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

Fisher, O.J., Watson, N.J. orcid.org/0000-0001-5216-4873, Escrig, J.E. et al. (5 more authors) (2020) Considerations, challenges and opportunities when developing data-driven models for process manufacturing systems. Computers & Chemical Engineering, 140. 106881. ISSN 0098-1354

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Item Type: Article
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© 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/)

Keywords: Data-driven models; Process resilience; Waste valorisation; Mathematical modelling; Machine learning; Industry 4.0
Dates:
  • Published: 2 September 2020
  • Published (online): 14 May 2020
  • Accepted: 20 April 2020
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Environment (Leeds) > School of Food Science and Nutrition (Leeds) > FSN Nutrition and Public Health (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 12 Jul 2024 09:35
Last Modified: 12 Jul 2024 09:35
Status: Published
Publisher: Elsevier
Identification Number: 10.1016/j.compchemeng.2020.106881
Sustainable Development Goals:
  • Sustainable Development Goals: Goal 9: Industry, Innovation, and Infrastructure
  • Sustainable Development Goals: Goal 12: Responsible Consumption and Production
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