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The digital twin in the manufacturing process. Five steps how Virtual Commissioning and Virtual Sensing work together.
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The food industry has improved product quality while reducing production time and cost by automating production using programmable logic controllers (PLC) over the last several decades. However, many production plants still require some level of manual expert interaction, mainly because the production processes are not 100% under control. Operators...
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... These research results are analyzing the technological impacts of digital twin solutions focusing on overall equipment effectiveness (OEE), shown, that xDT can significantly increase the performance of the technological system, but the financial impact of xDT application should have a deeper analysis, which can be based on simulation supported methodologies. The aggregation level of many types of data significantly influences an example in the semiconductor device manufacturing shows, the return of investment of factory scheduling, which is an integrity-related topic of big data [18]. Technologies like cloud computing, simulation, optimization, artificial intelligence, blockchain, smart sensors, cyber-physical systems, additive manufacturing, robotics, visualization, quantum computing, big data, virtual and augmented reality, Internet of Things (IoT), nanotechnology, radiofrequency identification, autonomous vehicles and machine learning are under the umbrella of Industry 4.0 solutions and these technologies can be used to integrate technological, human and logistics resources into a cloud [19]. ...
The object of this study is the impact of different digital twin solutions on the performance of job-shop manufacturing systems, while economic aspects are also taken into consideration. This paper proposes an approach to analyze the impact of different identification systems on the efficiency and ROI of digital twin deployment in production systems. In order to achieve this aim, let’s analyze the investment and operation cost of different Internet of Things technologies. The next phase of the research work was the definition of performance parameters, which makes it possible to analyze the impact of different digital twin solutions on the productivity of the job-shop manufacturing system. It is possible to choose four financial indicators to analyze the economic impact of digital twin solution on job-shop manufacturing: return on investment, compound annual growth rate, internal rate of return and net present value. Our approach is based on a novel agent-based simulation model using AnyLogic simulation tool. From the results of this productivity analyses, the model computes the financial indicators, which describe the expected financial impact of the investment and operation cost. It is compared the impact of barcodes and radiofrequency identification technologies on the financial and technological impact of the job-shop manufacturing environment. The numerical analysis of a job-shop manufacturing system shows, that the radiofrequency identification-based digital twin solution has 9.2 % higher return on investment, 53 % higher net present value and 1.6 % higher compound annual growth rate. The model can be easily converted to analyze other types of manufacturing systems, which can lead to increased efficiency of digital twin solutions
... Many other cases are realized or are under development, covering a wide range of domains, from design to manufacturing and process control (e.g. see [70]). ...
While the digital twin has become an intrinsic part of the product creation process, its true power lies in the connectivity of the digital representation with its physical counterpart. Data acquired on the physical asset can validate, update and enrich the digital twin. The knowledge contained in the digital representation brings value to the physical asset itself. When a dedicated encapsulation is extracted from the digital twin to model a specific set of behaviors in a specific context, delivering a stand-alone executable representation, such instantiated and self-contained model is referred to as an Executable Digital Twin. In this contribution, key building blocks such as model order reduction, real-time models, state estimation and co-simulation are reviewed, and a number of characteristic use cases are presented. These include virtual sensing, hybrid testing and hardware-in-the loop, model-based control and model-based diagnostics.
... To overcome these issues and increase the efficiency of the system, digital twin approaches have been used in post-harvest processing to continuously monitor the products and update the processing stages [80]. Digital twins, as an expanding family of digital farming could strengthen agri-food systems, affect knowledge and skills of farm management [44]. ...
... Food supply chain Thermophysical behavior of fruit during supply chain, storage at different airflow rate, understanding, recording, and predicting losses of temperature-based fruit quality [82] Beverage Predicting possible anomalies and preventing safety issues for employees [88] Food Machine learning-based models for real-time response and quality predictions, maintenance, and data collection [80] Food supply chain Development of practical implementation strategies, enhancing resilience food retail, and capacity management [83] Food Challenges, methodologies, and opportunities for implementation of digital twin in food processing, importance of realistic and accurate models in food processing [81] Food Modeling of equipment, humans, and space for fast-food producing, management of production chain, and performance evaluation [89] Post-harvest Monitoring of retail stores and detection of fruit quality lost [84] With rapid technological and sensor development, digital twin of the agricultural soil by considering the soil quality and properties may accommodate plant productivity, health, and yield, save water, and reduce chemical usage. Many elements of the soil, irrigation, and environmental parameters in agricultural land can be continuously monitored, analyzed, and their management strategies optimized using big data analytics, machine learning models, and decision support systems embedded in the digital twin concepts. ...
