Research Area:  Machine Learning

Abstract:

Optimal performance of the crystallization process is of utmost importance for industries handling bulk commodity chemicals to pharmaceuticals. Such an optimization exercise becomes extremely time expensive as the mathematical models mimicking such complex processes involve the solution of Integro-Differential Population Balance Equations using High Resolution Finite Volume Methods. In order to build a fast and robust data based alternative model, a surrogate assisted approach using Artificial Neural Networks has been proposed here. To overcome the heuristics-based estimation of the hyper-parameters in ANNs, we aim to contribute a novel Neural Architecture Search strategy for the auto-tuning of hyper-parameters integrated with sample size determination techniques. While solving a multi-objective optimization of crystallization process ensuring maximum productivity, the results from surrogates are compared with those of a high-fidelity physics driven model, which reports five order of magnitude speed improvement without sacrificing much on accuracy.

Keywords:  
crystallization process
Artificial Neural Networks
crystallization process
Crystallization process

Author(s) Name:  Srinivas Soumitri Miriyala,Keerthi NagaSree Pujari,Sakshi Naik

Publisher name:  Elsevier

DOI:  10.1016/j.powtec.2022.117527

Volume Information:  Volume 405

Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S0032591022004211