Production optimisation for refining and olefins: how best to achieve it

To suceed, companies must decisively implement production optimisation strategies using the latest AI-driven technologies, like digital twins and prescriptive maintenance.

Ron Beck

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Article Summary

The story of industry over the past several decades centres on advances in efficiency and production. Any enterprise that does not focus on continuously enhancing its production processes risks being surpassed by a competitor who has innovated their way to higher margins.

If you factor in today’s drivers around the cost of carbon, net-zero commitments, and rising energy costs, the imperative is for refineries, olefins operators, and other asset-intensive businesses to change their thinking about how to achieve their way of working while still making a profit is even greater.

That is why production optimisation, the practice of maximising results from a given facility with the available equipment, is so important but much more complex than ever before. It now requires optimising for multiple objectives, which include reducing waste (circular economy), achieving net-zero carbon, minimising energy use, and increasing margins. 

It is increasingly clear that to succeed in today’s market, companies must aggressively pursue and decisively implement production optimisation strategies. The only way to do that is with digital tools and solutions, and companies must be able to report transparently on their sustainability performance and be prepared to proactively take plant actions that ensure good results in that area.

Foundations for optimisation
A refinery or olefins operator’s practices, processes, and prioritisation are changing rapidly. Also, the workforce is changing even faster. Where operators in the past centred their improvement focus around ‘key economic units’ (such as FCCs or ethylene crackers), now the focus is around how quickly multiple units across a site can be optimised to reduce carbon emissions and help the site and plant management team achieve carbon reduction goals.

Similarly, sites are in a race to understand how they can incorporate bio-feedstocks and renewable energy into their site operations. Moreover, every facility or plant is different. What is optimal for one company or production process may not be optimal for another.

Therefore, the scope and pace of optimisation need to be faster than envisioned, strategies to overcome data gaps need to be addressed quickly, and digital-native workforces need to be catered to in designing optimisation initiatives.

Finding a solution
Managing a refinery or an olefins plant today requires a new way of thinking. The isolated processes used by planners, schedulers, engineers, and operators make it increasingly difficult to optimise safety, sustainability, and profitability simultaneously. The decisions are more complex. Tens of millions of dollars of margin are left uncaptured every year.

Moving to digital
To address the challenges they face, operators need to move to digital technologies to start removing these silos and optimise critical functions inside their plants dynamically.

Unified production optimisation, a vertically integrated approach to break down barriers and synchronise operations while employing more data, a more granular understanding of individual unit performance, and employing tools like AI to assist in the complexity of the tradeoffs, is a major part of the way forward here.

To undertake a production optimisation programme, a refinery needs high-quality data on how its operation has functioned prior to optimisation. This information should be as complete as possible, as there may be issues hidden in the data that only become clear over the longer term.

A key component of setting the stage for optimisation is to measure and model, in addition to performance data, detailed data regarding energy and water use, materials use and waste, and carbon emissions. Digital twin models of the asset’s processes play a crucial role in calculating those key sustainability parameters that cannot easily be measured.

A company that has adopted industrial internet of things (IIoT) technology should have a significant amount of information from the networked sensors that populate the production process. This data is invaluable for production optimisation, as it provides granular insight into the whole production process.

CEPSA, at its La Rabida refinery, is using this vertical integration approach to achieve production optimisation in its hydrogen networks, reducing hydrogen losses, flaring, and improving production. Air Products, operator of the largest blue and grey hydrogen production network in the US, is using digital twin models at its 20 hydrogen sites to optimise production, avoid production issues, and manage it all from one centralised technical centre.

Process of efficiency
Every industrial process will require a different approach, and each facility may have different requirements. There is no one-size-fits-all solution for production optimisation, at least in terms of the actual changes that need to be implemented. Companies should be prepared to undergo an iterative process; the changes and tweaks made at an early stage of optimising production may reveal further refinements and sources of waste. Some companies have changing feedstock while others function on long-term contracts, some are on an accelerated pace to incorporate biofeedstocks, some have started integrating distributed renewable power, and so forth.

This is where process simulation software can be a huge boon for optimisation. By creating a digital model of the process being optimised, companies can explore the impact of changes in a simulation instead of risking a loss of production by actually making changes to an asset. With unified production optimisation, refinery planners can, for example, take into account selected crudes and committed product deliveries to develop an optimised operations plan.

The digital twins can inform planning models through rapidly updated unit models that account for fouling and other changes. They can calculate key sustainability metrics and inform the advanced control systems (APC). And when digital twins are paired with APC, optimisation occurs. BPCL in India has won several sustainability awards for its combined digital twin and APC implementation, which help manage carbon emissions proactively while improving energy use and margins.

Digital twins to the fore
Digital twins are increasingly important to the planning and scheduling process within a modern refinery or olefins plant. The rationale behind their use is clear. Digital twins have a vital role to play in this context.

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