Review on Carbonaceous Catalysts for Transesterification of Feedstock Toward Biodiesel Production: Advances, Challenges, and AI Perspectives

The escalating energy demand has positioned biodiesel at the forefront of the global transition toward clean energy. As transesterification is a key pathway for biodiesel production, improving its efficiency requires the development of economic and sustainable catalysts. Waste-derived carbonaceous catalysts provide economically and ecologically sustainable alternatives in this regard. This Perspective presents an extensive analysis of emerging carbonaceous catalysts, highlighting the correlation between catalyst synthesis and its compositional-structural properties. This study further deciphers the stimulant influence of surface textural properties, acid–base functionalities, and reaction parameters on the underlying transesterification mechanistic pathways across various feedstocks. Furthermore, the sustainability dimensions of the transesterification process were evaluated through life-cycle assessment and technoeconomic prospects. Advanced characterization techniques, including Extended X-ray absorption fine structure (EXFAS), transmission electron microscopy (TEM), BET, X-ray diffraction (XRD), FTIR, X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and Raman spectroscopy, are explained to help understand the link between the structure and catalytic activity. Previously, experimental and empirical approaches have been used to optimize transesterification. However, this study proposes the use of advanced data-driven approaches, such as artificial intelligence (AI) and machine learning (ML), for recognizing reaction mechanisms and catalyst design owing to their strong predictive and interpretable capabilities. While previous studies primarily cover conventional catalysts and AI for only parameter optimization, this review uniquely intertwines carbonaceous catalysts with transesterification by integrating catalytic mechanisms with AI and catalyst compositional descriptors.