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Adaptive Closed-Loop Control System for the Optimization of Tablet Manufacturing Processes.

Xiaorong Luo1,2,3, Zhijian Zhong2,3, Pan Deng2,3

  • 1China Resources Jiangzhong Pharmaceutical Group Co., Ltd., Nanchang 330096, China.

Pharmaceutics
|December 31, 2025
PubMed
Summary
This summary is machine-generated.

A new Iterative Learning Model Predictive Control (IL-MPC) system enhances tablet manufacturing by using historical data for improved weight and hardness control. This data-driven approach optimizes production consistency and efficiency in pharmaceutical manufacturing.

Keywords:
constraint integrationiterative learning controlmodel predictive controlprocess parameter optimizationtablet quality

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Area of Science:

  • Pharmaceutical Manufacturing
  • Process Control Engineering
  • Data-Driven Optimization

Background:

  • Tablet manufacturing faces challenges with dynamic process parameter coupling, material variability, and delayed quality control, impacting tablet weight stability.
  • High-speed production exacerbates tablet weight and hardness variations, compromising efficiency due to limitations of traditional static optimization methods.

Purpose of the Study:

  • To develop a data-driven closed-loop control system for enhancing tablet manufacturing processes.
  • To address the critical need for improved tablet weight and hardness stability in high-speed production environments.

Main Methods:

  • Proposed a novel Iterative Learning Model Predictive Control (IL-MPC) architecture integrating iterative learning constraints within the Model Predictive Control (MPC) framework.
  • Enabled systematic utilization of historical batch data while maintaining real-time optimization capabilities through constraint-embedding.
  • Achieved enhanced batch-to-batch performance consistency with reduced computational load by combining learning and optimization.

Main Results:

  • Validated the IL-MPC algorithm's feasibility through simulations and industrial production data.
  • Demonstrated effective management of dynamic process variations, significantly improving control precision for tablet weight and hardness.
  • Showcased superior performance compared to conventional control methods in managing tablet quality attributes.

Conclusions:

  • Overcame the technical limitations in dynamic regulation for tablet manufacturing.
  • Established a reproducible closed-loop control paradigm for intelligent pharmaceutical manufacturing.
  • Promoted the industry's shift towards data-driven models and advanced intelligent drug production.