Building intelligent infrastructure with digital twins has helped several companies to collect, extract, and analyze data. Digital twin technology or virtual twin is overgrowing with increasing accessibility and adaptability. As Industry 4.0 comes closer, technologies surrounding digital twins are also maturing and continue to develop. With the incorporation of technologies like the Internet of Things (IoT), data analysis, and Artificial Intelligence (AI), digital twins enhance R&D innovation with intelligent services like automation, self-monitoring, and real-time optimization. It enables rapid design & development and smart solutions in production, sales, logistics, and overall supply chain.
With the ability to enhance current manufacturing & product development, industries worldwide are incorporating digital twin technology. We can already see this accelerating adoption of digital twins across the industry. Although the global twin market was currently at 5.4 billion US Dollars in 2020, much of its slump is due to the worldwide pandemic. In addition, several industries shut down due to lockdowns and social distancing being the new norm during 2020 because of COVID-19. Nevertheless, the digital twin market is slowly rising again, with a tremendous rise expected after 2021. As a result, the global digital twin market will likely reach 63 billion US Dollars by 2027 due to a high growth rate of 42.7% annually.
What is Digital Twin?
While the idea of building intelligent infrastructure with digital twins is not entirely a new concept, due to its current exponential rise and growth, digital twins are undoubtedly growing more and more prominent. Along with the advancement in IT and digital technology infrastructure, digital twins are also evolving rapidly. In general, the concept of digital twinning represents a physical object or environment in a digital form that possesses its accurate characteristics and behavior. While 3D models and simulations also can describe an object or environment, twins systems do more than that.
A digital twin generally represents a physical object or environment not just in a static manner but in a dynamic form. A digital twin represents every phase of the lifecycle of a physical object or environment. A digital twin represents a physical object or environment from its design phase to manufacturing and maintenance and changes due to re-resign, iteration, and refining the object.
Hence, a digital twin is less of a 3D model rather more like an information model. Unlike traditional 3D models, building intelligent infrastructure with digital twins needs a more dynamic and adaptive approach. They can evolve and change over time concerning changes and enhancement in information and data. Digital twins can communicate, update and even learn similarly to their physical counterparts through data exchange with Artificial Intelligence at its core.
Artificial Intelligence with technologies like Machine Learning and Deep Learning enables a digital twin to behave as accurately as possible in contrast with its physical counterpart. Due to this dynamic nature of digital twins, they are in use to explore solutions, detect and prevent problems even before they happen and essentially plan for the future. Armed with these intelligent and smart solutions, companies and organizations worldwide rapidly adopt these technologies in their operations and global supply chain.
Building Intelligent Infrastructure with Digital Twins
Digital twins have five levels of sophistication. Ranging from a level 1 twin that can describe and visualize the product to a level 5 twin model that can operate autonomously, different levels of digital twin require different levels of infrastructure. For instance, a level 1 twin does not require advanced Artificial Intelligence or Machine Learning systems, but a level 5 twin does need them. Level 2 digital twin is an informative twin that needs to incorporate additional operational and sensory data. Furthermore, level 3 is a predictive twin that can use these different data to infer and make predictions. On the other hand, the Level 4 digital twin is a comprehensive twin that can consider and simulate future scenarios to predict and learn from them.
Building digital twin technology includes converging technologies like IoT, data analysis, design & development of the twin either in 2D or 3D, and incorporating AI and technologies like machine learning and deep learning. The digital twin infrastructure is not only in a digital form but also in physical form. This is because a digital twin simulation model resides in a digital format and connects the physical world alongside it. This connection is the representation of both digital models and physical models such that they represent and replicate each other. Every change in the digital or physical model must be synchronized, and both should also respond to each other’s differences.
The actual connection is made through digital models. We can link the physical world with the virtual world by twins modeling and simulating the physical world to map and represent it in digital form. On the other hand, we can connect the virtual world with the physical one by replicating any changes and updates made in the virtual world in the physical world itself. It will ensure that neither the digital form nor the physical form is not synchronized.
