A Control Theory Informed Machine Learning-Based Modeling and Monitoring of Nanoparticle Generation Processes

Modeling, monitoring and control of nanoparticle generation processes are of critical importance in various industrial applications due to their impact on product quality and process efficiency. While traditional first-principles modeling is foundational, it often falls short due to incomplete knowledge and inherent simplifications. This article presents a novel approach using control theory informed machine learning to enhance the accuracy and reliability of these models. This innovative method is particularly useful in environments with uncertainties and noise, making it well-suited for complex processes in nanotechnology. The incorporation of control-theoretic preknowledge and the integration of kernel-based methods with neural networks enables the designed framework to address the challenges described above. The approach demonstrates significant improvements in predicting key performance indicators quantifying the relevant product parameters. The simulation results validate the effectiveness of the control theory informed machine learning framework, indicating its potential to be a robust and efficient solution for real-time process control in nanoparticle generation.