Evolutionary neural architecture search for surrogate models to enable optimization of industrial continuous crystallization process

Miriyala, S.S. and Pujari, K.N. and Mitra, Kishalay (2022) Evolutionary neural architecture search for surrogate models to enable optimization of industrial continuous crystallization process. Powder Technology, 405. pp. 1-16. ISSN 0032-5910

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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. © 2022 Elsevier B.V.

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IITH Creators:
IITH CreatorsORCiD
Mitra, Kishalayhttp://orcid.org/0000-0001-5660-6878
Item Type: Article
Additional Information: The authors would like to acknowledge the support provided by the project grant # BT/PR34209/AI/133/19/2019 received from the Depart- ment of Biotechnology, Government of India, and grant # DST/NSM/ R&D_HPC_Applications/2021/23 funded by the Department of Science and Technology, Government of India for this work
Uncontrolled Keywords: Artificial neural networks, Crystallization, Evolutionary algorithms, Multi objective optimization, Neural architecture search, Surrogate assisted optimization
Subjects: Chemical Engineering
Divisions: Department of Chemical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 23 Jun 2022 07:32
Last Modified: 29 Jun 2022 07:34
URI: http://raiith.iith.ac.in/id/eprint/9363
Publisher URL: https://doi.org/10.1016/j.powtec.2022.117527
OA policy: https://v2.sherpa.ac.uk/id/publication/16963
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