Digitalization has impacted agricultural and food production systems, and makes application of technologies and advanced data processing techniques in agricultural field possible. Digital farming aims to use available information from agricultural assets to solve several existing challenges for addressing food security, climate protection, and resource management. However, the agricultural sector is complex, dynamic, and requires sophisticated management systems. The digital approaches are expected to provide more optimization and further decision-making supports. Digital twin in agriculture is a virtual representation of a farm with great potential for enhancing productivity and efficiency while declining energy usage and losses. This review describes the state-of-the-art of digital twin concepts along with different digital technologies and techniques in agricultural contexts. It presents a general framework of digital twins in soil, irrigation, robotics, farm machineries, and food post-harvest processing in agricultural field. Data recording, modeling including artificial intelligence, big data, simulation, analysis, prediction, and communication aspects (e.g., Internet of Things, wireless technologies) of digital twin in agriculture are discussed. Digital twin systems can support farmers as a next generation of digitalization paradigm by continuous and real-time monitoring of physical world (farm) and updating the state of virtual world.
... The second most frequently assigned stage is the processing stage (31.37%). In this stage, the digital twins mainly concern processing machines, as pasteurizer [30,57] or packaging machines [45,72], or entire processing systems [6,45,[73][74][75][76]. A few use cases focus on the optimal product composition or quality [45,46,77]. ...
... These applications are used for monitoring and controlling plant growth environments, in particular greenhouses or fields [34,42,68,71]; the twinning of plants during growing itself [35,65]; the detection of pests and actions to tackle them [65]; the monitoring of animals [23]; or the determination of shocks and the adaptation of process parameters during potato harvesting [61,78,80]. In addition, applications concern the monitoring of cattle with regards to their health, dairy productivity, or growth (weight gain for meat production) [61,65,66] and the control of food processing parameters [75]. The applications use clustering methods to determine the states and conditions of animals and plants and to classify pests, and further ML techniques to improve the system continuously. ...
... Another prescriptive digital twin is applied in a pudding production system to assist in production planning [73]. Further use cases only recommend actions rather than fully automatizing the system [75]. Examples are the personalized design of foods regarding genetically caused diseases [79] or the design of food packaging [45]. ...
The food industry faces many challenges, including the need to feed a growing population, food loss and waste, and inefficient production systems. To cope with those challenges, digital twins that create a digital representation of physical entities by integrating real-time and real-world data seem to be a promising approach. This paper aims to provide an overview of digital twin applications in the food industry and analyze their challenges and potentials. Therefore, a literature review is executed to examine digital twin applications in the food supply chain. The applications found are classified according to a taxonomy and key elements to implement digital twins are identified. Further, the challenges and potentials of digital twin applications in the food industry are discussed. The survey revealed that the application of digital twins mainly targets the production (agriculture) or the food processing stage. Nearly all applications are used for monitoring and many for prediction. However, only a small amount focuses on the integration in systems for autonomous control or providing recommendations to humans. The main challenges of implementing digital twins are combining multidisciplinary knowledge and providing enough data. Nevertheless, digital twins provide huge potentials, e.g., in determining food quality, traceability, or designing personalized foods.
... For the campaign organizers, this technology also turned out to be very convenient: all the data was in the cloud. On their own, without contractors, they could control the course of the action in real time, make adjustments, come up with new marketing moves depending on sales, etc. [4]. ...
The article presents the development of the agro-industrial complex taking into account innovations in the field of digitalization of technological processes of the food industry. The tasks of digitalization, the evolution of the industry are described, digital technologies introduced into the processes of the food industry, the introduction of smart production, Big Data technologies, machine vision, additive technologies are listed. Examples of the introduction of digital technologies in different countries are shown. The factors limiting the introduction of digital technologies, lack of personnel, unstable economy, psychological and organizational factors, and lack of international standards in the field of digital transformation are shown. Production statistics are widely used to identify bottlenecks in production, search for hidden reserves and determine the reasons for reducing the efficiency of equipment. Blockchain technologies are beginning to be used in the production of food. Distributed registry systems help to increase the transparency of all stages. Successful implementation requires the interest of all participants in the production chain (from the farmer to the consumer). Digital transformation in Russia began to pay serious attention relatively recently, most of the projects are under implementation. Various innovations introduced in the food production and the food industry are reflected.
Digital Twins have emerged as an outstanding opportunity for precision farming, digitally replicating in real-time the functionalities of objects and plants. A virtual replica of the crop, including key agronomic development aspects such as irrigation, optimal fertilization strategies, and pest management, can support decision-making and a step change in farm management, increasing overall sustainability and direct water, fertilizer, and pesticide savings. In this review, Digital Twin technology is critically reviewed and framed in the context of recent advances in precision agriculture and Agriculture 4.0. The review is organized for each step of agricultural lifecycle, edaphic, phytotechnologic, postharvest, and farm infrastructure, with supporting case studies demonstrating direct benefits for agriculture production and supply chain considering both benefits and limitations of such an approach. Challenges and limitations are disclosed regarding the complexity of managing such an amount of data and a multitude of (often) simultaneous operations and supports.
