PTQ Q3 2026 Issue

fouling mechanisms at work, any redesign risks solving the wrong problem. For units with high-velocity inlets, Crystaphase’s proprie- tary Hold-Down Model adds a layer of quantitative analysis that is valuable when the goal is to load more catalyst in the reactor by moving the filtration system closer to the diffuser. The model estimates a Factor of Safety (FoS) for bed stability, a metric to help quantify the relative risk of bed movement under the unit’s actual operating conditions across different combinations of hold-down material and bed height. By running these combinations in sequence, operators can see how FoS shifts when, for example, HeavyTrap replaces conventional support balls, or when the upper surface of the bed is raised toward the tangent line to create space for additional catalyst. The model can give unit-specific answers to questions that used to be decided by conservative rules of thumb, such as how much more catalyst the reactor can safely hold before stability becomes a concern, and how far up in the reactor the bed can reasonably be loaded. When applied across an entire network, units can be tri- aged for maximum impact. Charting FoS against the dis- tance from diffuser to the top of the bed exposes which reactors carry the most untapped capacity and which are already constrained, giving engineering and economic teams a shared basis for prioritising optimisation investment across the network (see Figure 2 ). Units in yellow with high FoS values and high distances from the diffuser to the top of the bed are ideal targets for space optimisation analysis. The FoS limit (dashed line) provides a threshold below which the risk of bed movement is likely higher than preferred, but it is not a guarantee that no bed movement will occur above that limit. Iterative advantage Once the analytical framework is in place, the effort can pro- ceed reactor by reactor, with each turnaround representing an opportunity to implement an upgraded loading. Every reactor cycle also generates data: unloading observations, spent material analysis, pressure drop trends, and operating performance, all of which inform the next loading recommen- dation. This feedback loop enables continuous refinement, with each cycle building on the knowledge base established by the previous one. At Crystaphase, this iterative process is supported by a specialised analytical laboratory that has processed more than 10,000 samples from operating units worldwide. Each sample contributes to a growing body of knowledge about how different foulants behave under different conditions, how materials perform across various services, and how loading designs can be refined to deliver better results. This experience base is a critical differentiator. It can transform reactor loading from an exercise in following convention into a discipline grounded in empirical evidence. For the refiner, the practical implication is that results can improve over time. The first optimised loading in a unit can deliver measurable benefits. The second, informed by a detailed analysis of the first cycle’s performance, can deliver more. By the third or fourth cycle, the loading design has been refined to a high degree of precision, and the returns from that reactor’s space optimisation may be approaching

Unit H

Unit D

Unit G

Unit C

Unit E

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Unit A

Unit F

Distance from diuser to top of bed

their maximum potential. Multiply that trajectory across doz- ens of reactors, and the effect can be substantial. Conclusion Reactor space is a finite resource with a quantifiable eco - nomic value. Yet in many refineries, it remains an underopti - mised asset, consumed by conventional top-bed materials that have not kept pace with the demands of modern refin - ing operations. The opportunity to reclaim that space and convert it into catalyst performance can be significant, and it is available today. The tools to achieve this are proven and commercially deployed across hundreds of units worldwide. These tools include reticulated ceramics that can deliver superior filtra - tion in a smaller footprint, dual-function hold-down materi- als that filter while they stabilise, and quantitative modelling that can reveal how a proposed loading may affect cycle length and bed stability before the loading is ever installed. What distinguishes the most successful applications is not the technology alone, but the strategic framework in which it is deployed. Refiners who treat reactor space as a strategic asset, who pursue optimisation systematically across their networks, and who invest in the iterative cycle of analysis, design, and refinement have the potential to achieve the most substantial and sustained results. In an era of tightening margins and increasing operational complexity, the top bed is no longer a place to simply fill and forget. It can be a lever. For refiners willing to pull it, the returns can add up, cycle after cycle, reactor after reactor. CatTrap, ActiPhase, and HeavyTrap are marks of Crystaphase. Austin Schneider i s the Vice President of Technology at Crystaphase, with more than 20 years of experience in reactor performance optimi- sation. As an SME in fixed-bed reactor hydraulics, filtration, and feed purification, he leads the team responsible for technical service, prod - uct research and development, and intellectual property management, with a focus on eliminating bottlenecks, extending production cycles, and maximising reactor performance for refining, petrochemical, renew - ables, and chemical plants worldwide. He spearheads Crystaphase’s Pathways course and holds a Bachelor of Science in physics with a computational focus from the University of Texas at Austin and a Master of Science in mechanical engineering from the University of Houston. Figure 2 FoS benchmarking diagram showing how a the- oretical network of reactors can be mapped and prioritised for optimisation based on bed stability analysis

PTQ Q3 2026