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Simulation-optimization framework for the digital design of pharmaceutical processes using Pyomo and PharmaPy.

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This study introduces a new framework for pharmaceutical process design and optimization. It enables faster, more accurate solutions by directly using process simulators in optimization, simplifying complex model implementation.

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Area of Science:

  • Chemical Engineering
  • Process Systems Engineering
  • Pharmaceutical Manufacturing

Background:

  • Model-based process design and optimization in the pharmaceutical industry present significant computational and implementation challenges.
  • Existing frameworks for optimization often require extensive expertise in translating mechanistic models (ODEs and PDEs) into discretized algebraic formulations.
  • This complexity hinders efficient and accurate process optimization for pharmaceutical manufacturing.

Purpose of the Study:

  • To present a novel framework for direct utilization of process simulators within derivative-based optimization.
  • To enable users with limited experience to perform robust process optimization.
  • To achieve mathematically guaranteed optima with competitive computational efficiency.

Main Methods:

  • Developed a framework that allows direct use of a process simulator via callbacks during derivative-based optimization.
  • The framework bypasses the need for manual translation of mechanistic Ordinary Differential Equations (ODEs) and Partial Differential Equations (PDEs) into algebraic formulations.
  • Implemented simultaneous equation-oriented optimization techniques.

Main Results:

  • The framework successfully obtains mathematically guaranteed optimal solutions.
  • Demonstrated competitive computational efficiency compared to existing derivative-free and derivative-based optimization frameworks.
  • Validated accuracy and efficiency on two pharmaceutical process case studies: an anti-cancer API synthesis train and a synthesis-purification-isolation train.

Conclusions:

  • The presented framework simplifies the implementation of model-based optimization for pharmaceutical processes.
  • It offers a computationally efficient and accurate approach to finding optimal solutions.
  • The framework empowers users with less specialized modeling experience to achieve robust process design and optimization.