Reactive Distillation of Pharma Compounds Using Machine Learning Control Systems

Abstract

The integration of reactive distillation (RD) with machine learning (ML) control systems offers a transformative approach to pharmaceutical compound synthesis, combining reaction and separation in a single intensified process unit. RD significantly enhances efficiency, reduces solvent usage, and minimizes environmental impact, making it ideal for green pharma manufacturing. However, its inherent complexity, nonlinearity, and sensitivity to process variables pose significant challenges for traditional control methods. This study presents an intelligent control framework where machine learning algorithms—particularly reinforcement learning and neural network-based predictive control—are trained on dynamic process data to optimize temperature, pressure, and reactant feed rates in real-time. Simulation results and experimental validations demonstrate enhanced product yield, purity, and operational stability compared to conventional PID control. The approach also enables adaptive process optimization, anomaly detection, and self-learning capabilities, crucial for robust pharmaceutical production under variable conditions. This synergy between RD and ML represents a promising leap toward fully autonomous, efficient, and scalable drug manufacturing systems