What are Digital Twins?

Digital Twins are a digital replica of a physical asset or process and are used to simulate real-time or near real-time behaviour. This can range from the whole asset to just a much smaller scope size such as a single piece of equipment. Digital Twins are developed from a combination of IT (Information Technology) and OT (Operational Technology) and use real-time data from the physical asset to replicate and simulate real-life activity and their outcomes.

Through the collection of real time data via sensors and via data management solutions (e.g. data historians), a digital model can be developed and maintained to provide a virtual replica of the physical asset. As well the physical attributes (such as the dimensions), the behavioural attributes (e.g. performance, process conditions, and environmental factors) are all captured.

Digital Twins are frequently used for carrying out investigations and exploring what-if scenarios which can all be carried out in a risk-free environment and in a cost-effective way.  Digital Twins are increasingly used in optimising and de-risking product development and supply chains.  Some Digital Twins utilise predictive analytics and artificial intelligence (AI) capabilities to forecast future events, allowing for early identification of potential issues.

These digital replicas can be run both faster and slower than real-time to provide increased functional flexibility in their use. Operationally, Digital Twins can be left to run independently or out-of-hours, with results available for later review.
There are many value creating benefits of using Digital Twins that include:

•     Personnel Training: Training in a risk-free, virtual environment.
•     Performance Improvement: Monitoring and optimising processes.
•     Faster Decision Making: Real-time data leads to quicker, more informed decisions.
•     Defect Elimination: Identifying and solving issues without physical risks.
•     Process Optimisation: Fine-tuning operations to maximise efficiency.

Some practical uses cases of Digital Twins

 
A Flight simulator is probably one of the most common examples. As part of a pilots’ training, a key part is to put theory into practice and so use a simulator to learn and to demonstrate the required competency and skill to be a pilot. A flight simulator enables a host of operational and emergency use cases to be recreated in a safe and cost-effective way. As part of the training, assessments can be carried out to review progress and be used for ongoing training and assessments.

Jet engines are another example of where having a digital replica allows the ability to monitor its operation and provide insights that will lead to increased reliability and reduce operational inefficiencies. These learnings will also provide value input into future design specifications and improvements.

Geographical Information System (GIS) is a geospatial Digital Twin that is used in many industries that include Oil & Gas, Automotive, and the Public Sector. GIS systems are digital replicas of a physical asset.  In the Process Industries, any information (e.g. technical specifications) relating to the asset (e.g. a vessel or even a component such as a valve) can be linked to the asset. Any changes will be reflected so that the digital replica mirrors the physical version. Google Maps is one of the most widely used GIS platform. It is used for maps, directions, real-time information (e.g. traffic updates). Amongst its many functional use cases, we can use it to get a 3-D perspective of a location and identify physical buildings.

In the process industries such as Oil & Gas, Chemical and Manufacturing, Operations Training Simulators (OTS) are high value creation tools and are widely used as a result.  Understanding the dynamics and operational behaviour end-to-end of an asset provides a wide range of benefits.

When training and assessing plant operators/technicians for example, real-life scenarios such as equipment failure, a vessel/pipeline blockage, or a power failure can be initiated as events in the OTS to see how the user would respond in a given situation.

For Greenfield (i.e. new) sites, an OTS provides an opportunity to validate Standard Operating Procedures (SOPs) and engineering studies to take place in addition to training activities. For Brownfield sites, they are useful for debottlenecking and feasibility studies.  An OTS also provides an opportunity to execute operating strategies and identify defects.
From my personal experience of delivering Digital Twins, these solutions can have a large Return on Investment (ROI). Using the OTS as an example, operational issues can be resolved before they could possibly occur on the physical asset. In some instances where they were to occur, they would be very costly through loss of production and incurred operational downtime to resolve the issue and to bring the asset back online to normal production levels.
 
There are many more practical uses cases that can be discussed but the examples above provide a good starting point into some practical use cases and the benefit they bring in their respective application.
 

What are some of the challenges in developing, deploying and maintaining Digital Twins?

 
In the development phase, what can sometimes be challenging is getting the technical specifications of the physical asset required to build the virtual model. Close engagement with OEMs (Original Equipment Manufacturers) and support teams is very important.  Also having a competent technical team skilled in both IT and OT domains to develop and validate and fine-tune the model through the build phase.

It’s always best to start small (e.g. a pilot study) from which learnings can be gained with reduced risk before scaling up. These early insights will help to avoid costly failures in larger-scale implementations. As an example, the pilot could be a very small part of an asset and the larger scale implementation being the whole asset.

