s):  
Applied Sciences ◽  
Fluid Mechanics ◽  
Laboratory Experiments ◽  
Web Applications ◽  
Energy Systems ◽  
Sensor Data ◽  
Test Rig ◽  
Digital Twin ◽  
Digital Twins ◽  
University Of Applied Sciences

Abstract The Covid-19 pandemic has changed the university education, with most teaching moved off campus and students learning online or remote at home, but a cornerstone of undergraduate engineering education has been a big challenge, namely the laboratory classes. As the engineering and education communities continue to adapt to the realities of a global pandemic, it is important to recognize the importance of the laboratory-based courses. In order to address to this situation, an ambitious approach is taken to recreate the laboratory experience entirely online with the help of the digital twins of the fluid mechanics, thermodynamics, and turbomachinery laboratory experiments. Laboratory based undergraduate courses such as EFPLAB1, EFPLAB2 (Energy; Fluid and Process Laboratory 1 & 2) and EFPENG (Energy; Fluid and Process Engineering) are important parts of the “mechanical engineering” and “energy systems engineering” curricula of the Lucerne University of Applied Sciences (HSLU) in Switzerland. Each course mentioned above include six different laboratory experiments about fluid mechanics, thermodynamics, turbomachinery, energy efficiency, and energy systems, including mass- and energy flow balances in energy systems. During the Covid-19 pandemic, it was necessary to adapt to the new environment of remote learning courses and modify the laboratory experiments so that they can be carried out online. The approach was developing digital twins of each laboratory experiment with web applications and providing an environment together with supporting videos and interactive problems so that the laboratory experiments can be carried out remotely. A digital twin is a digital representation of a physical system, e.g., the test rig. It may contain a collection of various digital models with related physical equations and solutions, information related to the operation of the test rig, including 2D or 3D models, process models, sensor data records, and documentation. Ideally, all quantities and attributes that could be measured or observed from the real experiment should be accessible from its digital twin. The digital twin not only reproduces the experimental setup in the laboratory but also helps to improve the knowledge related to the theory and concepts of the laboratory experiments. One major advantage of the digital twin is that the number and range of the parameters, which can be manipulated or varied, is larger in comparison to the actual testing in the laboratory. This paper explains the development of the digital twins (web applications) of the laboratory experiments and provides information about the selected experiments such as potential vortex, linear momentum equation, diffuser flow, radial compressor, fuel cell, and pump test rig with the measurement of pump characteristics. A remote or distance learning has many hurdles, one of the largest being how to teach hands-on laboratory courses outside of an actual laboratory. The experience at the Lucerne University of Applied Sciences showed that teaching online labs using the digital twins of the laboratory experiments can work and the students can take part in remote laboratories that meet the learning outcomes and objectives as well as engage in scientific inquiry from a distance.

Building a Trustworthy Product-level Shape-performance Integrated Digital Twin with Multi-fidelity Surrogate Model

2021 ◽  
pp. 1-28
Author(s):  
Shuo Wang ◽  
Xiaonan Lai ◽  
Xiwang He ◽  
Yiming Qiu ◽  
Xueguan Song
Keyword(s):  
Real Time ◽  
Sensor Data ◽  
Test Case ◽  
Digital Twin ◽  
Digital Twins ◽  
Training Samples ◽  
Practical Engineering ◽  
Simulation Results ◽  
Visualization Techniques ◽  
Quantities Of Interest

Abstract Digital twin has the potential for increasing production, achieving real-time monitor, and realizing predictive maintenance by establishing a real-time high-fidelity mapping between the physical entity and its digital model. However, the high accuracy and instantaneousness requirements of digital twins have hindered their applications in practical engineering. This paper presents a universal framework to fulfill the requirements and to build an accurate and trustworthy digital twin by integrating numerical simulations, sensor data, multi-fidelity surrogate (MFS) models, and visualization techniques. In practical engineering, the number of sensors available to measure quantities of interest is often limited, complementary simulations are necessary to compute these quantities. The simulation results are generally more comprehensive but not as accurate as the sensor data. Therefore, the proposed framework combines the benefits of both simulation results and sensor data by using an MFS model based on moving least squares, named MFS-MLS. The MFS-MLS was developed as an essential part to calibrate the continuous field of the simulation by limited sensor data to obtain accurate results for the digital twin. Then single-fidelity surrogate models are built on the whole domain using the calibrated results of the MFS-MLS as training samples and sensor data as inputs to predict and visualize the quantities of interest in real-time. In addition, the framework was validated by a truss test case, and the results demonstrate that the proposed framework has the potential to be an effective tool to build accurate and trustworthy digital twins.

