Understanding that economies of scale and new technology don’t always guarantee success, streamlining maintenance and turnaround efforts goes a long way towards a company’s valuation and access to capital.
With an estimated $100 billion in refinery and petrochemical turnarounds expected over the next 2-to-3 years, the industry is interested in the scope and speed in which new analytical tools and integrated systems can be implemented.
Systems ranging from AI to data driven decision-making are delivering measurable improvements. Higher plant complexity amidst tighter margins has accelerated the urgency to close performance gaps. In fact, 95% of STO’s are late or over-budget. Equally pressing is the mandate to close the gap in sustainable OPEX control, safety and reliability, combined with the ability to predict equipment failures, process deviations and so forth.
Long discarded by top quartile performers are “fail and fix” maintenance procedures, such as with discovery of corrosion around critical equipment. Instead, embracement of digitalization based strategies facilitates turnaround success while also satisfying regulators and vigilant investors. The onus on optimizing critical equipment brings options like predictive digital solutions to the forefront.
Adopted from other industries like aviation, introduction of new tools for turnaround planning brings capabilities for optimizing workflow. For example, OMV’s three refineries are well into incorporating laser, thermal, hyper-spectral and imagery data into turnaround planning workflow at their facilities in Austria, Germany and Romania.
The ability to predict anomalies such as subtle vibrations of critical assets (e.g., compressors) ensures longer run lengths. Another important consideration is monitoring of a plant’s hundreds of heat exchangers, pumps and so forth to improve reliability, efficiency and save energy. Collecting process data on equipment cannot be overemphasized now that AI has the capability to look at thousands of sensors.
Challenges ranging from scope creep to staffing affecting the industry a decade ago still persist today. This is seen with the harmful conditions that can emerge during the startup and shutdown of turbomachinery.
Top-quartile and fourth quartile (poorest) performers are equally exposed to decarbonization pressures and related engineering and maintenance costs. Certain downstream processing assets are transitioning from fossil fuel-based processing to bio-based processing (or coprocessing), further complicating the ability to predict asset performance.
Going into 2022, flexible staffing models have been developed to deal with the COVID-19 pandemic’s affect on the industry’s ability to efficiently perform operations, maintenance and turnarounds. This is occurring while the industry is facing a talent crush. Most facility turnaround knowledge was owned by plant personnel, many of whom have retired since the pandemic.
Unexpected scenarios developing in the chemical industry are compelling adjustment of workplace structures, such as manual, uncoordinated work processes, creating the motivation for upskilling a cohesive workforce. Against this backdrop, the workforce is learning to use digitized systems, but circumstances being what they are, have made the implementation process more complicated.
By 2019, the transition to a low-carbon economy was well underway. However, the pandemic caught the industry unprepared for a shock that exposed inflexibility in technical functions and operational doctrine. For starters, on-site training and joint structured workshops between technology licensors and end-users had to be limited due to COVID restrictions.
A subconscious bias may persist against adopting new online training tools. For example, one conference panelist noted that adaptation of digital predictive strategies needs to be driven more by a change in corporate culture than by a change in technology. Even more challenging is trying to create a cohesive work group to deal with the challenges of integrating capital projects and turnarounds.
ESG and corporate social responsibility impact turnaround strategies, something that may not have been factored into turnaround planning a generation ago. Going forward, plant personnel focused on STO’s can ignore these added complexities at their own peril, such as when too many relatively simple tasks start piling up, requiring excessive coordination during a turnaround. Why is this happening?
Green infrastructure seen with the transition to net zero emissions is more capital and labor intensive than hydrocarbon-based infrastructure (by a factor of 1.5 to 3.0). This has led to excessive discovery work that couldn’t have been predicted during a turnaround, such as when converting a refinery distillate hydrotreater to process hydrogenated vegetable oil (HVO) for producing renewable diesel.
Regardless as to whether the turnaround objective is green infrastructure or improved refinery and petrochemical integration, they require maximum staff time and hundreds of contract employees to execute a timely turnaround. COVID-19 adds to the complexity of turnaround activities that include equipment inspections or repairs, timely part replacements and technology upgrades.
Fortunately, deployment of advanced analytics and machine learning applications along with being able to see what’s happening on a real-time basis goes a long way towards resolving stressful turnarounds. “Apps” connecting workers and managers to their digitized tasks on any mobile device continue to be a popular topic and STO-related conferences.
The consensus is that downstream turnarounds have improved during the pandemic with the incorporation of digitalized turnaround processes, such as what was discussed with scope optimization and the improvement of personnel safety protocols during a recent turnaround at a polyethylene terephthalate (PET) plant.
Increasing use of sensors to deliver a data-driven view of operations makes it easier to identify best practices and improve precision of turnaround work scope. What seems to be even more important is the industry-wide consensus that companies building a motivated, digitally driven workforce with the right skills and capabilities will be at the forefront of the transition to net-zero emissions.
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