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 cataly...