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.