Realising the vision of the self-optimising plant – How hybrid models can help to achieve it

Realising the vision of the self-optimising plant – How hybrid models can help to achieve it

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Industrial AI solutions are shaping a new autonomous era of self-optimising plants in the refining industry, according to Antonio Pietri, President and CEO, Aspen Technology. We hear how software technology such as Artificial Intelligence, can offer new opportunities for businesses.

Today’s refining market is characterised by extreme volatility, fuelled by dramatic fluctuations in oil and fuel demand and pricing. It is putting pressure on refinery executives and managers who are having to continuously evaluate new scenarios for economics, environmental impact, logistics and safety. The ability they demonstrate to analyse and react quickly and efficiently to these situations and develop enhanced operational resilience and reliability will be crucial in sustaining both their businesses and their competitive edge.

The long-term vision for the industry is the self-optimising, autonomous plant – and the increasing deployment of Artificial Intelligence (AI) across the sector is bringing the reality of this ever closer. However, while refining has been an early adopter of many digital tools, the industry has not yet fully realised the potential of Industrial AI.

That is largely because Machine Learning and AI are frequently locked in isolation today, rather than being combined with existing engineering capabilities – tools, models and expertise – to deliver a practical solution that effectively optimises refinery assets.

These are assets that typically rely on engineering models built from the ‘first principles’ (or fundamental basics) of chemistry and physics, which incorporate key domain knowledge such as process safety and understanding of the industry’s complex systems. 

These models draw on the expertise and experience of the world’s foremost scientists, process engineers and operators. They are highly accurate but have limitations in certain processes, to enhance their accuracy, plant data must be employed to calibrate them to observed plant conditions and performance. Currently, effective model calibration requires significant understanding and experience.

Building a hybrid model

This is where AI and Machine Learning have a key role to play. These technologies are fast emerging as tools that can greatly accelerate the ability to employ plant data, both to calibrate first-principles models and to quickly create data-based models of processes and phenomena. AI has the potential to lower the expertise required to model process systems, but it must be combined with domain expertise to create the real-world ‘guardrails’ that allow it to work safely, reliably and intuitively.

This combination enables what we call ‘hybrid models’, which effectively bring together AI and first-principles to deliver a comprehensive, accurate model more quickly and without requiring significant expertise. And crucially, they serve as a vital staging post on the way to the self-optimising plant.

Machine Learning is used to create the model, leveraging simulation, plant or pilot plant data. The model also uses domain knowledge, including first principles and engineering constraints, to build an enriched model — without requiring the user to have deep process expertise or be an AI expert.

The solutions supported by hybrid models act as a connection point between the first principles-focused world of today and the ‘smart refinery’ environment of the future. They are the essential driver of the self-optimising plant.

Many companies today are already experiencing the benefits of a hybrid modelling approach. Refining and olefin margins are closely related to plant planners’ and operators’ ability to achieve monthly production that is as close to the plan as possible, and gaps can usually be traced to out-of-date or inaccurate planning models. One of the largest global refiners projects the ability to generate up-to-date revisions of these detailed reactor models as often as needed, will deliver value over US$10 million annually for a typical 200,000-barrel-per-day refinery. This technology is especially timely as refineries contend with dramatic changes in the products they must produce.

Fulfilling objectives

The development of hybrid model solutions will also, for many refiners, be the first step in realising the vision of the self-optimising plant. At AspenTech, we define this as a facility that can automatically adapt and respond to changing operating conditions.

Relying on a combination of AI and key domain knowledge, the self-optimising plant will rapidly assess all available data streams, both within an asset and beyond its boundaries. It will rapidly react to changing conditions to achieve the best possible outcome, taking into account safety, sustainability, asset health and operational objectives. Furthermore, it will use AI to anticipate future behaviour and provide workers and managers with alternative operational scenarios moving forward.

In the self-optimising plant of the future, operators and engineers will be supervising faster and more agile decision-making, freed up from low value-add, repetitive tasks, by the systems that have closed the loop to operate the plant close to intended limits and automatically react to unforeseen scenarios. Moreover, asset reliability information and operating data will inform the models to achieve safer, more sustainable designs.

That’s the end goal we are all driving towards. There is still some way to go on the journey for the refining industry, but the advancement achieved through hybrid modelling capabilities has opened up a completely new opportunity and is a transformative step forward on the route map to the self-optimising plant.  

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