Applications of machine learning for modeling and advanced control of crystallization processes: Developments and perspectives

Abstract

Crystallization is a separation method relevant to the production of medicines, food and many other products. An efficient crystallization process must obtain a product with the desired size, length, and purity. Therefore, models and control schemes are applied to achieve this goal. Artificial intelligence techniques, such as machine learning (ML), are applied for modeling and controlling these processes. The current review aims to present the use of ML for modeling and advanced control of crystallization processes. Considering modeling crystallization processes, this paper presents the advances and different uses of ML, such as neural networks, symbolic regression, and transformer algorithms. This review also presents the development of hybrid models combining ML with physical laws for crystallization processes. For the advanced control of crystallization processes, this review presents the development of advanced control strategies based on ML approaches, such as applying neural networks in a nonlinear model predictive controller and based on reinforcement learning. This work can be a relevant reference for the progress of the application of ML in the process systems engineering (PSE) to crystallization processes. It is also expected to encourage industry and academy to use these approaches for different crystallization processes.

Keywords

• Crystallization models
• Crystallization control
• Machine learning
• Hybrid models
• Reinforcement learning
• Nonlinear model predictive control