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Why bother with a Digital Twin?

September 4, 2019 Joseph Ting and Duncan Micklem

Digital twin technology is not a new trend in the energy industry, yet there is still a lot of confusion as to what exactly these twins are and what is their use value. Here, Joseph Ting, Vice President of Yokogawa’s Digital Transformation Platform Center, and Duncan Micklem KBC’s Executive Vice President, Strategy & Marketing, provide an overview of the role of digital twins in the modern energy, chemical, and process manufacturing industries.

The growing proliferation of data, new data sources and computer speeds within an increasingly volatile business environment are slowly but steadily disrupting decision cycles across the energy and chemical industries. Yet, making better decisions, faster, that can be executed perfectly, every time, is vital for delivering superior results, sustained.

This is of course easier said than done because every individual perspective is underpinned by a series of unique cognitive biases that drive swift action in adversity but make accurately weighing evidence, assessing probabilities, and deciding logically a challenge. A single view to the truth and analytics are therefore key to situational awareness and effective organizational decision-making.

Many players in the industry are stuck on what type of analytics they need. Increasing process plant complexity requires more sophisticated ways of approaching key performance indicators (KPIs) and targets. This is where deeper analytics technology, utilizing digital twins, comes to the fore as it accounts for the multi-dimensional factors and non-linear trade-offs that make effective decision-making a challenge.

The digital twin allows "What if?" and "What's best?" scenarios to be run automatically to determine available strategies that maximize profitability. Experts can then review the recommended strategies to assess the impact of each recommended approach.

A digital twin works in the present, mirroring the actual device, system or process in simulated mode, but with full knowledge of its historical performance and accurate understanding of its future potential.

Therefore, the digital twin can exist at any level within the traditional ISA-95 architecture and can be defined as a decision support tool that enables improved safety, reliability and profitability in design or operations.

As a virtual/digital copy of a device, system or process, a digital twin is useful across the entire lifecycle of an asset. It is ideally created during the initial study to evaluate the feasibility and process model of the asset. It is then used and further developed during the design, construction, and commissioning of the asset; thereby facilitating the optimum design of the asset and the training of the staff who will operate the asset. During the bulk of a plant's lifecycle, operation, and maintenance, the digital twin can be employed for optimization and predictive maintenance.

The digital twin enables everyone to see inside assets and processes and perceive things that are not being directly measured. They are wired up so that insights are instantly available without data and model wrangling by end users and run in a consistent way that everyone can understand and agree on. In this way the digital twin is able to drive agility and convergence in understanding and action across the whole business, for example from Engineering to Operations; Operations to Supply Chain; Reservoir to Facilities; Shopfloor to Board room; etc.

In order to achieve the desired levels of accuracy, source data must be gathered in real-time, be validated and reconciled to ensure that all physical and chemical laws are respected, and electronic noise and dynamic effects eliminated through filtering. Only through this approach can data quality issues be identified and mitigated, and the digital twin can be trusted to reflect reality and relied on for quality and accuracy of its predictions.

While individual point solution digital twins exist today, a future digital nirvana has one multi-purpose digital twin. It is unrealistic to get to the future state in one step, and it is likely to be achieved by the connectivity of valuable high performing individual elements. Therefore, the mantra has to be agile – think big, start small, scale fast and drive adoption.

Yokogawa and KBC have successfully achieved the development of an integrated production management system digital twin that operates across the entirety of the process manufacturing supply chain and asset lifecycle to align production management and reliability, energy and supply chain optimization and strategic asset investment planning.

Overall, connectivity of the plant to the wider knowledge pool and thus completing the digital twin will provide a lot of value. Risks can be managed with governance, cybersecurity measures, and localized operational applications. A focus of the outcomes and an agile organization are key to making the balance right for the plant.

Digitalization is accelerating and disrupting the decision cycle. For the first time, a digital nirvana can be within reach for the energy, chemical, and process manufacturing industries. A digital twin initiative can rally your operations around this vision and create lasting, sustainable business value.

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