In digital twin technology, synchronization must be in real-time when building intelligent infrastructure with digital twins. Real-time synchronization and simulation of the product is the following infrastructure for digital twins. Whenever a product is in the developmental phase of production, the status of the digital twin must also reflect that. The changes occurring in the digital twin must also be replicated in the physical product. Therefore, the changes in materials, processes, environmental, and every other change must be synchronized across physical and digital forms.
Apart from this, the digital twin infrastructure also requires data analysis for deep learning and intelligent systems. Artificial Intelligence generally powers these intelligent systems along with Machine Learning and Deep Learning capabilities. This is necessary for smart analytics and prediction. ML and deep learning systems must be capable of analyzing substantial amounts of data. This data must be representing the actual physical product in real-world environments. Such data are generated and collected by sensors placed in the physical world and physical development.
The data collection is a crucial metric for a system to detect anomalies or errors through analysis in the digital twins platform. Usually, ML systems process these data types and perform pattern recognition to make predictions or suggestions. Thus, these systems enable self-monitoring, predictive maintenance and diagnosis, alert systems for possible future errors, and detection of abnormalities or inconsistencies in the product.
Due to this, the data must be accurate and representative of the actual physical product and environment with great precision. These types of data also are helpful for the corporations or organizations for their product analysis and study.
These infrastructures together enable all the digital twin advantages. The convergence of these technologies is a complex task. Nevertheless, the resultant solution offers an intelligent system that can track past system analytics to predict future solutions and real-time product optimization. Companies are rapidly advancing towards implementing digital twin technologies in their platforms and systems to leverage such benefits.
Building the Infrastructures
Building digital twin infrastructures is a very complicated and complex process. Since digital twins incorporate various technologies together, it is tough to integrate these technologies to work together flawlessly. Only with such integration can one enable proper digital twin technology and can leverage its benefits.
Since the technologies part of the model twins infrastructures are different, companies must be willing to take on R&D for every technology when building intelligent infrastructure with digital twins. Moreover, if not for flawless integration, the technologies must at least be working together, which is a challenging task. However, technology is rapidly growing, and so is its accessibility and ease of use. Hence, integrating these technologies is increasingly easier to enable the tech stack for digital twins.
With the power of the cloud, technology today is dependent mainly upon real-time computing. With the help of the cloud, companies can leverage virtually endless amounts of computing to enable various services, including digital twins. Furthermore, cloud computing allows companies to build intelligent systems that are ideal for integrating multiple infrastructures of the digital twin technology.
One of the most prevalent uses of cloud computing is Artificial Intelligence. Due to the nature of Machine Learning and deep learning, immense computing power is necessary to develop these systems. Cloud computing shines brightly in this field due to its vast pre-built infrastructure and network of computer systems. In cloud computing, these computer systems are connected through an extensive network of servers and processing systems. Cloud computing service providers serve this network of different systems as a single system with enormous computing power.
Alongside this, a system for efficient and accurate modeling of the physical world with high-performance systems for real-time optimization and synchronization is mainly necessary. Moreover, deep learning and data analytics with intelligent AI systems to enable smart solutions with automation at its core is also imperative. Furthermore, a unified system integrating all these technologies is crucial while building an infrastructure for digital twins.
Companies like FS Studio pioneer product innovation and transformative R&D technology through already established and proven digital infrastructure. Since deploying and building intelligent infrastructure with digital twins is very complex and challenging for companies and organizations, FS Studio provides innovative and smart solutions for these problems. Consequently, companies can focus on their primary product innovation rather than shifting their resources towards building a digital infrastructure.
Digital Twin decarbonizes the energy systems by bringing all technologies together. The virtual twin can model energy flows and changes of variables in real-time.
Initially, designers and engineers used it to build prototypes of new products, but it proved to have more practical uses. Moreover, integrating the Internet of Things and AI has strengthened it to carry out multiple tasks.
Digital Twin can model a raft of internally connected systems through big data analytics. So, Digital twin can aid the reduction of CO2 and other greenhouses gases released into the earth's atmosphere.