A robust test plan needs to be developed to ensure all use cases have been thoroughly tested and validated. Taking the example of an OTS, a Factory Acceptance Testing (FAT) would be carried out after development. Site Acceptance Test (SAT) would be carried out after site installation.

A maintenance plan also needs to be developed for BAU (Business as Usual) activities to ensure the model is kept up to date with necessary software and security updates & patches.
Something that can easily be overlooked but is important is to a keep a Lesson Learned log to capture all learnings both in development and in operational use. These learnings can then be used in any future deployments and in Continuous Improvement (CI) initiatives.

Through incorporating learnings and continuously monitoring for improvement opportunities, Digital Twin implementations can become more robust, scalable, and beneficial for organisations.
 

Conclusion

It can be seen that Digital Twins are powerful transformative tools for improving efficiency, reducing risks, and facilitating innovation across many industries.  By creating virtual replicas of physical systems, they allow for training, real-time monitoring, predictive maintenance and many other real-world use cases.

As the technology continues to evolve, Digital Twins will have increasing value in their use and value creation.

What is Operational Technology?

Operational Technology (OT) are the computing and communication systems used to monitor, control, and manage physical processes and machinery in several industrial sectors that include Oil & Gas, Automotive and Manufacturing. This includes both hardware and software systems.

This will include machinery, monitoring systems and control systems. Some examples of Operational Technology are:

• Oil & Gas: Industrial Control Systems (ICS) - includes Distributed Control Systems (DCS) and Supervisory Control and Data Acquisition (SCADA) systems.

• Automotive: Automated Assembly Line in the manufacturing process of vehicles.

• Manufacturing: Internet of Things (IoT) technology allows machinery to be connected to the internet and exchange data with other devices.

How does Operational Technology (OT) differ to Information Technology (IT)?

Information technology (IT) relates to the data and the flow of digital information and how it is stored, transmitted, processed. It is the technology backbone for any organisation. The digital flow and the availability of data are key enablers for organisations to increase their profitability and create new opportunities for business growth.

Operational Technology is focused on the production side and is more associated with hardware and the physical equipment used in industrial systems and processes. OT devices are more likely to be purpose built, have specialised software and proprietary protocols.

There is increasing overlap between OT and IT that includes of enabling technology such as Internet of Things (IoT) that allows operational data to be collected throughout the asset, machine, and product life cycle. This allows intelligent insights to be created by way of analytical models, predictive maintenance, and operational dashboards.

This provides benefits that include:

• Increased Safety

• Minimising production downtimes

• Extending the life of assets

• Reduced maintenance costs

• Increasing Productivity

 

Operational Technology (OT) vs Information Technology (IT)

 

Digital solutions typically used with Operational Technology

Digital Twins

Digital Twins are a digital replica of a physical asset and are used to simulate their behaviour. This can range from the whole asset to just a much smaller scope such as a single piece of equipment. Digital twins used real-time data just like the physical asset to simulate asset behaviour and monitor operations.

Common examples are Flight Simulators used in the aviation industry to train and provide ongoing refresher training for pilots. Likewise in the process industries, Operator Training Simulators (OTS) are commonly used to primarily train plant operators and are also used for operational use cases that include engineering studies and identifying potential faults. In the Automotive industry, Digital Twins are used to create digital model that provide insights into the physical behaviour of a vehicle.

Key benefits: Digital Twins help to provide performance improvement by enabling a better understanding of the asset and by exploring operational scenarios in a safeand cost effective way. Addtional benefits include leveraging predictive capabilities enabling early detection and reducing costly downtimes.

Data Management Solutions/Data Historians

Data Management Solutions/Data Historians collect plant/asset data in Real-Time and can store several years’ worth of data. The data stored in the historian can also be leveraged by a whole host of applications would typically include:

• Predictive Maintenance solutions

• Plant Visualisation software

• Operational Dashboards

• Alarm Management software

Data Historians in particular are typically connected to the Process Control Network (PCN) and so the architectural design needs to contain the additional necessary security protocols to protect the asset from cybersecurity threats.

Key benefits: Provides real-time and secure access to plant data that can be leveraged for a wide range of operational use cases.

Advanced Process Controllers (APC)

Advanced Process Controllers are software solutions that are used to optimise plant performance. APC solutions continuously collect plant parameters and have calculation engines to determine optimal operating conditions based on current plant conditions.  By doing so they seek to minimise plant disturbances, reduce energy consumption, and optimise throughput. They are widely used in industries such as Oil & Gas, Chemical and Utilities.

These control solutions can connect to a wide array of supporting solutions to monitor controller performance and provide alerts that will include detecting signs of controller performance degradation.

