Comprehensive evaluation of a data driven control strategy: Experimental application to a pharmaceutical crystallization process

Abstract

In this contribution, a data-driven control approach was developed and applied experimentally to a pharmaceutical batch cooling crystallization process. In this approach, a radial basis functions (RBF) network model was trained in real-time with experimental data (time varied temperature and chord length distribution) with two different input data update strategies. The control objective was to optimize the cooling profile with the aid of trained RBF to achieve the desired crystal population profile throughout the process. The robustness of the proposed control strategy was tested with 10 comprehensive experiments in the presence of several disturbances (initial supersaturation, impeller speed, water composition and seed size). The presented control strategy was able to easily handle all the case scenarios. In 8 cases, the experimental crystal population profile followed successfully the reference with less than 10% offset. In the remaining 2 cases, the offset was 17% that was due to the absence of the supersaturation. The proposed RBF network-driven control is a promising strategy that is easy to implement, fully-automated and relies on relatively limited data for training. Therefore, the RBF control is expected to contribute to quick process development and control, especially when there is a lack of comprehensive process understanding and historical data especially in the pharmaceutical industry.