Digital Twin can map out all the aspects of the energy systems to help with decarbonization. Starting from electrical production to distribution losses and localized demand, it would lay out the whole system in the virtual environment.
The use of Digital Twin is expanding as we are developing the technology to be more effective with time. For example, it has proven effective in energy management, electrically powered public transport infrastructure management, and sector coupling.
It is not possible to derive the best outcomes from the Digital Twin without knowing the factors. Therefore, one needs to know these factors below to decarbonize the energy systems without failure with Digital Twin.
Get Digital Twin Experts on Board:
When you are up for decarbonization of energy systems, you'll need technical know-how. So we are expecting that your in-house talents already possess the understanding of electrical generation, supply, and distribution.
Next, you will need a more profound working knowledge of Digital Twin to use this for the digital decarbonization of energy systems.
You must have a clear understanding of building digital twins aligned with each phase of the decarbonization process. Additionally, you need to know how to deal with data modeling and intelligent data technology to make digital twin work for you.
It's better to outsource the Digital Twin project to experts if you lack the in-house skills needed to make this project successful. Companies have to deal with the unique sets of challenges of building and deploying a digital twin.
Digital Twin is a framework for building a bridge between the physical and digital worlds. Therefore, you must satisfy a range of requirements to develop and deploy virtual twins successfully, including:
The biggest challenge of deploying a digital twin is that it shares the same characteristics as the physical entity. The Digital Twin (DT) decarbonizes the energy systems better if the team can deal with the challenges below:
Make use of your best Datasets:
Preparing the data set should be the next step in your list before building the digital twin.
We're living in the big data age where high-quality data sets are crucial to success. Feed high-quality data into the Digital Twin and you can expect fantastic outcomes. Feed trash into the twin system and the result will be the other way round.
To sum it up, it means that you need to clean up, gather and structure data sets to set up the twin system. Also, cross-check the Data's relevancy in real-world settings to ensure you will be feeding high-quality sensible data into the twin system.
When you are swamped with data and need to analyze different sets for different purposes, it adds complexity.
For instance, analyzing meteorological data like temperature, dew point, wind direction, and speed next to electrical consumption in buildings from photovoltaic, solar thermal, and hydropower plants at locations worldwide can become messy.
In brief, you'll need to combine patience and years of professional experience to locate, collect and prepare the best data sets.
Digital Twin Decarbonizes the Energy Systems Better if You Avoid Preconceptions About Model or Configuration:
Start building and deploying Digital Twin with an open mind. Don't keep any preconceived notions about which specific model or design will work for your program.
Keeping an open mind will give you the flexibility to draw the best model and configuration from a variety of ideas and solutions.
Your digital twin will end up with average quality outcomes if you have a preconceived technological pathway and follow it.
Examine all the possible solutions to decarbonization before going with one specific approach. In this way, you can make the best out of the digital modeling and AI computing calculations and stats.
Adopt Digital Twin for the whole system:
Before starting your digital twin program, you must figure out the pathway to optimize the entire energy system.
You'll need to conduct an end-to-end analysis of the initial situation and the entire technological framework conditions. Digital Twin decarbonizes the energy systems better if you consider the parameters below of each site individually for decarbonization of the energy system:
Prepare the decarbonization strategy before jumping into the program:
You must set clear goals before starting the decarbonization program with the digital twin. Digital Twin decarbonizes the energy systems better if these factors are implemented:
Don't look for Instant success:
It's not possible to achieve success within a short period with digital decarbonization as it's a high multi-dimensional program.
The digital twinning of the energy system will need care because it's a complex model. So, building the model may take a few weeks to months, depending on the size of the venture. Also, onboarding the right people for the fieldwork and synchronizing all the steps together cannot happen overnight.
How Digital Decarbonization can Change the World?
Digital Twin decarbonizes the energy systems in a different class when you apply a tactical approach. Reducing carbon emissions in the atmosphere will help climate change and keep our earth greener. Both the private and public sectors will feel the impact of using digital twins in decarbonization.
Adopting decarbonization applies mainly to the utility sector. However, other high energy demanding sectors such as the chemical industries should think about it as well.
