JUL-AUG 2019

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24 INTECH JULY/AUGUST 2019 WWW.ISA.ORG FACTORY AUTOMATION everyone can understand and agree on. In this way the digital twin drives agil- ity and convergence in understanding and action across the whole business, for example from engineering to opera - tions, operations to supply chain, reser- voir to facilities, and shop floor to board room. The digital twin aims to be an accu- rate representation of a device, system, or process over its full range of opera- tion and its full life cycle. Ideally, the digital twin should be able to transition from design to operations with ease. To achieve the desired levels of ac curacy, source data must be gath- ered in real time and be validated and reconciled to ensure that all physical and chemical laws are respected. Elec- tronic noise and dynamic effects must be eliminated through filtering. Only through this approach can data qual- ity issues be identified and mitigated, and the digital twin be trusted to reflect reality and relied on for the quality and accuracy of its predictions. Although individual point solution digital twins exist today, a future digi- tal nirvana has one multipurpose digi- tal twin (figure 3). Getting to the future state in one step is unrealistic, and it is likely to be achieved by connecting valuable high-performing individual elements. Therefore, the mantra has to be one of agility—think big, start small, scale fast, and drive adoption. Some examples of what digital twins are mirroring today include: l instrument/device l control system l 3D design and engineering l worker l process/optimization l energy/utilities l supply chain. Considering the above, some can un- derstandably believe that "digital twin" is a marketing term used to repackage cer - tain technologies that have been avail- able in the market for a long time. To some extent that might be true, but not all digital twins are made equal. Their perceived use value varies, for example, a 3D computer-aided design model of a plant may be of less value to a process engineer than a digital copy of the plant's operating conditions and the way in which molecules behave and transform. If anything, the term has been a catalyst for driving clarity and understanding of the value that it represents. Comprehensive digital twin solu- tions have been developed for an inte- grated production management sys- tem. These operate across the entirety of the process manufacturing supply chain and asset life cycle to align pro - duction management and reliability, energy and supply chain optimiza - tion, and strategic asset investment planning (figure 4). From an enterprise technology stack perspective, digital twin technology can benefit multiple levels of the orga - nization: l Digital board room. This could com- prise a series of business and finan - cial KPIs that are updated in real time as part of an enterprise-wide bal- anced scorecard. The underlying KPI calculation aims to combine a simple dashboard of measured pa rameters with integrated logic linked to the process, energy, supply chain, and asset digital twins. l Simulation and optimization. This Value engineering Value engineering Value chain optimization FEED/basic engineering Engineering Procurement Construction Commissioning Feasibility Asset life cycle Value chain The market Today Delivery Product, logistics, waste management Production planning & scheduling Feedstock sourcing Upgrade, revamp, expand or decommission Production/ manufacturing • Multiple different digitial twins covering aspects of the asset life cycle and value chain • Serving different purposes • Running off siloed/limited data sources • At fit-for-purpose compute speeds Future digital nirvana • One multipurpose digital twin • Which aligns asset life cycle and value chain • Running off ubiquitous data source(s) • At fit-for-purpose compute speeds Production management • Backcasting • Production accounting • Yield and energy optimization • Unit monitoring • Equipment monitoring • Asset performance management • Operator training Supply chain optimization • Supply chain planning • Production planning • Production scheduling • Plantwide optimization • Real-time optimization and APC • Operator decision support Capital planning Data Strategic investment evaluation Data Data Enterprise asset management Maintenance activities Process and condition data Integrated process/ energy/reliability digital twin Figure 3. Evolution of the digital twin. Figure 4. Alignment of production management, supply chain optimization, and strate - gic asset investment planning.

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