Downtime damages environmental performance too
Advanced technologies can reduce the environmental impact of unexpected shutdowns.
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While unplanned downtime will always impact productivity and profitability in an oil refinery or petrochemical plant, the effects of unexpected stoppages often have implications that reach beyond financial. Safety is critically important, but so too is environmental efficiency. Today, as energy providers face increasing pressure to set and meet sustainability targets and reduce emissions, there is a growing focus on the environmental impact of unexpected shutdowns at oil and gas refineries.
Plant downtime is highly damaging in this context, with a single, unplanned shutdown lasting just hours leading to the release of a year’s worth of toxins into the atmosphere for example. An emission event following a forced shutdown at a California refinery in 2017 resulted in 31000 lbs of sulphur dioxide being released in one day – more than the refinery had released over 2015 and 2016 combined. This is just one of many such examples.
And that is in addition to the losses in profitability that we know these events result in, stemming from reduced productivity, higher maintenance costs, and the waste that comes from irregular operations. If there is a single plant process that illustrates this issue clearly, it is gas flaring, or the combustion of excess product that is typically released when a plant experiences over-pressuring operation, such as during an unplanned shutdown. Excessive flaring is a visual sign that something is outside of normal parameters in the facility, which means the safety risk is increased.
Flaring is also a significant source of greenhouse gas emissions. In fact, according to satellite data published by the World Bank’s Global Gas Flaring Reduction (GGFR) programme, each year 145 billion cubic metres of gas is released into the atmosphere from gas flaring. That is equivalent to 270 million tonnes of CO2 emissions per year.
These figures paint a grim picture, but there is good news from the industrial technology front. By tapping into the power of machine learning and predictive analytics, companies can begin to reduce unplanned upsets and capture all the benefits that come with that. With technology that eliminates the surprise of unplanned downtime, companies can minimise the most dangerous conditions, reduce the amount of gases released into the environment, and realise significant financial gains by maximising uptime.
Without question, there is a lot at stake – financially and beyond –in avoiding unexpected shutdowns.
Technology for decision agility
So, what if energy companies could actually plan for downtime? What if it was possible to know which pieces of equipment are going to fail and when, so repairs could be performed as part of a managed shutdown? The benefits are significant, for both improvements in emissions reduction and profitability.
Today’s asset performance management technology can deliver advanced warning of failures through a combination of predictive and prescriptive analytics, enabled by integrated software that incorporates artificial intelligence (AI) and machine learning. This type of solution provides a detailed view of all equipment, systems, facilities,, and networks, thereby enabling a capability we call ‘decision agility’.
This means that, with the time to plan around predicted downtime and a holistic view of the operation, plant personnel can see exactly how a decision that changes any business process also affects the entire organisation. They will immediately know how it impacts planning and scheduling, how it determines which feedstocks are purchased, how it affects inventory, and even how it may impact the sales team and the potential for missed orders.
The right technology can simulate how any event will impact the system, the process, and the asset. When the outcome is known in advance, operators and engineers can collaborate to make the safest and most profitable decisions; they can work together to develop a plan. That plan becomes a clear roadmap of where to spend every pound to maximise the return on capital employed. The technology can even be scaled to cover multiple plants across a region to provide a look at how facilities are tied together and to better understand their co-dependencies.
So, when there is an issue in one location, the software can show how it will affect the pipeline coming in, the ships going out, and whether the facility is at risk of defaulting on any contracts.
By driving the best decisions, this technology also reduces risk across the entire operation, and there is a recognised value in doing that. Some providers in the insurance industry, which is also driven by data, have actually begun advising their customers about digital solutions for prescriptive maintenance and decision support. They are promoting these technologies as ways to reduce unplanned downtime and associated events — and also as an incentive to lower their insurance rates.
The ability to see wide and deep enables new ways of running the business. Digital transformation is knocking down the data silos and delivering the tools necessary to make sense of the data available at the enterprise scale.
Putting it Into practice
Achieving this level of technological integration starts with a ramping up of the organisation’s digital capabilities. Companies in every sector now have access to technologies such as high performance computing, artificial intelligence, and advanced analytics to generate deeper insights from their operating data.
Fuelled by these data-driven insights, leading edge simulation programs enable operators to quantify the true value or cost of any renovation or improvement project, maintenance change, operations improvement, or supply chain constraint. This technology utilises statistical sampling techniques to predict the future performance of a system, analysing equipment behaviour patterns to derive a ‘time to failure’ estimate.
With the broad view of operations that simulation programs provide, plant personnel can be alerted to impending failures and understand the potential impacts to the wider system. Operators can also model flow through the pipes and tank levels, as well as the utilised and available capacities of all units.
This is how it is possible to discover exactly which events are robbing an operation of money or negatively impacting performance in ways that can lead to environmental issues, for example. With a prioritised list of every single event in the business that is negatively impacting performance, the company can apportion budgets and put people where they are needed — and every decision is based on data.
If the software is in place at a refinery for example, it might alert to a failure of a fluid catalytic cracker or part of a cooling tower, likely to occur within the month, which would cause significant disruptions throughout the business. But with the advance notice provided by the software and time to plan before the failure happens, personnel can then use scheduling models to find the best time to take that part of the plant offline, and even insert additional maintenance activities to make the most out of the planned downtime.
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