Africa: Data centre and fibre investment sustained in forward looking markets

The debate has ended. Not implementing artificial
intelligence (AI) is no longer an option. Every company should have an
effective AI strategy, not least because as the pace of innovation accelerates,
such an approach will present them with new opportunities to transform their
business.

The growing ability of businesses to employ streams of
business and operational data to drive machine intelligence and access insights
is driving AI’s momentum.

Currently, companies typically only use a small amount of
the data they have collected. This provides huge potential for implementing
digital twins (virtual copies of a company’s assets and processes,) which can
unlock the potential value from all that data.

In 1984, “Neuromancer”, a scientific fiction novel by
William Gibson, captured the imagination of readers – as a prelude to the world
of AI.

Gibson envisioned the massive value and power that digital
twins can bring and how they can change the world.

More than three decades later, this vision is materialising
in the enterprise world. Virtual copies of physical locations and activities
provide an insightful way for companies to harness the true value of data, as
AI helps humans access this massive world of multi-dimensional data.

The power of AI and the interconnected industrial world to
unlock critical insights via data mining and by leveraging domain expertise
helps technology innovators create turnkey solutions for digital twins.

No longer dreamtime…

The reality is here with advanced technology available on
demand. The golden question now is where to invest, as digital twins transform
asset-intensive businesses, especially those in energy and chemical sectors.

In today’s volatile, uncertain, complex and ambiguous (VUCA)
marketplace, the deployment of digital twins can help companies achieve
sustainability and operational excellence.

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Digital twinning technology provides a valuable model of the
physical asset to help explore ‘what-if’ scenarios safely and provide
forecasting capabilities and advice on degradation, asset failure events and
more. 

This using self-learning systems as well as by capturing
knowledge of experts. Digital twins also function as business models to optimise
various business scenarios.

Based on models and real-time data, the digital twin is an
evolving digital profile of behaviour of a physical object or process that
optimises business performance.

This provides important insights into system performance
which, in turn, leads to actions in the physical world.

The digital twin takes advantage of asset data to stay
updated and is increasingly made more intelligent by AI agents.

First, the digital twin ensures that the process plant is
modelled vigorously using engineering models, enhanced via AI techniques with
embedded cost and risk models.

Second, the operational digital twin ensures that plant
operations are modelled and viewed virtually as planning, scheduling, control
and utility models.

Areas covered include planning and scheduling, demand
models, distribution models, energy demand and supply, as well as control and
optimisation. 

We expect autonomously optimized production optimisation in
refining to be available soon.

Third, the operational integrity digital twin provides
tactical and strategic decision guidance around prescriptive maintenance and
real-time decision-making to maximise uptime, adjust production, minimise
environmental impact and production losses, and prioritise safety.

The digital twin also covers asset condition and
sustainability.  And can feed back to
engineering to improve weak points in the asset.

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Overall, companies need a future-proof digital reference
architecture to structure the implementation of digital twins supporting
collaboration and integration across business functions.

Powered by business
value

Scaling up digital twins can deliver significant value for the
enterprise. Unit level models, for example, can generate very high value
returns for digital twins – involving process, asset condition, control and
optimisation online models.

Energy and utility models, refinery and bulk chemical
planning, specialty chemical scheduling, debottlenecking and de-risking and
emissions present high-value opportunities for plants to adopt digital twin
models.

A new but important area, enterprise-level visualization
tied to actionable work flows, allows rapid analysis of available enterprise
profit opportunity options and effectively presents insights and operational
status at the executive level.

Examples of success with digital twins include:

•           YPFB
Andina, a Bolivian upstream company, has increased yield by millions of dollars
via an asset-wide digital twin model.

•           A major
US-based international refiner adopted machine learning digital twins to
improve uptime and margins, saving tens of million dollars in avoided equipment
degradation.

•           Bharat
Petroleum (BPCL) implemented an integrated digital twin and achieved 90%
reduction in sulfur emissions and derived economic value from recovered sulfur
for sale – all within six months.

•           A polymer
producer implemented a multivariate analysis-based digital twin approach which
manages a wide range of specialty chemical applications, where product quality
is key and often problematic.

Companies are progressing with new, advanced technology –
but it is also necessary to be strategic and have a roadmap to get ahead.

As businesses invest in digital twins, it is critical to
observe at a high level how this technology will help them overtake the
competition.

Beyond technology, companies should also take note of their organisational change and evolution. Organisational adaption, enthusiasm and readiness must be managed regularly, as business value creation is a key driver of technology.

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