Catalysis 2024 Issue

more easily. This can improve overall catalytic performance, especially in reactions where mass transfer limitations can be a factor due to their superior diffusion properties, which allow reactants and products to move more freely within the catalyst particles. Refinery processes often involve high temperatures, so the thermal stability of the catalyst is crucial. Low-density TiO 2 catalysts can be designed to exhibit excellent thermal stability under the operating conditions of refinery pro - cesses. The surface of TiO 2 catalysts can be modified to enhance specific catalytic properties. For example, doping with other elements or adding promoters can improve cat - alytic activity and selectivity. AI applications Artificial intelligence (AI) is increasingly being applied in developing refinery and petrochemical catalysts to improve efficiency, optimise processes, and accelerate innovation. AI algorithms are used to analyse vast datasets related to catalyst materials, properties, and performance. This helps in identifying patterns and correlations that experienced engineers might overlook, leading to more effective cata - lyst design. AI models can predict the behaviour of different catalyst formulations, allowing researchers to focus on the AI is increasingly being applied in developing refinery and petrochemical catalysts to improve efficiency, optimise processes, and accelerate innovation most promising candidates while reducing the time and resources required for experimentation. AI is employed to develop sophisticated control sys - tems that can optimise the operation of refining and petrochemical processes in real time, including the adjust - ment of catalyst concentrations, temperatures, and other variables to maximise efficiency and product yield while predicting when catalysts are likely to degrade or require maintenance. For example, in FCC operations, this capability is impor - tant in mitigating the impact of higher concentrations of contaminants seen in resid feedstocks (Ni, Cu, V, and Fe) that can poison dehydrogenation catalysts. Instead, AI can help provide insights into shifting operating conditions, such as reactor temperature, feed preheat temperature, catalyst cooler duty or charge rate, all of which could signif - icantly impact coke make. AI techniques can analyse large datasets generated from various stages of catalyst development and usage. This allows for a deeper understanding of catalyst performance, leading to informed decisions in the development and selection of catalysts, including maximising catalyst reuse while meeting unit performance targets and valorisation of

non-reusable catalyst loads. Its ability to identify patterns and anomalies in data helps researchers gain insights into the factors influencing catalyst performance and refining processes. To reduce the need for expensive and time-consuming physical experiments, AI-powered simulations and virtual testing can be used to model catalyst behaviour under dif - ferent conditions. AI can analyse supply chain data to opti - mise the procurement and distribution of catalyst materials, ensuring a steady supply at the lowest possible cost while assisting in monitoring and ensuring compliance with envi - ronmental regulations by continuously analysing data from petrochemical processes. AI facilitates collaboration among researchers and experts by providing tools for data sharing, analysis, and communication. This accelerates the pace of innovation and knowledge dissemination in the field. The application of AI in developing refinery and petro - chemical catalysts is an evolving field, and ongoing research and technological advancements continue to enhance its impact on efficiency, sustainability, and cost-effectiveness in the industry (see Question 2 in this issue’s Q&A section). Overall, the integration of machine learning and compu - tational approaches is transforming the catalyst discovery and optimisation process. Computational models are used to predict catalytic activity, selectivity, and stability, reduc- ing the time and resources required for experimentation. This trend accelerates the development of novel catalysts and catalytic processes. Innovation Other recent innovations revolve around 3D printing, also known as additive manufacturing, which has gained trac - tion due to its ability to create complex and customised structures. Within the context of olefins and polymer cat - alysts, researchers are exploring the use of 3D printing for catalyst design and fabrication. 3D printing allows for the customisation of catalysts based on the desired proper- ties, making it possible to optimise performance for select chemical reactions. It is important to note that the development and use of catalysts in the refining industry are highly specialised and involve proprietary technologies. Researchers and engi - neers in the field continuously work on optimising catalyst properties to meet the specific requirements of different refining processes. Refinery and petrochemical catalysis are driven by sus - tainability goals, technological advancements, and the quest for increased efficiency. The bespoke trends outlined in this discussion underscore the industry’s commitment to reducing environmental impact, optimising processes, and embracing innovation. Without competitive process design and industrial and technical support, exceptional catalyst performance may not be possible. References 1 An Z, Yang P, Duan D, et al, Highly active, ultra-low loading single-atom iron catalysts for catalytic transfer hydrogenation, Nat Commun, 14, 6666, 2023. https://doi.org/10.1038/s41467-023-42337-9

Catalysis 2024