A Bibliometric Review and Visualization of Research Progress in Computational Modeling for Biofuels

Abstract

The conversion of biomass into biofuels is a crucial step towards sustainable energy transitions. These processes are multi‐scale phenomena that require advanced computational modeling. This study synthesizes bibliometric analysis and visualization to examine the scientific advancements and future trends in computational modeling for biomass‐to‐biofuel transformation. To identify thematic clusters, research hotspots, and evolving collaborations from 2003 to 2025, network mapping and keyword co‐occurrence analysis were conducted on a dataset of 3,689 Scopus‐indexed publications. Publication trends were analyzed using VOSviewer and Microsoft Excel, highlighting the most active authors, institutions, countries, journals, and collaboration networks. The findings reveal exponential growth in research after 2017, accompanied by an increasing focus on response surface methodology (RSM), machine learning, CFD, and hybrid modeling approaches. China leads in publication output, while the United States records the highest citation impact, with Tianjin University achieving the greatest productivity and National Cheng Kung University the highest citation‐per‐publication ratio among institutions. Recent research increasingly focuses on hybrid physics‐informed and data‐driven models, multi‐scale coupling, high‐performance computing, and their integration with techno‐economic and life‐cycle assessments for sustainability. These trends outline the path for further development of computational strategies, with implications for scalability, cost efficiency, and environmentally sustainable biofuel production.