naRTC 2026
ers offer more reliable solutions designed for liquid- or gas-phase applications to act as first-line barriers against solid contami- nants. These systems prevent early foul- ing, protect downstream catalyst beds, and reduce the grading bed materials required, leaving more room for the bulk catalyst. In liquid-phase applications, scale catch- ers route gas directly to the distributor tray while directing liquid through sedimentation chambers where larger particles settle by gravity. The liquid then passes through dou- ble-layer filter cassettes engineered to trap smaller particles. When filters become sat- urated, feed automatically bypasses them to avoid pressure drop build-up. Field data shows that implementing scale catchers can extend cycle length significantly com- pared to operations without this protection. Gas-phase scale catchers remove parti- cles entrained in the process stream based on physical parameters like size, shape, and particle density. They typically employ two-stage separation: gravity separation removes particles over 100 microns, while momentum and terminal velocity separation capture finer particles in a secondary zone. Measurable economic benefits Topsoe’s advanced grading bed cata- lyst technologies deliver measurable eco-
nomic benefits by extending cycle lengths and enabling the use of more challeng- ing feedstocks. Its optimised grading con- figurations are designed to push catalyst replacement cycles to the point where activity, rather than pressure drop, dic- tates replacement, resulting in a significant increase in the profitability of the unit sav- ings. By protecting main catalyst beds from contaminants while maintaining high activ- ity, these solutions allow refiners to use feed with higher levels of contaminants, including renewable feed and opportu- nity crudes, without compromising perfor- mance or product quality. When choosing a customised grading solution from Topsoe, comprehensive sup- port services are available for long-term success. One example is Topsoe’s con- nected platform ClearView™, which pro- vides real-time performance dashboards with intuitive key performance indicators for optimisation and faster support. Its spent catalyst analysis examines unloaded catalysts to determine metal content, poi- son pick-up capacity, particle size distribu- tion, and more. These insights are used to optimise future catalyst loadings to prove continuous performance improvements.
poisoning than silicon, phosphorus, nickel, iron, or vanadium, leading to irreversible catalyst deactivation. High-nickel surface area arsenic traps provide volume-based pick-up efficiency. Topsoe’s portfolio includes TK-49 and TK-51 as extrudates, and TK-45 and TK-41 as ring-shaped traps, offering structural robustness and effective arsenic capture. Heavy metals challenges Vanadium, nickel, and iron are well-known hydrotreating catalyst poisons. These met- als enter as organometallic compounds in the feed and deposit within catalyst pore structures and on external surfaces. While concentrated in residuum fractions, some organometallic compounds volatilise dur- ing distillation and appear in lower-boiling fractions, especially those boiling above 1,100°F. When feeds such as atmospheric tower bottoms enter vacuum gas oil (VGO) hydro- treaters, they significantly impact catalyst performance and reduce cycle length. This is particularly problematic because VGO hydrotreaters operate at higher space veloc- ities than resid hydrotreaters, increasing metal migration risk into main catalyst beds. High-activity demetallation catalysts with extremely high metal pick-up capac-
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Sulphur Vanadium Nickel Aromatic rings Naphthenic rings
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Figure 4 Vanadium and nickel in entrained VGO fractions
ity on a volume basis can be installed above main catalyst beds. Topsoe offers TK-453, TK-743, and TK-773 for heavy vacuum gas oil (HVGO) and resid applications. These large-pore materials accommodate metal- containing molecules that require high sur- face-to-volume ratios for effective trapping. From traditional trash baskets to modern scale catchers Traditional trash baskets often disrupt flow profiles during operation, especially when fouled. Topsoe’s modern scale catch-
Contact: HWR@topsoe.com
Artificial intelligence and process centricity
Dipankar DAS MARATHON petroleum
Artificial intelligence (AI) is not a short- cut; it can serve as a significant advan- tage when deployed well and is poised to redefine business operations. AI is trans- forming the downstream hydrocarbon industry by enhancing process efficiency and operational effectiveness. This arti- cle emphasises the need to shift from peo- ple-centric to process-centric approaches to fully harness AI’s potential for business improvement. During my Fellowship in energy econom- ics in the mid-90s, I read articles from 1965 in which scientists argued that the information processing involved in cogni- tive performance could be formulated as a program and simulated on a digital com- puter. Fast forward to 2025, and the use of AI is now prevalent and here to stay. At the time, many experts felt that AI was not fully understood and often misused to describe any automated process. Some industry leaders and scientists even used terms like ‘red herring’ and suggested it could ‘spell the end of the human race’. Industry leaders, including Dr Stephen Hawking, Elon Musk, Sundar Pichai, and Ginni Rometty, have debated what con- stitutes AI and what does not. The key question remains: how can we responsibly design and implement AI in our field? This centres on process efficiency. Downstream Business Context Our industry is run by engineers who are focused, capable, and experienced. Indirectly, this engineering passion has naturally led us to adopt a people-centric approach to managing operations. While procedures such as standard operat-
V irtuous cycle of continuous i mprovement
Process centricity f irst: First adopt a process-focused approach to identify map and document ' ways of working ' (workflows).
