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Updated: Jun 27, 2026

Preparing Silica Aerogel Monoliths via a Rapid Supercritical Extraction Method
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Digital Twin-Driven Optimization of Pilot-Scale Polyurethane Aerogel Production Using SVR Modelling.

Óscar Brandón-Basdediós1, Laura Miguélez-Riádigos1, Esther Pinilla-Peñalver2

  • 1Instituto Tecnológico de Galicia (ITG), Cantón Grande 9, Planta 3, 15003 A Coruña, Spain.

Gels (Basel, Switzerland)
|June 26, 2026
PubMed
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This summary is machine-generated.

This study introduces a Digital Twin (DT) framework to optimize polyurethane (PU) aerogel development. The DT framework aids in designing sustainable, energy-efficient materials by reducing experimental workload and identifying optimal synthesis conditions.

Area of Science:

  • Materials Science
  • Chemical Engineering
  • Computational Modeling

Background:

  • Aerogels, particularly polyurethane (PU) aerogels, are highly sought after for sustainable and energy-efficient insulation due to their thermal properties and mechanical versatility.
  • Traditional PU aerogel development relies on time-consuming and resource-intensive trial-and-error experimentation.
  • There is a need for efficient methodologies to accelerate the design and optimization of PU aerogels.

Purpose of the Study:

  • To present a Digital Twin (DT) framework for supporting the design of PU aerogels.
  • To reduce the experimental workload in PU aerogel synthesis and development.
  • To demonstrate a data-driven approach for advancing aerogel manufacturing.

Main Methods:

  • Development of a pilot-scale Digital Twin (DT) framework using data from 21 synthesis experiments.
Keywords:
data-driven modellingdigital twinspolyurethane aerogelssustainable manufacturing

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  • Evaluation of two predictive models, selecting Support Vector Regression (SVR) for its accuracy (R² = 0.964).
  • Utilizing the DT to map process parameters, analyze the synthesis, and estimate aerogel density.

    • Main Results:

    • The Support Vector Regression (SVR) model accurately predicted PU aerogel density (R² = 0.964).
    • The DT framework successfully identified synthesis conditions linked to lower aerogel density, potentially enhancing thermal insulation.
    • The study validated the efficacy of DT-assisted modeling in material development.

    Conclusions:

    • Digital Twin frameworks offer a powerful tool for optimizing material design and improving process understanding in PU aerogel synthesis.
    • This data-driven approach facilitates more efficient experimentation and guides the development of sustainable, scalable aerogel manufacturing.
    • The developed DT framework shows significant potential for accelerating innovation in advanced insulation materials.