This paper primarily aims at the application of DT in petrochemical industry from an operation perspective. The rest of this paper is organized as follows: Section 2 discusses the definition and
An increasing number of Digital Twin implementations are made in many industries. Given the novelty of the concept, companies from any industry type face significant challenges
A digital twin is a virtual replica of a system''s behavior in its operating environment. That system, which might be a product, a manufacturing process, or even an entire supply chain, is represented by a collection of digital models. Those models process and react to various stimuli, which are data representing the external environment.
Digital twin in process automation Easy comparison between As Planned and As Built Current, real data from the plant is available at all times with automatic as-built documentation Tours for manually recording the
The global digital twin market size was valued at USD 12.91 billion in 2023. The market is projected to grow from USD 17.73 billion in 2024 to USD 259.32 billion by 2032, exhibiting a CAGR of 39.8% during the forecast period. North America dominated the global market with a share of 34.55% in 2023. A digital twin is a virtual representation or
The best understood and most common type of digital twin a chemical engineer will encounter is the process digital twin – an integrated, real-time digital version of the plant. The process twin
A digital twin can be defined, fundamentally, as an evolving digital profile of the historical and current behavior of a physical object or process that helps optimize business performance. The digital twin is based on massive, cumulative, real-time, real-world data measurements across an array of dimensions.
A framework for utilizing Energy Digital Twin technologies in the process and energy sectors, encompassing Digital Model, Digital Shadow, and Digital Manager [149]. Overall, the application of digital twins in the renewable energy industry is expected to grow as renewable energy sources become more prevalent and complex.
Abstract: Digital twins are now one of the top trends in Industry 4.0, and many companies are using them to increase their level of digitalization, and, as a result, their productivity and reliability. However, the development of digital twins is difficult, expensive, and time consuming. This article proposes a semi-automated methodology to generate
Introduction. A digital twin is a virtual representation of a physical system or process that allows for real-time monitoring, analysis, and optimization. In the context of smart manufacturing, a digital twin can be used to simulate and optimize the production process, predict and prevent equipment failures, and improve efficiency and quality of
The tool introduced can also serve the requirements arising for online use of the models as digital shadows of the physical systems, in the context of digital twinning the process industry. The implemented models in conjunction with tools from other scientific fields can be used for monitoring, root cause analysis, performance optimization, limitation and
See how digital twins apply in the digital transformation of factories and Industry 4.0. In manufacturing, safety, and consistency are always a top focus. From machinery performance to factory floor workflow, teams are always looking for ways to optimize their manufacturing process and improve efficiency.
Digital twins can iterate and evolve through the seamless connection and fusion between the virtual and physical spaces1, and this consistency and synchronization can bring many benefits to numer
Business Performance Management & Intelligence System. Single view to monitor and manage all critical business processes and activities Data integration technology to be real-time and data source vendor agnostic Integration of refinery systems and business applications (DCS, IP21, MES, LIMS, SAP, In-House Apps) Annual savings of 13.5M USD.
Digital Twin is a cutting-edge technology that has revolutionized the industry by mirroring almost every facet of a product, process, or service. It has the potential to replicate everything in the physical world in the digital space and provide engineers with feedback from the virtual world [7] .
The published applications of digital twins in the process industry are elaborated: design of digital twins, the process synthesis, the modernization of existing
FIGURE 1. Versatile digital twin technology supports a wide array of functionality across the plant lifecycle. 1. A digital twin should be a practical investment. Digital twin architecture can be a practical investment to make, regardless of the size of the plant or industry where it will be used. To help drive better projects and operations
1. Improved construction resource management. The use of digital twins can improve the procurement process and ease supply chain holdups, fostering harmonization between the current progress and future deliverables and helping construction firms better allocate resources and physical assets. 2. Easier communication
A digital twin is a digital representation of a real-world entity or system. The implementation of a digital twin is an encapsulated software object or model that mirrors a unique physical object, process, organization, person or other abstraction. Data from multiple digital twins can be aggregated for a composite view across a number of real
Digital process twin risk control. The research of the aviation industry DT covers the whole lifecycle, which improves the ability of aircraft to operate efficiently and safely but also increases the risk and control difficulty of digital process twin. Once the risk occurs, it will bring property losses and even endanger life safety.
