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

Resumen

Chemical engineering has progressively expanded from its traditional focus on unit operations and large-scale petrochemical processes to a broader interaction with biotechnology, nanotechnology, and sustainability-oriented practices. In parallel, artificial intelligence (AI) has transformed industrial systems through enhanced process optimization, predictive modeling, and the integration of smart and adaptive production environments. This paper examines the intersection of these developments by conducting a bibliometric analysis of publications indexed in Scopus between 2015 and 2024, complemented with a foresight perspective to anticipate future trajectories. The results identify five thematic clusters—adsorption and material characterization, process optimization and prediction, computational chemistry, neural networks and chemometrics, and chemical process automation—together illustrating the breadth of AI applications across the discipline. A temporal review highlights the transition from early applications in process control and multivariate analysis to recent advances in biotechnological, pharmaceutical, and materials engineering contexts. The study also situates these global trends within the Peruvian industrial environment, characterized by limited diversification and high dependence on raw material exports, but with emerging opportunities in mining analytics, agro-industry, and pharmaceutical innovation. The conclusions emphasize the strategic relevance of integrating AI into chemical engineering education and research as a means to enhance competitiveness and guide sustainable development pathways.