Advanced Control of Continuous Pharmaceutical Manufacturing Processes: A Case Study on the Application of Artificial...

The adoption of continuous pharmaceutical manufacturing has driven increased use of modeling, simulation, and advanced process control strategies. Artificial intelligence (AI) model-based approaches, like neural network predictive control (NNPC), offer advantages in providing insights, predictions, and process adjustments. However, evaluating the credibility of such models and accurately quantifying their impact on product quality remains challenging. In this study, a digital twin model of a continuous direct compression (CDC) line was developed based on residence time distribution theory. A two-layer neural network model was trained using data from the digital twin to predict system outputs. The NNPC model combined the trained neural network with an optimization block to adjust control signals and minimize tracking error and control effort. A proportional-integral-derivative (PID) controller was also developed for comparison. The developed neural network model accurately represented the dynamics of the nonlinear system. The tuned NNPC outperformed PID in setpoint tracking (zero overshoot, shorter settling times) and disturbance rejection (≤1.6% peak deviation, settling time of zero) for ±20% and ±50% changes. In conclusion, the NNPC model demonstrated remarkable performance in setpoint tracking and disturbance rejection for the simulated CDC line, underscoring the potential of AI-based control strategies in enhancing product quality and regulatory assessment.