Real-Time Optimization and Genetic Algorithm for Enhanced Process Control of Coupling Cooling and Antisolvent Crystallization

• Published in: Industrial & engineering chemistry research Vol. 64; no. 4; pp. 2199 - 2207
• Main Authors: XuanYuan, Shutian, Sun, Ying, Zhou, Xiaomeng, Bai, Yunhe, Ye, Yang, Hu, Cuihong, Hao, Hongxun, Xie, Chuang
• Format: Journal Article
• Language: English
• Published: American Chemical Society 29.01.2025
• Subjects: algorithms; carnitine; cooling; crystallization; process control; Process Systems Engineering; temperature profiles
• ISSN: 0888-5885; 1520-5045; 1520-5045
• DOI: 10.1021/acs.iecr.4c04048

Crystallization process control has attracted extensive research interest in recent years within the field of industrial crystallization. This study focused on controlling the crystallization process of coupling cooling and antisolvent crystallization (CCAC), a traditional crystallization method widely used for enhancing the product yield and process efficiency. By employing model-free control, real-time self-feedback control, and preset process routes optimized by genetic algorithm (GA), the simultaneous control of the temperature profile and antisolvent addition was achieved during CCAC. The kinetic behavior of l-carnitine during the CCAC process under different control logics was characterized. It is revealed that the real-time self-feedback control and preset process route based on genetic algorithm exhibited better performance than the model-free control strategy and linear process. Specifically, real-time optimization based on the genetic algorithm (GA-RTO) can achieve a larger D90, while the optimized design route of the process can result in a larger D50. However, for simple process development, a preset process path may yield acceptable preliminary results.