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Modelling the effect of base component properties and processing conditions on mixture products using probabilistic,

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Area of Science:

  • Materials Science
  • Chemical Engineering
  • Data Science

Background:

  • Developing new materials by mixing components is common but predicting mixture properties is difficult without understanding underlying physics.
  • Current modeling approaches often rely on assumptions of ideal mixing, limiting their applicability.

Purpose of the Study:

  • To propose a new data-based methodology for predicting the properties of mixture-based materials.
  • To develop a model that integrates base component properties, mixing proportions, and processing conditions.
  • To estimate prediction uncertainty and incorporate expert knowledge.

Main Methods:

  • Utilized probabilistic, knowledge-guided artificial neural networks (ANNs).
  • Developed a model to jointly predict properties based on component characteristics, mixture ratios, and manufacturing conditions.
  • Incorporated monotonicity constraints for expert knowledge integration.

Main Results:

  • The methodology accurately predicts pharmaceutical tablet quality attributes (e.g., mass variation, tensile strength).
  • The model overcomes limitations of previous approaches by not assuming ideal mixing.
  • The approach allows for the estimation of aleatoric uncertainty in predictions.

Conclusions:

  • The proposed data-driven methodology effectively predicts properties of mixture-based materials, including pharmaceutical products.
  • This approach enhances material design by providing accurate predictions without requiring deep prior knowledge of physical phenomena.
  • The integration of expert knowledge and uncertainty estimation improves model reliability and applicability.