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  • Considering that new catalyst formulations play a significant role in successful reactor performance, what else is necessary for optimal reactor/catalyst performance?

    Jul-2022

Answers


  • Erick D Gamas, The Business Shop-Chemical Engineering Services, edgamas@hotmail.com

    The most important reasons for introducing a new catalyst formulation are to increase process profitability, maintain or increase process stability, or both. This can be achieved via higher conversion, increased product yields or extended catalyst life cycle.

    A robust approach to guarantee performance of a new catalyst formulation is that the reactor design and its internals maximize contacting of feed and catalyst particles, otherwise channelling and dead flow zones will develop leading to decreased catalyst bed effectiveness. Accurate catalyst particles geometry and catalyst bed design are also required as, depending on the chemical reaction and the physical state of feed and product streams, the conversion process might benefit from a bed configuration that is shallow as opposed to deep, or particle geometries that lead to higher diffusion in/out of materials or heat. Heat and mass transfer patterns in the bed also contribute to maximized  catalyst performance efficiency.

    The process economics with the new catalyst formulation must determine its viability. New formulations might required expensive equipment revamps, new vessels or new process configuration in order to take advantage of a much better catalyst. These issues can be properly addressed by collaborative work between the catalyst vendor, sharing lab and pilot plant data of chemical reaction tests with the old and new formulations, and the plant engineering partner responsible for assessing flow distribution, heat and mass transfer patterns and their effect on catalyst particles (poor gas contacting, overheating, hydrogen starvation, attrition, etc.).

    While most improved catalyst formulations tend to be designed as drop-in technologies, an additional and highly relevant aspect of introducing a new catalyst formulation is found on determining what needs to happen upstream as well as downstream the conversion process. A new catalyst formulation might require revamping or building feed pre-treatment facilities to remove contaminants in upstream operations. In downstream operations the new catalyst formulation might require that separation and purification units reach higher efficiency in order to realize the benefit of a new catalyst on process profitability.

     

    Aug-2022

  • Peter Nymann, Haldor Topsoe, pan@topsoe.com

    New and improved catalysts do indeed play a significant role in achieving good performance of hydrotreating and hydrocracking reactors, but the catalysts also needs to be operated properly.

    Assuring good distribution of gasses and liquids across the entire volume of the catalyst helps assure utilization of the catalysts capabilities. Reactor internals needs to be clean and in good mechanical conditions so that they work as per design. In case the reactor internals are not performing they may be replaced by newer design, high performance reactor internals like, Topsoe VLT distribution trays and Topsoe Vortex mixers for quench sections. Good thermometry in the reactor with good coverage of the inlet and outlet part of each bed helps determine whether good distribution is achieved. Performance may also be impaired by plugging of the catalyst beds originating from material being brought in with the feed. This may be alleviated by good feed filtration and by installation of Topsoe HELPE Scale catchers in the top reactor head.

    Furthermore, good control of feed quality is imperative to obtain good performance of the installed catalyst and even short periods of processing feedstocks with worse severity, like high end point or high content of contaminants may have detrimental effect on reactor performance and unit cycle length.

     

    Aug-2022

  • Dinesh-Kumar Khosla, Axens, dinesh-Kumar.KHOSLA@axens.net

    In units featuring fixed-bed reactors, along with optimum catalyst design, overall reactor/catalyst performance can be enhanced using high-efficiency reactor internals. Axens’ proprietary EquiFlow reactor internals ensure a uniform gas/liquid distribution and optimum mixing in the reactor, thereby minimising channelling and hot spots to ensure optimal use of the entire catalyst inventory in the reactor.

    This enhances catalyst activity, selectivity, and stability, minimising catalyst change-out frequency while ensuring safe and reliable operation. EquiFlow distributor trays employ a dispersive system located below a chimney tray to ensure close-to-ideal vapour/liquid distribution throughout the catalytic bed underneath.

    EquiFlow quench systems (i.e., the proprietary Hy-Quench-XM and Hy-Quench-NG) feature a more compact design. This results in smaller reactors in grassroots configurations and increased catalyst volume for existing reactors. These quench systems provide higher thermal efficiency over a wider range of operating conditions. These systems result in longer catalyst cycles and/or higher throughput operation.

    For reactors prone to fouling, the EquiFlow smart filtering tray system (i.e., proprietary Hy-Clean) limits recurrent pressure drop problems while ensuring a perfect gas/liquid distribution in reactors. It will prevent plugging of the bed by catching and retaining feed impurities that are often responsible for crust formation between the different catalyst layers. Notably, with the use of Hy-Clean, there is no additional pressure drop compared to conventional distributors or quench systems. Overall, Hy-Clean will enable a significant increase in catalyst cycle length, leading to higher profitability.

    The right combination of catalyst and reactor internals is thus essential for reliable and profitable reactor operation. A reduction in reactor operating temperature and pressure drop with Axens’ EquiFlow reactor internals also results in a lower CO₂ footprint associated with specific unit operation.

     

    Jul-2022

  • Ron Beck, AspenTech, ron.beck@aspentech.com

    Operating conditions affecting catalyst performance include temperature, pressure, composition, and other aspects that can impact catalyst degradation. Accurate modelling solutions are crucial to monitor catalyst performance and troubleshoot operations to prevent deactivation.

    Because reactors have a complex series of chemical and physical reactions and dynamics going on inside, accurate modelling requires high fidelity and ongoing validation and tuning. Traditionally, process licensors guard the proprietary nature of their technology and don’t release such models.

    Owners who want to be independent of the licensors’ reactors employ rigorous models to predict conditions leading to catalyst degradation or deactivation. Molecular-level modelling of crude characteristics, and conversely a more detailed understanding of reactor performance and catalyst interaction, is a key digital technology that helps in this area. But AI analytics can be a game-changer.

    AI is already changing the game in terms of the formerly time-consuming task of calibrating these models. Refiners using hybrid models in this area have seen it as a breakthrough in the practical use of these models to optimise catalyst economics. As biofeedstocks are blended into the refining process, this will only get more complicated.
    To fully optimise refining performance with respect to biofuels, carbon intensity, energy use, and catalysts, a combination of digital tools comes into play. Effective use of models that can predict unit fouling (rigorous engineering models in a digital twin mode) benefits from:
    • Planning models that can plan the blending of feedstocks and biofuels to achieve carbon, energy use, and margin results
    • Energy and utility models
    • Mass balance accounting.

    A synchronisation of the models is needed to achieve results that can be used to make the trade-off decisions needed. To provide that, AspenTech has innovated a concept we term model alliance. We use AI-based reduced-order models to synchronise across these different refining models and enable the optimisation engineers, plant managers, and technical teams to achieve the desired optimisation.

     

    Jul-2022



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