We cannot deny that buildings are responsible for about 40 percent of all current carbon emissions. So, digital decarbonization of energy systems can make a massive difference to the climate of tomorrow.
However, the question may arise: Is decarbonization possible?
Digital decarbonization would be hard to implement but not impossible to achieve. We can reduce indirect carbon emissions through electrification and clean energy only. It is hard to control direct carbon emissions. So, we cannot deny that heavy industries would still be responsible for at least 20% of GHG emissions.
Digitalization of industries has already begun. So, companies that won't embrace modern trends like digital decarbonization processes will be left out. Decarbonization and digitalization are getting popular. As a result, industries have to switch from their traditional business models to stay relevant.
Every company must adopt the digital decarbonization process. At present, businesses are in a situation where they need to decide on a sustainability strategy that will work from the economic perspective. To do that, they must transform into a lower-carbon business model company.
Suddenly, this change of direction cannot take place overnight. Therefore, cooperation between industries is required to implement a lower-carbon business model. The enterprises will need to review climate strategy, set measurable goals, and clearly define their action plans. They will also need to assess their performance from time to time and optimize as necessary.
Adopting digital decarbonization can benefit industries in many ways. For example, businesses can run cost-efficient systems designed for specific localities when Digital Twin decarbonizes the energy systems.
Digital decarbonization can aid district heating systems and energy storage management systems of the community. In addition, it can help to increase the capacity of electric vehicle charging stations while ensuring saving potentials in environments. Moreover, digital decarbonization can reduce electricity waste and daily operational costs.
Businesses that adopt digital decarbonization will gain the rewards of CSR too. First, they will achieve better brand recognition as environmentally friendly companies. It, in turn, will strengthen their brand image among the public.
Therefore, implementing Digital Decarbonization is a win-win situation for businesses, our environment, and society.
Simulation experts in industry 4.0 must have a passion for digital twin technology today for industry research. Digital Twin simulation is entering mainstream use as more industries are adopting this technology. According to Gartner's IoT implementation 2019 survey, 75% of organizations already use Digital Twin or plan to in a year. Notably, all the companies willing to adopt Digital Twin are implementing Internet-of-Things.
Digital Twinning is not a new technology. In 2002, Michael Grieves of the Florida Institute of Technology introduced the concept of Digital Twin publicly. In 2010, NASA showcased the first practical implementation of Digital Twinning to improve the physical model simulation of spacecraft.
We can see Digital Twin has been around for two decades now. Yet, many businesses are confused about the value of Digital Twin Technology. In addition, many companies don't know the use of Digital Twin in the modern energy, chemical, and process manufacturing industries.
Many companies still don't know that Digital Twin can enjoin the disconnected processes cutting out manual efforts that can be time-consuming.
For instance, interns or low-level employees will still follow the outdated method to gather engineering information. They would walk around one department to another to collect data required for engineering research.
Many engineers use CAD and PLM software and other sources to collect data to make informed design decisions. A few engineers are lucky enough to use enterprise search engines to pull information from various departments from hundreds of documents, folders, presentations, etc.
Now that we are entering the age of automation, companies must adapt to cultural change and access the right technology. They need to integrate technologies like Digital Twin to help teams gain information without any hassle. They also need to save employees from the pain of surfing through numerous record systems.
There are other reasons to consider Digital Twin to accelerate business innovation. Here we've discussed 3 of them below:
1) The Rapid build-up and expansion of data:
The business environment of the energy and chemical industries is already volatile. On top of that, the decision-making cycle is in an array across these industries due to piled-up data sources.
System Digital Twins made for entire plants, or factory systems can rescue energy and chemical industries. A massive amount of operational data can be collected, organized, and analyzed from various devices and products.
Human decisions are not rational, even if we make sound judgments after weighing evidence and assessing probabilities. It happens because the human brain tends to simplify information processing. So, cognitive biases, including memory and attention biases, influence human decision-making.
System digital twins can eliminate human bias for critical decision makings. System digital twins can also provide a single logical view of the actual situation based on evidence, probabilities, and analytics.