Key benefits: Provide a key layer of process automation for both plant safety and improved plant performances. Also provide a foundation for other real-time plant solutions such as Real-Time Optimisers.

Operational Dashboards

Operational Dashboards leverage plant data and are effective tools for capturing key operational metrics either in Real-Time or near Real-Time. Dashboards can be configured to provide alerts when thresholds are reached and show data and trends based on custom time periods. This can be very useful in trying to understand plant behaviour prior or post events, and to detect any anomalies or any performance degradation.

Dashboards can be configured to provide Role Based Access (RBA) as an important security measure and can also be accessed remotely with only the appropriate level of information shared based on permissions. This is useful particularly in scenarios where support teams are located remotely. This can be the case for Offshore platforms in the Oil & Gas industry, for example. RBA also enables a wider stakeholder audience to access the data.

Key benefits: Provides key operational performance data that can be used to increase productivity, plant investigations and accessibility.

 

Internet of Things (IoT)

The Internet of Things (IoT) provides a bridge between OT and IT. Using devices such as sensors, physical equipment plant data can be captured and utilised by connecting and exchanging data with other devices and systems over the internet.

In do so, The Internet of Things (IoT) play a significant role in enhancing and transforming Operational Technology (OT). By integrating IoT with OT, industrial operations can increase efficiency, enable greater security, and extend asset life.

More specifically, Industrial Internet of Things (IIoT) focuses on the connection of machines and devices in the several industry sectors. This enables information to be received in real-time enabling faster decision making. Application examples that benefit from this technology include predictive maintenance, automated inventory management, and Machine Learning/Artificial Intelligence applications.

 

What are the challenges in deploying OT solutions?

Deploying Operational Technology (OT) solutions present several challenges that can significantly impact the effectiveness, security, and profitability of industrial operations.

Maintaining operations with aging infrastructure

Challenge: Older plants often lack compatibility with modern OT solutions making the integration with new technologies challenging.

Impact: The impact of upgrading or replacing aging infrastructure can be costly and time-consuming. This is because many industrial facilities have been in operation for decades. In some cases, the risks and costs associated with upgrading may lead to the prolonged use of outdated systems. 

Cybersecurity

Challenge: Industrial systems are increasingly connected to IT networks and the internet, they are coming under increasing cybersecurity threats such as ransomware, malware, and unauthorised access.

Impact: The impact of Cyberattacks on OT systems can result in severe consequences, including operational disruptions, safety-related incidents, financial losses, and reputational damage. Protecting OT systems requires specialised cybersecurity measures that can differ from traditional IT security practices.

Safety

Challenge: Industrial environments have inherent safety risks, and so deploying new OT solutions can introduce additional complexities if not properly managed. Ensuring that new technologies do not compromise the safety of personnel, asset equipment, and the environment are critical challenges that need to be managed accordingly.

Impact: The impact of safety issues during OT deployments can be severe, including injury to personnel, damage to equipment, environmental harm, and potential legal liabilities. Safety considerations must be integrated into the deployment process, with rigorous testing, adherence to safety standards, and ongoing monitoring to mitigate risks.

Minimising operational downtime

Challenge: This is an important consideration to ensure the high availability of industrial systems. Downtime, whether unplanned or scheduled, can have significant financial implications on profitability. Downtimes are typically reserved for scheduled periods of time like plant turnarounds where scheduled maintenance activities can take place.

Impact: Unexpected downtimes can disrupt production, lead to missed production deadlines, and incur significant costs. Planning for minimal disruption during OT deployment is crucial, often necessitating extensive testing, simulation, and phased rollouts. This approach aims to ensure that the deployment process is as seamless as possible, minimising any impact on ongoing operations.

Profitability

Challenge: Deploying OT solutions often requires substantial upfront investment in new technologies, training, and infrastructure upgrades. These costs can be a significant barrier, especially for companies with tight budgets or those operating in industries with low margins.

Impact: The financial impact of deploying OT solutions must be carefully managed to ensure that the benefits outweigh the costs. If not properly planned, the deployment could lead to financial strain, reducing profitability. However, successful deployment can lead to long-term gains in efficiency, productivity, and cost savings, ultimately enhancing profitability.

 

Conclusion

Operational Technology (OT) play a pivotal role in industrial operations by enhancing safety, optimising operational performance, and driving profitability. The integration of OT with Information Technology (IT) has become increasingly important as it allows organisations to leverage the latest technological advancements and create a competitive edge.

However, the strategic implementation of these solutions is not without its challenges. Organisations must carefully plan and execute their digital transformation journey to achieve benefit realisation that includes minimising operational downtime, extending asset life and increasing productivity.