Salt and sediment fouling in heat exchangers: a bibliometric analysis 2010–2025

Review Article

Authors

  • Gianfranco Di Mattia-Castro Carrera de Ingeniería Industrial, Facultad de Ciencias de la Industria y Producción, Universidad Técnica Estatal de Quevedo. Quevedo, Ecuador. ORCID iD https://orcid.org/0009-0001-1048-9554
  • Víctor Moreno-Riquero Carrera de Ingeniería Industrial, Facultad de Ciencias de la Industria y Producción, Universidad Técnica Estatal de Quevedo. Quevedo, Ecuador. ORCID iD https://orcid.org/0000-0002-1516-5823
  • José Navia-Zamora Carrera de Ingeniería Industrial, Facultad de Ciencias de la Industria y Producción, Universidad Técnica Estatal de Quevedo. Quevedo, Ecuador. ORCID iD https://orcid.org/0009-0009-2442-0513
  • Marco Osorio-Trávez Carrera de Ingeniería Mecánica, Facultad de Ciencias de la Ingeniería, Universidad Técnica Estatal de Quevedo. Quevedo, Ecuador. ORCID iD https://orcid.org/0009-0009-8695-959X

DOI:

https://doi.org/10.33936/riemat.v11i1.8312

Keywords:

fouling, heat exchangers, crystallization, bibliometric analysis, machine learning

Abstract

Fouling caused by salt deposition and sediment accumulation in heat exchangers remains one of the most critical factors limiting thermal performance in process industries, directly affecting energy efficiency and operational costs. This study aimed to examine, through a bibliometric approach, the evolution, structure, and emerging trends of scientific production indexed in Scopus between 2010 and 2025 on crystallization and particulate fouling. A retrospective mixed-method design was adopted, integrating productivity, impact, and collaboration indicators using Bibliometrix/Biblioshiny, together with science mapping in VOSviewer to identify thematic clusters and collaboration networks. The final dataset comprised 682 journal articles, revealing sustained growth and a strong collaborative pattern (97.2% multi-authored publications). China, the United States, and Germany lead global output, while a small core of journals concentrates nearly one-third of the publications. Keyword co-occurrence analysis indicates a clear transition from predominantly experimental studies focused on thermal resistance and crystallization mechanisms toward a dual paradigm that combines computational fluid dynamics and, more recently, machine learning–based predictive modeling. Gaps persist in the integration between thermal engineering and data-driven approaches, as well as in the explicit linkage to sustainable energy agendas. The findings provide a comprehensive intellectual mapping that supports strategic research planning and technological development in applied thermal engineering.

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Published

2026-06-01

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Vol. 11 No. 1 (2026): January - June

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Copyright (c) 2026 Gianfranco Di Mattia-Castro, Víctor Moreno-Riquero, José Navia-Zamora, Marco Osorio-Trávez

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