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Updated: Jan 15, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
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Machine learning real-time control of continuous granulation process.

Maksym Dosta1, Moritz Schneider2, Christopher W Geis2

  • 1Pharmaceutical Development CMC NCE, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Str. 65, 88397 Biberach an der Riss, Germany.

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|October 6, 2025
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Summary

This study introduces a machine learning (ML) model for real-time control in continuous pharmaceutical manufacturing. The developed system effectively adjusts critical process parameters (CPPs) to achieve desired critical material attributes (CMAs) in wet granulation.

Keywords:
Continuous granulationMachine learningPharmaceutical manufacturingProcess controlProcess development

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Area of Science:

  • Pharmaceutical Manufacturing
  • Process Control
  • Machine Learning Applications

Background:

  • Continuous manufacturing requires efficient process development and operation, posing challenges in understanding critical process parameters (CPPs) and critical material attributes (CMAs).
  • Implementing active process control is crucial for maintaining a stable control state in complex pharmaceutical plants.

Purpose of the Study:

  • To develop a data-driven process model using machine learning (ML) for real-time control of a continuous wet granulation line.
  • To integrate mechanistic models as soft-sensors to enhance ML model training and create a hybrid architecture.

Main Methods:

  • Utilized historical process data and targeted new data collection to build an ML kernel.
  • Implemented a control system for the granulation plant based on the developed ML model.
  • Extended process data with mechanistic models (soft-sensors) for a hybrid model approach.

Main Results:

  • Successfully built an ML kernel and implemented a control system for the continuous granulation plant.
  • The hybrid model architecture effectively supported ML training.
  • Demonstrated efficient real-time control of the continuous plant.

Conclusions:

  • The proposed ML-based strategy enables effective real-time control of continuous pharmaceutical manufacturing processes.
  • The developed system can achieve desired critical material attributes (CMAs), such as granule size and loss on drying, by adjusting critical process parameters (CPPs).