AI implementation: Process interactions and quality data are prerequisites , unleashing the power of AI.
Quality d ata & i nteractions: Standardised process ensures quality data is generated.
Mutually reinforcing , or symbiotic , relationship between AI and b usiness p rocess w orkflows
Continuous improvement: AI improves process
Process enhancement: Automates, optimises , and extracts insights from processes to improve decision - making.
Value capture: Process owners to ensure value attribution a cross all attributes.
Generating
better data
Furthers AI
improvement.
Figure 1 AI and process centricity form a self-reinforcing cycle
actions align with operational practices. Comprehensive process knowledge sup- ports AI projects in delivering measurable and strategic outcomes. How AI Enables Process Centricity AI supports process centricity by automat- ing high-volume, rule-based tasks, such as data entry and invoice processing, freeing employees to focus on more complex pro- jects. It enables intelligent decision-making by analysing large datasets for patterns, predictions, and insights. AI tools quickly detect inefficiencies and risks, facilitat- ing prompt corrections and enhancing pro- cess agility. Leveraging natural language processing, AI can automatically build or update process maps from documents and discussions, reducing manual effort. Additionally, AI acts as a copilot, enhancing human judgement-based decisions. In conclusion, AI is no longer a C-Suite curiosity; it has become a board-level imperative to enhance organisational effi- ciency, adaptability to a changing environ- ment, and competitive power.
focus on finding the real value by address- ing bottlenecks and uncovering significant challenges. Let us examine how these complement each other. The good news is that AI can help us build processes from unstructured paper-based procedures and manuals, find opportunities for improvement, and complete the full cycle – updating the pro- cedures and instructions for a complete digitisation cycle. Why is this so relevant for North America? First, the availability of required skills and the ability to attract talent glob- ally. Second, both national and global downstream industry leaders here are con- tinuously investing in transformation for improvement in their business operations. How Process Centricity Enables AI A process-centric approach provides standardised, consistent, and reliable data critical for accurate AI outcomes. Process mapping identifies automation and optimi- sation opportunities and guides AI imple- mentation. This approach enables AI to understand workflows, dependencies, and decision points, ensuring AI-driven
ing procedures (SOPs) and manuals exist and are followed, business process work- flows are often set aside as ’unneces- sary paperwork’. Initiatives to document, improve, and mine have been undertaken, but often remain buried in SharePoint fold- ers, untouched and unmanaged. Meanwhile, the downstream segment – and probably the entire hydrocarbon indus- try – is scrambling today to identify use cases for AI deployment. While use cases are necessary and such focused atten- tion will yield benefits, I would argue that a more process-centric and structured approach is needed to leverage such phe- nomenal scientific potential in new ways. It may be time for us to challenge ourselves and change our path. AI and Process Centricity: Virtuous circle AI and process centricity form a self-rein- forcing cycle in which momentum reduces effort needed over time, creating a virtu- ous cycle of growth and efficiency through strategies that attract, engage, and delight customers while aligning with inter- nal business aspirations (see Figure 1 ). More importantly, this approach helps us
Contact: DDas@Marathonpetroleum.com
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