It is also essential that you know your needs before adopting Digital Twin Technology. Therefore, you must ask these three questions to ensure your success with Digital Twin:
2) What type of analytics should Industry players seek?
The factory systems and manufacturing plants involve complex processes today. So, measuring KPIs isn't easy now.
The digital twin can resolve this problem by providing deeper analytics from factory systems and plants, taking multi-dimensional factors and non-linear trade-offs into account.
The digital twin can build an accurate understanding of the future based on historical and present performances data. The digital twin can recommend the best strategies that can maximize profitability for the industries. Next, the experts will need to assess each recommendation and its impact to make the best decision for the businesses.
Therefore, industries can use the digital twin technology as a supporting tool to aid decisions enabling improved safety, reliability, and profitability.
3) Digital Twin Model Utility across the entire lifecycle of the asset:
Manufacturers use digital twins differently at each stage of the product development cycle.
Initially, manufacturers start working with Digital Twin Prototype or DTP. Then, manufacturers use DTP to design, analyze, and plan out the process to predict the future shape of the actual product.
In the next phase, manufacturers use Digital Twin Instance or DTI. DTI is the virtual twin of a physical asset. Developers will use DTI to run multiple tests and determine how the product will behave in different scenarios.
The DTI stays connected with the physical asset throughout its lifecycle. As a result, developers will add more operational data to improve it over time.
In the final phase, manufacturers will use Digital Twin Aggregate or DTA. Manufacturers use DTA to cross-examine the physical product, predictions, and learning based on the collected data from the previous phase.
People from engineering, operations, supply chain, shop floor even board room can look inside the assets and processes of the Digital Twin technology at every stage.
Companies integrate AI, machine learning, predictive analytics, etc., into the system with high hopes. They do it because they believe that digital transformation will cut out all the manual workload. However, when they realize that a lot of the work still depends on the human end, they get shocked.
Industries may have entered the automated age and have innovative IoT solutions at their disposal. However, automated systems cannot replace the human touch in many critical areas of business. For example, humans still need to implement and monitor automated systems in manufacturing plants.
Automation cannot replace other tasks like enhancing product design, building strategies, and growth roadmap, decision making, communicating with stakeholders, applying creativity to solve problems, etc. These areas will continue to need human intervention.
Companies need to set clear expectations when moving forward with the digital transformation of the assets.
The purpose of digital transformation and digital twin is to make the technical aspects of the job easier. In addition, Digital Twin technology will provide you the intelligence to help you focus your hard work on beneficial outputs.
Companies building Digital Twin Technology today are the pioneers of shaping the agile and intelligent industries of tomorrow. So, they need to develop the right digital twin platforms to leverage the full potential of digital transformation to create an exemplary model that others can follow.
The first steps will always be challenging. You can expect objections and hurdles to come your way. However, all these troubles are manageable if you know the proper ways to manage them.
Here is a brief guideline to follow for successful digital twin adoption in business:
To sum it up, digital twin adoption has the scope to attract more stakeholders' buy-in. Companies can show them the data-driven rewards based on concrete analysis instead of flawed predictions. So, the stakeholders will always have the know-how of the direction they are heeding with you.
"The true benefit of a digital twin: it gives you business intelligence to make better decisions in the future. It doesn't eliminate or minimize the work you're doing now, but it fundamentally changes what you're going to do next." - Former chief executive of Cambridge City Council, Andrew Grant
As we deduce the statement of Andrew Grant, it's a life lesson that industries have learned the most brutal way around the world after the Covid-19 shock. Thus, many enterprises are seriously considering the concept of Digital Twin and thinking big to expand it.
Companies are now interested in optimizing business operations based on the real-time insights gained from manufacturing plants and product use. As a result, they are more focused on satisfying orders, resolving root causes that are hindering growth, and maximizing factories' performance based on solid predictions.
We have already entered the age of automation as the 4.0 industrial revolution has begun. Today, companies maybe just interested in predictive maintenance. However, the use of Digital Twin Technology will expand where it will be integrated not for products but into manufacturing processes and entire factory systems.