Artificial Intelligence in Chemical Engineering: A Bibliometric and Foresight Analysis with Insights from Peru

Gusukuma M.; Guillermo Hernández-Cenzano C. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2026) · DOI: 10.18687/laccei2026.1.1.2153

Resumen

Chemical engineering has expanded from its traditional focus on unit operations and petrochemical processing to areas such as biotechnology, nanotechnology, advanced materials, environmental engineering, and sustainability. In parallel, artificial intelligence (AI) has gained relevance in process modeling, optimization, monitoring, and decision support for industrial systems. This study examines the relationship between AI and chemical engineering through a bibliometric review of Scopus-indexed publications from 2015 to 2024, complemented by a foresight-oriented analysis of emerging research directions. The bibliometric results identify five thematic clusters: adsorption and material characterization, process optimization and prediction, computational chemistry, neural networks and chemometrics, and chemical process automation. The temporal analysis shows a shift from early applications in neural networks, process control, and multivariate analysis toward recent developments in bioprocessing, pharmaceutical design, materials discovery, and agri-food quality assessment. The paper also relates these global trends to the Peruvian productive context, where dependence on primary exports coexists with opportunities for selective technological upgrading in mining analytics, agro-industry, pharmaceuticals, and bioprocessing. The findings indicate that data-driven methods can support chemical engineering education, research agendas, and sectoral innovation strategies, particularly when they are linked to human capital formation, data infrastructure, and sustainability-oriented industrial development.

Instituciones: Pontificia Universidad Católica del Perú