ANFIS-Driven Machine Learning Automated Platform for Cooling Crystallization Process Development
Cha Yong Jong, Akshay Mittal, Geordi Tristan, Vanessa Noller, Hui Ling Chan, Y. R. Goh, Eunice Wan Qi Yeap, Srinivas Reddy Dubbaka, Harsha Rao Nagesh, Shin Yee Wong
Abstract
Manual crystallization trials have historically posed significant challenges, demanding substantial expertise for process development and often offering unpredictable outcomes. This study addresses these difficulties by introducing an automated system that alleviates the need for manual iterations and intuitive deductions. The system leverages machine learning algorithms capable of learning from high-quality data to discern patterns and recommend optimal actions for subsequent runs. The automation process commences with a direct chord length (DCL) control system, generating system-specific training data via universal crystallization rules. After that, the automation process will progress into a machine learning iteration loop using adaptive neuro-fuzzy inference system (ANFIS) models. In this iteration loop, multiple models will be built (with accumulative historical data) and deployed to the crystallization process until predefined exit criteria are met or a maximum of five iterative cycles are reached. Results from the two campaigns are presented. It is evident that the automated crystallization platform with machine learning’s ability can confidently explore the operational space, proposing credible processing conditions that yield desirable process outcomes.