Advances on hybrid modelling for bioprocesses engineering: Insights into research trends and future directions from a bibliometric approach

Abstract

• Novel combination of tools for data processing and visualization. • Combining Scopus® and Web of Science® for comprehensive meta-data collection. • Systematically review of 5890 contributions leading to 331 unique papers. • Top authors are contributors of 52 % of scientific contributions in core journals. • Strong international collaboration in the field, especially among European countries. Hybrid modeling in bioprocess engineering has emerged as a promising approach to strengthen process system engineering applications. However, understanding evolution of the field structure is a challenge. To address this gap, we conducted a comprehensive bibliometric analysis of the field. This study aims to assess publications metadata quantitatively and qualitatively to map the research landscape. Through a systematic review of Scopus and Web of Science databases, 360 contributions have been identified within chemical or biochemical engineering. Using Bibliometrix®, Tree of Science®, VantagePoint®, VOSViewer®, and Python, metadata was analyzed and visualized, revealing "hybrid model" and "neural networks" are the central keywords on the field, with notable contributions from countries like Portugal and the United States of America. Thematic analysis unveiled three clusters: one dealing with control applications and other two that combine machine learning terminology with bioprocesses concepts. Furthermore, the field exhibits a high level of collaboration, with leading researchers such as Rui Oliveira and Moritz von Stosch making significant contributions. Based on these findings, insights into the research trends and future directions are presented.

Topics & Concepts