scholarly journals Generic Digital Twin Architecture for Industrial Energy Systems

2020 ◽  
Vol 10 (24) ◽  
pp. 8903
Author(s):  
Gernot Steindl ◽  
Martin Stagl ◽  
Lukas Kasper ◽  
Wolfgang Kastner ◽  
Rene Hofmann
Keyword(s):  
Industry 4.0 ◽  
Energy Systems ◽  
Energy Transition ◽  
Packed Bed ◽  
Reference Architecture ◽  
Semantic Web Technologies ◽  
Architecture Model ◽  
Digital Twin ◽  
Digital Twins ◽  
Industrial Energy

Digital Twins have been in the focus of research in recent years, trying to achieve the vision of Industry 4.0. In the domain of industrial energy systems, they are applied to facilitate a flexible and optimized operation. With the help of Digital Twins, the industry can participate even stronger in the ongoing renewable energy transition. Current Digital Twin implementations are often application-specific solutions without general architectural concepts and their structures and namings differ, although the basic concepts are quite similar. For this reason, we analyzed concepts, architectures, and frameworks for Digital Twins in the literature to develop a technology-independent Generic Digital Twin Architecture (GDTA), which is aligned with the information technology layers of the Reference Architecture Model Industry 4.0 (RAMI4.0). This alignment facilitates a common naming and understanding of the proposed architectural structure. A proof-of-concept shows the application of Semantic Web technologies for instantiating the proposed GDTA for a use case of a Packed-Bed Thermal Energy Storage (PBTES).

scholarly journals Research on Monitoring and Prewarning System of Accident in the Coal Mine Based on Big Data

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Xu Xia ◽  
Zhigang Chen ◽  
Wei Wei
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Coal Mine ◽  
Data Aggregation ◽  
Delay Time ◽  
Assessment Model ◽  
Sensor Nodes ◽  
Sensor Data ◽  
Working Face ◽  
State Of The Environment

More and more big data come from sensor nodes. There are many sensor nodes placed in the monitoring and prewarning system of the coal mine in China for the purpose of monitoring the state of the environment. It works every day and forms the coal mine big data. Traditional coal mine monitoring and prewarning systems are mainly based on mine communication cable, but they are difficult to place at coal working face tunnels. We use WSN to replace mine communication cable and build the monitoring and prewarning system. The sensor nodes in WSN are energy limited and the sensor data are complicated so it is very difficult to use these data directly to prewarn the accident. To solve these problems, in this paper, a new data aggregation strategy and fuzzy comprehensive assessment model are proposed. Simulations compared the energy consumption, delay time, cooperation cost, and prewarning time with our previous work. The result shows our method is reasonable.

scholarly journals Differentiating Digital Twin from Digital Shadow: Elucidating a Paradigm Shift to Expedite a Smart, Sustainable Built Environment

Buildings ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 151
Author(s):  
Samad M. E. Sepasgozar
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Industry 4.0 ◽  
Construction Projects ◽  
Data Exchange ◽  
Smart Cities ◽  
Information Modeling ◽  
Digital Data ◽  
Digital Twin ◽  
Industrial Sectors

Construction projects and cities account for over 50% of carbon emissions and energy consumption. Industry 4.0 and digital transformation may increase productivity and reduce energy consumption. A digital twin (DT) is a key enabler in implementing Industry 4.0 in the areas of construction and smart cities. It is an emerging technology that connects different objects by utilising the advanced Internet of Things (IoT). As a technology, it is in high demand in various industries, and its literature is growing exponentially. Previous digital modeling practices, the use of data acquisition tools, human–computer–machine interfaces, programmable cities, and infrastructure, as well as Building Information Modeling (BIM), have provided digital data for construction, monitoring, or controlling physical objects. However, a DT is supposed to offer much more than digital representation. Characteristics such as bi-directional data exchange and real-time self-management (e.g., self-awareness or self-optimisation) distinguish a DT from other information modeling systems. The need to develop and implement DT is rising because it could be a core technology in many industrial sectors post-COVID-19. This paper aims to clarify the DT concept and differentiate it from other advanced 3D modeling technologies, digital shadows, and information systems. It also intends to review the state of play in DT development and offer research directions for future investigation. It recommends the development of DT applications that offer rapid and accurate data analysis platforms for real-time decisions, self-operation, and remote supervision requirements post-COVID-19. The discussion in this paper mainly focuses on the Smart City, Engineering and Construction (SCEC) sectors.