Improvements are necessary for the performance improvements of the digital twin technology developed for the virtual energy market on the Metaverse platform. However, more important factors need to be improved first to avoid excessive increases in costs. Thus, a priority analysis needs to be carried out to determine the variables that most affect the performance of technology investments. Accordingly, the purpose of this study is to evaluate the investments of digital twin technologies for virtual energy market in the Metaverse. A novel artificial intelligence-based fuzzy decision-making model is constructed to reach this objective. Firstly, the expert choices are prioritized with artificial intelligence-based decision-making method. Secondly, the investment priorities are analyzed for digital twin technologies with quantum picture fuzzy rough sets (QPFRS)-based Multi Stepwise Weight Assessment Ratio Analysis (M-SWARA). Finally, the alternatives for virtual energy market in the metaverse are ranked by VIKOR (VIsekriterijumska optimizacija i KOmpromisno Resenje). There are limited studies in the literature that computes the weights of the experts while generating a decision-making model. Therefore, the main contribution of this study is integrating the artificial intelligence approach and fuzzy multi-criteria decision-making methodology. Within this scope, an artificial intelligence-based application is performed when creating the decision matrix. Owing to this issue, the importance weights of experts are determined according to the qualifications of these people. This situation contributes to the results obtained being more realistic. The findings demonstrate that operational performance is the most important indicator for the improvements of the digital twin technology investments for virtual energy markets in metaverse platform because it has the greatest weight (0.267). Furthermore, integrated data production is another critical factor for the performance increase of these projects with the weight of 0.257. It is also concluded that optimization of energy consumption with smart grids has the best ranking performance among the alternatives.
The world is facing challenges in the loss of agri-food products due to poor management at all levels of the supply chain. Even though there are trials in the implementation of technologies to minimize the waste of fresh produce from farm to fork, a nearly huge volume of product loss still occurs. The application of the digital twin is becoming one of the promising solutions to improve the management of perishable food items by enhancing visibility. This article aims to present a detailed analysis of work by researchers in the field focusing on the current advances of digital twin applications in enhancing supply chain interoperability of the agri-food sector. The finding showed that the research on the application of digital twins technology in the agri-food supply chain is in its incipient stage; therefore, more efforts are needed to utilize this technology.
Predictive maintenance has been considered fundamental in industrial applications over the last few years. It contributes to improving reliability, availability, and maintainability of the systems and decreasing production efficiency in manufacturing plants. This article aims to explore the integration of predictive maintenance into production scheduling through a systematic review of literature. The review includes 165 research articles published in international journals indexed in the Scopus database. Press articles, conference papers, and non-English papers are not considered in this study. After carefully evaluating each study for its purpose and scope, 50 research articles are selected for this review, following the 2020 Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA) statement. Benchmarking of predictive maintenance methods was used to understand the parameters contributing to improved production scheduling. The results of our comparative analysis, which assessed various methods for prediction, underscore the promising potential of artificial intelligence in anticipating breakdowns. An additional insight from this study is that each equipment has its own parameters that must be collected, monitored, and analyzed.
The current state of the art of the digital twin is portrayed in this chapter with a focus on the process plant. For this purpose, an exhaustive sample of recent scientific literature was analyzed, and different approaches are explained. While it should be noted that a variety of definitions of the digital twin exist, several definitions of the digital twin are compared here. A digital twin is understood as a digital representation of an active, unique product or unique product-service-system with its selected characteristics during certain lifecycle phases. Countless publications document comprehensive approaches, advanced methods, and convincing practical solutions on how products, processes, and services can be optimized, and new ones created by using a digital twin. A taxonomy of the digital twin is elaborated. The multitude of practical applications of the digital twin can be structured according to the digital twin paradigm: functions, components, lifecycle, architecture, and context. An overview of the main application fields of the digital twin in different industrial domains is also drawn. The published applications of digital twins in the process industry are elaborated. The applications in the process industry are explored for use cases such as the design of digital twins, the process synthesis, the modernization of existing plants, incident studies, the planning of maintenance measures, the virtual monitoring of plant operation, and the training of operating and maintenance personnel. In the discussion section, the match is presented on how the digital twin provides the suitable approaches to business challenges a modern company in the process industry is faced with by timely sharing information about the products, processes, and resources across the lifecycle. The benefits of the digital twin are highlighted such as acceleration of business tasks and processes such as analytics, decision support, forecast/prediction, and recommendations. The distinction between the data-based and the system-based approach for the generation of the digital twin is elaborated. The gap in coherence between the real and the digital twin in a brownfield environment was detected. Finally, based on the digital twin characteristics, an approach for the generation and update of the digital twin of a process plant is drafted which should be offered as a commercial service.
