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Considerations, challenges and opportunities when developing data-driven models for process manufacturing systems

Watson, Nicholas J; Fisher, Oliver J; Escrig, Josep E; Witt, Rob; Porcu, Laura; Bacon, Darren; Rigley, Martin; Gomes, Rachel L

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Authors

Nicholas J Watson

Oliver J Fisher

Josep E Escrig

Rob Witt

Laura Porcu

Darren Bacon

Martin Rigley

RACHEL GOMES rachel.gomes@nottingham.ac.uk
Professor of Water & Resource Processing



Abstract

The increasing availability of data, due to the adoption of low-cost industrial internet of things technologies, coupled with increasing processing power from cloud computing, is fuelling increase use of data-driven models in manufacturing. Utilising case studies from the food and drink industry and waste management industry, the considerations and challenges faced when developing data-driven models for manufacturing systems are explored. Ensuring a high-quality set of model development data that accurately represents the manufacturing system is key to the successful development of a data-driven model. The cross-industry standard process for data mining (CRISP-DM) framework is used to provide a reference at to what stage process manufacturers will face unique considerations and challenges when developing a data-driven model. This paper then explores how data-driven models can be utilised to characterise process streams and support the implementation of the circular economy principals, process resilience and waste valorisation.

Citation

Watson, N. J., Fisher, O. J., Escrig, J. E., Witt, R., Porcu, L., Bacon, D., …Gomes, R. L. (2020). Considerations, challenges and opportunities when developing data-driven models for process manufacturing systems. Computers and Chemical Engineering, 140, Article 106881. https://doi.org/10.1016/j.compchemeng.2020.106881

Journal Article Type Article
Acceptance Date Apr 20, 2020
Online Publication Date May 14, 2020
Publication Date Sep 2, 2020
Deposit Date May 19, 2020
Publicly Available Date Mar 28, 2024
Journal Computers and Chemical Engineering
Print ISSN 0098-1354
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 140
Article Number 106881
DOI https://doi.org/10.1016/j.compchemeng.2020.106881
Keywords General Chemical Engineering; Computer Science Applications
Public URL https://nottingham-repository.worktribe.com/output/4469329
Publisher URL https://www.sciencedirect.com/science/article/pii/S0098135419308373

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