To ensure the stability of product quality and production continuity, quality control is drawing increasing attention from the process industry. However, current methods cannot meet requirements with regard to time series data, high coupling parameters, delayed data acquisition, and ambiguous operation control. A digital
Here we provide a brief overview of digital twin advancements in industry and highlight the main pitfalls to avoid and challenges to overcome, to improve the
Four steps to build process digital twins. Step 1: Data collection - the first step is to collect process data, typically from mining event logs but increasingly also through Work API using hybrid process intelligence. Step 2: Visualization - process intelligence software is then used to visualize both end-to-end processes and individual tasks.
This essay of the temporary Working Group ''''100% Digital'''' of the Association ''''Process, Apparatus and Plant Engineering'''' PAAT in ProcessNet addresses char-acteristics of a
from scoping a Digital Twin project at an international process manufacturing company. The four steps included in the framework are: (1) Identification of key stakeholders and. their requirements
SOLUTION BENEFITS. Replacing handover with a seamless migration. The creation of a "Central Information Store" of asset lifecycle information –for operational powered by
Digital twin (DT) technology (see Table 10.1 for basic terminology) has itself been a phenomenon practiced since the 1960s. For space programming, NASA used simple twinning ideas during this time. Additionally, in order to balance the systems in space, they constructed physically duplicated structures at ground level.
For example, in chemical manufacturing, digital twins can be used to simulate chemical processes, predict equipment failures, and optimize production schedules. Thereby, leading to increased safety, reduced costs, and improved product quality. Digital twins can be applied to a wide range of industries, from manufacturing and energy to
Digital twin in process automation Easy comparison between As Planned and As Built Current, real data from the plant is available at all times with automatic as-built documentation Tours for manually recording the installed HART field devices are no longer necessary due to the field devices being continuously connected Rapid troubleshooting
1 · In the pages that follow, we discuss the digital twin—its definition, the way it can be created, how it could drive value, its typical applications in the real world, and how a company can prepare for the digital twin planning process. Digital twin: What it is, and why it matters. Industry and academia define a digital twin in several
This essay of the temporary Working Group "100% Digital" of the Association "Process, Apparatus and Plant Engineering" PAAT in ProcessNet addresses characteristics of a digital twin from the user''s
In recent years, the emerging Digital Twin (DT) paradigm under Industry 4.0 has been attracting more attention from both practitioners and academia due to its dynamic capabilities. Most DT studies are theoretical and deal with hypothetical analysis, whereas fewer studies are available on real-life empirical cases.
As a highly secure and reliable system, aviation faces challenges including digital transformation, high production operation costs, low maintenance efficiency, and intensive technology. One of the most beneficial technologies is the digital twin (DT), which can solve the above issues. This paper investigates aviation industry DT from
The digital twin in the process industry has already gained some attention [11,12], but can be purposefully structured as a product-process-resource model (Fig.1), since these sub-mod-els are created and maintained by different disciplines, depart-ments, and
In today''s fast-paced market, companies are challenged to meet increasing customer demands and shorter product life cycles. To successfully respond to these demands, companies must produce a wide variety of different products. This requires the determination of necessary processes and resources for each product, which can be
Industries are increasingly adopting digital twin technologies to use data-driven insights as part of their digital transformation efforts. It allows for immediate monitoring, analysis, and optimization of industrial activities, resulting in improved efficiency, productivity, and cost-effectiveness [ 4, 5, 6 ].
The concepts of Hybrid and Cognitive Digital Twin are introduced as elements of the next level of process control and automation in the process and manufacturing industry. We