Catalysis 2026 Issue

catalysis q&a

More answers to these questions can be found at www.digitalrefining.com/qanda

Q Can you point out cases where hydrotreater catalyst regeneration met the refiner’s expectations? A Steve Mayo, VP Technology & Business Development, smayo@eurecat.com and Michael Martinez, Application Technology Manager, mmartinez@eurecat.com, Eurecat Regenerating hydrotreating catalysts is a proven strategy to reduce operating costs and environmental impact while maintaining reliable performance when applied in suitable services. Effective regeneration requires balancing carbon and sulphur removal with carefully controlled tempera- tures to prevent surface area loss and damage to the active phase. Traditionally, regeneration restored 90% or more of original catalyst activity. Newer-generation catalysts may require rejuvenation in addition to regeneration to restore activity to near-original fresh (or even above it in some cases). Given the high level of activity recovery, regenerated/rejuvenated catalysts can usually be applied in units with operating conditions comparable to the prior fresh catalyst installation. Typical applications include naphtha, kerosene, distillate, and some vacuum gas oil (VGO) hydrotreaters. Successful catalyst reuse begins with disciplined unload- ing. Each container should be clearly labelled and sampled immediately upon discharge. Proper identification and rep - resentative sampling enable accurate analysis, ensuring that contaminated material is segregated from uncontami- nated catalyst for more effective regeneration. The catalyst unloading method is critical for minimising particle length reduction and preserving structural integrity. For free-flowing catalysts, gravity dumping is preferred because it imposes significantly lower mechanical stress compared to vacuum unloading. When catalysts exhibit poor flowability, specialised technologies such as Eurecat’s proprietary CarboDump and UltiCat can be used to break up agglomerates and enable gravity-assisted discharge, thereby reducing particle damage and maximising catalyst suitability for reuse. For units operating at higher activity, regenerated cata- lysts can be further enhanced through rejuvenation treat- ments that modify the active phase and transform its morphology. In this way, performance can be restored to near-fresh levels or beyond. Commercial rejuvenation tech- nologies, such as Eurecat’s proprietary EBoost, have dem - onstrated activity improvements over regeneration alone, exceeding 30 RVA points. Residual vanadium activity (RVA) is a numerical measure of how much catalytic activity has been lost due to vanadium poisoning, and how much of that activity can be recovered through rejuvenation. This increase in performance expands the window for reusing catalysts to more demanding services. It also allows the use of hybrid loading strategies, where rejuve- nated catalysts are positioned in reaction zones governed by mass transfer rather than intrinsic activity, while fresh or

Q What aspects of AI can be leveraged to optimise reac- tor and catalyst design? A Jumal Shah, Hydrogen Market Manager, Jumal.shah@ matthey.com, and Paul Clark, Digital Transformation Director, Catalyst Technologies, paul.clark@matthey.com, Johnson Matthey We will not provide answers in isolation; its real value lies in how we apply proven techniques to strengthen existing processes and accelerate innovation. From a JM perspec- tive, the opportunity is to combine AI with deep domain expertise to deliver practical, scalable solutions that improve productivity, enable tailored designs and break new boundaries. Scale and productivity AI can enable greater throughput and efficiency by automat - ing scientific workflows, accelerating candidate screening, and simplifying research tasks. Increasingly, JM expects to be able to integrate agentic tools tailored for scientific and engineering processes, which will rapidly increase the pro- ductivity of our scientists and engineers. Accelerated computational chemistry techniques enable rapid screening of catalyst formulation candidates. Once AI-generated models are validated with experimental data, high-throughput screening can be streamlined through more targeted testing plans, reducing time, resources, and overall development costs. These approaches drive internal processes with greater efficiency while maintaining rigor - ous validation. Personalisation Leveraging AI to gather, process, and interpret data from operational reactors and catalysts helps tailor solutions to specific operational challenges. For example, understanding performance under variable conditions enables the design of catalyst and reactor configurations that address intermit - tency in renewably powered plants. This data-driven insight ensures solutions are optimised for real-world outcomes rather than generic scenarios. Pushing the boundaries of design and operation Generative AI techniques open the door to catalyst and reactor structures that would be impossible for a human to conceive alone. By combining these algorithms with experimental validation, novel geometries and formula- tions that push the boundaries of conventional design can be explored. Once in the field, we can help our customers maintain optimum operation with advanced simulations for real-time performance modelling, while facilitating multi- objective optimisation to balance conversion, selectivity, efficiency, and cost. Additionally, data-driven approaches such as active learning help continuously review process requirements and adapt to changing operational needs.

Catalysis 2026