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Predictive Modelling and Optimisation of Rubber Blend Mixing Using a General Regression Neural Network.

Ivan Kopal1, Ivan Labaj1, Juliána Vršková1

  • 1Department of Numerical Methods and Computational Modelling, Faculty of Industrial Technologies in Púchov, Alexander Dubček University of Trenčín, Ivana Krasku 491/30, 020 01 Púchov, Slovakia.

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|July 12, 2025
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Summary

This study introduces an intelligent system using a General Regression Neural Network (GRNN) for real-time rubber blend mixing control. It accurately predicts process parameters and optimizes mixing early, enhancing industrial quality and productivity.

Keywords:
elastomersgeneral regression neural networkintelligent modellingmixing processoptimisationrubber blends

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Area of Science:

  • Materials Science
  • Chemical Engineering
  • Artificial Intelligence

Background:

  • Rubber blend mixing is critical for product quality.
  • Real-time process control is essential for efficiency.
  • Predictive modeling can optimize complex industrial processes.

Purpose of the Study:

  • To develop an intelligent predictive system for real-time control of rubber blend mixing.
  • To accurately predict key process parameters like viscosity, temperature, and energy consumption.
  • To enable early detection of mixing progress for process optimization.

Main Methods:

  • Implementation of a General Regression Neural Network (GRNN) model.
  • Utilizing experimental data from a Brabender Plastograph EC Plus.
  • Optimizing GRNN kernel width (σ) via 10-fold cross-validation.

Main Results:

  • High predictive accuracy for viscosity, temperature, and energy consumption.
  • Accurate evaluation of mixing progress from initial 10% of data.
  • Achieved R² values close to 1 and low RMSE, confirming model reliability.

Conclusions:

  • The GRNN-based system provides robust and scalable intelligent control for rubber mixing.
  • The system enhances productivity and quality assurance in industrial applications.
  • The predictive approach is applicable beyond rubber blending processes.