AI-Driven Hydrocarbon Upgrading #worldresearchawards #researchaward #researcher #aiinchemistry

Converting long-chain alkanes such as C22 into high-value aromatic compounds is one of the most important challenges in modern petrochemistry and sustainable chemical engineering. Traditionally, this transformation requires harsh conditions, complex catalyst systems, and extensive experimental optimization. Today, machine learning (ML) is revolutionizing this process by enabling smarter catalyst design, faster reaction discovery, and more efficient process control.

In this video, we explore how artificial intelligence models analyze massive datasets containing catalyst compositions, reaction conditions, and product distributions to predict optimal pathways for alkane aromatization. ML algorithms can identify subtle patterns linking metal types, support materials, pore structures, and operating parameters that influence selectivity toward aromatics such as benzene, toluene, and xylenes. This dramatically reduces experimental trial-and-error and accelerates innovation.

AI-driven catalyst screening allows researchers to virtually test thousands of material combinations before synthesizing the most promising candidates in the lab. This saves time, lowers cost, and improves sustainability by minimizing waste and energy consumption. Combined with real-time reactor data, machine learning also enables adaptive process optimization, allowing reaction conditions to be adjusted dynamically for maximum efficiency and yield.

Beyond petrochemical upgrading, this approach supports greener chemistry by improving atom economy and enabling more precise control over product distributions. The integration of data science with catalysis represents a paradigm shift turning complex chemical transformations into predictable, designable processes.

Join us as we uncover how machine learning is transforming C22 alkane conversion into aromatics, unlocking smarter, cleaner, and more efficient chemical manufacturing for the future.

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