Will generative AI make the digital twin promise real in the energy and utilities industry?

Generative AI the Digital twins represent an asset’s virtual reflection in reality. Data from real-world assets seamlessly weaved through virtual reality can be seen here; their appeal extends far beyond simple replication as its vitality echoes engineering excellence, simulation echoes, and machine learning models used as models within its veins – not simply reflecting but offering enhanced models capable of driving operations forwards through complex waters.

What is a digital twin?

The definition of a Digital Twin (DT) differs across industries and organizations, each providing its own perspective. At its core, however, a DT represents an exact digital copy of something physical such as an asset, system, or process, integrating real-time data to form complex duplicates for efficient testing, optimization planning as well as security, safety, and stability assurance.

Imagine this: Before crossing a bridge for the first time, digital twins may be used to simulate and evaluate their structural integrity and strength. Furthermore, when developing new office buildings their environmental impact can also be evaluated using digital twins.

Digital twins in the energy industry take four forms, each serving specific functions: 

renewable energy utility companies can harness the power of digital twins to create networks resembling real-world components, including grids, power plants, wind turbines, and related infrastructure components. This concept plays an essential role when considering energy networks – an interconnected digital twin can cover various assets owned or managed by different entities yet integrated into one digital twin model.

Digital twin networks enable green energy utility companies to better comprehend how components interrelate by gathering data from various systems and employing this methodology to form digital twin networks, transcending individual asset analysis.

Overcome hurdles to optimize digital twin benefits

To maximize the potential of digital twins, the full integration of logic and data layers must occur together with specific presentation techniques (Figure 1). For industries that rely heavily on significant assets (utilization of energy for example), therefore the integration of various data sets must occur (in this instance Figure 1)

  • Real-time operational technology data that covers sensors, equipment, and IoT.
  • IT systems including enterprise asset management systems like Maximo or SAP
  • Plant lifecycle management systems.
  • ERP systems as well as various unstructured data such as P&ID visual images and Acoustic information can all play an integral part of business operations.

The presentation layer offers various methods of presenting information, such as 3D modeling and augmented reality capabilities as well as health scores derived from predictive models or criticality indicators. IBM strongly advocates open technologies as the basis of digital twins.

Traditional machine learning (ML) and AI modeling techniques require intensive human supervision when training AI models individually, creating significant barriers to using historical, real-time, and predictive data generated within siloed processes and technology.

As illustrated in Figure 2, applying generative AI improves digital twin networks’ efficiency by creating physically plausible object states and feeding them back into digital twin networks.

These capabilities help in consistently evaluating the state of physical objects. Heat maps can assist in identifying potential bottlenecks in an electricity grid due to anticipated heat waves caused by heavy use of air conditioning (and provide smart solutions for switching). As part of an open technologies framework, ensuring model reliability and alignment with business requirements is of utmost importance.

The energy and materials sector is uniquely positioned to take advantage of Gen AI

Highly sophisticated heavy industries have taken to data analytics as a means of improving efficiency. Industries such as oil and gas, agriculture electric power, chemicals, and mining materials all stand to gain from next-generation AI’s capabilities in terms of optimizing back office operations to solving cross-function issues or even optimizing core business processes.

Dependence on analytics is built upon enormous data reservoirs. Modern facilities, from plants and mines to farms and mines, build up years of data through sensors historians, failure mode and effects analysis databases, engineering reports, work orders, and maintenance logs. Exploration and extraction of resources generate further seismic and electromagnetic data while OEM instructions and troubleshooting manuals reside in storage rooms.

Unstructured and structured data present an ideal opportunity for AI technologies to explore and analyze, offering businesses a distinct advantage when choosing to leverage it. Key considerations when using it in these sectors are asset intensity and optimizing utilization of energy

assets as well as algorithm compression and predictive analytics; two different use case categories exist – simple cases as well as more ambitious ones known as moonshots.

Simple use cases involve features that require only limited technical skills to deploy and can quickly be made available for sale, such as virtual assistants to automate administrative tasks or chatbots to interact with customers; “copilots” for software developers enhance productivity further still.

“Moonshot” use cases are more imaginative and require modifications; sometimes this requires teaching LLMs from scratch! Although such projects could offer substantial value, they require substantial upfront investments in infrastructure and capabilities as well as significant upfront payments in return. How next-generation AI is applied depends upon specific industry sectors or value chain segments.

Moonshot applications across industries may include:

1. Utilities: Establishing corrosion – and predictive maintenance models using infrequently used inspection records to preserve asset integrity is beneficial in increasing asset integrity.

2. Gas and oil: Utilizing special models that interpret seismic data reduces the need for data while improving exploration results.

3. Mining: Implementing AI assistance for maintenance technicians using large databases can streamline processes and increase reliability, saving them both time and effort.

4. Chemicals: Utilizing large chemical databases, we can predict the properties and prototype pathways of new chemicals as well as provide predictions for their synthesis.

5. Agriculture: Employing AI-powered virtual advisors to identify individual risks and opportunities for farmers, grower managers, and operators.

“Moonshot” endeavours are aspirational goals. Yet forward-thinking companies have already taken steps toward their development, acknowledging their ability to add significant value to core business functions.

AI Tools

Popular use cases in the energy sector

Imagine digital twin technology entering the energy and utilities industry consulting sector as an avenue to solve real-life problems: imagine having a digital twin (DT) help duplicate power plants, uncover bottlenecks, and take preventive actions against impending issues; or DTs overseeing virtual representations of wind turbines to measure efficiency without needing physical contact between parties involved. The possibilities are limitless!

Below we explore the numerous capabilities that DTs can bring to the transformation of energy utility software operations:

Also Read: Notion AI vs Chatgpt: Which is a Better Tool in 2024?

What are the four types of solar energy utilization

1. Light – heat

Utilizing solar radiation efficiently This method transforms solar radiation energy into heat after interaction with materials, such as flat plates, vacuum tubes, or focused collectors.

2. Light – electricity

The solar industry is an innovator in renewable electricity generation; this can be accomplished via two mechanisms.

A. Heat-to-Electricity Conversion Utilizing solar radiation’s thermal energy for electricity production, collectors aid in the conversion of heat energy into steam for propelling gas turbines that generate generators. In its initial stage light-to-heat conversion takes place before transitioning to conversion of heat energy into electricity production.

B. Optic-to-Electric Converter: Based on the photovoltaic effect, this method directly converts solar radiation to electricity using solar cells as its foundation.

3. Light – chemical

Innovatively utilizing solar radiation, this technique enables the direct decomposition of water for photochemical conversion to hydrogen production.

4. Light – biomass

Utilizing photosynthesis, this technique converts solar energy into biomass through plants with rapid growth (e.g. coppice forests, oil crops, or large seaweeds). This practice illustrates green energy conversion.

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