scholarly journals Towards Integrated Digital Twins for Industrial Products: Case Study on an Overhead Crane

2021 ◽  
Vol 11 (2) ◽  
pp. 683
Author(s):  
Juuso Autiosalo ◽  
Riku Ala-Laurinaho ◽  
Joel Mattila ◽  
Miika Valtonen ◽  
Valtteri Peltoranta ◽  
...  
Keyword(s):  
System Integration ◽  
Sensor Data ◽  
Overhead Crane ◽  
Application Development ◽  
Multiple Systems ◽  
Digital Twin ◽  
Industrial Companies ◽  
Digital Twins ◽  
Industrial Internet ◽  
Application Programming

Industrial Internet of Things practitioners are adopting the concept of digital twins at an accelerating pace. The features of digital twins range from simulation and analysis to real-time sensor data and system integration. Implementation examples of modeling-oriented twins are becoming commonplace in academic literature, but information management-focused twins that combine multiple systems are scarce. This study presents, analyzes, and draws recommendations from building a multi-component digital twin as an industry-university collaboration project and related smaller works. The objective of the studied project was to create a prototype implementation of an industrial digital twin for an overhead crane called “Ilmatar”, serving machine designers and maintainers in their daily tasks. Additionally, related cases focus on enhancing operation. This paper describes two tools, three frameworks, and eight proof-of-concept prototypes related to digital twin development. The experiences show that good-quality Application Programming Interfaces (APIs) are significant enablers for the development of digital twins. Hence, we recommend that traditional industrial companies start building their API portfolios. The experiences in digital twin application development led to the discovery of a novel API-based business network framework that helps organize digital twin data supply chains.

scholarly journals Towards Digitalization in Bio-Manufacturing Operations: A Survey on Application of Big Data and Digital Twin Concepts in Denmark

2021 ◽  
Vol 3 ◽  
Author(s):  
Isuru A. Udugama ◽  
Merve Öner ◽  
Pau C. Lopez ◽  
Christan Beenfeldt ◽  
Christoph Bayer ◽  
...  
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Business Case ◽  
Digital Twin ◽  
Manufacturing Operations ◽  
Current State ◽  
Production Processes ◽  
Big Data Applications ◽  
Digital Twins ◽  
Biotech Industry

Digitalization in the form of Big Data and Digital Twin inspired applications are hot topics in today's bio-manufacturing organizations. As a result, many organizations are diverting resources (personnel and equipment) to these applications. In this manuscript, a targeted survey was conducted amongst individuals from the Danish biotech industry to understand the current state and perceived future obstacles in implementing digitalization concepts in biotech production processes. The survey consisted of 13 questions related to the current level of application of 1) Big Data analytics and 2) Digital Twins, as well as obstacles to expanding these applications. Overall, 33 individuals responded to the survey, a group spanning from bio-chemical to biopharmaceutical production. Over 73% of the respondents indicated that their organization has an enterprise-wide level plan for digitalization, it can be concluded that the digitalization drive in the Danish biotech industry is well underway. However, only 30% of the respondents reported a well-established business case for the digitalization applications in their organization. This is a strong indication that the value proposition for digitalization applications is somewhat ambiguous. Further, it was reported that digital twin applications (58%) were more widely used than Big Data analytic tools (37%). On top of the lack of a business case, organizational readiness was identified as a critical hurdle that needs to be overcome for both Digital Twin and Big Data applications. Infrastructure was another key hurdle for implementation, with only 6% of the respondents stating that their production processes were 100% covered by advanced process analytical technologies.

Sign in / Sign up

Export Citation Format

Share Document